Investigation of advanced satellite image analysis techniques for forest mapping and monitoring

This dissertation addresses the general research topic of efficient mapping and monitoring of forest regions using two advanced analysis methodologies of Very High Resolution (VHR) satellite data, namely, the Support Vector Machines (SVM) and Object Based Image Analysis (OBIA). It is mainly focused on techniques for mapping burned forest areas, along with new methodologies for characterizing the environmental impact of destructive fire events (Burn Severity) and monitoring the recovery of the affected areas after the event, using very high resolution satellite data (VHR). The individual objectives of this study are: a) to investigate modern classification methodologies of satellite images for mapping burned regions, and generate highly accurate thematic maps, b) to investigate the use of topographic conditions and burn severity, as measured on the field using CBI index, for explaining the post-fire vegetation response that is measured using VHR satellite images, and c) to monitor the progression of burn severity using the composite burned index (CBI) and GeoEye imagery. To address each of these objectives, this dissertation introduces three main methodologies that enable the efficient monitoring and studying of the immediate and short-term effects of a fire event. The first methodology aimed at achieving a very accurate mapping of a burned area by using a set of classification models (object-based and pixel-based). The second methodology investigated the connection between the topography and field-measured CBI, with the post-fire vegetation response, using the Redundancy Analysis (RDA) statistical methodology. A secondary target of this methodology was to identify which of the satellite parameters, that were estimated using the VHR satellite images (as parameters the used images’ channels and their NDVI index), can be used to detect and monitor post-fire vegetation response. Finally, the third methodology aimed at monitoring and capturing the environmental impact of regions after a forest fire. Overall, to map the burn severity of the studied region, we developed three object-based classification models. To do so, we used satellite images that were captured at three different time points (2011, 2012 and 2014). To study the spatiotemporal structure of the post-fire landscape, the thematic maps that were produced from our classification process were further analyzed using landscape metrics. The analysis results highlighted the inter-temporal variation trends that are present in areas with variable degree of impact severity, as well as, helped us identify the key factors that are associated with such trends.Η παρούσα διδακτορική διατριβή πραγματεύεται το γενικότερο ζήτημα της χαρτογράφησης και παρακολούθησης των δασικών εκτάσεων, αξιοποιώντας δύο προηγμένες μεθόδους ανάλυσης δορυφορικών δεδομένων, τις Μηχανές Διανυσμάτων Υποστήριξης και την Αντικειμενοστραφή Ταξινόμηση. Κύριος σκοπός της ήταν η χαρτογράφηση των καμένων δασικών εκτάσεων, η εκτίμηση της καταστροφής που αυτές υφίστανται, αλλά και η παρακολούθηση της εξέλιξης τους, μέσω της χρήσης δορυφορικών εικόνων πολύ υψηλής χωρικής ευκρίνειας (VHR). Επιμέρους στόχοι της διατριβής αποτελούν α) η διερεύνηση σύγχρονων μεθόδων ταξινόμησης δορυφορικών εικόνων για την χαρτογράφηση καμένων εκτάσεων και την παραγωγή θεματικών χαρτών υψηλής ακρίβειας, β) η διερεύνηση του βαθμού στον οποίο οι τοπογραφικές συνθήκες και η σφοδρότητα καύσης που μετράται στο πεδίο με το δείκτη CBI, μπορούν να εξηγήσουν τη μεταπυρική δυναμική της βλάστησης που υπολογίζεται μέσω των VHR δορυφορικών δεδομένων και γ) η παρακολούθηση της εξέλιξης της σφοδρότητας καύσης με τη χρήση του δείκτη Composite Burned Index (CBI) και των εικόνων GeoEye. Για την υλοποίηση των παραπάνω στόχων προτείνονται τρείς μεθοδολογίες οι οποίες επιτρέπουν την αποτελεσματική καταγραφή και μελέτη των άμεσων και βραχυπρόθεσμων συνεπειών μιας πυρκαγιάς. Η πρώτη μεθοδολογία που αναπτύχθηκε είχε ως σκοπό την ακριβή και λεπτομερή χαρτογράφηση της καμένης έκτασης με τη χρήση διάφορων μοντέλων ταξινόμησης εικόνας. Η δεύτερη μεθοδολογία που αναπτύχθηκε είχε ως σκοπό να αποκαλύψει τη σχέση που υπάρχει ανάμεσα στην τοπογραφία και τις μετρήσεις πεδίου CBI, με τη μεταπυρική δυναμική της βλάστησης, μέσω της χρήσης της στατιστικής μεθόδου ανάλυσης πλεονασμού (Redundancy Analysis-RDA.) Επιπρόσθετος στόχος της ήταν να αναδείξει ποια από τις δορυφορικές μεταβλητές που υπολογίστηκαν μέσω των VHR δορυφορικών εικόνων (ως μεταβλητές θεωρήθηκαν τα κανάλια των εικόνων και ο δείκτης NDVI), μπορούν να χρησιμοποιηθούν για να ανιχνεύσουν και να παρακολουθήσουν τη μεταπυρική δυναμική της βλάστησης. Η τρίτη και τελευταία μεθοδολογία που αναπτύχθηκε αποσκοπούσε στην παρακολούθηση και καταγραφή του βαθμού περιβαλλοντικής αλλαγής που υφίστανται οι περιοχές που επλήγησαν από τις δασικές πυρκαγιές. Στα πλαίσια της εν λόγω διαδικασίας αναπτύχθηκαν τρία μοντέλα ταξινόμησης βασισμένα σε αντικείμενα (object based classification models), με σκοπό τη χαρτογράφηση της σφοδρότητα καύσης στην περιοχή μελέτης. Η επίτευξη αυτού του στόχου πραγματοποιήθηκε με τη χρήση δορυφορικών εικόνων που ελήφθησαν σε τρεις διαφορετικές χρονικές στιγμές (2011, 2012 και 2014). Οι θεματικοί χάρτες που προέκυψαν από τη διαδικασία τη διαδικασία της ταξινόμησης αναλύθηκαν περαιτέρω με τη χρήση δεικτών τοπίου για να μελετηθεί η διαχρονική εξέλιξη της δομής τους. Από την ανάλυση που πραγματοποιήθηκε αναδείχτηκαν τις διαχρονικές τάσεις μεταβολής που παρουσιάζουν οι περιοχές με διαφορετικό βαθμό σοβαρότητας επιπτώσεων καθώς και οι παράγοντες που ενδέχεται να σχετίζονται με αυτές τις τάσει

