ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΣΧΟΛΗ ΔΑΣΟΛΟΓΙΑΣ ΚΑΙ ΦΥΣΙΚΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ

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1 ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΣΧΟΛΗ ΔΑΣΟΛΟΓΙΑΣ ΚΑΙ ΦΥΣΙΚΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ ΕΡΓΑΣΤΗΡΙΟ ΔΑΣΙΚΗΣ ΔΙΑΧΕΙΡΙΣΤΙΚΗΣ ΚΑΙ ΤΗΛΕΠΙΣΚΟΠΗΣΗΣ ΔΙΕΥΘΥΝΤΗΣ ΕΡΓΑΣΤΗΡΙΟΥ: ΚΑΡΤΕΡΗΣ ΜΙΧΑΛΗΣ, ΚΑΘΗΓΗΤΗΣ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΓΗΤΑΣ ΙΩΑΝΝΗΣ, ΛΕΚΤΟΡΑΣ Η ΑΝΑΠΤΥΞΗ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΟΥΣ ΜΟΝΤΕΛΟΥ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΜΕ ΤΗ ΧΡΗΣΗ ΔΟΡΥΦΟΡΙΚΩΝ ΕΙΚΟΝΩΝ ASTER ΜΕΤΑΠΤΥΧΙΑΚΗ ΔΙΑΤΡΙΒΗ της ΑΝΑΣΤΑΣΙΑΣ ΠΟΛΥΧΡΟΝΑΚΗ ΘΕΣΣΑΛΟΝΙΚΗ 2007

2 ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΣΧΟΛΗ ΔΑΣΟΛΟΓΙΑΣ ΚΑΙ ΦΥΣΙΚΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ ΕΡΓΑΣΤΗΡΙΟ ΔΑΣΙΚΗΣ ΔΙΑΧΕΙΡΙΣΤΙΚΗΣ ΚΑΙ ΤΗΛΕΠΙΣΚΟΠΗΣΗΣ ΔΙΕΥΘΥΝΤΗΣ ΕΡΓΑΣΤΗΡΙΟΥ: ΚΑΡΤΕΡΗΣ ΜΙΧΑΛΗΣ, ΚΑΘΗΓΗΤΗΣ ΕΠΙΒΛΕΠΩΝ ΚΑΘΗΓΗΤΗΣ: ΓΗΤΑΣ ΙΩΑΝΝΗΣ, ΛΕΚΤΟΡΑΣ THE DEVELOPMENT OF A TRANSFERABLE OBJECT-BASED CLASSIFICATION MODEL FOR BURNED AREA MAPPING USING ASTER IMAGERY ΜΕΤΑΠΤΥΧΙΑΚΗ ΔΙΑΤΡΙΒΗ της ΑΝΑΣΤΑΣΙΑΣ ΠΟΛΥΧΡΟΝΑΚΗ ΕΞΕΤΑΣΤΙΚΗ ΕΠΙΤΡΟΠΗ Ι. ΓΗΤΑΣ Μ. ΚΑΡΤΕΡΗΣ Α. ΔΗΜΗΤΡΑΚΟΠΟΥΛΟΣ ΘΕΣΣΑΛΟΝΙΚΗ 2007

3 CONTENTS CONTENTS... 3 LIST OF FIGURES... 4 LIST OF TABLES... 5 ACKNOWLEDGEMENTS ΕΙΣΑΓΩΓΉ ΠΕΡΙΓΡΑΦΗ ΠΕΡΙΟΧΩΝ ΜΕΛΕΤΗΣ ΚΑΙ ΔΕΔΟΜΕΝΩΝ ΠΡΩΤΗ ΠΕΡΙΟΧΗ ΜΕΛΕΤΗΣ ΔΕΥΤΕΡΗ ΠΕΡΙΟΧΗ ΜΕΛΕΤΗΣ ΔΕΔΟΜΕΝΑ ΕΠΕΞΕΡΓΑΣΙΑ ΔΕΔΟΜΕΝΩΝ ΜΕΘΟΔΟΛΟΓΙΑ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΗΣ ΜΕΘΟΔΟΣ ΑΝΑΛΥΣΗΣ ΚΑΤΑΤΜΗΣΗ (SEGMENTATION) ΤΑΞΙΝΟΜΗΣΗ ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ (PROCESSES) ΑΝΑΠΤΥΞΗ ΔΥΟ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΩΝ ΜΟΝΤΕΛΩΝ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΜΕ ΤΗ ΧΡΗΣΗ ΕΙΚΟΝΑΣ ASTER ΣΥΓΚΡΙΣΗ ΤΩΝ ΑΠΟΤΕΛΕΣΜΑΤΩΝ ΤΑΞΙΝΟΜΗΣΗΣ ΤΩΝ ΔΥΟ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΩΝ ΜΟΝΤΕΛΩΝ ΕΛΕΓΧΟΣ ΤΗΣ ΕΦΑΡΜΟΓΗΣ ΤΟΥ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΟΥΣ ΜΟΝΤΕΛΟΥ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΣΕ ΑΛΛΕΣ ΠΕΡΙΟΧΕΣ ΈΛΕΓΧΟΣ ΤΗΣ ΕΦΑΡΜΟΓΗΣ ΤΟΥ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΟΥΣ ΜΟΝΤΕΛΟΥ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΠΟΥ ΕΙΧΑΝ ΑΠΟΤΥΠΩΘΕΙ ΣΤΗΝ ΙΔΙΑ ΕΙΚΟΝΑ ASTER ΈΛΕΓΧΟΣ ΤΗΣ ΕΦΑΡΜΟΓΗΣ ΤΟΥ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΟΥΣ ΜΟΝΤΕΛΟΥ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΠΟΥ ΕΙΧΑΝ ΑΠΟΤΥΠΩΘΕΙ ΣΕ ΔΙΑΦΟΡΕΤΙΚΗ ΕΙΚΟΝΑ ASTER ΣΥΜΠΕΡΑΣΜΑΤΑ ABSTRACT INTRODUCTION AN EXAMINATION OF THE ROLE OF FIRES IN THE MEDITERRANEAN AND AN INTRODUCTION TO OBJECT-ORIENTED IMAGE ANALYSIS THE MEDITERRANEAN ECOSYSTEM AND ITS CHARACTERISTICS FOREST FIRES IN THE MEDITERRANEAN BASIN MAPPING OF BURNED AREAS USING REMOTE SENSING An overview of the different sensors and techniques employed for burned area mapping NEW SENSORS FOR BURNED AREA MAPPING NEW CLASSIFICATION TECHNIQUES FOR BURNED AREA MAPPING Image understanding in object-oriented image analysis Segmentation Classification Basics of object oriented image analysis CHAPTER SUMMARY STUDY AREA AND DATA PREPROCESSING

4 3.1 DESCRIPTION OF THE FIRST STUDY AREA DESCRIPTION OF THE SECOND STUDY AREA DATASETS IMAGE PRE-PROCESSING Atmospheric correction The effect of atmospheric interactions Methods of atmospheric correction Basic concepts of The Spatially-Adaptive Fast Atmospheric Correction Algorithm (ATCOR2) Implementation of the atmospheric correction The Haze Removal Algorithm Resulting images CHAPTER SUMMARY DEVELOPMENT OF AN OBJECT-BASED CLASSIFICATION MODEL FOR BURNED AREA MAPPING USING ASTER IMAGERY BACKGROUND WORKFLOW OF THE DEVELOPMENT OF AN OBJECT-ORIENTED CLASSIFICATION MODEL Segmentation Classification Processes DEVELOPMENT OF AN OBJECT-BASED CLASSIFICATION MODEL USING AN ATMOSPHERICALLY CORRECTED ASTER IMAGE Classification accuracy of the object-based classification model using an atmospherically corrected ASTER image DEVELOPMENT OF AN OBJECT-BASED CLASSIFICATION MODEL USING A NON-ATMOSPHERICALLY ASTER IMAGE Classification accuracy accuracy of the object-based model using an non-atmospherically corrected ASTER image A COMPARISON BETWEEN THE TWO OBJECT-BASED CLASSIFICATION MODELS CHAPTER SUMMARY EXAMINATION OF THE TRANSFERABILITY OF THE OBJECT-BASED CLASSIFICATION MODEL DEVELOPED FOR BURNED AREA MAPPING BACKGROUND EXAMINATION OF THE TRANSFERABILITY OF THE OBJECT-BASED CLASSIFICATION MODEL WHEN MAPPING ANOTHER BURNED AREA LOCATED ON THE SAME ASTER IMAGE EXAMINATION OF THE TRANSFERABILITY OF THE OBJECT-BASED MODEL WHEN MAPPING ANOTHER BURNED AREA USING A DIFFERENT ASTER IMAGE CHAPTER SUMMARY GENERAL CONCLUSIONS AND RECOMMENDATIONS GENERAL CONCLUSIONS RECOMMENDATIONS AND FUTURE INVESTIGATIONS LIST OF FIGURES Figure 2.1 Map of the five regions with Mediterranean-type climate (red). For the Mediterranean Basin, the yellow area corresponds to the Mediterranean steppe domain (mean annual rainfall between 100 and 400 mm) according to Le Houérou, 1997 (Source: Joffre et al. 2002) Figure 2.2 Burned area (a) and number of fires (b) in the five Southern Member States for the last 26 years (Source: Forest Fires Report No.6) Figure 2.3 Characteristics of the three ASTER Sensor Systems (Table on top) and Comparison of Spectral Band between ASTER and Landsat-Thematic Mapper (Figure below) (Source: ASTER Users Handbook Version 2 and 42 Figure 2.4 An approach to visual cognition: when one sees a red round area he/she classifies it as a red, round object (a). Underneath (b) one sees another object, which he/she already knows is a human being. Immediately one combines both figures and relates them to each other. Both objects may be located in front of a third large blue object (c). The 3 objects are connected by defined relationships ( underneath, in front of ). The features of the objects and their relationships allow one to - 4 -

5 recognize a child playing with a ball in the sky. This cognition process compares one s view of the objects and their relationships with the existing knowledge of one s memory (Source: Definies, Professional 5, Userguide) Figure 2.5 Procedure sequence of object-oriented image analysis Figure 2.6 Image object hierarchy (Source: Definiens Professional 5, User Guide) Figure 3.1 Map of the study area and satellite image showing fire burns (left). Map of the major fires occurred in Portugal in the summer of 2003 (right) Figure 3.2 Maps of the environmental and landscape characteristics of the study area in relation to the official fire perimeters Figure 3.3 Map of the second study area in Spain...55 Figure 3.4 Flowchart of the preprocessing of the ASTER image Figure 3.5 Schematic sketch of solar radiation components in flat terrain Figure 3.6 Gain represents the gradient of the calibration and bias defines the spectral radiance of the sensor for a DN of zero (also called dark current ) Figure 3.7 Calibration file for the atmospheric correction of the ASTER image, where c0 is the BIAS and c1 is the GAIN Figure 3.8 Spectra main display setup Figure 3.9 Spectral profile for ASTER image before (left) and after (right) atmospheric correction Figure 3.10 ASTER image before (left) and after (right) atmospheric correction Figure 4.1 Flowchart of Chapter Figure 4.2 The workflow of the development of an object-based classification model Figure 4.3 Workflow of a process sequence (Source: Definiens Professional 5, User Guide) Figure 4.4 The sequence of the steps followed and the parameters used for the development of the object-based classification model Figure 4.5 Top: Burned area classification result of the atmospherically corrected ASTER image (in grey) and the official fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map Figure 4.6 Interface of the Definiens Professional software Figure 4.7 Top: Burned area classification result of the non-atmospherically corrected ASTER image (in grey) and the fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map Figure 5.1 Flowchart of Chapter Figure 5.2 LEFT: Burned areas (b) located on the same ASTER image as the one used for the development of the model (a), (see Chapter 4). RIGHT: Map of the major fires that occurred in Portugal in the summer of Figure 5.3 The three classification levels created by the implementation of the object-based classification model Figure 5.4 Top: Burned area classification result of the implementation of the object-based classification model (in grey) and the official fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map Figure 5.5 The three classification levels created by the implementation of the object-based classification model in fire affected areas in Spain Figure 5.6 LEFT: Burned area classification result of the implementation of the object-based model on the second ASTER image (in grey) and the fire perimeter (in red). RIGHT: Spatial overlay of the burned area resulting from the model and the official map LIST OF TABLES Table 2.1 Different techniques employed for burned area mapping (first column) and corresponding authors (second column) Table 4.1 Segmentation parameters Table 4.2 Error matrix generated for the assessment of the classification accuracy of object-based model that was developed on the atmospherically corrected ASTER image Table 4.3 Error matrix generated for the assessment of the classification accuracy of object-based model that was developed on the atmospherically corrected ASTER image Table 5.1 Accuracy statistics for the classification of the burned areas Table 5.2 Accuracy statistics for the classification of the burned areas

6 ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my supervisor, Professor Ioannis Gitas, for his support during this research. His valuable help, advice and expertise were essential for the completion of this work. I would like to express my appreciation to Professor Michel Karteris, director of the Lab of Forest Management and Remote Sensing, and Professor Alexandros Dimitrakopoulos for the time they have put in reading and evaluating my work. I am grateful to George Mitri, Kostas Douros, Chara Minakou and Thomas Katagis who provided invaluable assistance and advice during all the stages of this work, but I also would like to thank them for the nice time we had together in the Laboratory. Additional thanks to my colleagues Nikos Oikonomakis and Dinos Dimitrakopoulos for their help and company. I would also like to thank Dr. Jose Pereira and Aná Sa for their generous contribution of ancillary data and Dr. Paulo Barbosa for his literary contribution. I want to express my sincere thanks to Linda Lucas for her help reading and correcting the English content of my thesis. Finally, I am eternally grateful to my parents for supporting and encouraging me throughout all these years in which I sought to pursue my dreams

7 1 ΕΙΣΑΓΩΓΉ Η διαμόρφωση των μεσογειακών οικοσυστημάτων στο πέρασμα των χρόνων έχει επηρεαστεί καταλυτικά από τις δασικές πυρκαγιές (Trabaud et al. 1993). Ωστόσο, τις τελευταίες δεκαετίες, οι δασικές πυρκαγιές στην περιοχή της Μεσογείου έχουν αυξηθεί σημαντικά, γεγονός που σχετίζεται με την αύξηση στη συγκέντρωση καύσιμης ύλης στο περιβάλλον λόγω τις εγκατάλειψης καλλιεργήσιμων εκτάσεων ή της αλλαγής των παραδοσιακών τρόπών καλλιέργειας και βόσκησης (Giralt 1990; Debussche et al. 1999; Pausas and Vallejo 1999). Επιπλέον, η αύξηση του αριθμού των δασικών πυρκαγιών μπορεί να αποδοθεί στις παρατηρούμενες κλιματικές αλλαγές (Piñol et al. 1998; Pausas and Vallejo 1999), οι οποίες χαρακτηρίζονται από την αύξηση της μέσης θερμοκρασίας σε παγκόσμιο επίπεδο (Houghton et al. 2001), ενώ όλες οι προβλέψεις δείχνουν μείωση των αποθεμάτων πόσιμου νερού. Το αποτέλεσμα των κλιματικών αυτών αλλαγών θα είναι η αύξηση της ξηρότητας της βλάστησης, η αλλαγή της κατάστασης της καύσιμης ύλης και η αύξηση του κινδύνου έναρξης πυρκαγιών. Ο μεγάλος αριθμός των δασικών πυρκαγιών, που εμφανίζονται κάθε χρόνο και στην ίδια περιοχή, γεγονός το οποίο μεταφράζεται σε εκατοντάδες εκτάρια καμένης έκτασης στη Μεσόγειο, αποτελεί την μεγαλύτερη απειλή για τα φυσικά οικοσυστήματα (Koutsias et al. 1999). Οι άμεσες επιπτώσεις των πυρκαγιών είναι η απώλεια του προστατευτικού στρώματος βλάστησης, η μείωση της αξίας της αισθητικής του δασικού τοπίου (González-Pelayo et al. 2006), ενώ ακόμα οι δασικές πυρκαγιές μπορεί να οδηγήσουν στο θάνατο πολιτών και δασοπυροσβεστών. Επιπλέον στις μακροπρόθεσμες επιπτώσεις των πυρκαγιών μπορούν να συμπεριληφθούν οι σημαντικές αλλαγές στη διαμόρφωση του φυσικού τοπίο (Romme 1982; Turner and Romme 1994; Knick and Rotenberry 1997; Turner et al. 1997) και η αύξηση του κινδύνου διάβρωσης των εδαφών (Pausas and Vallejo 1999; Giovannini et al. 2001). Κατά συνέπεια, οι οικονομικές, κοινωνικές, οικολογικές και κλιματικές επιπτώσεις που σχετίζονται με τις δασικές πυρκαγιές δεν καταδεικνύουν μόνο τη διαστάση του προβλήματος των δασικών πυρκαγιών, αλλά επιβάλλουν και την ανάγκη για την - 7 -

8 ανάπτυξη μιας αξιόπιστης και αποτελεσματικής μεθόδου για την εκτίμηση των περιβαλλοντικών επιπτώσεων των πυρκαγιών και τη χαρτογράφηση των καμένων εκτάσεων στα Μεσογειακά οικοσυστήματα (Gitas 1999; Koutsias et al. 1999). Η τηλεπισκόπηση μπορεί να αποτελέσει ένα πολύτιμο εργαλείο για την ανάπτυξη μιας τέτοιάς μεθοδολογίας αφού παρέχει όλα τα κατάλληλα δορυφορικά δεδομένα για αυτό το σκοπό με άμεσο και οικονομικό τρόπο. Πράγματι, τα δεδομένα της τηλεπισκόπησης έχουν χρησιμοποιηθεί εκτεταμένα τα τελευταία χρόνια για την ανίχνευση και χαρτογράφηση καμένων εκτάσεων (Chuvieco and Congalton 1988; Siljeström and Moreno 1995; Koutsias et al. 2000; Roy and Lewis 2002; Mitri and Gitas 2004), ενώ η διάθεση σήμερα καινούργιων υψηλής χωρικής και φασματικής ευκρίνειας δορυφορικών εικόνων, όπως οι εικόνες ASTER, εκτιμάται ότι θα συμβάλει στην καλύτερη αποτύπωση των καμένων εκτάσεων. Η διακοπή επίσης της λειτουργίας του δορυφόρου LANDSAT, τα προϊόντα του οποίου έχουν χρησιμοποιηθεί συχνά για τη χαρτογράφηση καμένων εκτάσεων, καθιστά την εξέταση των καινούργιων δορυφορικών δεδομένων απαραίτητη. Ο καταγραφέας ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) είναι ένα από τα πέντε όργανα που βρίσκονται πάνω στο δορυφόρο TERRA, που κατασκευάστηκε από τη NASA και το υπουργείο Οικονομίας και Βιομηχανίας της Ιαπωνίας (METI), με τη συνεργασία ερευνητικών και βιομηχανικών οργανισμών και στις δυο χώρες και εκτοξεύθηκε στις 18 Δεκεμβρίου του Ο ASTER καταγράφει τη φασματική πληροφορία σε 14 διαύλους από το ορατό μέχρι το θερμικό υπέρυθρο με υψηλή χωρική, φασματική και ραδιομετρική ανάλυση: τρεις δίαυλοι στο ορατό-κοντινό υπέρυθρο (VNIR), έξι δίαυλοι στο μέσο υπέρυθρο (SWIR) και τέσσερις δίαυλοι στο θερμικό υπέρυθρο (TIR). Επιπλέον, υπάρχει η δυνατότητα λήψης εικόνων εκτός ναδήρ, για τη συλλογή στερεοεικόνων και τη δημιουργία ψηφιακών μοντέλων εδάφους (DEM). Η χωρική ανάλυση διαφέρει ανάλογα με το φασματικό εύρος: 15 μέτρα στο VNIR, 30 μέτρα στο SWIR και 90 μέτρα στο TIR, ενώ το εύρος σάρωσης είναι 60 χιλιόμετρα (ASTER Users Handbook Version 2)

