Characterizing forest fragmentation : Distinguishing change in composition from configuration

This project was funded by the Government of Canada through the Mountain Pine Beetle Program, a three-year, $100 million program administered by Natural Resources Canada, Canadian Forest Service. Additional information on the Mountain Pine Beetle Program may be found at: http://mpb.cfs.nrcan.gc.ca.Forest fragmentation can generally be considered as two components: 1) compositional change representing forest loss, and 2) configurational change or change in the arrangement of forest land cover. Forest loss and configurational change occur simultaneously, resulting in difficulties isolating the impacts of each component. Measures of forest fragmentation typically consider forest loss and configurational change together. The ecological responses to forest loss and configurational change are different, thus motivating the creation of measures capable of isolating these separate components. In this research, we develop and demonstrate a measure, the proportion of landscape displacement from configuration (P), to quantify the relative contributions of forest loss and configurational change to forest fragmentation. Landscapes with statistically significant forest loss or configurational change are identified using neutral landscape simulations to generate underlying distributions for P. The new measure, P, is applied to a forest landscape where substantial forest loss has occurred from mountain pine beetle mitigation and salvage harvesting. The percent of forest cover and six LPIs (edge density, number of forest patches, area of largest forest patch, mean perimeter area ratio, corrected mean perimeter area ratio, and aggregation index) are used to quantify forest fragmentation and change. In our study area, significant forest loss occurs more frequently than significant configurational change. The P method we demonstrate is effective at identifying landscapes undergoing significant forest loss, significant configurational change, or experiencing a combination of both loss and configurational change.PostprintPeer reviewe

Regionalization of landscape pattern indices using multivariate cluster analysis

This project was funded by the Government of Canada through the Mountain Pine Beetle Program, a six-year, $40 million program administered by Natural Resources Canada, Canadian Forest Service. Additional information on the Mountain Pine Beetle Program may be found at: http://mpb.cfs.nrcan.gc.ca.Regionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape patterns (e.g., forest harvesting). The objective of this research is to develop an approach for investigating the spatial distribution of forest pattern across a study area where forest harvesting, other anthropogenic activities, and topography, are all influencing forest pattern. We generate spatial pattern regions (SPR) that describe forest pattern with a regionalization approach. Analysis is performed using a 2006 land cover dataset covering the Prince George and Quesnel Forest Districts, 5.5 million ha of primarily forested land base situated within the interior plateau of British Columbia, Canada. Multivariate cluster analysis (with the CLARA algorithm) is used to group landscape objects containing forest pattern information into SPR. Of the six generated SPR, the second cluster (SPR2) is the most prevalent covering 22% of the study area. On average, landscapes in SPR2 are comprised of 55.5% forest cover, and contain the highest number of patches, and forest/non-forest joins, indicating highly fragmented landscapes. Regionalization of landscape pattern metrics provides a useful approach for examining the spatial distribution of forest pattern. Where forest patterns are associated with positive or negative environmental conditions, SPR can be used to identify similar regions for conservation or management activities.PostprintPeer reviewe

Semi-automatización de los procesos de monitorización empleando imágenes de satélite en un entorno orientado a objetos. Cartografía, cuantificación y clasificación de áreas quemadas en Galicia (2006)

La cartografía y cuantificación de las áreas quemadas y los efectos post-fuego es crucial para priorizar las actuaciones de gestión, especialmente cuando los incendios son muy numerosos y dispersos, como es el caso de los ocurridos en Galicia en el verano de 2006. El protocolo para la obtención de esta información debe ser operativo y asequible, y alcanzar el mayor grado de automatización posible. La consistencia y calidad de los resultados están vinculadas a los procesos de corrección rediométrica, normalización y cálculo de índices espectrales, de modo que los errores de cálculo y el tiempo de procesamiento disminuyen cuando estas etapas se combinan algebraicamente. Se describen los elementos individuales del algoritmo (imágenes Landsat 5 TM, datos calibrados, radiancia TOA, índices espectrales) así como el protocolo IDL resultante. Los índices espectrales obtenidos se emplearon como input para la clasificación de segmentos espectralmente homogeneos empleando un método no paramétrico de determinación de umbrale

Assessment of Human African Trypanosomiasis Foci using Change Detection Algorithms