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through higher-order features is added to the classification process of VHR imagery for burned area mapping

Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece

The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models’ performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from −0.3% to −7.3%. Moreover, in all of the cases examined, apart from one (Sartis’ fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements

Meteorological Analysis of the 2021 Extreme Wildfires in Greece: Lessons Learned and Implications for Early Warning of the Potential for Pyroconvection

The 2021 fire season in Greece was the worst of the past 13 years, resulting in more than 130,000 ha of burnt area, with about 70% consumed by five wildfires that ignited and spread in early August. Common to these wildfires was the occurrence of violent pyroconvection. This work presents a meteorological analysis of this outbreak of extreme pyroconvective wildfires. Our analysis shows that dry and warm antecedent weather preconditioned fuels in the fire-affected areas, creating a fire environment that alone could effectively support intense wildfire activity. Analysis of surface conditions revealed that the ignition and the most active spread of all wildfires coincided with the most adverse fire weather since the beginning of the fire season. Further, the atmospheric environment was conducive to violent pyroconvection, as atmospheric instability gradually increased amid the breakdown of an upper-air ridge ahead of an approaching long-wave trough. In summary, we highlight that the severity and extent of the 2021 Greek wildfires were not surprising considering the fire weather potential for the period when they ignited. Continuous monitoring of the large- and local-scale conditions that promote extreme fire behavior is imperative for improving Greece’s capacity for managing extreme wildfires

Deliverables

Access point for public deliverables of WP3

Open Codes

Access point for public codes of WP2

Deliverables

Access point for public deliverables of WP4

Open Data

Access point for open data of WP4