9 Επιπλέον, σήμερα έχουν αναπτυχθεί προηγμένες μέθοδοι ταξινόμησης δορυφορικών εικόνων, όπως η αντικειμενοστραφής μέθοδος ανάλυσης εικόνων, η οποία έχει εφαρμοστεί με επιτυχία για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση δορυφορικών εικόνων LANDSAT και NOAA-AVHRR (Gitas et al. 2004; Mitri and Gitas 2004). Σε αντίθεση με τις παραδοσιακές μεθόδους ταξινόμησης δορυφορικών εικόνων όπου η μονάδα ταξινόμησης είναι το εικονοστοιχείο, στην αντικειμενοστραφή μέθοδο ως μονάδα ταξινόμησης θεωρείται το αντικείμενο (object). Τα πλεονεκτήματα της αντικειμενοστραφούς ανάλυσης είναι οι σημαντικές στατιστικές αναλύσεις και η ανάλυση της δομής της εικόνας, η δημιουργία ενός σημαντικά ασυσχέτιστου φασματικού χώρου (feature space) με τη χρήση χαρακτηριστικών σχήματος των αντικειμένων (π.χ. μήκος, αριθμός γωνιών των αντικειμένων), και τη χρήση τοπολογικών χαρακτηριστικών (π.χ. η σχέση των αντικειμένων με τα γειτονικά αντικείμενα) (Benz et al. 2004). Ο σκοπός της εργασίας αυτής ήταν η ανάπτυξη ενός αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση δορυφορικών εικόνων ASTER. Συγκεκριμένα, οι επιμέρους στόχοι ήταν: Η ανάπτυξη ενός αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση μιας ατμοσφαιρικά διορθωμένης εικόνας ASTER Η ανάπτυξη ενός αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση μιας μη ατμοσφαιρικά διορθωμένης εικόνας ASTER Η σύγκριση και η αξιολόγηση των αποτελεσμάτων της χαρτογράφησης που προέκυψαν από τα δυο μοντέλα που αναπτύχθηκαν Η εξέταση της δυνατότητας εφαρμογής του μοντέλου που έδωσε το καλύτερο αποτέλεσμα για τη χαρτογράφηση καμένων εκτάσεων σε άλλη περιοχή με τη χρήση της ίδιας εικόνας ASTER Η περαιτέρω εξέταση της δυνατότητας εφαρμογής του μοντέλου για τη χαρτογράφηση καμένων περιοχών με τη χρήση μιας διαφορετικής εικόνας ASTER

10 2 ΠΕΡΙΓΡΑΦΗ ΠΕΡΙΟΧΩΝ ΜΕΛΕΤΗΣ ΚΑΙ ΔΕΔΟΜΕΝΩΝ 2.1 Πρώτη περιοχή μελέτης Το καλοκαίρι του 2003 ήταν από τις χειρότερες αντιπυρικές περιόδους για την Πορτογαλία, αφού μεγάλες πυρκαγιές και μικρότερες ήταν υπεύθυνες για την καταστροφή εκταρίων δασικής έκτασης (European Commission Forest Fires in Europe. 2004). Μεγάλες πυρκαγιές εκδηλώθηκαν και στην πρώτη περιοχή μελέτης, η οποία βρίσκεται στην κεντρική Πορτογαλία, ανάμεσα στις περιφέρειες Santarem και Castelo Branco (Figure 3.1, Chapter 3). Η περιοχή μελέτης καλύπτεται κυρίως από δάση με κυριότερο δασοπονικό είδος τη θαλάσσια πεύκη (Pinus pinaster atlantica) και αγροτικές καλλιέργειες. Το υπερθαλάσσιο ύψος στην περιοχή κυμαίνεται μεταξύ 300 και 400 μέτρων, ενώ το κυρίαρχο γεωλογικό πέτρωμα που συναντάτε είναι ο σχιστόλιθος. Στην περιοχή η συχνότητα εκδήλωσης πυρκαγιών είναι υψηλή τόσο λόγω της παρατηρούμενης αστυφιλίας (από το 1960 ο αγροτικός πληθυσμός της περιοχής έχει μειωθεί κατά 50%), που είχε σαν αποτέλεσμα την εγκατάλειψη των αγροτικών εκτάσεων και την αύξηση της συγκέντρωσης καύσιμης ύλης, όσο και από τις κλιματικές συνθήκες που επικρατούν στην περιοχή (παρατεταμένο θερμό και ξηρό καλοκαίρι, χωρίς βροχές). 2.2 Δεύτερη περιοχή μελέτης Η δεύτερη περιοχή μελέτης έχει χαρακτηριστεί ως Ζώνη Ειδικής Προστασίας (Special Protected Area SPA), ονομάζεται Encinares de los rios Alberche y Cofio, και βρίσκεται 40 χιλιόμετρα νοτιοδυτικά της Μαδρίτης (Figure 3.3, Chapter 3). Λιβάδια, θαμνότοποι και διάφορα είδη δέντρων, όπως η κουκουναριά (Pinus pinea) και η παραθαλάσσια πεύκη ( Pinus pinaster) αποτελούν κυρίως τη «φυσική» βλάστηση της περιοχής. Η ανθρωπογενής επίδραση στο περιβάλλον είχε σαν αποτέλεσμα να δημιουργηθεί στην περιοχή ένα χαρακτηριστικό είδος τοπίου γνωστό ως «dehesa», το οποίο χαρακτηρίζεται από την υπερβόσκηση των λιβαδιών και καλλιεργειών και την ύπαρξη διάσπαρτων βελανιδιών (Quercus ilex). 2.3 Δεδομένα Τα δεδομένα που χρησιμοποιήθηκαν σε αυτή την εργασία ήταν:

11 Μια εικόνα ASTER (L1A) στην οποία έχουν αποτυπωθεί οι καμένες περιοχές στην Πορτογαλία με ημερομηνία λήψης το καλοκαίρι του 2003 Μια εικόνα ASTER (L1B) στην οποία έχουν αποτυπωθεί οι καμένες περιοχές στην Ισπανία με ημερομηνία λήψης το καλοκαίρι του 2003 Οι χάρτες με τις περιμέτρους των πυρκαγιών οι οποίοι παράχθηκαν από την Πορτογαλική και την Ισπανική Δασική υπηρεσία, αντίστοιχα. 2.4 Επεξεργασία δεδομένων Η επεξεργασία των δορυφορικών δεδομένων είναι ουσιαστικά η διόρθωση λαθών και η αφαίρεση ελαττωμάτων που τυχόν υπάρχουν στις εικόνες. Γενικά, η επεξεργασία των εικόνων βελτιώνει την ποιότητα τους και παρέχει τη βάση για περαιτέρω ανάλυση. Στα πλαίσια της εργασίας αυτής, η δορυφορική εικόνα ASTER διορθώθηκε ατμοσφαιρικά (παράγραφος 3.4. Chapter 3). Συγκεκριμένα, ο Spatially-Adaptive Fast Atmospheric Correction Algorithm (ATCOR2) εφαρμόστηκε. Οι κατάλληλες ατμοσφαιρικές συνθήκες για τη στιγμή της λήψης της εικόνας έπρεπε να βρεθούν, και το αρχείο με τους συντελεστές co και c1 έπρεπε να δημιουργηθεί. Οι εικόνες 3.9 και 3.10 (Chapter 3) απεικονίζουν τις φασματικές υπογραφές για τις κυριότερες κατηγορίες χρήσης/κάλυψης γης και την εικόνα ASTER πριν και μετά την ατμοσφαιρική διόρθωση. 3 ΜΕΘΟΔΟΛΟΓΙΑ Η μεθοδολογία που ακολουθήθηκε στην εργασία αυτή συμπεριέλαβε την ανάπτυξη δυο αντικειμενοστραφών μοντέλων με τη χρήση μιας δορυφορικής εικόνας ASTER. Το πρώτο μοντέλο που αναπτύχθηκε βασίστηκε στην ατμοσφαιρικά διορθωμένη εικόνα ASTER, ενώ το δεύτερο μοντέλο βασίστηκε στην ίδια μη ατμοσφαιρικά διορθωμένη εικόνα ASTER. Οι ακρίβειες των ταξινομήσεων των καμένων περιοχών που προέκυψαν από τα δυο μοντέλα υπολογίστηκαν χρησιμοποιώντας ως σημεία αναφοράς την περίμετρο που παραχωρήθηκε από τη Δασική Υπηρεσία της Πορτογαλίας και συγκρίθηκαν μεταξύ τους. Το διάγραμμα ροής (Figure 4.1) στο Chapter 4 απεικονίζει τα βήματα και τη σειρά που ακολουθήθηκε στη μεθοδολογία της εργασίας

12 3.1 Αντικειμενοστραφής μέθοδος ανάλυσης Το πρώτο βήμα για την ανάπτυξη του αντικειμενοστραφούς μοντέλου ήταν η δημιουργία αντικειμένων. Διάφορες παράμετροι όπως η κλίμακα (scale), το βάρος των διαύλων της εικόνας (layer weights) και το κριτήριο ετερογένειας (heterogeneity criterion) έπρεπε να καθοριστούν, έτσι ώστε να δημιουργηθούν τα κατάλληλα αντικείμενα που θα δώσουν τις απαραίτητες πληροφορίες για την ταξινόμηση των καμένων εκτάσεων. Η συνέχεια της μεθοδολογίας για την ανάπτυξη του αντικειμενοστραφούς μοντέλου περιλάμβανε την αλληλεπίδραση μεταξύ της διαφοροποίησης του σχήματος των αντικειμένων και της ταξινόμησης των αντικειμένων σε διάφορα επίπεδα. Αυτό πραγματοποιήθηκε μέσω ειδικών διαδικασιών (processes) οι οποίες σκοπό έχουν την επίλυση προβλημάτων ανάλυσης της εικόνας για την ταξινόμηση των επιθυμητών κατηγοριών κάλυψης γης (Figure 4.2, Chapter 4). Οι τελικές ταξινομήσεις των αντικειμένων σε κάθε επίπεδο έγιναν με τη χρήση των κατάλληλων χαρακτηριστικά (feature objects) τους με σκοπό τον καλύτερο διαχωρισμό των καμένων από τις μη καμένες περιοχές. Οι βασικές διαδικασίες για την ανάπτυξη αντικειμενοστραφούς μοντέλου περιγράφονται στις παραγράφους που ακολουθούν. 3.2 Κατάτμηση (segmentation) Με τη διαδικασία της κατάτμησης της εικόνας πραγματοποιείται η δημιουργία των αντικειμένων, ενώ στις περισσότερες περιπτώσεις τα αρχικά αντικείμενα δεν αντιστοιχούν στις επιθυμητές κλάσης για ταξινόμηση. Συνεπώς, τα αρχικά αυτά αντικείμενα αποτελούν ουσιαστικά μέσα διακίνησης πληροφοριών και βάσεις για περαιτέρω ταξινομήσεις ή για περαιτέρω κατατμήσεις. Το καλύτερό λοιπόν αποτέλεσμα από τη διαδικασία της κατάτμησης είναι αυτό που παρέχει την καταλληλότερη πληροφορία για περαιτέρω ανάλυση. Για την εφαρμογή της διαδικασίας της κατάτμησης πρέπει να καθοριστούν οι εξής παράμετροι: (1) η κλίμακα, (2) το βάρος για κάθε δίαυλο της εικόνας και (3) το κριτήριο της ετερογένειας: (1) αν και στην τηλεπισκόπηση ως κλίμακα βασικά θεωρείται η χωρική ανάλυση ενός εικονοστοιχείου, τις περισσότερες φορές η πληροφορία που επιδιώκεται να εξαχθεί από τη δορυφορική εικόνα έχει τη δική της κλίμακα. Η κλίμακα (τιμές από

13 μέχρι 1) λοιπόν καθορίζει αν μια συγκεκριμένη κλάση αντικειμένων θα αποτυπωθεί στο χάρτη ή όχι, ενώ η ίδια κατηγορία αντικειμένων μπορεί να αποτυπωθεί διαφορετικά σε διαφορετικές κλίμακες. (2) οι δίαυλοι της εικόνας μπορούν να επεξεργαστούν διαφορετικά ανάλογα με τη σπουδαιότητα ή κατά πόσο απαραίτητοι είναι για το αποτέλεσμα της κατάτμησης. Όσο πιο μεγάλο είναι το βάρος που δίνεται σε ένα δίαυλο τόσο πιο πολύ πληροφορία θα εξαχθεί από αυτόν κατά τη διαδικασία της κατάτμησης. Συνεπώς, στους δίαυλους που δεν παρέχουν πληροφορία για να χρησιμοποιηθεί από τα αντικείμενα πρέπει να δίνεται λίγο ή καθόλου βάρος. (3) το κριτήριο της ετερογένειας αποτελείται από δύο μέρη: το κριτήριο για το χρώμα και το κριτήριο για το σχήμα (τιμές από 0 μέχρι 1). Το κριτήριο για το χρώμα εκφράζει ουσιαστικά η αλλαγή της ετερογένειας που παρατηρείται κατά τη συγχώνευση των αντικειμένων, ενώ το δεύτερο κριτήριο περιγράφει τη βελτίωση του σχήματος των αντικειμένων. 3.3 Ταξινόμηση Για την ταξινόμηση των αντικειμένων έπρεπε να βρεθούν τα κατάλληλα χαρακτηριστικά για τον καλύτερο διαχωρισμό των καμένων από τις μη καμένες περιοχές. Με τη χρήση των κατάλληλων εργαλείων έγιναν αναλύσεις και συγκρίσεις μεταξύ των διαφόρων χαρακτηριστικών των αντικειμένων με σκοπό την εύρεση των πιο κατάλληλών για την εξαγωγή της απαραίτητης πληροφορίας. Υπάρχει ένας μεγάλος αριθμός από χαρακτηριστικά για τον υπολογισμό του χρώματος, του σχήματος και της υφής των αντικειμένων, επειδή τα αντικείμενα μιας εικόνας περικλείουν πολύ περισσότερη πληροφορία από ότι τα εικονοστοιχεία. Επιπλέον πληροφορία μπορεί να εξαχθεί τόσο από τις σχέσεις που δημιουργούνται μεταξύ των αντικειμένων στο ίδιο ή σε διαφορετικά επίπεδα μέσω του ιεραρχικού δικτύου που σχηματίζεται κατά τη διαδικασία της κατάτμησης, όσο και από την ταξινόμηση των αντικειμένων. 3.4 Ειδικές διαδικασίες (processes) Το λογισμικό Definiens Professional 5, που χρησιμοποιήθηκε για την ανάπτυξη του αντικειμενοστραφούς μοντέλου εμπεριέχει μια τεχνική «γλώσσα» για την ανάπτυξη

14 προηγμένων αλγόριθμων για την ανάλυση της εικόνας. Αυτοί οι αλγόριθμοι χρησιμοποιούν τις αρχές της αντικειμενοστραφούς ανάλυσης και μια προσαρμοσμένη τοπική επεξεργασία. Αυτό επιτυγχάνεται με τις ειδικές διαδικασίες. Μια ειδική διαδικασία αποτελεί τη στοιχειώδη μονάδα μιας ομάδας κανόνων (rule set) η οποία δίνει λύση σε συγκεκριμένα προβλήματα ανάλυσης της εικόνας. Τα βασικά μέρη μιας ειδικής διαδικασίας είναι ο αλγόριθμος και το πεδίο ορισμού (domain) (Figure 4.3, Chapter 4). Μια ειδική διαδικασία επιτρέπει την εφαρμογή ενός αλγόριθμου σε μια συγκεκριμένη περιοχή της εικόνας. Ο αλγόριθμος περιγράφει τι θα κάνει η ειδική διαδικασία: τη δημιουργία αντικειμένων, τη συγχώνευση ή τον διαχωρισμό των αντικειμένων, την ταξινόμηση των αντικειμένων κτλ. Το πεδίο ορισμού περιγράφει την περιοχή του ενδιαφέροντος όπου θα εφαρμοστεί ο αλγόριθμος: σε ολόκληρη την εικόνα, σε ένα επίπεδο ή σε όλα τα αντικείμενα που ανήκουν σε μια συγκεκριμένη ομάδα. 4 ΑΝΑΠΤΥΞΗ ΔΥΟ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΩΝ ΜΟΝΤΕΛΩΝ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΜΕ ΤΗ ΧΡΗΣΗ ΕΙΚΟΝΑΣ ASTER Στην παράγραφο 4.1 δίνεται η περιγραφή της ανάπτυξης αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων στην Πορτογαλία με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας ASTER και εκτίμηση του αποτελέσματος της ταξινόμησης, ενώ στην παράγραφο 4.2 δίνεται η περιγραφή της ανάπτυξης αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση των ίδιων καμένων εκτάσεων με τη χρήση της ίδιας αλλά μη ατμοσφαιρικά διορθωμένης εικόνας ASTER και εκτίμηση του αποτελέσματος της ταξινόμησης. Στο τέλος αυτού του κεφαλαίου τα αποτελέσματα των ταξινομήσεων των δυο μοντέλων συγκρίνονται. 4.1 Ανάπτυξη αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας ASTER Εκτός από τους εννέα διαύλους της δορυφορικής εικόνας ASTER, χρησιμοποιήθηκε και η πληροφορία από τον δείκτη βλάστησης NDVI για την ανάπτυξη του αντικειμενοστραφούς μοντέλου με σκοπό τον καλύτερο διαχωρισμό των καμένων από τις μη καμένες περιοχές. Το πρώτο βήμα για την ανάπτυξη του μοντέλου ήταν η δημιουργία αντικειμένων με την εφαρμογή της διαδικασίας της κατάτμησης. Η