Environ-climatic change influences the occurrence and propagation of Human African Trypanosomiasis (HAT), focusing on two foci; Delta State and Jigawa State Nigeria where HAT has been reported. Geospatial and temporal based ground truthing exercise carried out to harvest HAT vector in Jigawa state did not yield any results; this indicates that the disease might have been phased out in the state.  In the same vein, resurging of HAT disease in the Delta State has been reported of recent. Thus, a change detection analysis was conducted in a geographic information systems (GIS) environment, to investigate the foci landscape. Using normalised difference vegetation index (NDVI), normalised difference water index (NDWI) and tasseled cap transformation (TCT), changes with a lag time of two decades was assessed for the two foci.  The analysis suggested that the landscape has changed considerably over the years that show Delta State as the potential active HAT foci as explained from the regression analysis of 0.9868 (99%) ahead of Jigawa state 0.0000 (0%) that can be regarded as non-active foci.  However, on-going programs, such as afforestation, forestation, irrigation farming and water reservoir projects may result in re-introduction of favourable landscape, thus, re-invasion of the area by the HAT vector. Therefore strategies that will maintain the present HAT-free status of the non-active foci, without adverse effect on the environment should be a government priority. To effectively reduce or control HAT propagation, integrated prevention schemes should be developed and executed.  The two HAT foci are of great economic importance; Delta State landscape is rich in hydrocarbons while Jigawa State is known for its extensive grazing and arable landscape. Keywords: Trypanosomiasis, Afforestation, Foci, HAT, Landscape, Environ-climatic, Spatial, Irrigatio

Hurricane Induced Land and Vegetation Changes in the Breton Sound Estuary and Chandeleur Islands Using Landsat 5 TM

This study focuses on hurricane-induced changes in land and vegetation primarily in two study areas, the Breton Sound Estuary and the Chandeleur Islands, southeast of New Orleans, Louisiana. Breton Sound Estuary consists of the Caernarvon Diversion, a fresh water diversion of the Mississippi River that supplies this region with managed pulses of fresh water and sediments. The Chandeleur Islands are a chain of barrier islands that are uninhabited and transgressive in nature. A sequence of hurricanes in the past two decades has greatly altered both areas significantly. Satellite data were analyzed for a period of 24 years (1987-2011) of Breton Sound Estuary region and for 14 years (1997-2010) of the Chandeleur Islands. Landsat 5 Thematic Mapper data were used to classify and analyze changes using ERDAS IMAGINE 9.3 software. Images were classified into land and water classes using a hybrid classification technique that is unlike the techniques used in the past. Quantitative spatial analyses of the extent of land loss, vegetation changes and beach loss/gain over time were performed. Three change detection techniques were used in this research, which include post-classification spatial intersection, Change Vector Analysis (CVA) and image differencing. Maximum land loss in the Breton Sound Estuary region was due to Hurricane Katrina in 2005 when 196 km2 of land was converted to water from November 2004 to October 2005. Marsh area loss in the 24-year time series coincided with the overall land area loss. An increase in marsh area was detected in three segments of the time series i.e. 1987 to 1991, 1992 (after Hurricane Andrew) to 2003 (before Hurricane Ivan) and 2006 (after Hurricane Katrina) to 2010 indicating some recovery between hurricane years. At the Chandeleur Islands, most of the land loss over the past decade was due to four major hurricanes since 1997; Hurricane Georges in 1998, Hurricane Ivan in 2004, Hurricane Katrina in 2005 and Hurricane Gustav in 2008. The most significant hurricane that impacted these islands was Hurricane Georges in 1998 that resulted in a land loss of 76.5% measured from 1997. The land area increase after the impact of Hurricane Gustav in 2008 to 2011 was very low ranging from 0 km2 to 2 km2. Shoreline change detection results indicated that the barrier islands moved westward (landward), a maximum of 1.7 km in the southern section. Seven kilometres of the linear coastline was lost in the northern tip and 15 km in the southern tip. The change detection analysis and the shoreline change analysis indicated that the southern section of these islands has undergone greater damage due to erosion than the northern section

The Impact of Sensor Characteristics and Data Availability on Remote Sensing Based Change Detection