15 συνέχεια περιλάμβανε ειδικές διαδικασίες που εμπεριείχαν αλγόριθμους τροποποίησης και ταξινόμησης οι οποίες εφαρμόστηκαν με την κατάλληλη σειρά έτσι ώστε να γίνει η χαρτογράφηση των καμένων περιοχών. Η σειρά με την οποία εφαρμόστηκαν οι ειδικές διαδικασίες αποτέλεσε τελικά και την ομάδα κανόνων. Τρία επίπεδα ταξινόμησης (Figure 4.4, Chapter 4) δημιουργήθηκαν τελικά για τη χαρτογράφηση των καμένων εκτάσεων: Επίπεδο 2: Το επίπεδο 2 ήταν αυτό που δημιουργήθηκε πρώτο ενώ το πεδίο ορισμού όπου εφαρμόστηκαν οι αλγόριθμοι ήταν όλη η εικόνα. Το μέγεθος της κλίμακας που χρησιμοποιήθηκε ήταν 10 και δόθηκε βάρος μόνο στο κανάλι 3 και στον δείκτη βλάστησης (NDVI). Στο κριτήριο ετερογένειας πλήρης βάρος δόθηκε στο χρώμα. Για την επίτευξη του καλύτερου διαχωρισμού στην ταξινόμηση των καμένων από τις μη καμένες περιοχές, δημιουργήθηκε ένα αριθμητικό χαρακτηριστικό (arithmetic feature). Τα αριθμητικά χαρακτηριστικά συντάσσονται από υπάρχοντα χαρακτηριστικά, μεταβλητές και σταθερές μέσω αριθμητικών πράξεων. Σε αυτή την περίπτωση, τα χαρακτηριστικά «μέσος όρος του διαύλου 3» (mean of layer 3) και «μέσος όρος του δείκτη βλάστησης» (mean of NDVI) προστέθηκαν, με αποτέλεσμα τη δημιουργία ενός καινούργιου χαρακτηριστικού, το οποίο ονομάστηκε «μέσος όρος & ndvi». Εκτός από το καινούργιο χαρακτηριστικό, τα χαρακτηριστικά «μέσος όρος του διαύλου 3», «μέσος όρος του διαύλου 1» και «λόγος του διαύλου 3» (ratio of layer 1) χρησιμοποιήθηκαν για την ταξινόμηση των καμένων εκτάσεων. Όλα τα αντικείμενα ταξινομήθηκαν χρησιμοποιώντας τα χαρακτηριστικά που προαναφέρθηκαν, με αποτέλεσμα τη δημιουργία δύο κλάσεων σε αυτό το επίπεδο: «Καμένη έκταση στο επίπεδο 2» και «νερό». Επίπεδο 3: Σε αυτό το επίπεδο, το πεδίο ορισμού όπου όλοι οι αλγόριθμοι εφαρμόστηκαν ήταν όλα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 2» η οποία είχε δημιουργηθεί στο προηγούμενο επίπεδο. Έτσι, η διαδικασία της κατάτμησης εφαρμόστηκε μόνο στα αντικείμενα που είχαν ταξινομηθεί ως «Καμένη έκταση στο επίπεδο 2». Το μέγεθος της κλίμακας που χρησιμοποιήθηκε

16 ήταν 300, βάρος δόθηκε μόνο στον δίαυλο 3 και στον δείκτη βλάστησης και στο κριτήριο ετερογένειας πλήρης βάρος δόθηκε στο χρώμα. Για την ταξινόμηση χρησιμοποιήθηκαν χαρακτηριστικά όπως «μέσος όρος του διαύλου 3» και «μέσος όρος του δείκτη βλάστησης», ενώ ο αλγόριθμος της ταξινόμησης εφαρμόστηκε μόνο στα αντικείμενα που αποτελούνταν από υπόαντικείμενα ταξινομημένα ως «Καμένη έκταση στο επίπεδο 2». Τελικά, η κλάση «Καμένη έκταση στο επίπεδο 3» δημιουργήθηκε. Εξαιτίας, ωστόσο ότι μερικά αντικείμενα δεν ταξινομήθηκαν σωστά, δύο κατάλληλοι αλγόριθμοι ( find enclosed by class και merge region, Appendix I) εφαρμόστηκαν στα αντικείμενα που ανήκαν στη κλάση «Καμένη έκταση στο επίπεδο 3», με σκοπό τη βελτίωση της ταξινόμησης. Η εφαρμογή των αλγόριθμων είχε σαν αποτέλεσμα την τροποποίηση του σχήματος και μεγέθους των αντικειμένων. Έτσι, επιλέχθηκαν τα κατάλληλα χαρακτηριστικά, όπως ο «μέσος όρος» για την ταξινόμηση των καινούργιων αντικειμένων και τελικά δημιουργήθηκαν δυο κλάσεις στο επίπεδο 3: «Καμένη έκταση στο επίπεδο 3(β)» και «νερό 2». Επίπεδο 1: Το επίπεδο 1 ήταν το τελευταίο που δημιουργήθηκε, ενώ ήταν και το τελευταίο και στην ιεραρχία των επιπέδων. Το πεδίο ορισμού για την εφαρμογή του αλγόριθμου της κατάτμησης ήταν όλα τα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 2», το μέγεθος της κλίμακας που χρησιμοποιήθηκε ήταν 1, βάρος δόθηκε στον δίαυλο 3 και στον δείκτη βλάστησης και στο κριτήριο ετερογένειας δόθηκε 0.9 βάρος στο χρώμα. Για την εφαρμογή του αλγόριθμου της ταξινόμησης το πεδίο ορισμού ήταν όλα τα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 3(β)». Χαρακτηριστικά των αντικειμένων, όπως «μέσος όρος του διαύλου 3» και «λόγος του διαύλου 3» χρησιμοποιήθηκαν για τη δημιουργία των τελικών κλάσεων: «Καμένη έκταση στο επίπεδο 1», «μερικώς καμένη έκταση», «λιγότερο καμένη έκταση», «έκταση με βλάστηση» και «γυμνή έκταση». Οι κλάσεις «Καμένη έκταση στο επίπεδο 1», «μερικώς καμένη έκταση», «λιγότερο καμένη έκταση» ομαδοποιήθηκαν σε μια κλάση, «Καμένη έκταση»

17 4.2 Ακρίβεια ταξινόμησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο (με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας) Για τον έλεγχο των αποτελεσμάτων της ταξινόμησης των καμένων εκτάσεων από αντικειμενοστραφές μοντέλο που αναπτύχθηκε, παράχθηκε ο πίνακας σφαλμάτων (error matrix). Τα περιγραφικά στατιστικά μεγέθη που υπολογίζονται από τον πίνακα σφαλμάτων είναι η συνολική ακρίβεια (overall accuracy), ενώ ο υπολογισμός της ακρίβειας για κάθε κλάση μπορεί να γίνει με την ακρίβεια του κατασκευαστή ή σφάλμα παράλειψης (producer s accuracy) και την ακρίβεια του χρήστη ή σφάλμα αναπλήρωσης (user s accuracy). Ένα επιπλέον, στατιστικό μέγεθος που προκύπτει από τον πίνακα σφαλμάτων είναι ο συντελεστής Kappa, ο οποίο σε σύγκριση με τη συνολική ακρίβεια, λαμβάνει υπόψη τον παράγοντα της τυχαιότητας (Zhang and Goodchild 2002). Ο πίνακας 4.2 (Chapter 4) παρουσιάζει τα αποτελέσματα του πίνακα σφαλμάτων για τον έλεγχο της ακρίβειας της ταξινόμησης. Η περίμετρος της πυρκαγιάς που κατασκευάστηκε από την Δασική Υπηρεσία της Πορτογαλίας θεωρήθηκε ως τα δεδομένα αναφοράς. Από τα 800 δειγματοληπτικά σημεία που επιλέχθηκαν τυχαία, προβλέφθηκαν ότι από τα 307 ανήκαν στην κλάση «Καμένη έκταση», μόνο τρία είχαν ταξινομηθεί λάθος. Η συνολική ακρίβεια της ταξινόμησης των καμένων εκτάσεων εκτιμήθηκε ότι ήταν 98.38%, η ακρίβεια του κατασκευαστή 96.02% και η ακρίβεια του χρήστη 99.02%. Ο συντελεστής Κappa υπολογίστηκε ότι ήταν , αποτέλεσμα που δείχνει τη μεγάλη ακρίβεια της ταξινόμησης (Figure 4.5, Chapter 4). Επιπλέον, το αποτέλεσμα της χαρτογράφησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο συγκρίθηκε με την επίσημη περίμετρο της πυρκαγιάς σε σχέση με τη χωρική τους αλληλοκάλυψη εκτάρια χαρτογραφήθηκαν ως καμένα από το μοντέλο, ενώ εκτάρια χαρτογραφήθηκαν από τη Δασική Υπηρεσία. Μια έκταση εκταρίων (95.8%) ήταν κοινή και στους δύο χάρτες και 1129 εκτάρια (2.27%) ήταν η μεταξύ τους διαφορά (Figure 4.5, Chapter 4). Η διαφορά αυτή μπορεί να αποδοθεί στους εξής λόγους: Στην υψηλή χωρική ικανότητα των δεδομένων ASTER, που επέτρεψαν τον καλύτερο διαχωρισμό των καμένων από τις μη καμένες εκτάσεις

18 Στην εξελιγμένη μέθοδο ταξινόμησης που χρησιμοποιήθηκε σε σύγκριση με τις παραδοσιακές μεθόδους που χρησιμοποιεί η Δασική Υπηρεσία Στη διαφορά μεταξύ της ημερομηνίας λήψης της δορυφορικής εικόνας ASTER και της ημερομηνίας εκδήλωσης της πυρκαγιάς. Στην ύπαρξη παλαιότερων πυρκαγιών που έχουν αποτυπωθεί στην δορυφορική εικόνα. 4.3 Ανάπτυξη αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση της μη-ατμοσφαιρικά διορθωμένης εικόνας ASTER Σε προηγούμενη παράγραφο έγινε η περιγραφή της ανάπτυξης αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας. Όπως έχει ήδη αναφερθεί, η ατμοσφαιρική διόρθωση δορυφορικών εικόνων συμβάλλει στη βελτίωση των δεδομένων, αλλά δεν παύει να είναι μια χρονοβόρος διαδικασία. Επίσης, η έλλειψη στις περισσότερες περιπτώσεις, ραδιομετρικών και ατμοσφαιρικών δεδομένων τη χρονική διάρκεια της λήψης της δορυφορικής εικόνας, δεν επιτρέπει τον έλεγχο της ακρίβειας της διαδικασίας της ατμοσφαιρικής διόρθωσης. Λαμβάνοντας τα παραπάνω υπόψη, έγινε η ανάπτυξη αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση της ίδιας αλλά μηατμοσφαιρικά διορθωμένης εικόνας. Ο δείκτης βλάστησης χρησιμοποιήθηκε για την ανάπτυξη του μοντέλου και δημιουργήθηκαν τελικά τρία επίπεδα ταξινόμησης για τη χαρτογράφηση των καμένων εκτάσεων: Επίπεδο 2: Το επίπεδο 2 ήταν αυτό που δημιουργήθηκε πρώτο ενώ το πεδίο ορισμού όπου εφαρμόστηκαν οι αλγόριθμοι ήταν όλη η εικόνα. Το μέγεθος της κλίμακας που χρησιμοποιήθηκε ήταν 10 και δόθηκε βάρος μόνο στο κανάλι 3 (0.8) και στον δείκτη βλάστησης (NDVI, 1). Στο κριτήριο ετερογένειας πλήρης βάρος δόθηκε στο χρώμα. Χαρακτηριστικά όπως «μέσος όρος του διαύλου 3», «μέσος όρος του διαύλου 1», «μέσος όρος του δείκτη βλάστησης» και «λόγος του διαύλου 3» (ratio of layer 1) χρησιμοποιήθηκαν για την ταξινόμηση των καμένων εκτάσεων. Όλα τα αντικείμενα ταξινομήθηκαν χρησιμοποιώντας τα χαρακτηριστικά που προαναφέρθηκαν, με

19 αποτέλεσμα τη δημιουργία δύο κλάσεων σε αυτό το επίπεδο: «Καμένη έκταση στο επίπεδο 2» και «νερό». Επίπεδο 3: Σε αυτό το επίπεδο, το πεδίο ορισμού όπου όλοι οι αλγόριθμοι εφαρμόστηκαν ήταν όλα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 2» η οποία είχε δημιουργηθεί στο προηγούμενο επίπεδο. Έτσι, η διαδικασία της κατάτμησης εφαρμόστηκε μόνο στα αντικείμενα που είχαν ταξινομηθεί ως «Καμένη έκταση στο επίπεδο 2». Το μέγεθος της κλίμακας που χρησιμοποιήθηκε ήταν 300, βάρος δόθηκε μόνο στον δίαυλο 3 και στον δείκτη βλάστησης και στο κριτήριο ετερογένειας πλήρης βάρος δόθηκε στο χρώμα. Για την ταξινόμηση χρησιμοποιήθηκαν χαρακτηριστικά όπως «μέσος όρος του διαύλου 3» και «μέσος όρος του δείκτη βλάστησης», ενώ ο αλγόριθμος της ταξινόμησης εφαρμόστηκε μόνο στα αντικείμενα που αποτελούνταν από υπόαντικείμενα ταξινομημένα ως «Καμένη έκταση στο επίπεδο 2». Τελικά, η κλάση «Καμένη έκταση στο επίπεδο 3» δημιουργήθηκε. Εξαιτίας, ωστόσο ότι μερικά αντικείμενα δεν ταξινομήθηκαν σωστά, δύο κατάλληλοι αλγόριθμοι ( find enclosed by class και merge region, Appendix I) εφαρμόστηκαν στα αντικείμενα που ανήκαν στη κλάση «Καμένη έκταση στο επίπεδο 3», με σκοπό τη βελτίωση της ταξινόμησης. Η εφαρμογή των αλγόριθμων είχε σαν αποτέλεσμα την τροποποίηση του σχήματος και μεγέθους των αντικειμένων. Έτσι, επιλέχθηκαν τα κατάλληλα χαρακτηριστικά, όπως ο «μέσος όρος» για την ταξινόμηση των καινούργιων αντικειμένων και τελικά δημιουργήθηκαν δυο κλάσεις στο επίπεδο 3: «Καμένη έκταση στο επίπεδο 3(β)» και «νερό 2». Επίπεδο 1: Το επίπεδο 1 ήταν το τελευταίο που δημιουργήθηκε, ενώ ήταν και το τελευταίο και στην ιεραρχία των επιπέδων. Το πεδίο ορισμού για την εφαρμογή του αλγόριθμου της κατάτμησης ήταν όλα τα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 2», το μέγεθος της κλίμακας που χρησιμοποιήθηκε ήταν 1, βάρος δόθηκε στον δίαυλο 3 και στον δείκτη βλάστησης και στο κριτήριο ετερογένειας δόθηκε 0.9 βάρος στο χρώμα

20 Για την εφαρμογή του αλγόριθμου της ταξινόμησης το πεδίο ορισμού ήταν όλα τα αντικείμενα που ανήκαν στην κλάση «Καμένη έκταση στο επίπεδο 3(β)». Χαρακτηριστικά των αντικειμένων, όπως «μέσος όρος του διαύλου 3» και «λόγος του διαύλου 3» χρησιμοποιήθηκαν για τη δημιουργία των τελικών κλάσεων: «Καμένη έκταση στο επίπεδο 1», «μερικώς καμένη έκταση», «λιγότερο καμένη έκταση», «έκταση με βλάστηση» και «γυμνή έκταση». Οι κλάσεις «Καμένη έκταση στο επίπεδο 1», «μερικώς καμένη έκταση», «λιγότερο καμένη έκταση» ομαδοποιήθηκαν σε μια κλάση, «Καμένη έκταση». 4.4 Ακρίβεια ταξινόμησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο (με τη χρήση της μη-ατμοσφαιρικά διορθωμένης εικόνας) Για τον έλεγχο των αποτελεσμάτων της ταξινόμησης των καμένων εκτάσεων από αντικειμενοστραφές μοντέλο που αναπτύχθηκε, παράχθηκε ο πίνακας σφαλμάτων (error matrix) και ο συντελεστής Kappa. Ο πίνακας 4.3 (Chapter 4) παρουσιάζει τα αποτελέσματα του πίνακα σφαλμάτων για τον έλεγχο της ακρίβειας της ταξινόμησης. Η περίμετρος της πυρκαγιάς που κατασκευάστηκε από την Δασική Υπηρεσία της Πορτογαλίας θεωρήθηκε ως τα δεδομένα αναφοράς. Η συνολική ακρίβεια της ταξινόμησης των καμένων εκτάσεων εκτιμήθηκε ότι ήταν 98.88%, η ακρίβεια του κατασκευαστή 97.99% και η ακρίβεια του χρήστη 97.50%. Ο συντελεστής Κappa υπολογίστηκε ότι ήταν , αποτέλεσμα που δείχνει τη μεγάλη ακρίβεια της ταξινόμησης. Επιπλέον, το αποτέλεσμα της χαρτογράφησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο συγκρίθηκε με την επίσημη περίμετρο της πυρκαγιάς όσον αφορά η με τη χωρική τους αλληλοκάλυψη εκτάρια χαρτογραφήθηκαν ως καμένα από το μοντέλο, ενώ εκτάρια χαρτογραφήθηκαν από τη Δασική Υπηρεσία. Μια έκταση εκταρίων (97.27%) ήταν κοινή και στους δύο χάρτες και 71 εκτάρια (0.23%) ήταν η μεταξύ τους διαφορά (Figure 4.6, Chapter 4). Η διαφορά αυτή μπορεί να αποδοθεί στους λόγους όπως αναφέρθηκαν στην παράγραφο

21 4.4 Σύγκριση των αποτελεσμάτων ταξινόμησης των δύο αντικειμενοστραφών μοντέλων Στις προηγούμενες παραγράφους δόθηκε η περιγραφή της ανάπτυξης δύο αντικειμενοστραφών μοντέλων. Το συμπέρασμα που μπορεί να εξαχθεί από τη σύγκριση των δύο μοντέλων, είναι ότι οι δομές τους είναι παρόμοιες: Τρία επίπεδα ταξινόμησης δημιουργήθηκαν τελικά και στις δυο περιπτώσεις Οι παράμετροι για τη διαδικασία της κατάτμησης και τα χαρακτηριστικά των αντικειμένων που χρησιμοποιήθηκαν για την ταξινόμηση ήταν σχεδόν ίδια Τα πεδία ορισμού στα οποία εφαρμόστηκαν οι αλγόριθμοι κατάτμησης και ταξινόμησης ήταν ίδια. Από τη σύγκριση των αποτελεσμάτων της ακρίβειας της ταξινόμησης των δύο αντικειμενοστραφών μοντέλων προέκυψε ότι η ακρίβεια της ταξινόμησης των καμένων εκτάσεων από το μοντέλο που αναπτύχθηκε χρησιμοποιώντας τη μηατμοσφαιρικά διορθωμένη εικόνα ήταν κατά 0.5% υψηλότερη. Επίσης, οι καμένες εκτάσεις που προέκυψαν από τα δύο μοντέλα συγκρίθηκαν μεταξύ τους όσον αφορά τη χωρική τους αλληλοεπικάλυψη. Το αποτέλεσμα που προέκυψε ήταν ότι το αντικειμενοστραφές μοντέλο που αναπτύχθηκε χρησιμοποιώντας την ατμοσφαιρικά διορθωμένη εικόνα ταξινόμησε 1202 εκτάρια λιγότερα καμένης έκτασης από το δεύτερο μοντέλο. Αυτό μπορεί να αποδοθεί στο γεγονός ότι με την ατμοσφαιρικά διορθωμένη εικόνα ο διαχωρισμός των καμένων από τις μη καμένες περιοχές, όπως οι δρόμοι, οι κατοικημένες περιοχές και οι γυμνές εκτάσεις, είναι πιο εύκολος. 5 ΕΛΕΓΧΟΣ ΤΗΣ ΕΦΑΡΜΟΓΗΣ ΤΟΥ ΑΝΤΙΚΕΙΜΕΝΟΣΤΡΑΦΟΥΣ ΜΟΝΤΕΛΟΥ ΓΙΑ ΤΗ ΧΑΡΤΟΓΡΑΦΗΣΗ ΚΑΜΕΝΩΝ ΕΚΤΑΣΕΩΝ ΣΕ ΑΛΛΕΣ ΠΕΡΙΟΧΕΣ Ο σκοπός του κεφαλαίου 5 είναι ο έλεγχος της εφαρμογής του αντικειμενοστραφούς μοντέλου που έδωσε το καλύτερο αποτέλεσμα, όπως αναφέρθηκε στο προηγούμενο κεφάλαιο, για τη χαρτογράφηση καμένων εκτάσεων σε άλλες περιοχές. Συγκεκριμένα, το αντικειμενοστραφές μοντέλο το οποίο αναπτύχθηκε χρησιμοποιώντας τη μη-ατμοσφαιρικά διορθωμένη δορυφορική εικόνα ASTER

22 εφαρμόστηκε με σκοπό τη χαρτογράφηση καμένων εκτάσεων σε δύο άλλες περιοχές: η πρώτη περιοχή, η οποία βρίσκεται στην Πορτογαλία είχε αποτυπωθεί στην ίδια εικόνα ASTER με αυτή που χρησιμοποιήθηκε για την ανάπτυξη του μοντέλου και η δεύτερη περιοχή, η οποία βρίσκεται στην κεντρική Ισπανία είχε αποτυπωθεί σε διαφορετική εικόνα ASTER. Τα αποτελέσματα της ακρίβειας της ταξινόμησης των καμένων εκτάσεων από την εφαρμογή του μοντέλου έγινε με τη χρήση των περιμέτρων των πυρκαγιών από τις αντίστοιχές Δασικές Υπηρεσίες. Το διάγραμμα ροής του κεφαλαίου αυτού απεικονίζεται στην εικόνα 5.1 στο Chapter Έλεγχος της εφαρμογής του αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων που είχαν αποτυπωθεί στην ίδια εικόνα ASTER Στην ίδια εικόνα ASTER που είχε χρησιμοποιηθεί για την ανάπτυξη του αντικειμενοστραφούς μοντέλου είχαν αποτυπωθεί και άλλες καμένες εκτάσεις οι οποίες οφείλονται σε πυρκαγιές που εκδηλώθηκαν το καλοκαίρι του 2003 ( Figure 5.2, Chapter 5). Σε αυτές λοιπόν τις καμένες εκτάσεις εφαρμόστηκε το μοντέλο που αναπτύχθηκε για τον έλεγχο της δυνατότητας του για ακριβή χαρτογράφηση Figure 5.3, Chapter 5). Ο πίνακας σφαλμάτων (Table 5.1, Chapter 5) που δημιουργήθηκε για τον έλεγχο της ακρίβειας της ταξινόμησης, έδειξε ότι το αντικειμενοστραφές μοντέλο εφαρμόστηκε σε άλλες καμένες εκτάσεις με μεγάλη επιτυχία: η συνολική ακρίβεια χαρτογράφησης των καμένων εκτάσεων βρέθηκε ότι είναι 98.50%, η ακρίβεια του κατασκευαστή 97.74%, η ακρίβεια του χρήστη 98.86%, ενώ ο συντελεστής Kappa Επιπλέον, το αποτέλεσμα της χαρτογράφησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο συγκρίθηκε με την επίσημη περίμετρο της πυρκαγιάς όσον αφορά η με τη χωρική τους αλληλοκάλυψη εκτάρια χαρτογραφήθηκαν ως καμένα από το μοντέλο, ενώ εκτάρια χαρτογραφήθηκαν από τη Δασική Υπηρεσία. Μια έκταση εκταρίων (84.65%) ήταν κοινή και στους δύο χάρτες και 2112 εκτάρια (15.64%) ήταν η μεταξύ τους διαφορά (Figure 5.4, Chapter 5). Πρέπει να τονιστεί όμως, ότι το αντικειμενοστραφές μοντέλο έπρεπε να ρυθμιστεί όσον αφορά τα όρια των συναρτήσεων συμμετοχής (membership functions) των

23 χαρακτηριστικών των αντικειμένων (π.χ «μέσος όρος διαύλου», «λόγος διαύλου») πριν την εφαρμογή του στις διαφορετικές καμένες εκτάσεις. Ο κύριος λόγος στον οποίο οφείλεται το γεγονός αυτό είναι η διαφορετική φασματική συμπεριφορά που παρατηρείται μεταξύ των καμένων εκτάσεων στις οποίες αναπτύχθηκε το μοντέλο και στις καμένες εκτάσεις στις οποίες εφαρμόστηκε. Η αλλαγή στη φασματική συμπεριφορά μπορεί να αποδοθεί στους παρακάτω λόγους: Στον διαφορετικό βαθμό έντασης της πυρκαγιάς Στο χρονικό διάστημα που μεσολάβησε μεταξύ της εκδήλωσης των πυρκαγιών και της ημερομηνίας λήψης της δορυφορικής εικόνας. 5.3 Έλεγχος της εφαρμογής του αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων που είχαν αποτυπωθεί σε διαφορετική εικόνα ASTER Ο περαιτέρω έλεγχος της δυνατότητας του αντικειμενοστραφούς μοντέλου για τη ακριβή χαρτογράφηση, πραγματοποιήθηκε με την εφαρμογή του σε καμένες εκτάσεις που είχαν αποτυπωθεί σε διαφορετική εικόνα ASTER στην κεντρική Ισπανία (Figure 5.5, Chapter 5). Ο πίνακας σφαλμάτων (Table 5.2, Chapter 5) που δημιουργήθηκε για τον έλεγχο της ακρίβειας της ταξινόμησης, έδειξε ότι το αντικειμενοστραφές μοντέλο εφαρμόστηκε σε άλλες καμένες εκτάσεις με μεγάλη επιτυχία: η συνολική ακρίβεια χαρτογράφησης των καμένων εκτάσεων βρέθηκε ότι είναι 97.75%, η ακρίβεια του κατασκευαστή 94.94%, η ακρίβεια του χρήστη 97.99%, ενώ ο συντελεστής Kappa Επιπλέον, το αποτέλεσμα της χαρτογράφησης των καμένων εκτάσεων από το αντικειμενοστραφές μοντέλο συγκρίθηκε με την επίσημη περίμετρο της πυρκαγιάς όσον αφορά η με τη χωρική τους αλληλοκάλυψη. 799 εκτάρια χαρτογραφήθηκαν ως καμένα από το μοντέλο, ενώ 858 εκτάρια χαρτογραφήθηκαν από τη Δασική Υπηρεσία. Μια έκταση 766 εκταρίων (89.3%) ήταν κοινή και στους δύο χάρτες και 59 εκτάρια (6.84%) ήταν η μεταξύ τους διαφορά (Figure 5.6, Chapter 5). Και σε αυτή την περίπτωση όμως, το αντικειμενοστραφές μοντέλο έπρεπε να ρυθμιστεί όσον αφορά τα όρια των συναρτήσεων συμμετοχής (membership

24 functions) των χαρακτηριστικών των αντικειμένων (π.χ. «μέσος όρος διαύλου», «λόγος διαύλου») πριν την εφαρμογή του στις διαφορετικές καμένες εκτάσεις. Το γεγονός αυτό οφείλεται τόσο στους λόγου που αναφέρθηκαν στην προηγούμενη παράγραφο, αλλά και (1) στις διαφορετικές κλιματικές συνθήκες που μπορεί να επικρατούσαν μεταξύ των δύο περιοχών και (2) στα διαφορετικά προϊόντα ASTER που χρησιμοποιήθηκαν. 6 ΣΥΜΠΕΡΑΣΜΑΤΑ Το βασικό συμπέρασμα που προκύπτει από την αυτή την εργασία, είναι ότι ο συνδυασμός της αντικειμενοστραφής μεθόδου ταξινόμησης με τα δορυφορικά δεδομένα ASTER έχει σαν αποτέλεσμα την επιτυχημένη και ακριβή χαρτογράφηση καμένων εκτάσεων. Επιπλέον, τα αποτελέσματα της εργασίας κατέδειξαν τη μεγάλη δυνατότητα εφαρμογής του αντικειμενοστραφούς μοντέλου που αναπτύχθηκε για τη χαρτογράφηση καμένων εκτάσεων σε διαφορετικές περιοχές. Σύμφωνα με τους στόχους της εργασίας, τα κυριότερα συμπεράσματα συνοψίζονται παρακάτω: Στόχος 1: Η ανάπτυξη ενός αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση μιας ατμοσφαιρικά διορθωμένης εικόνας ASTER: Το αντικειμενοστραφές μοντέλο που αναπτύχθηκε με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας είχε ως αποτέλεσμα την πολύ επιτυχημένη χαρτογράφηση των καμένων εκτάσεων (98.38% συνολική ακρίβεια) Στόχος 2: Η ανάπτυξη ενός αντικειμενοστραφούς μοντέλου για τη χαρτογράφηση καμένων εκτάσεων με τη χρήση μιας μη ατμοσφαιρικά διορθωμένης εικόνας ASTER: Το αντικειμενοστραφές μοντέλο που αναπτύχθηκε με τη χρήση της ατμοσφαιρικά διορθωμένης εικόνας είχε ως αποτέλεσμα την πολύ επιτυχημένη χαρτογράφηση των καμένων εκτάσεων (98.88% συνολική ακρίβεια)

25 Στόχος 3: Η σύγκριση και η αξιολόγηση των αποτελεσμάτων της χαρτογράφησης που προέκυψαν από τα δυο μοντέλα που αναπτύχθηκαν: Από τη σύγκριση των αποτελεσμάτων της χαρτογράφησης των καμένων εκτάσεων από τα δύο μοντέλα, προέκυψε ότι το μοντέλο που αναπτύχθηκε με βάση τη μη-ατμοσφαιρικά διορθωμένη εικόνα παρουσίασε μεγαλύτερη συνολική ακρίβεια χαρτογράφησης του. Από τη σύγκριση, προέκυψε επίσης ότι η διαδικασία της ατμοσφαιρικής διόρθωσης της εικόνας ASTER δεν είναι αναγκαία όταν χρησιμοποιείται η αντικειμενοστραφής μέθοδος ταξινόμησης. Στόχος 4: Η εξέταση της δυνατότητας εφαρμογής του μοντέλου που έδωσε το καλύτερο αποτέλεσμα για τη χαρτογράφηση καμένων εκτάσεων σε άλλη περιοχή με τη χρήση της ίδιας εικόνας ASTER: Η συνολική ακρίβεια της χαρτογράφησης καμένων εκτάσεων σε άλλες περιοχές με την εφαρμογή του μοντέλου που αναπτύχθηκε ήταν πολύ μεγάλη (98.50%). Ωστόσο, πρέπει να τονιστεί ότι πριν την εφαρμογή του μοντέλου που αναπτύχθηκε, απαραίτητη ήταν η προσαρμογή του όσον αφορά τις τιμές των χαρακτηριστικών των αντικειμένων που χρησιμοποιήθηκαν. Το γεγονός αυτό μπορεί να αποδοθεί στο διαφορετικό βαθμό έντασης της πυρκαγιάς και το χρονικό διάστημα που μεσολάβησε μεταξύ της εκδήλωσης της πυρκαγιάς και της ημερομηνίας λήψης της δορυφορικής εικόνας. Στόχος 4: Η περαιτέρω εξέταση της δυνατότητας εφαρμογής του μοντέλου για τη χαρτογράφηση καμένων περιοχών με τη χρήση μιας διαφορετικής εικόνας ASTER: ο περαιτέρω έλεγχος της δυνατότητας εφαρμογής του μοντέλου για τη χαρτογράφηση καμένων εκτάσεων σε άλλες περιοχές, έδωσε πάλι πολύ μεγάλη συνολική ακρίβεια (97.75%). Όπως και στην προηγούμενη περίπτωση, έτσι και σε αυτή κρίθηκε αναγκαία η προσαρμογή του μοντέλου πριν την εφαρμογή του στην άλλη περιοχή

26 ABSTRACT Forest fires in the Mediterranean basin have always been a natural factor constituting a part of the ecosystem. However, in recent decades, the general trend in the number of fires and surface burns has increased spectacularly. Since forest fires are a major source of concern, both for environmental and safety reasons, information about them must be available in a timely and harmonized way. Remote sensing from space is especially suitable for forest-fire-related studies, including those focusing on mapping the affected areas. Commercial satellites have long been used for mapping burned areas. Nevertheless, the new generation of higher spatial and spectral resolution satellite sensors, such as ASTER, is expected to provide a good basis for enhanced classification results in burned area mapping. In addition to the potential of the new satellite sensors, advanced image analysis methods can contribute to more accurate mapping of burned areas. Such a new approach is the object-oriented image classification, which is based on fuzzy logic and allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. This study aimed to map the spatial extent of the burned areas in central Portugal by developing an object-based model using ASTER imagery. The main objectives of the study were to develop two different object-based models for mapping a burned area using an atmospherically and a non-atmospherically corrected ASTER image. Subsequently, the performance of the better performing object-based classification model was examined on another burned area located on the same ASTER image in order to examine the model s transferability. In addition, the model was further tested by its implementation on a different ASTER image showing fire burns in another study area located in central Spain. In general, the combination of the new satellite sensor ASTER and the objectoriented image analysis technique proved to be successful in burned area mapping (98.38% and 98.88% overall accuracies). On the other hand, the transferability of the better performing model proved to be successful, that is 98.50% accuracy for the burned areas located on the same ASTER image and 97.75% accuracy for the burned

27 areas located in central Spain, even if the appropriate adjustment of the thresholds of the feature values that were incorporated in the model, thus providing strong evidence for the potential use of the classification model on an operational basis in burned area mapping

28 Chapter 1 1. INTRODUCTION Mediterranean ecosystems are recognized to have been modelled by fire for a long time (Trabaud et al. 1993). However, during the last decades, wildfires have increased in the Mediterranean region, and this increase is related to fuel accumulation as a consequence of the abandonment of cultivated fields or the change in traditional agriculture and cattle raising (Giralt 1990; Debussche et al. 1999; Pausas and Vallejo 1999). In addition, the increase in fire activity can also be attributed to the observed climatic changes (Piñol et al. 1998; Pausas and Vallejo 1999) characterized by an increase in mean temperature on a global scale (Houghton et al. 2001) and with all predictions suggesting a future increment in water deficit. These climatic changes, already obvious at the present time, would lead to an increase in water stress conditions for plants, changes in fuel conditions and an increase in fire risk (Pausas and Vallejo 1999). On the other hand, vegetation burning is recognised as an important contributor to global climate change of its potential to modify atmospheric composition and chemistry [e.g. biomass burning is estimated to contribute about 40% of the gross carbon dioxide (CO 2 )](Justice et al. 2002). The high number of forest fires occurring every year and over the same areas, which in turn amounts to thousands of hectares of burned land in the Mediterranean Basin, constitutes a real threat to natural ecosystems (Koutsias et al. 1999). The immediate consequences of wildfires are the loss of protective vegetation cover, a strong visual impact on the landscape (González-Pelayo et al. 2006) and even the cause of casualties among forest fire fighters and civilians (Forest Fire Report No6). Fire is an important ecosystem process affecting vegetation structure and composition, biogeochemical cycling and hydrology (Dull and Lee 2001) and in the long-term, wildfires have led among others, to landscape changes (Romme 1982; Turner and Romme 1994; Knick and Rotenberry 1997; Turner et al. 1997) and an increased risk of soil erosion (Pausas and Vallejo 1999; Giovannini et al. 2001). The economic, social, ecological, atmospheric, and climatic consequences associated with fire activity denote not only the magnitude of the problem, but also impose the need for the development of reliable monitoring and effective analysis techniques in

29 Chapter 1 order to estimate the ecological impact of fire on the Mediterranean ecosystems (Gitas 1999; Koutsias et al. 1999). In this context, remote sensing could effectively be involved in burned land mapping, since it provides the necessary means for gathering information about the Earth s surface in a less expensive and timely fashion (Koutsias et al. 1999). Satellite data have been used extensively for many years for the detection and mapping of fire-affected areas (Chuvieco and Congalton 1988; Siljeström and Moreno 1995; Koutsias et al. 2000; Roy and Lewis 2002; Mitri and Gitas 2004). Indeed, remotely sensed data can contribute to an improved, more cost effective, objective and time-saving method that can be used to quantify the location, aerial extent and intensity of fire events (Chuvieco 1999). The new generation of high spatial and spectral resolution sensors, such as ASTER, has been estimated to contribute better to the recognition and detection of burned areas because of their advanced and improved abilities. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an advanced multispectral imager with a unique combination of wide spectral coverage and high spatial resolution in the visible near-infrared through shortwave infrared to the thermal infrared regions that was launched on board NASA s Terra spacecraft in December ASTER data is expected to contribute to a wide array of global change-related application areas including vegetation and ecosystem dynamics (Muukkonen and Heiskanen 2005), hazard monitoring (Tralli et al. 2005; Fourniadis et al. 2007), geology and soils (Chikhaoui et al. 2005; Vaughan et al. 2005; Wolken 2006), land surface climatology, hydrology (Boḧme et al. 2006), and land cover change (Ostwald and Chen 2006; Nakayama et al. 2007). Moreover, advanced classification techniques, such as object-oriented image analysis, have also been introduced recently and have showed promising results in burned area mapping (Gitas et al. 2004; Mitri and Gitas 2004). In contrast to the traditional classification techniques, object-oriented classification acts on image objects (segments) that are generated by initial image segmentation and may result in increased accuracy, a more appropriate and realistic representation of the