Land cover and land use change are among the major drivers of global change. In a time of mounting challenges for sustainable living on our planet any research benefits from interdisciplinary collaborations to gain an improved understanding of the human-environment system and to develop suitable and improve existing measures of natural resource management. This includes comprehensive understanding of land cover and land use changes, which is fundamental to mitigate global change. Remote sensing technology is essential for the analyses of the land surface (and hence related changes) because it offers cost-effective ways of collecting data simultaneously over large areas. With increasing variety of sensors and better data availability, the application of remote sensing as a means to assist in modeling, to support monitoring, and to detect changes at various spatial and temporal scales becomes more and more feasible. The relationship between the nature of the changes on the land surface, the sensor properties, and the conditions at the time of acquisition influences the potential and quality of land cover and land use change detection. Despite the wealth of existing change detection research, there is a need for new methodologies in order to efficiently explore the huge amount of data acquired by remote sensing systems with different sensor characteristics. The research of this thesis provides solutions to two main challenges of remote sensing based change detection. First, geometric effects and distortions occur when using data taken under different sun-target-sensor geometries. These effects mainly occur if sun position and/or viewing angles differ between images. This challenge was met by developing a theoretical framework of bi-temporal change detection scenarios. The concept includes the quantification of distortions that can occur in unfavorable situations. The invention and application of a new method – the Robust Change Vector Analysis (RCVA) – reduced the detection of false changes due to these distortions. The quality and robustness of the RCVA were demonstrated in an example of bi-temporal cross-sensor change detection in an urban environment in Cologne, Germany. Comparison with a state-of-the-art method showed better performance of RCVA and robustness against thresholding. Second, this thesis provides new insights into how to optimize the use of dense time series for forest cover change detection. A collection of spectral indices was reviewed for their suitability to display forest structure, development, and condition at a study site on Vancouver Island, British Columbia, Canada. The spatio-temporal variability of the indices was analyzed to identify those indices, which are considered most suitable for forest monitoring based on dense time series. Amongst the indices, the Disturbance Index (DI) was found to be sensitive to the state of the forest (i.e., forest structure). The Normalized Difference Moisture Index (NDMI) was found to be spatio-temporally stable and to be the most sensitive index for changes in forest condition. Both indices were successfully applied to detect abrupt forest cover changes. Further, this thesis demonstrated that relative radiometric normalization can obscure actual seasonal variation and long-term trends of spectral signals and is therefore not recommended to be incorporated in the time series pre-processing of remotely-sensed data. The main outcome of this part of the presented research is a new method for detecting discontinuities in time series of spectral indices. The method takes advantage of all available information in terms of cloud-free pixels and hence increases the number of observations compared to most existing methods. Also, the first derivative of the time series was identified (together with the discontinuity measure) as a suitable variable to display and quantify the dynamic of dense Landsat time series that cannot be observed with less dense time series. Given that these discontinuities are predominantly related to abrupt changes, the presented method was successfully applied to clearcut harvest detection. The presented method detected major events of forest change at unprecedented temporal resolution and with high accuracy (93% overall accuracy). This thesis contributes to improved understanding of bi-temporal change detection, addressing image artifacts that result from flexible acquisition features of modern satellites (e.g., off-nadir capabilities). The demonstrated ability to efficiently analyze cross-sensor data and data taken under unfavorable conditions is increasingly important for the detection of many rapid changes, e.g., to assist in emergency response. This thesis further contributes to the optimized use of remotely sensed time series