30 Chapter 1 environment, and a powerful and flexible framework for further data analysis (Benz et al. 2004). The aim of this study was to develop a transferable object-oriented model for ASTER imagery to be used in burned area mapping. The specific objectives were: to develop an object-based classification model for mapping a burned area in Portugal by employing an atmospherically corrected ASTER image and to evaluate the classification accuracy; to develop an object-based classification model for mapping a burned area in Portugal by employing a non-atmospherically corrected ASTER image and to evaluate the classification accuracy; to compare and evaluate the results of the two developed object-based classification models; to test the transferability of the better performing model by mapping another burned area on the same ASTER image; and to further test the transferability of the object-based model by mapping a burned area in Spain using a different ASTER image. The following paragraphs provide an overview of the main structure of the thesis with a brief description of each chapter s contents. Chapter 2 comprises a brief description of the Mediterranean ecosystems and their characteristics and focuses on the problem of forest fires in the region. In addition, an overview of different classification techniques is given, with emphasis on objectoriented image analysis concepts and methods. In the beginning of Chapter 3, a brief description of the study area is given, followed by an introduction to the datasets used in this work. The rest of the Chapter is devoted to the pre-processing of the ASTER imagery. In the first section of Chapter 4, an object-based classification model is developed for fire mapping using an atmospherically corrected ASTER image, while in the second section, a different model is developed for burned area mapping based on the same

31 Chapter 1 ASTER image which was not atmospherically corrected. Conclusions drawn from the two case studies are then discussed. Chapter 5 deals with the evaluation of the transferability of the better performing object-based classification model (i.e. the model that was developed on the nonatmospherically corrected ASTER image). In the first section of this chapter the model is applied at another fire affected site located on the same non-atmospherically corrected ASTER image as that used for the development of the model (see Chapter 4). In the subsequent section, the transferability of the model is further examined by its implementation in a different study area located in Spain (a brief description of the area is also given) and was affected by fire located on another ASTER image. Finally, Chapter 6 provides a discussion of the findings of this research and draws general conclusions

32 Chapter 2 2. AN EXAMINATION OF THE ROLE OF FIRES IN THE MEDITERRANEAN AND AN INTRODUCTION TO OBJECT-ORIENTED IMAGE ANALYSIS Chapter 1 presented the aim and the specific objectives of this study. This chapter, which focuses on the role fire plays in the Mediterranean and discusses objectoriented image analysis, is divided into 5 sections. Section 2.1 gives a description of the Mediterranean ecosystems and its characteristics. Section 2.2 discusses the problem of forest fires in the Mediterranean and the reasons for their outbreak. Section 2.3 highlights the importance of mapping fire-affected areas, while in Section 2.4, the most common mapping techniques are described. Finally, the concepts of the object-oriented classification methods are introduced in Section The Mediterranean ecosystem and its characteristics In one of the first efforts to divide the earth into a limited number of geographical regions, Köppen, in 1900, defined Mediterranean regions as regions subject to climates receiving rain primarily during the winter season from the mid-latitude cyclone. These areas experience climatic conditions characterized by a hot dry period in the summer and a cool wet period in the winter, which can be crudely considered to be the transition between dry tropical and temperate climates. This basic definition constitutes the core of what is called the Mediterranean climate (Di Castri and Mooney 1973). This climate occurs on the west coasts of all continents between latitudes 308 and 458, and is due to global air circulation patterns as the dry subtropical high-pressure cells move poleward in the warm season, blocking the entry of mid-latitude storm (Figure 2.1). Eastward penetration of Mediterranean climates occurs when they are not arrested by high relief or confrontation with moist climates with summer rainfall: the two greatest penetrations correspond to western Asia and to Argentina. Two of the main corollaries of this climate-based definition are clearly the identification of Mediterranean regions as zonal and the exclusion of limited geographical definitions considering only the Mediterranean Sea. It is important to note that the Mediterranean

33 Chapter 2 climate is very recent in geological terms and first appeared approximately 3.2 million years ago during the Pliocene (Di Castri and Mooney 1973). Mediterranean climates have attained their greatest extent at present (Joffre and Rambal 2002). Figure 2.1 Map of the five regions with Mediterranean-type climate (red). For the Mediterranean Basin, the yellow area corresponds to the Mediterranean steppe domain (mean annual rainfall between 100 and 400 mm) according to Le Houérou, 1997 (Source: Joffre et al. 2002) Mediterranean-type ecosystems are presently represented by five widely separated regions of the world that present strong similarities not only in terms of climatic trends but also in the structure of vegetation and in the general land-use patterns and appearance of the landscape. These regions are parts of the Mediterranean Basin, California, southwestern and southern Australia, central Chile and southern Africa. The seasonality in air temperature and precipitation, which is the most distinctive feature of the Mediterranean-type climates, has important implications for vegetation functioning, as it limits the active growing season to the humid period between fall and spring. The length of this period ranges from 5 to 10 months according to distinct climatic subtypes. The vegetation of the Mediterranean Basin region is quite distinct from that of any other region in Europe. It is dominated by evergreen trees, shrubs, and shrublets which can survive long hot summers without rain. Most of the herbaceaous plants die right down and remain inactive in the summer with dormant buds in the soil, while

34 Chapter 2 the annuals complete their life cycle by the summer. Such a contrasting and unique type of climate has favoured types of vegetation that occur nowhere else in Europe. Early human colonisation of the Mediterranean shores has resulted in a prolonged and intensive influence on the vegetation of the region. The spread of the Neolithic civilization, present more than 9000 years ago in the Middle East (Zohary and Hopf 1994), transformed the landscape that was dominated by primary forests into a mosaic of areas with stands of trees and agriculture. The remaining woodlands were used as a source of fuel such as charcoal, for timber and wood for buildings and for weapon tools. This use of biomass is the most important factor in the relationship between man and forest in the Mediterranean over the last eight millennia, from the Neolithic Age until the first half of the twentieth century. In this context, some natural vegetation has followed civilization through time and space; besides the well-known history of cereals, vine and olive trees, an economic history of forest resources has helped to shape the landscape of the Mediterranean Basin. Indeed, the distribution of some species around the Mediterranean, such as the chestnut tree (Castanea sativa) and the Italian stone pine (Pinus pinea), represents a problem in traditional bio-geographical study because man has strongly influenced the distribution of these species in the last 8000 years (Quézel and Barbero 1990). The current distribution of Pinus halepensis, Cupressus sempervirens, Quercus coccifera, Ceratonia siliqua and others specie are probably linked more to human management than to ecological factors. Moreover, not only trees but even shrubby species have been an important economic resource since the beginning of the twentieth century. Plant communities persisting to the present day are either scattered woodlands, which survive in localities that have not been destroyed by humans or domesticated animals, or more commonly dense evergreen sclerophyllous scrub known as maquis or, more widespread still, dwarf, scattered, mostly evergreen shrublets, the garigue. The dominant trees in the Mediterranean zone are evergreen oaks and pines with many evergreen shrubs such as juniper, heathers, cistus, erica, spiny broom, strawberry trees, and lentisc being also present. The dominant characteristic species are the olive and carob trees

35 Chapter 2 Processes often linked to anthropisation such as degradation, fire and grazing, are frequently used in a generic way as causes of degradation. In fact, little is known of the dynamic processes that have produced the current vegetation and landscape in the Mediterranean, but an important factor is likely to be the use, planned or otherwise, of the forest resource. Thus, one extends to the notion that if the Mediterranean landscapes are the result of man-induced changes, it is to be expected that the abandonment, cessation of human practices, as is obvious in recent years, should lead to the progressive loss of the Mediterranean ecosystem (Di Pasquale et al. 2004). 2.2 Forest fires in the Mediterranean Basin In the Mediterranean European Basin natural forest fires have probably influenced vegetation since the appearance of the first terrestrial plants and they have represented one of the most important evolutionary factors. The origin of the use of fire by man has been dated to a period during the middle Paleolithic Age (Muller-Karpe 1992). Naveh (1975) reports that the first landscape transformations, through the processes of post-fire regeneration, had been induced to favour hunting and the spread of vegetation species used by man and for livestock nutrition. Fire seems to be the most ancient technique for the management of vegetation (Singh et al. 1981) and may exert a positive or negative influence upon ecosystem dynamism, depending on the particular characteristics of fire occurrence such as intensity, type, periodicity etc. However, nowdays, fire represents one of the greatest disturbances of the forest ecosystem, since the high number of forest fires occurring every year and over the same areas, which in turn amounts to thousands hectares of burned land in the Mediterranean Basin, constitutes a real threat to natural ecosystems (Koutsias et al. 1999). Fire statistics compiled for the European Mediterranean areas show a clearly increasing tendency in the number of forest fires in the last decades (Source: Forest Fire Report No6). Figure 2.2a shows the total burned area per year in the five Mediterranean European countries (Portugal, Spain, Italy, France and Greece) since 1980 and Figure 2.2b shows the yearly number of fires since 1980 clearly indicating an increasing trend during the 1990s

36 Chapter 2 Figure 2.2 Burned area (a) and number of fires (b) in the five Southern Member States for the last 26 years (Source: Forest Fires Report No.6) The increase in the general trend in the number of fires and in the surfaces burned is due to: a. land-use changes: As mentioned in the previous section, in the Mediterranean basin, many centuries of severe human pressure resulting in burning, cutting and grazing on non-arable lands and clearing, terracing, cultivating, and later abandonment of arable portions, have created a strongly human-influenced landscape. The cultivation of marginal areas under increasing population pressure have been common in southern Europe since the 16 th century (Roxo and Mourao 1995; Kosmas 1996).The changes in fire occurrence during the last decades closely reflect the recent socio-economic changes in the European Mediterranean countries (Vélez 1993; Moreno et al. 1998). With industrial development, European Mediterranean countries have experienced: depopulation of rural areas, increases in agricultural mechanization, decreases in grazing pressure and wood gathering, and increases in the urbanization of rural areas (LeHouérou 1993). These changes in traditional land-use and lifestyles have implied the abandonment of large areas of farmland, which has led to the recovery of vegetation (García- Ruiz et al. 1996; Roxo et al. 1996), homogenization of the landscapes (Moreno and Oechel 1994; Sala and Rubio 1994) and an increase in accumulated fuel (Rego 1992). According to Smith and Woodgate (1985), doubling the amount of available fuel also doubles the rate of fire spread, which in turn produces a fourfold increase in fire severity. In general, land-use changes produced during the last decades in southern Europe are parallel to the changes in the fire regime,

37 Chapter 2 from being few in number and affecting small areas, to becoming very numerous and affecting large areas every year (Pausas and Vallejo 1999). b. climatic warming: The relationship between meteorological conditions and fire occurrence is well known (Chandler et al. 1983). Fires tend to be concentrated in summer when temperatures are high, and air humidity and fuel moisture are low. Pausas (2004) showed that the annual area burned in a study area in Spain was significantly related to regional annual rainfall and more closely to summer rainfall. Predictions, however, on climate warming in the Mediterranean basin indicate an increase in air temperature and a reduction in summer rainfall (Houghton et al. 1996). Several studies have showed that average summer rainfall has tended to decrease over the last decades (Amanatidis et al. 1993; Lebourgeois et al. 2001; Piervitali and Colacino 2003; Pausas 2004) and temperatures have increased (Piñol et al. 1998; Esteban-Parra et al. 2003; Pausas 2004) in areas of the Mediterranean region. In summary, there is evidence that summers have become warmer and drier over the last five decades (Pausas 2004). These changes imply a decrease in fuel moisture and a consequent increase in fire hazard (Piñol et al. 1998), with a consequent increase in ignition probability and fire propagation (Pausas and Vallejo 1999). Pausas (2004) suggested that if the current climatic trends remained constant, fuel conditions in summer would become drier each year and, as a consequence, the risk of large burned areas would increase. Future climatic scenarios based on global circulation models (GCM) also predict warmer summers and drier conditions for the Mediterranean Basin (due to increase in temperature and evapotranspiration), although there is no clear agreement regading the predictions of future precipitation (Houghton et al. 2001). Likewise, the IPCC (Intergovernmental Panel on Climate Change) report predicted a probable increase in the wildfire risk in the Mediterranean ecosystems. However, to predict the future fire regime, other factors are also important and show effects in different directions, such as the continuously increasing urban-wildland interface (which will favour fire ignitions), fuel management and increased prevention and suppression efforts (Pausas 2004)

38 2.3 Mapping of burned areas using remote sensing Chapter 2 After a forest is damaged by fire, detailed and current information concerning the location and extent of the burned areas is important in order to assess economic losses and ecological effects (Pereira et al. 1997). Moreover, accurate assessments aid in evaluating the effectiveness of measures taken to rehabilitate the fire damaged area and allow forest managers to identify and target areas for intensive or special restoration (Jakubauskas et al. 1990), thus avoiding long-term site degradation. Nowadays, most of the National Forest Services in the Mediterranean Europe do not provide cartographic representation of the burned areas (Chuvieco 1999) and this lack of information results in a poor understanding of the spatial consequences of fires. Traditional methods of burned area mapping, though they can be used with small fires, are not applicable in the case of large fires due to cost and time limitations (Gitas 1999). According to Chuvieco (1997), satellite remote sensing has supplied an alternative to conventional techniques in that it is especially suitable for monitoring burned areas. Indeed, given the very broad spatial extent and often limited accessibility of the areas affected by fire, satellite remote sensing is an essential technology for gathering the required information in a timely and methodologically consistent manner (Pereira et al. 1997). The importance of remote sensing in mapping burned areas seems to be becoming more and more recognized each year. An excellent example is the European Forest Fire Information System (EFFIS), an activity initiated by the Directorate-General Join Research Centre (DG JRC) in order to estimate the annual damage caused by forest fires in the south of the EU (Forest Fire Report No6). Modern methods based on satellite remote sensing and geographic information analysis are used for this purpose. This activity produced the first cartography of forest fire damage in the south of the EU in Since then, cartography of all burned areas larger than 50 ha is produced every year through the processing of satellite imagery (in particular,

39 Chapter 2 imagery MODIS satellite images). Further to the mapping of burned areas, the analysis of which types of land cover classes have been affected by fires is performed. Satellite remote sensing enables the acquisition of data on the Earth s surface even in areas with limited accessibility and on a regular and permanent basis. In general, according to Caetano et al. (1994), the use of satellite remote sensing technology for burned area mapping has the following potential: the acquisition of data that present different spectral reflectance characteristics between burned and unburned healthy vegetation (Tanaka et al. 1983), especially in the infrared part of the electromagnetic spectrum; the effectiveness of the cost/benefit ratio compared to field measurements or aerial photography, especially in cases of large geographic extent (Lauer and Krumpe 1973); the periodical acquisition of the required information (Lee et al. 1977) combined with the synoptic view and the minimal time required for data acquisition, and the digital form of the data with all the accompanying advantages, such as the speed and objectivity of data processing (Richards 1984) An overview of the different sensors and techniques employed for burned area mapping The use of remote sensing images for burned land mapping has greatly increased in recent years and covers a wide range of sensors and techniques (Koutsias et al., 1999; Pereira et al. 1997). Most of these attempts have relied on low resolution sensors such as the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) (Martin and Chuvieco 1994; Pereira et al. 1999; Fraser et al. 2000; Chuvieco et al. 2005), SPOTVEGETATION (Fraser et al. 2000; Stroppiana et al. 2002; Grégoire et al. 2003), the Along Track Scanning Radiometer (ATSR) data (Eva and Lambin 1998) and, more recently, the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (Barbosa et al. 2001; Roy and Lewis 2002). Within high resolution sensors, Landsat Thematic Mapper (TM) data has been the most widely used (Koutsias and Karteris 2000; Koutsias et al. 2000; Miller and Yool 2002 ), while medium spatial resolution sensors ( m)

40 Chapter 2 (Salvador et al. 2000; Vázquez et al. 2001; García and Chuvieco 2004) and SAR data (Gimeno and San-Miguel-Ayanz 2004) have been rarely used. Although a large number of different methodologies have been developed, no standard classification procedure can be applied to remotely sensed data for the identification and mapping of burned areas. Methodologies vary according to the specific characteristics of the case study (Karteris 1995), although the spectral, spatial or temporal resolution of the satellite data also determines the type of method used (Pereira et al. 1997). The most commonly used methods for monitoring, detecting and mapping burned areas can be seen in Table 2.1. Table 2.1 Different techniques employed for burned area mapping (first column) and corresponding authors (second column) Despite the advantages of remote sensing, several problems have been reported in burned area mapping using satellite data. Multispectral classification, one of the most common methods for mapping burned areas, is based on the spectral properties of different classes of interest and employs special algorithms designed to perform

41 Chapter 2 various types of spectral analysis. However, the use of these classifications has been repeatedly reported to create various types of confusion that can affect the accuracy of mapping, the most important of which can be summarized as follows: spectral overlapping between slightly burned areas and other non-vegetated categories, especially water bodies, urban areas and bare soil (Tanaka et al. 1983; Huete et al. 1985; Ponzoni et al. 1986; Parnot 1988; Pereira and Setzer 1993; Siljeström and Moreno 1995; Caetano et al. 1996); spectral overlapping between burned areas and shaded unburned areas (Caetano et al. 1994; Pereira et al. 1997); spectral overlapping between burned areas and unburned forest (Chuvieco and Congalton 1988; Simpson 1990). 2.4 New sensors for burned area mapping As mentioned in previous paragraphs many satellite sensors, including most recently the MODIS sensor on Terra satellite, have been used over the years for burned area mapping. ASTER is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industrial organizations in both countries and was launched along with MODIS on board NASA s Terra spacecraft in December The ASTER instrument provides the next generation in remote sensing imaging capabilities compared with the older Landsat Thematic Mapper, and Japan's JERS-1 OPS scanner. ASTER covers a wide spectral region with 14 bands from the visible to the thermal infrared with high spatial, spectral and radiometric resolution: three bands in the VNIR, six bands in the SWIR and four bands in the TIR (Figure 2.3). An additional backward-looking near-infrared band provides stereo coverage for digital elevation model creation. The spatial resolution varies with wavelength: 15 m in the visible and near-infrared (VNIR), 30m in the short wave infrared (SWIR), and 90m in the thermal infrared (TIR). Each ASTER scene covers an area of 60 x 60 km. As the "zoom lens" for Terra, ASTER data are used by other Terra and space-borne instruments for validation and calibration

42 Chapter 2 Figure 2.3 Characteristics of the three ASTER Sensor Systems (Table on top) and Comparison of Spectral Band between ASTER and Landsat-Thematic Mapper (Figure below) (Source: ASTER Users Handbook Version 2 and The primary scientific objective of the ASTER mission is to improve understanding of the local- and regional-scale processes occurring on or near the earth s surface and lower atmosphere, including surface-atmosphere interactions. ASTER data are expected to contribute to a wide array of global change-related application areas, though up until now, ASTER data have been mainly employed in geological application (Yamaguchu and Naito 2003; Pieri and Abrams 2004; Gomeza et al. 2005; Rowan et al. 2005) environmental and ecological studies (FalkowskiI et al. 2005; Marçal et al. 2005; Muukkonen and Heiskanen 2005) and in DEM generation (Fujisada et al. 2005; San and Süzen 2005). However, as yet, the use of ASTER imagery has had rather limited applications in the mapping of burned areas. ASTER is an on-demand instrument. This means that data are only acquired over a location if a request, which comprises a submission of a proposal, has been made to observe that area. Nonetheless, for a variety of reasons, even if a proposal is accepted, there is no guarantee that ASTER can acquire the requested data. Furthermore, even if

43 Chapter 2 ASTER observes the right area at the right time of the year, the resulting data may not meet the user s requirements (e.g. the site might have been covered by clouds). The ASTER instrument produces two types of Level-1 data: Level-1A (L1A) and Level-1B (L1B). ASTER L1A data are formally defined as reconstructed, unprocessed instrument data at full resolution. They consist of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data, thus maintaining original data values. The L1B data are generated by applying these coefficients for radiometric calibration and geometric resampling (ASTER Users Handbook Version 2). 2.5 New classification techniques for burned area mapping In order to overcome the limitations of most of the commonly used classification techniques, as discussed in the previous section, new classification techniques such as neural network techniques and object-oriented image analysis for burned area mapping need to be examined. Even though neural networks is a powerful signal processing method, nonetheless it is applied on pixels or rectangular areas and does not take into account contextual information. On the other hand, the basic processing units of object-oriented image analysis are segments, so-called image objects, and not single pixels. The advantages of object-oriented analysis are meaningful statistic and texture calculation, an increased uncorrelated feature space using shape (e.g. length, number of edges, etc.) and topological features (neighbour, super-object, etc), and close relation between real-world objects and image objects. This relation improves the value of the final classification and cannot be fulfilled by common, pixel-based approaches (Benz et al. 2004). Furthermore, recent studies on mapping burned areas using LANDSAT-TM imagery, as well as NOAA-AVHRR, have shown that using the object-oriented technique produces satisfactory classification results, while the different land cover classes of confusion can be discriminated (Gitas et al. 2004; Mitri and Gitas 2004)

44 Chapter 2 Taking into consideration the advantages of the method and the apparent promising results when it is implemented for burned area mapping, the advanced classification technique of object-oriented image analysis was investigated for burned area mapping using the ASTER image. The following sections introduce the basic concepts behind object based image analysis Image understanding in object-oriented image analysis When human beings make use of their eyes, they are performing a complex mental process. This process is called image understanding (Figure 2.4). That is, when surveying a region with the eyes, certain areas are brought into focus in one s surrounding and a certain area is registered to have a particular size, form, and colour. Thus, in one s vision, the area becomes an object. Similar to human vision, the object-oriented analysis concept of image understanding is based on a correct segmentation of the visual image content of interest against other visual image contents. Segmentation is performed by splitting the image into zoned partial areas of differing characteristics. The segments are called image objects. Figure 2.4 An approach to visual cognition: when one sees a red round area he/she classifies it as a red, round object (a). Underneath (b) one sees another object, which he/she already knows is a human being. Immediately one combines both figures and relates them to each other. Both objects may be located in front of a third large blue object (c). The 3 objects are connected by defined relationships ( underneath, in front of ). The features of the objects and their relationships allow one to recognize a child playing with a ball in the sky. This cognition process compares one s view of the objects and their relationships with the existing knowledge of one s memory (Source: Definies, Professional 5, Userguide). Hence, in contrast to traditional image processing methods, the basic processing units of object oriented image analysis are image objects or segments, and not single pixels. Even the classification acts on image objects. The theory behind the two basic steps carried out (Figure 2.5), namely segmentation and classification, and the basics of object oriented image analysis are reviewed in turn overleaf

45 Chapter 2 Figure 2.5 Procedure sequence of object-oriented image analysis Segmentation The first step in object-oriented image analysis is segmentation. Segmentation is the subdivision of an image into separated regions. The segmentation method followed in object based approach to image analysis is a data driven method (bottom-up). In general, in bottom-up approaches the segments are generated based upon a set of statistical methods and parameters for processing the whole image. As such, bottomup methods can also be seen as a kind of data abstraction or data compression. Nevertheless, as in the beginning the generated image segments have no meaning, they can be better called image object primitives. It is up to the user to determine what kind of real world objects the generated image objects represent. The basic difference between both approaches is: top-down methods usually lead to local results because they just mark pixels or regions that meet the model description, whereas bottom-up methods perform a segmentation of the complete image. They group pixels to spatial clusters which meet certain criteria of homogeneity and heterogeneity Classification Classification in object-based analysis is based on fuzzy logic. Fuzzy logic is a mathematical approach to quantifying uncertain statements. The basic idea is to replace the two strictly logical statements yes and no by the continuous range of [0 1], where 0 means exactly no and 1 means exactly yes. All values between 0 and 1 represent a more or less certain state of yes and no. Thus, fuzzy logic is able to emulate human thinking and to take into account even linguistic rules. Fuzzy

46 Chapter 2 classification systems are suited to handling most vagueness in remote sensing information extraction. Parameter and model uncertainties are considered using fuzzy sets defined by membership functions. Instead of binary true and false, the multivalued fuzzy logic allows transitions between true and false. Additionally, there are more or less strict realizations of the logic operations AND or OR. The output of the fuzzy classification system is a fuzzy classification, where the membership degree to each land cover and land use is given for each object. This enables a detailed performance analysis and gives an insight into the class mixture for each image object Basics of object oriented image analysis One motivation for the object oriented approach is the fact that the expected result of many image analysis tasks is the extraction of real world objects, proper in shape and proper in classification. This expectation cannot be fulfilled by common, pixel-based approaches. Thus, in order to become acquainted with the object oriented approach, its basic characteristic aspects are discussed in the following paragraphs: 1. Directly connected to the representation of image information by means of objects is the networking of these image objects. Whereas the topological relation of single, adjacent pixels is given implicitly by the raster, the association of adjacent image objects must be explicitly worked out in order to address neighbour objects. In consequence, the resulting topological network has a big advantage as it allows the efficient propagation of many different kinds of relational information. 2. Each object-based classification task addresses a certain scale. The scale is a crucial aspect of image understanding. Although in the domain of remote sensing a certain scale is always presumed by pixel resolution, the desired objects of interest often have their own inherent scale. Scale determines the occurrence or nonoccurrence of a certain object class and the same type of objects appears differently on different scales. Note the difference between scale and resolution of satellite image data: as resolution commonly expresses the average size of the

47 Chapter 2 area that a pixel covers on the ground, scale describes the magnitude or the level of abstraction on which a certain phenomenon can be described. Thus, it is important that the average resolution of image objects can be adapted to the scale of interest. Image information can be represented on different scales based on the average size of image objects. The same imagery can be segmented into smaller or larger objects, with considerable impact on practically all information which can be derived from image objects. Thus, specific scale information is accessible. 3. Furthermore, it is possible to represent image information on different scales simultaneously by different object layers. Bringing different object layers in relation to each other can contribute to the extraction of further valuable information. This can be achieved, for instance, by a hierarchical networking and representation of image objects. Figure 2.6 Image object hierarchy (Source: Definiens Professional 5, User Guide) During the segmentation process, a hierarchical network of all generated image objects which represents image information in different spatial resolutions simultaneously is constructed. Starting at the level of pixels the levels are consecutively numbered and the image objects are networked so that each image object knows its context (neighborhood), its super-object and its sub-objects (see Figure 2.6). Thus, it is possible to define relations between objects, and to utilize this kind of local context information. This hierarchical network is topologically definite, i.e., the border of a superobject is consistent with the borders of its sub-objects. The area represented by a specific image object is defined by the sum of its sub-objects areas. Each level is

48 Chapter 2 constructed based on its direct sub-objects, i.e., the sub-objects are merged into larger image objects on the next level. Merging is limited by the borders of superobjects; adjacent image objects cannot be merged when they are sub-objects of different super-objects. The hierarchical network of image objects provides possibilities for innovative techniques: Structures of different scales can be represented simultaneously and thus classified in relation to each other. Different hierarchical levels can be segmented based on different data; an upper layer, for instance, can be built based on thematic land register information, whereas a lower layer is segmented using remote sensing data. Classifying the upper level, each land register object can be analyzed based on the composition of its classified sub-objects. By means of this technique different data types can be analyzed in relation to each other. The shape of image objects can be corrected based on a regrouping of subobjects. Analysis of image objects based on sub or super-objects. 4. An astonishing characteristic of object-oriented image analysis is the multitude of additional information which can be derived based on image objects: tone, shape, texture, area, context, and information from other object layers. Using this information, classification leads to better semantic differentiation and to more accurate and specific results. In a conceptual perspective, the available features can be distinguished as follows overleaf: intrinsic features: the object s physical properties, which are determined by the imaging situation, basically sensor and illumination. Such features describe colour, texture and form of the objects; topological features: features which describe the geometric relationships between the objects or the whole scene, such as being left, right or at a certain distance to a certain object or being in a certain area within the image; context features: these describe the semantic relationship of objects to one another

49 Chapter 2 5. Based on classification the processing of image objects can be done locally in a specific way; that is, instead of processing all areas of an image with the same algorithms, a differentiated procedure can be much more appropriate. This is a specific strength of object oriented image analysis. 6. Characteristic of the object oriented approach is, finally, a circular interplay between the processing and classifying of image objects. Based on segmentation, scale and shape of image objects, specific information is available for classification. In turn, based on classification, specific processing algorithms can be activated. In many applications, the desired geoinformation and objects of interest are extracted step by step, by iterative loops of classifying and processing. Thereby, image objects as processing units can continuously change their shape, classification and mutual relations. Similar to human image understanding processes, this kind of circular processing results in a sequence of intermediate states, with an increasing differentiation of classification and an increasing abstraction of the original image information. At each step of abstraction, new information and new knowledge is generated and can be used beneficially for the next analysis step. Thereby, the abstraction not only concerns the shape and size of image objects, but also their semantics. It is interesting that the result of such a circular process is by far not only a spatial aggregation of pixels to image regions, but also a spatial and semantic structuring of the image s content. Whereas the first steps are more data driven, more and more knowledge and semantic differentiation can be applied in later steps. The resulting network of classified image objects can be seen as a spatial, semantic network. After successful analysis, much interesting, and additional information can be derived just by processing requests over this network

50 2.6 Chapter summary Chapter 2 Chapter Two can be summarized as follows: Mediterranean regions are defined as regions subject to climates receiving rain, primarily during the winter season from the mid-latitude cyclone. These areas experience climatic conditions characterized by a hot dry period in the summer and a cool wet period in the winter, which can be crudely considered to be the transition between dry tropical and temperate. The vegetation of the Mediterranean Basin region is quite distinct from that of any other region in Europe. It is dominated by evergreen trees, shrubs and shrublets which can survive long hot summers without rain; There are two main reasons for the increase in fire outbreaks in the Mediterranean basin: (a) land-use changes (rural depopulation results in increasing land abandonment and consequently, fuel accumulation; and (b) climatic warming (which reduces fuel humidity and increases fire risk and fire spread); The main reasons for mapping fire-affected areas are to assess the impact of fires in the ecosystems, to establish effective solutions in order to control and reduce the spread of fires and to restore the fire damaged areas; Satellite remote sensing allows the acquisition of data on the Earth s surface, even in areas with limited accessibility and on a regular and permanent basis; Remote sensing studies for burned area mapping have been conducted using satellite data of different spatial resolution and employing a large number of different classification methodologies; Most of the commonly used techniques for fire mapping using satellite images have major limitations, mainly because of the confusion created between burned land and other land types in the process of mapping; The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is an advanced multispectral imager that was launched on board NASA s Terra spacecraft in December ASTER covers a wide spectral region with 14 bands from the visible to the thermal infrared with high spatial, spectral and radiometric resolution;

51 Chapter 2 In contrast to classic image processing methods, the basic processing units of object-oriented image analysis are image objects or segments and not single pixels; moreover, classification acts on image objects. One motivation for the object-oriented approach is the fact that, in many cases, the expected result of most image analysis tasks is the extraction of real world objects, proper in shape and proper in classification. This expectation cannot be fulfilled using traditional, pixel-based approaches

52 3. STUDY AREA AND DATA PREPROCESSING Chapter 3 The aim of this research, as presented in Chapter 1, was to develop a transferable object-based classification model for burned area mapping. The methodology (see Chapter 4) followed to achieve this aim initially required the development of a model by mapping a burned area located in central Portugal. Furthermore, the performance of the developed object-based classification model had to be tested (see Chapter 5) in order to evaluate the degree of its transferability for burned area mapping. Hence, the developed model was implemented for mapping different burned areas located in central Spain. The current Chapter deals in the beginning with a description of the two aforementioned study areas: the first study area, which is located in Portugal, is presented in Section 3.1, while the second study area, which is located in Spain, is described in Section 3.2. In the remaining sections of Chapter 3, a description of the datasets and their preprocessing is given. 3.1 Description of the first study area In 2003, Portugal faced the worst fire season ever recorded. There were large fires, and small fires (area<1 ha), which were responsible for ha of burned forest land (Source: Forest Fire in Europe-2003 fire campaign). Major fire incidences occurred in the study area located in Central Portugal (Figure 3.1), between the districts of Santarem and Castelo Branco, which are separated by the Zézere River, a tributary of the Tagus River. The study area is mainly covered by woodland and agricultural land. The woodland mainly consists of Pinus pinaster atlantica, Pinus pinea, Quercus suber and Quercus fraginea. The agricultural areas in the southern parts are covered by vineyards, olive groves and fruit trees. Moreover, given the favourable climatic conditions for timber production in Portugal, large areas of new plantations of both coniferous and deciduous species, particularly eucalyptus, have recently been established (Wilkinson 1995). The altitude range within the area of interest lies between 300 and 400m above sea level, while the geology of the area is characterized by the presence of shale, sands and quartzite

53 Chapter 3 Figure 3.1 Map of the study area and satellite image showing fire burns (left). Map of the major fires occurred in Portugal in the summer of 2003 (right) The frequency of fire incidences in the area is high, which can be attributed to the fact that the area has experienced a very substantial rural land abandonment during the last 50+ years (since 1960 the population of the area has decreased almost by half), thus entering the typical southern European process of fuel accumulation and increased fire risk. One additional factor that makes the area particularly fire-prone is the considerably warm/hot, dry, and long summer, even if it has relatively high net primary productivity, since it receives a reasonable amount of rainfall yearly, and thus vegetation tends to regenerate relatively fast after the fire

54 Chapter 3 Figure 3.2 Maps of the environmental and landscape characteristics of the study area in relation to the official fire perimeters

55 Chapter Description of the second study area The second study area, the Special Protected Area (SPA) num. 56 Encinares de los rios Alberche y Cofio, is located 40 km south west of Madrid (Central Spain-Figure 3.3). It comprises an area of some ha. (comprising 10.3% of the Madrid Region), and encompasses 19 municipalities falling within the Madrid Region. Figure 3.3 Map of the second study area in Spain The topography of the landscape can be described as flat and low plain in the east Depresión del Tajo-and mountainous areas Sierra de Guadarrama and Siera de Gredos-in the west. Grassland, shrubs, and various tree species (such as Pinus pinea, Pinus pinaster, Quercus ilex and Castanea sativa) dominate the natural vegetation in this area. The human influence in this area has led to a characteristic landscape type known as dehesa, which comprises a combination of extensive grazing of natural pastureland and crop cultivation with a scattering of Holm oaks (Quercus ilex). This mountainous area has been included in the list of Community Important Sites because of its high environmental value regarding its flora and fauna; notable species in the park include Aquila adalberti (Spanish Imperial Eagle), which nests in the SPA, and Lynx pardina (the Iberian Lynx), a species at high risk of extinction. SPA num.56 is a mountainous area with a low population density that has experienced aging of agrarian employers and consequent abandonment of traditional land use. The abandonment of crops (e.g. vineyard, olive groves) and pastures has resulted in old-field succession in these areas. Urbanization and recreational uses

56 Chapter 3 have also increased in recent years due to the natural beauty of the landscape and its proximity to Madrid (Romero-Calcerrada and Perry 2002). 3.3 Datasets The datasets that were used in this research were: An ASTER L1A data product showing burns that occurred during the summer of 2003 in central Portugal was obtained for this study. The image particularly was used for the development of the object based classification model; An ASTER L1B image data showing fire burns from a forest fire which occurred in central Spain on the 26 th August 2003 was additionally acquired in order to test the transferability of the developed object based classification model; The map of the official fire perimeters was provided by the Portuguese Forest Service and the Spanish Forest Service in order to evaluate the classification result. 3.4 Image pre-processing Preprocessing is the correction of deficiencies and the removal of flaws present in the data; in principle, it improves image quality and provides the basis for later analyses in which information is extracted. In this research, the preprocessing procedure involved mainly the atmospheric correction of the ASTER image. A brief introduction to the theory behind atmospheric correction, the methods used and the procedure of the atmospheric correction of the image are described in the following paragraphs (Figure 3.4). Figure 3.4 Flowchart of the preprocessing of the ASTER image

57 3.4.1 Atmospheric correction Chapter 3 A very large portion of optical remotely sensed imagery is severely contaminated by aerosols, clouds and their shadows. It is greatly beneficial for land surface characterization if these atmospheric effects from imagery are removed. As the utility of these data becomes more quantitative, the accurate retrieval of surface reflectance becomes increasingly important. The procedure for retrieving surface reflectance from remotely sensed imagery is called atmospheric correction in optical remote sensing (Liang 2004). The atmospheric correction of satellite mages is an important step in improving the data analysis in many ways (Richter 1996a): The influence of the atmosphere and the solar illumination is removed or at least greatly reduced; Multi-temporal scenes recorded under different atmospheric conditions can be better compared after atmospheric correction. Changes observed will be due to changes on the earth s surface and not due to different atmospheric conditions; The results of change detection and classification algorithms can be improved if careful consideration of the sensor calibration aspects is taken into account Classification algorithms, which are object oriented [e.g. ecognition by Definiens Imaging], will yield significantly improved results; The ground reflectance data of different sensors with similar spectral bands (e.g. Landsat TM band 3, SPOT band 2) can be compared. This is a particular advantage for multitemporal monitoring, since data of a certain area may not be available from one sensor for a number of orbits due to cloud coverage. The probability of getting data with low cloud coverage will increase with the number of sensors The effect of atmospheric interactions The Sun is the source of radiation, and electromagnetic radiation (EMR) from the Sun, which is reflected by the earth (the amount reflected depending on the reflectivity or albedo of the surface) and detected by the satellite or aircraft-borne

58 Chapter 3 sensor, must pass through the atmosphere twice-once on its journey from the Sun to the Earth, and once after being reflected by the surface of the Earth back to the sensor. During its passage through the atmosphere, EMR interacts with particulate matter suspended in the atmosphere and with the molecules of the constituent gases. This interaction is usually described in terms of two processes. The process called scattering deflects the radiation, while the second process, absorption, converts the energy present in electromagnetic radiation into the internal energy of the absorbing molecule. Both absorption and scattering vary in their effect from one part of the spectrum to another. Remote sensing is impossible in those parts of the spectrum that are seriously affected by scattering and/or absorption, since these mechanisms effectively render the atmosphere opaque to incoming or outgoing radiation. Regions of the spectrum that are relatively free from the effects of scattering and absorption are called atmospheric windows*: electromagnetic radiation in these regions passes through the atmosphere with less change than does the radiation at other wavelengths. The effect of the processes of scattering and absorption is to add a degree of haze to the image; that is, to reduce the contrast of the image and to reduce the amount of radiation returning to the sensor from the Earth s surface. A certain amount of radiation that is reflected from the neighbourhood of each target may also be recorded by the sensor as originating from the target. This is because scattering deflects the path taken by electromagnetic radiation as it travels through the atmosphere, while absorption involves the interception of photons or particles of radiation. *atmospheric windows cover the μm (visible, near infrared), , and μm (middle infrared) and μm (thermal infrared) wavebands (Mather, 1987)

59 Chapter Methods of atmospheric correction Preprocessing operations to correct for atmospheric degradation fall into three rather broad categories: 1. First are those procedures based upon efforts to model the physical behavior of the radiation as it passes through the atmosphere. The application of such models (i.e., LOWTRAN 7, MODTRAN) permits observed reflectances to be adjusted to approximate true values that might be observed under a clear atmosphere, thereby improving image quality and accuracies of analyses; 2. A second group of atmospheric correction procedures is based upon examination of the reflectances from objects of known or assumed brightness recorded by multispectral imagery (i.e., histogram minimum method); 3. The last category consists of a more sophisticated approach than the previous one. It retains the idea of examining brightness of objects within each scene, but attempts to exploit the knowledge of interrelationships between separate spectral bands (i.e. the covariance matrix method). The atmospheric correction process, which is the inversion process of converting the digital numbers detected by the sensor into radiance values, is not generally a simple matter. Because the atmosphere is multiple-scattering, its composition is often at least partially unknown, and the lower boundary is spatially heterogeneous with usually unknown bi-directional reflectance properties. According to Schott (1997), it is a fact that well-accepted operational calibration methods do not yet exist and some compromises must therefore be made depending upon the level of knowledge about atmospheric conditions at the time of image acquisition and the computational resources available. Ideally, ground truth data on the atmosphere collected at the time of overflight should be used for the atmospheric calibration of the satellite images. However, this is impractical in many cases. In this study, the atmospheric correction used is the Spatially-Adaptive Fast Atmospheric Correction Algorithm (ATCOR2) developed by Richter (1997). The method was developed mainly for optical satellite sensors with a small angle, where the scan angle dependence of the radiance and transmittance

60 Chapter 3 functions is negligible. The algorithm works with a database of atmospheric correction functions stored in look-up tables (LUTs) and calculates a ground reflectance image in each spectral band Basic concepts of The Spatially-Adaptive Fast Atmospheric Correction Algorithm (ATCOR2) Figure 3.5 shows a schematic sketch of the total radiation signal at the sensor. It consists of three components: 1. path radiance (L1), i.e., photons scattered into the sensor s instantaneous fieldof-view, without having ground contact 2. reflected radiation (L2) from a certain pixel: the direct and diffuse solar radiation incident on the pixel is reflected from the surface. A certain fraction is transmitted to the sensor. The sum of direct and diffuse flux on the ground is called global flux 3. reflected radiation from the neighborhood (L3), scattered by the air volume into the current instantaneous direction, the adjacency radiance. Figure 3.5 Schematic sketch of solar radiation components in flat terrain Only radiation component 2 contains information about the currently viewed pixel. The task of atmospheric correction is the calculation and removal of components 1 and 3, and the retrieval of the ground reflectance from component 2. So, the total radiance signal L can be written as: L = L + L + L E.q 3.1 path reflected adjacency

61 Chapter 3 For each spectral band of a sensor a linear equation describes the relationship between the recorded brightness or the digital number DN and the at-sensor radiance L = L + L + L E.q 3.2 path reflected adjacency The c 0 and c 1 (bias and gains, respectively) are called radiometric calibration coefficients (Figure 3.6). The transformation of the DN values to radiance is based on a calibration curve of DN to radiance which has been calculated by the operators of the satellite system and the calibration is carried out before the sensor is launched. Thus, eq. 3.2 can be written: L = c + 0 c1 DN E.q 3.3 where: DN max = maximum DN recorded (255 for 8-bit) L max = saturation point of the sensor, which is maximum detectable radiance; and L min = minimum activation for the sensor to respond, which is lowest detectable radiance Offset = L min Gain = Lmax L DN max min Figure 3.6 Gain represents the gradient of the calibration and bias defines the spectral radiance of the sensor for a DN of zero (also called dark current ) As a result of this, the following is obtained: L = Offset + ( Gain) DN or L = c + 0 c1 DN Disregarding now the adjacency component equation 3.2 can be written L = L + L = L + τρe / π = c + c DN path reflected path g 0 1 E.q

62 Chapter 3 Where τ, ρ, and E g are the ground-to-sensor atmospheric transmittance, surface reflectance, and global flux on the ground, respectively. Solving for the surface reflectance equation 3.4 becomes π ρ = { d ( c + c DN ) L } 2 E.q τe g path Factor d 2 takes into account the sun-to-earth distance (d is in astronomical units), because the LUT s for path radiance and global flux are calculated for d=1 in ATCOR. Equation 3.5 is a key formula to atmospheric correction Implementation of the atmospheric correction According to the above, it can be concluded that in order to atmospherically correct an image two parameters have to be determined: 1. an accurate radiometric calibration is required, i.e., a knowledge of c 0, c 1 in each spectral band; and 2. an accurate estimate of the main atmospheric parameters because these influence the values of path radiance, transmittance, and global flux. As previously, radiometric correction of the satellite images is applied before the sensor is launched. However, the radiometric coefficients that are used to calibrate the images vary over time, and consequently the relationship between the pixel value recorded at a particular location and the reflectance of the material making up the surface of the pixel area will not be constant. Therefore, the preflight calibration may not be applicable and, if used, will result in a smaller atmospheric correction. Nevertheless, with ATCOR, the influence of sensor calibration on the derived spectra can interactively be assessed. A file with the results of the latest in-flight calibration, which in most cases are embedded in the metadata of the image, may easily be added to the existing calibration files (Figure 3.7)

63 Chapter 3 Figure 3.7 Calibration file for the atmospheric correction of the ASTER image, where c0 is the BIAS and c1 is the GAIN As already mentioned, ATCOR2 is spatially-adaptive fast atmospheric correction algorithm working with an atmospheric database (Richter 1996a, 1996b) which contains the atmospheric correction functions stored in look-up tables and consists of several parameters, such as: standard atmospheres (altitude profile of pressure, air temperature, water vapor content, ozone concentration), taken from model MODTRAN-2. The atmospheres available are: midlatitude summer, US standard, tropical, fall (autumn) and midlatitude winter aerosol types: rural, urban, other (desert, maritime) a range of aerosol concentrations (aerosol optical depth), defined by the horizontal surface meteorological range, shortly called visibility) the visibility range is km a range of ground elevations: km above sea level solar zenith angles from These ground truth data about the atmosphere collected at the, time of overflight are almost never available for the atmospheric calibration of the satellite images. Nevertheless, ATCOR module facilitates a tool (SPECTRA module) for the determination of the appropriate choice of the atmospheric correction functions (Figure 3.8)

64 Chapter 3 Figure 3.8 Spectra main display setup It was found that the atmospheric correction function for the ASTER image was for a fall (autumn) atmosphere, rural aerosol type, at a visibility of 50 km The Haze Removal Algorithm Prior to atmospheric correction, the haze removal algorithm was applied to the image. In many cases of satellite imagery, the scene contains haze and cloud areas. The optical thickness of cloud areas is so high that the ground surfaces cannot be seen, whereas in hazy regions some information from the ground is still recognizable. In ATCOR, the scene is portioned into clear, hazy, and cloud regions. As a first approximation, haze is an additive component to the radiance signal at the sensor. It can be estimated and removed as described below. Cloud areas have to be masked to exclude them from haze areas and to enable a successful haze removal Resulting images Figure 3.9 shows the result of the atmospheric correction for the nine bands of the image. A broadening is evident and can be attributed to the dependence of the atmospheric correction on the type of surface which tends to increase the contrast between low and high reflective surfaces. Also noticeable is a general reduction in the reflectance values of the first bands. This is normal since the contribution of atmospheric Raleigh and aerosol is higher than the ozone absorption, and correction

65 Chapter 3 of the atmospheric effects tends to decrease the reflectance values at that wavelength. Figure 3.10 shows the ASTER image before and after atmospheric correction. In the atmospherically corrected image, land features can be more easily discriminated compared to the non-atmospherically corrected image. Figure 3.9 Spectral profile for ASTER image before (left) and after (right) atmospheric correction. Figure 3.10 ASTER image before (left) and after (right) atmospheric correction

66 3.5 Chapter summary Chapter 3 The study area, which is characterized as fire-prone, is located between the districts of Santarem and Castelo Branco and is mainly covered by woodland and agricultural land. During the summer of 2003 major fires incidences occurred in the area; An ASTER L1A data product showing burns was obtained for this study. The preprocessing of the image mainly involved the processing of the atmospheric correction; The imagery collected by the satellites was contaminated by effects of atmospheric particles through absorption and scattering of the radiation from the earth s surface. The objective of the atmospheric correction was to retrieve the surface reflectance (which characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects. The atmospheric correction of satellite mages is an important step in improving the data analysis; Prior to atmospheric correction, haze was removed from the image. As a first approximation, haze is an additive component to the radiance signal at the sensor, which can be estimated and removed using the haze removal algorithm; The Spatially-Adaptive Fast Atmospheric Correction Algorithm (ATCOR2) developed by Richter (1997) was used in this study. The equations and parameters used by the model ATCOR2 were presented

67 4. DEVELOPMENT OF AN OBJECT-BASED CLASSIFICATION MODEL FOR BURNED AREA MAPPING USING ASTER IMAGERY Chapter 4 The aim of Chapter 4 is to accurately map burned areas by employing object-oriented image analysis and ASTER imagery. In the beginning of Chapter 4, a description of the main steps followed for the development of an object-based classification model is given. Section 4.3 describes the development of an object-based model for burned area mapping using the atmospherically corrected ASTER image, while section 4.4 describes the development of another object-based classification model using the same but not atmospherically corrected ASTER image. At the end of Chapter 4, the classification results of the two models are compared. 4.1 Background As was explained in Chapter 3, section 2.5, the object-oriented approach takes the form, textures and spectral information of image objects into account. Its classification phase starts with the crucial initial step of grouping neighbouring pixels into meaningful areas, which can be handled in the later step of classification. Such segmentation and topology generation must be set according to the resolution and the scale of the expected objects. In this method, single pixels are not classified but homogeneous image objects are extracted during a previous segmentation step. This segmentation can be done in multiple resolutions, thus allowing differentiation of several levels of object categories. An ASTER image was used to build two object oriented models for burned area mapping. The first model was based on the atmospherically corrected image, while the second on the same non-atmospherically corrected image. The accuracy of the two classified images was assessed by comparing the classification results of the object-based models with the fire perimeters provided by the Portuguese Forest Service. The methodology followed in this work is presented in the flowchart overleaf (Figure 4.1)

68 Chapter 4 Figure 4.1 Flowchart of Chapter Workflow of the development of an object-oriented classification model The development of the object based model started with a segmentation procedure to create image objects. Several parameters such as scale and the heterogeneity criterion had to be determined in order to generate as meaningful objects as possible. Afterwards, the process sequence involved interactions between subsequent modifications (i.e. segmentations, object merging) and classifications (Figure 4.2) at different levels. This was possible through individual operations called processes which give solutions to specific image analysis problems. The final classifications of the generated image objects at each level were realized using the appropriate feature values to best separate the different classes. The main procedures, namely segmentation, classification and processes, of the object oriented analysis are described in turn overleaf

69 Chapter 4 Figure 4.2 The workflow of the development of an object-based classification model Segmentation Segmentation is not an aim in itself. A segmentation procedure following a relatively general homogeneity criterion will, in most cases, not be able to directly extract the final areas or objects of interest due to their considerable heterogeneity. Thus, image objects resulting from a segmentation procedure are intended to be rather image object primitives, serving as information carriers and building blocks for further classification or other segmentation processes. In this sense, the best segmentation result is one that provides optimal information for further processing. In order to determine the outcome of the segmentation algorithm, several parameters, such as (1) the scale parameter, (2) the single layer weights, and (3) the mixing of the heterogeneity criterion concerning tone and shape have to be determined: (1) A comparison of a fusion value with the scale parameter defines the break-up criterion. As mentioned in previous paragraphs, the scale parameter (values range from 1 to infinite) is a measure for the maximum change in heterogeneity that may occur when merging two image objects. Internally, this value is squared and serves as the threshold which terminates the segmentation algorithm. When a possible merging of a pair of image objects is examined, a fusion value between these two objects is calculated and compared to the squared scale parameter

70 Chapter 4 (2) Image layers can be assessed differently depending on their importance or suitability for the segmentation result. The higher the weight assigned to a layer, the more of its information will be used during the segmentation process. Consequently, image layers that do not contain the information intended for representation by the image objects should be given little or no weight. (3) The heterogeneity criterion consists of two parts: a criterion for tone and a criterion for shape (values range from 0 to 1 for both). The spectral criterion is the change in heterogeneity that occurs when merging two image objects as described by the change of the weighted standard deviation of the spectral values regarding their weightings. The shape criterion is a value that describes the improvement of the shape with regard to two different models describing ideal shapes (compact or smooth) Classification In the second step of classification, the best discriminating object features for successful classification results had to be found. Since regions in the image provide much more information than single pixels, there is a large number of different image object features for measuring the colour, shape, and texture of the associated regions. Even more information may be extracted by taking the network structure and the classification of the image objects into account. Comparisons and analyses of image objects in terms of the different features were performed using the appropriate tools, which provided all the information necessary to achieve high quality classifications. Different feature values were visualized in order to find features separating different classes of image objects. Statistical analyses were then performed on the appropriate feature values from sample objects. Thus, it was possible to evaluate whether image objects would be classified correctly or not before performing the classification (Appendix I) Processes The software (Definiens Professional) used for the object oriented image analysis provides an artificial language for developing advanced image analysis algorithms

71 Chapter 4 These algorithms use the principles of object oriented image analysis and local adaptive processing. This is achieved by processes. A single process is the elementary unit of a rule set providing a solution to a specific image analysis problem. Processes are the main working tools for developing rule sets. The main functional parts of a single process are the algorithm and the image object domain (Figure 4.3). A single process allows the application of a specific algorithm to a specific region of interest in the image. All conditions for classification, as well as region of interest selection, may incorporate semantic information. Processes may have an arbitrary number of child processes. The so formed hierarchy defines the structure and flow control of the image analysis. Arranging processes containing different types of algorithms allows the user to build a sequential image analysis routine. The algorithm describes what the process will do. The algorithm describes what operation the process will perform. This can be generating image objects, merging or splitting image objects, classifying objects etc. The two main functions of algorithms are to generate or modify image objects and to classify image objects. In addition to these, a set of other algorithms (chessboard segmentation, assign class, find enclosed by class algorithms) help to define all necessary operations to set up an image analysis routine. Figure 4.3 Workflow of a process sequence (Source: Definiens Professional 5, User Guide)

72 Chapter 4 The image object domain describes the region of interest where the algorithm of the process will be executed in the image object hierarchy. The image object domain is defined by a structural description of the corresponding subset. Examples of image object domains are the entire image, an image object level or all image objects of a given class. By applying the usual set operators to the basic image object domains, many different image object domains can be generated. The process will then loop over the set of image objects in the image object domain and apply the algorithm to every single image object. 4.3 Development of an object-based classification model using an atmospherically corrected ASTER image In addition to the nine bands of the ASTER image, the Normalized Difference Vegetation Index (NDVI) was generated and used as an additional input in the development of the object-based classification model. Since the NDVI show a severe decrease after fire occurrence as a result of the deterioration of leaf pigments and leaf structure (Kasischke et al. 1993) its use in the development of the model can lead to a more successful separation of the burned and non-burned areas. The first step in the development of the object-based model was the generation of image objects through the procedure of segmentation. Subsequently, different processes that mainly consisted of modification (i.e. segmentation, merging of image objects) and classification algorithms were arranged in a proper sequence in order to produce the final outcome, i.e. the classification of the burned areas. The resulting process sequence ultimately comprised the rule se of the classification model

73 Chapter 4 Figure 4.4 The sequence of the steps followed and the parameters used for the development of the object-based classification model Three levels of classifications were finally created (Figure 4.4) for the development of the model. As mentioned in previous sections (4.2.1 and 4.2.2), several parameters have to be set for the segmentation and the classification of the image. The parameters that were used, along with the processes sequence followed and the domain the algorithms were implemented (see section 4.2.3), are described in the paragraphs that follow: Level 2: At this level, the entire image was the domain where the algorithms were executed was segmented. The scale parameter for the segmentation algorithm used was 10, while weight was given only to layer three and the NDVI layer. For the heterogeneity criterion, full importance was given to colour (Table 4.1). In order to achieve the best separation in the classification of the burned area from the other landcover types, an arithmetic feature was created. Arithmetic features are composed of existing features, variables and constants which are combined via arithmetic operations. In this case, features mean of layer 3 and mean of NDVI were arithmetically added producing a new feature, namely mean&ndvi. In addition to the new feature, features mean of layer 3, mean of layer 1 and ratio of layer 1 were evaluated as the most appropriate for the discrimination of the burned areas

74 Chapter 4 Classification was implemented for all image objects using the aforementioned features and two classes were finally created at this level: Burned area at level 2 and water. Level 3: At this level, the region of interest where the algorithms were implemented was all objects of the class Burned at level 2 which was created at level 2 which was created above level 2. Thus, the segmentation procedure was implemented only on objects that were classified as Burned at level 2 and the scale parameter that was used was 300, weight was given only to layer three and the NDVI layer and for the heterogeneity criterion full importance was given to colour. For the classification, features such as mean of layer 3 and mean&ndvi were used, while the classification algorithm as mentioned above was executed on all objects that comprised sub-objects that were classified as Burned at level 2. Class Burned at level 3 was created. Some objects, however, were misclassified and in order to enhance the classification result, two appropriate algorithms ( find enclosed by class and merge region, see Appendix I for explanation) were implemented on all objects classified as Burned at level 3. The execution of these algorithms resulted in modification of the objects sizes. Thus, the suitable features such as mean of layer were found and the final classification at this level resulted in the generation of two classes: Burned at level 3(b) and water 2. Level 1: Level 1 was the final level to be created and was the lowest in the level s hierarchy. The domain for the execution of the segmentation algorithm was all objects that were classified as Burned at level 2. The scale parameter that was used was 1, weight was given to layer three and the NDVI layer and, for the heterogeneity criterion, weight of 0.9 was given to colour. The image object domain for the execution of the classification algorithm was all objects classified as Burned at level 3(b). Features such as, mean of layer 3 and ratio of layer 1 were used and classes Burned at level 1, slightly burned, burned 20, water 3, vegetated, and bare land were finally created

75 Chapter 4 Table 4.1 Segmentation parameters Classification accuracy of the object-based classification model using an atmospherically corrected ASTER image Classification accuracy determines the quality of the information derived from remotely sensed data and for this purpose the error matrix, a standard descriptive reporting tool for accuracy assessment, was generated. The most common and simplest descriptive statistic generated by the error matrix is the overall accuracy, which is computed by dividing the total correct by the total number of sample units (i.e. pixels of the resulting classification). In addition, individual category accuracies can be computed in a similar manner. Producer s accuracy is a measure that relates to the probability of a reference sample unit s (i.e. a sample from the official fire perimeter map) being correctly classified and is really a measure of omission error. User s accuracy, on the other hand, is a measure that is indicative of the probability that the sample unit classified on the map actually represents that category on the ground (Story and Congalton, 1986). In addition, a second discrete multivariate technique of use in accuracy assessment is called Kappa (Cohen 1960). The overall classification accuracy, as previously mentioned, represents the probability that a randomly selected pixel is correctly classified. However, it takes no account of chance agreement, because even a purely random assignment of class labels will result in a positive value. In other words, the overall accuracy classification accuracy tends to give an inflated index of classification accuracy, as described by Veregin (1995)

76 Chapter 4 To remedy this shortcoming, the Kappa coefficient (values range between 0 and 1) of agreement has been recommended. This measure of agreement is based on the difference between the actual agreement in the error matrix (i.e. the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals in the error matrix. The Kappa analysis has become a standard component of accuracy assessment (Hudson and Ramm 1987; Congalton 1991) because all elements in the classification error matrix contribute to its calculation, and because it compensates for chance agreement (Zhang and Goodchild 2002). Table 4.2 shows the results of the descriptive statistics, as described above, generated in order to estimate of the classification accuracy. The fire perimeter produced by the Forest Service was considered to be the reference data. 800 sample units (i.e. pixels) were randomly distributed on the study area. Of the 307 pixels predicted by the logistic model to be burned, only three were erroneously classified as unburned. The overall classification was estimated to be 98.38%. Users accuracy and Producers accuracy for burned area mapping were estimated at 99.02% and 96.82%, respectively, while the Kappa coefficient was found to be , which signifies strong agreement of accuracy. Table 4.2 Error matrix generated for the assessment of the classification accuracy of objectbased model that was developed on the atmospherically corrected ASTER image Moreover, the burned area map resulting from the object-oriented image analysis was compared with the fire perimeter provided by the Portuguese Forest Service in terms of spatial overlap and size in order to determine to what extent they were compatible (Figure 4.5). An area of ha was mapped as burned by the object-based

77 Chapter 4 classification model, while an area of ha was mapped as burned on the official map. An area of ha (95.8% of the official map) was common to both maps. A difference of 1129 ha (2.27%) in the area classified as burned was noted between the two maps. Figure 4.5 Top: Burned area classification result of the atmospherically corrected ASTER image (in grey) and the official fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map The difference between the classification result of the object-based model for burned area mapping and the official fire perimeter provided by the Forest Service could be attributed to: the high spatial resolution of the ASTER satellite image, which allowed better discrimination of the burned and unburned areas;

78 Chapter 4 the advanced classification method (i.e. object-oriented image analysis) used in this research in comparison to the traditional methods used by the Forest Service; the difference between the date of the acquisition of the satellite image and the date the fire occurred. The official fire perimeter (i.e. the reference data) produced immediately after the fire incidence, while in the time interval between the fire and the acquisition of the image, the landscape is subject to changes (e.g. regeneration of burned areas in small scales) which could produce wrongly estimated classification accuracies; the existence of old fire scars and recently burned areas in the same image frame. As mentioned above, the official fire perimeter (i.e. reference data) included only the recently burned areas, while in the object-based classification procedure, no discrimination between the old fire scars and the recently burned areas was taken into account-a fact that could lead to erroneous classification results. 4.4 Development of an object-based classification model using a non-atmospherically ASTER image Section 4.3 dealt with the description of the structure of the object-based classification model that was developed on the ASTER image which had previously been atmospherically corrected (see section 3.3). As has already been mentioned, atmospheric correction of satellite images represents an important step in the improvement of the data analysis in many ways, nonetheless it is a time-consuming procedure. Moreover, the lack of field radiometry data in most cases at the time of image acquisitions does not allow a qualitative determination of the accuracy of the radiometric correction method. In this sense, an object-based classification model was developed on the same ASTER image, but without previously being atmospherically corrected in order to minimize the time of the classification process. The development of the second model followed the same methodology sequence that of the previous model (see Figures 4.4 and 4.6). The three classification levels that were created are discussed in turn below:

79 Chapter 4 Level 2: At this level, the entire image was the domain where the algorithms were executed. The scale parameter for the segmentation algorithm used was 10. Layers three and the NDVI where the only layers that were used for the segmentation, while more weight was given to the NDVI layer (1 for the NDVI and 0.8 for layer three). For the heterogeneity criterion, full importance was given to colour. Features such as mean&ndvi (see section 4.3) mean of layer 3, mean of layer 1 and ratio of layer 1 were evaluated to be the most appropriate for the discrimination of the burned areas. Classification was implemented for all image objects using the aforementioned features and two classes were finally created at this level: Burned area at level 2 and water. Level 3: At this level, the region of interest where the algorithms were implemented was all objects of the class Burned at level 2, which was created at level 2, which was created above level 2. Thus, the segmentation procedure was implemented only on objects that were classified as Burned at level 2 and the scale parameter that was used was 300, weight was given only to layer three (0.5) and the NDVI layer (1) and, for the heterogeneity criterion full importance was given to colour. For the classification, features such as mean of layer 3 and mean&ndvi were used, while the classification algorithm, as mentioned above, was executed on all objects that comprised sub-objects that were classified as Burned at level 2. Class Burned at level 3 was created. However, some objects were misclassified and, in order to enhance the classification result, two appropriate algorithms ( find enclosed by class and merge region, see Appendix I for explanation) were implemented on all objects classified as Burned at level 3. The execution of these algorithms resulted in modification of the objects sizes. Thus, the suitable features such as mean of layer were found and the final classification at this level resulted in the generation of two classes: Burned at level 3(b) and water

80 Chapter 4 Level 1: Level 1 was the final level to be created, which was the lowest in the level s hierarchy. The domain for the execution of the segmentation algorithm was all objects that were classified as Burned at level 2. The scale parameter that was used was 1, weight was given to layer three and the NDVI layer and, for the heterogeneity criterion a weight of 0.9 was given to colour. The image object domain for the execution of the classification algorithm was all objects classified as Burned at level 3(b). Features such as mean of layer 3 and ratio of layer 1 were used and classes Burned at level 1, slightly burned, burned 20, water 3, vegetated, and bare land were finally created. Figure 4.6 Interface of the Definiens Professional software Classification accuracy accuracy of the object-based model using an nonatmospherically corrected ASTER image Table 4.3 shows the descriptive statistics (see section for explanation) generated in order to assess the classification accuracy of the object-based classification model that was developed based on the non-atmospherically corrected ASTER image. The burned area mapping resulted in an overall accuracy of 98.88%. Burned area

81 Chapter 4 classification Producers Accuracy was 97.99%, Users Accuracy was 97.50%, while the overall Kappa coefficient was found to be Table 4.3 Error matrix generated for the assessment of the classification accuracy of objectbased model that was developed on the atmospherically corrected ASTER image In addition, the burned area resulting from the object-oriented classification of the non-atmospherically corrected ASTER image was compared with the fire perimeter provided by the Forest Service, in terms of spatial overlay and size (Figure 4.7). An area of ha was mapped as burned by the object-based classification model, while an area of ha was mapped as burned on the official map. An area of ha (97.27% of the official map) was common to both maps. A difference of 71 ha (0.23%) in the area classified as burned was noted between the two maps. The reasons that explain the difference between the classification results of the object-based classification model and the fire perimeter produced by the Forest Service do not differ from those mentioned in section

82 Chapter 4 Figure 4.7 Top: Burned area classification result of the non-atmospherically corrected ASTER image (in grey) and the fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map 4.5 A comparison between the two object-based classification models In the previous sections, the procedure sequence for the development of the two object-based classification models for burned area mapping was described. It could be concluded that the structure of the two models was similar, since in both cases: three levels of classification were finally created

83 Chapter 4 the segmentation parameters and the features used for the classification were almost identical the image object domains at which the algorithms were executed were the same. In addition, the accuracies of the resulting classifications of the two object-based models for burned area mapping were estimated. It was found that the two objectbased models developed mapped the burned area with high accuracy. Specifically, the burned area mapping of the first model that was developed based on the atmospherically corrected ASTER image resulted in an overall accuracy of 98.38%, while the overall accuracy of the model developed on the non-atmospherically corrected ASTER image was 98.88%. Thus, in terms of figures, the overall accuracy of the second object-model was 0.50% more, even though the confusion among the different land covers was higher for the non-atmospherically corrected image. The reason for the higher-albeit small-accuracy of the second model lies in the fact that the official fire perimeter, against which the classification accuracies of the models were estimated, represents a rougher estimation of the real burned area (either because it was produced using a spatially coarser satellite image or because it was produced on the basis of field measurements). Thus, even though the mapping of the burned areas using the atmospherically corrected image was slightly more detailed and accurate, the estimated overall classification accuracy was less. Furthermore, the burned areas resulting from the two object-based classification models were compared in terms of spatial overlap and size. An area of ha was mapped as burned by the first object-based classification model, which was developed based on the atmospherically corrected ASTER image, while an area of ha was mapped as burned by the second model, which was developed based on the same non-atmospherically corrected ASTER image. The area mapped as burned by the first model was by 1202 ha smaller due to the ability of the model to discriminate better the unburned areas, such as roads, residential areas and bare land, which were located within the image frame and especially within the burned area. An area of ha was common to both maps

84 Chapter 4 From the aforementioned, it could be concluded that the performances of the two object-based classification models developed for burned area mapping were both successful, since the difference between the two classification accuracies was insignificant. However, it would be preferable to choose t the second object-based classification model (i.e. the model that was developed based on the nonatmospherically corrected image) for burned area mapping, not only because it is the better performing model, but also because it renders the time-consuming procedure of atmospheric correction unnecessary. 4.6 Chapter summary Two object-based classification models were developed for mapping burned areas. The first was based on the atmospherically corrected ASTER image, while the second was based on the same but not atmospherically corrected image; The classification procedure sequence of the burned areas in both cases involved the interaction of modifications (i.e. segmentations, merging of image objects) and classification algorithms executed on specific image objects domains ; Three levels of classification using the appropriate feature (i.e. layer mean and ratio, class-related features) were finally created for both models; The overall accuracy of the resulting classification of the first object-based classification developed on the atmospherically corrected ASTER image model was estimated to be 98.38% while for the second model that was developed based on the non-atmospherically corrected image, the overall accuracy was estimated to be 98.88%; The results of the comparison of the burned area maps resulting from the object-oriented analysis in both cases, with the fire perimeter provided by the Forest Service indicated a high degree (95% for the first model and 97% for the second model) of spatial agreement; A comparison between the classification accuracies of the two objectbased classification models showed that the object-based classification model that was developed on the non-atmospherically corrected image

85 Chapter 4 was performed better. Thus, the results strongly indicated that the implementation procedure of the atmospheric correction in burned area mapping could be rendered unnecessary

86 Chapter 5 5. EXAMINATION OF THE TRANSFERABILITY OF THE OBJECT-BASED CLASSIFICATION MODEL DEVELOPED FOR BURNED AREA MAPPING The aim of Chapter 5 is to examine the transferability of the better performing objectbased classification model for burned area mapping. Hence, in Section 5.2 the objectbased classification model was applied on a second area affected by fire and located on the same, as that used in Chapter 4, non-atmospherically corrected ASTER image. In Section 5.3, the performance of the model was further tested for burned area mapping in another study area located in central Spain, using a different ASTER image. 5.1 Background As mentioned in the previous Chapter, the need for the development of a reliable technique for estimating forest fire impact on the environment has become even greater, given the increasing number of fires that occur each year. Such a technique should incorporate satellite data, which constitute a very useful tool for mapping forest areas that have recently been burned (Patterson and Yool 1998), and advanced classification methods, to enable forest managers, who are not necessarily familiar with remotely sensed methods, to assess the fire damage, estimate the vegetation losses and plan a speedy restoration strategy for the affected areas. Even though, a considerable amount of research has been done on the development of techniques to accurately detect and map burned areas, little has been done in the way of developing of an automatic methodology for burned area mapping using remotely sensed data. Fernández et al. (1997) built two automatic algorithms for the evaluation of burned land and the mapping of large forest fires with the use of NOAA-AVHRR images, while Mitri and Gitas (2004) developed an object-based classification model for burned area mapping using Landsat-TM imagery. Furthermore, the MODIS active fire product mapped the locations of burning fires detected within a pixel size of 1 km (Giglio et al. 1999) and, more recently, a MODIS burned area product was developed that maps the extent of fire-affected areas at a pixel size of 500m (Roy et al. 2002,

87 Chapter a) even though this latter product will be released in the near future. Nevertheless, it should be noted that the transition to an operational mode for burned area mapping pre-requisites the development and validation of robust satellite image analysis algorithms that can be extended through space and time (Gitas et al. 2004). In this research, the better performing object-based classification model that was developed (see Chapter 4) was examined in order to test its transferability for burned area mapping. In particular, the model that was developed on the non-atmospherically corrected ASTER image was applied for mapping two different burned areas: the first one was located on the same ASTER image used when developing the model, while the second was located on a different ASTER image that showed fire burns in central Spain. The resulting burned area maps were compared with the official fire perimeter provided by the Forest Services and the transferability of the model was evaluated. The flowchart of Chapter 5 is given below (Figure 5.1). Figure 5.1 Flowchart of Chapter Examination of the transferability of the object-based classification model when mapping another burned area located on the same ASTER image Burned areas that resulted from fires which occurred in the district of Santarem during the summer of 2003 are depicted in Figure 5.2. The figure shows the location of this second fire-affected area on the ASTER image, along with the burned area that was used for the development of the object-based classification model

88 Chapter 5 Figure 5.2 LEFT: Burned areas (b) located on the same ASTER image as the one used for the development of the model (a), (see Chapter 4). RIGHT: Map of the major fires that occurred in Portugal in the summer of In order to examine the transferability of the object-based classification model for burned area mapping, the model was applied on the aforementioned fire affected area (Figure 5.3). The results showed that the transferability of the model itself was not successful when applied to the fire affected areas (classification accuracy less than 30%). The main reason could be attributed to the different spectral behaviour of the burned area in comparison to the spectral characteristics of the burned area in the first case study. The difference in the spectral behaviour was due to the following: the different degrees of fire severity of the burned areas; the time period which intervened between the fire incident and the acquisition of the satellite images. The longer this time period, the more chances the ecosystem has to recover-even to a small degree; the difference in the natural environments of the two study areas

89 Chapter 5 Figure 5.3 The three classification levels created by the implementation of the object-based classification model Nonetheless, and considering the aforementioned reasons, the model was modified in terms of the thresholds of the feature values (i.e. layer mean and ratio, the customized feature mean&ndvi ) used for the classification of the burned areas, while the same developed rule set was kept. An accuracy assessment of the outcome of the implementation of the developed object-based classification model indicated that the adjusted model was successful in mapping the burned area at the second site. The classification confusion matrix or error matrix (Table 5.1) shows the emissions and emission errors in the classification. The fire perimeter produced by the Forest Service was considered to be the reference data and 800 points were randomly distributed in the study area. The overall classification was estimated to be 98.50%. Users accuracy and Producers accuracy for burned area mapping were estimated at 98.86% and 97.74%, respectively. Table 5.1 Accuracy statistics for the classification of the burned areas

90 Chapter 5 Figure 5.4 Top: Burned area classification result of the implementation of the object-based classification model (in grey) and the official fire perimeter (in red). Bottom: Spatial overlay of the burned area resulting from the model and the official map The burned area resulting from the object-oriented classification of the ASTER image was compared with the fire perimeter provided by the Forest Service of Portugal in terms of spatial overlay and size (Figure 5.4). An area of ha was mapped as burned by the object-based classification model, while an area of ha was mapped as burned on the official map. An area of ha (84.65% of the official map) was common to both maps. A difference of 2112 ha (15.64%) in the area classified as burned was noted between the two maps

91 Chapter 5 The difference between the classification result of the object-based model for burned area mapping and the official fire perimeter provided by the Forest Service could again be attributed to the same reasons as mentioned in section (i.e. high spatial resolution of ASTER image, the classification method, the differences in the dates between the acquisition of the image and the fire incidence, the existence of old and recently burned areas). 5.2 Examination of the transferability of the object-based model when mapping another burned area using a different ASTER image The transferability of the second object-based classification model for burned area mapping was further tested by employing a different ASTER image that showed fire burns resulting from a forest fire that occurred in Spain (Figure 5.5). The results showed that the performance of the model when applied to the second ASTER image was not successful. The reasons for the inability to transfer the model, besides those mentioned in the previous cases, were (1) the differing ASTER product (L1B instead of L1A used in the previous cases, even if the embedded radiometric and geometric coefficients of the image were applied), and (2) the differing natural environments of the two study areas. Figure 5.5 The three classification levels created by the implementation of the object-based classification model in fire affected areas in Spain However, as in the case before (see section 5.1), the classification accuracy of the model, as estimated from the generation of the error matrix, increased spectacularly

92 Chapter 5 only by adjusting the thresholds of the feature values used, without changing the developed rule set of the model. The classification confusion matrix or error matrix (Table 5.1) shows the emissions and emission errors in the classification. The fire perimeter produced by the Forest Service was considered to be the reference data and 800 points were randomly distributed in the study area. The overall classification was estimated to be 97.75%. Users accuracy and Producers accuracy for burned area mapping were estimated at 97.99% and 94.94%, respectively, while the Kappa coefficient was found to be Table 5.2 Accuracy statistics for the classification of the burned areas In addition, the burned area resulting from the object-oriented classification of the ASTER image was compared with the fire perimeter provided by the Forest Service, in terms of spatial overlay and size (Figure 5.6). An area of 799 ha was mapped as burned by the object-based classification model, while an area of 858 ha was mapped as burned on the official map. An area of 766 ha (89.3% of the official map) was common to both maps. A difference of 59 ha (6.87%) in the area classified as burned was noted between the two maps. This difference could be attributed to: the mapping of a seemingly older fire scar some shadowed areas erroneously mapped as burned the ability of the model and the high spatial resolution of the sensor to discriminate unburned patches such as the roads located mainly within the burned areas in comparison to the rougher estimation of the burned area by the Spanish Forest Service

93 Chapter 5 Figure 5.6 LEFT: Burned area classification result of the implementation of the object-based model on the second ASTER image (in grey) and the fire perimeter (in red). RIGHT: Spatial overlay of the burned area resulting from the model and the official map 5.3 Chapter summary The better performing object-based classification model (i.e. the model that was developed on the non-atmospherically corrected image) was examined for a different fire affected area, recorded on the same ASTER image;