for improving the understanding, accuracy, and reliability of forest cover change detection. Additionally, the thesis demonstrates the usability of and also the necessity for continuity in medium spatial resolution satellite imagery, such as the Landsat data, for forest management. Constellations of recently launched (e.g., Landsat 8 OLI) and upcoming sensors (e.g., Sentinel-2) will deliver new opportunities to apply and extend the presented methodologies.Der Einfluss von Sensorcharakteristik und Datenverfügbarkeit auf die fernerkundungsbasierte Veränderungsdetektion Landbedeckungs- und Landnutzungswandel gehören zu den Haupttriebkräften des Globalen Wandels. In einer Zeit, in der ein nachhaltiges Leben auf unserem Planeten zu einer wachsenden Herausforderung wird, profitiert die Wissenschaft von interdisziplinärer Zusammenarbeit, um ein besseres Verständnis der Mensch-Umwelt-Beziehungen zu erlangen und um verbesserte Maßnahmen des Ressourcenmanagements zu entwickeln. Dazu gehört auch ein erweitertes Verständnis von Landbedeckungs- und Landnutzungswandel, das elementar ist, um dem Globalen Wandel zu begegnen. Die Fernerkundungstechnologie ist grundlegend für die Analyse der Landoberfläche und damit verknüpften Veränderungen, weil sie in der Lage ist, große Flächen gleichzeitig zu erfassen. Mit zunehmender Sensorenvielfalt und besserer Datenverfügbarkeit gewinnt Fernerkundung bei der Modellierung, beim Monitoring sowie als Mittel zur Erkennung von Veränderungen in verschiedenen räumlichen und zeitlichen Skalen zunehmend an Bedeutung. Das Wirkungsgeflecht zwischen der Art von Veränderungen der Landoberfläche, Sensoreigenschaften und Aufnahmebedingungen beeinflusst das Potenzial und die Qualität fernerkundungsbasierter Landbedeckungs- und Landnutzungsveränderungs-detektion. Trotz der Fülle an bestehenden Forschungsleistungen zur Veränderungsdetektion besteht ein dringender Bedarf an neuen Methoden, die geeignet sind, das große Aufkommen von Daten unterschiedlicher Sensoren effizient zu nutzen. Die in dieser Abschlussarbeit durchgeführte Forschung befasst sich mit zwei aktuellen Problemfeldern der fernerkundungsbasierten Veränderungsdetektion. Das erste sind die geometrischen Effekte und Verzerrungen, die auftreten, wenn Daten genutzt werden, die unter verschiedenen Sonne-Zielobjekt-Sensor-Geometrien aufgenommen wurden. Diese Effekte treten vor allem dann auf, wenn unterschiedliche Sonnenstände und/oder unterschiedliche Einfallswinkel der Satelliten genutzt werden. Der Herausforderung wurde begegnet, indem ein theoretisches Konzept von Szenarien dargelegt wurde, die bei der bi-temporalen Veränderungsdetektion auftreten können. Das Konzept beinhaltet die Quantifizierung der Verzerrungen, die in ungünstigen Fällen auftreten können. Um die Falscherkennung von Veränderungen in Folge der resultierenden Verzerrungen zu reduzieren, wurde eine neue Methode entwickelt – die Robust Change Vector Analysis (RCVA). Die Qualität der Methode wird an einem Beispiel der Veränderungsdetektion im urbanen Raum (Köln, Deutschland) aufgezeigt. Ein Vergleich mit einer anderen gängigen Methode zeigt bessere Ergebnisse für die neue RCVA und untermauert deren Robustheit gegenüber der Schwellenwertbestimmung. Die zweite Herausforderung, mit der sich die vorliegende Arbeit befasst, betrifft die optimierte Nutzung von dichten Zeitreihen zur Veränderungsdetektion von Wäldern. Eine Auswahl spektraler Indizes wurde hinsichtlich ihrer Tauglichkeit zur Erfassung von Waldstruktur, Waldentwicklung und Waldzustand in einem Untersuchungsgebiet auf Vancouver Island, British Columbia, Kanada, bewertet. Um die Einsatzmöglichkeiten der Indizes für dichte Zeitreihen bewerten zu können, wurde ihre raum-zeitliche Variabilität untersucht. Der Disturbance Index (DI) ist ein Index, der sensitiv für das Stadium eines Waldes ist (d. h. seine Struktur). DerNormalized Difference Moisture Index (NDMI) ist raum-zeitlich stabil und zudem am sensitivsten für Veränderungen des Waldzustands. Beide Indizes wurden erfolgreich zur Erkennung von abrupten Veränderungen getestet. In der vorliegenden Arbeit wird aufgezeigt, dass die relative radiometrische Normierung saisonale Variabilität und Langzeittrends von Zeitreihen spektraler Signale verzerrt. Die relative radiometrische Normierung wird daher nicht zur Vorprozessierung von Fernerkundungszeitreihen empfohlen. Das wichtigste Ergebnis dieser Studie ist eine neue Methode zur Erkennung von Diskontinuitäten in Zeitreihen spektraler Indizes. Die Methode nutzt alle wolkenfreien, ungestörten Beobachtungen (d. h. unabhängig von der Gesamtbewölkung in einem Bild) in einer Zeitreihe und erhöht dadurch die Anzahl an Beobachtungen im Vergleich zu anderen Methoden. Die erste Ableitung und die Messgröße zur Erfassung der Diskontinuitäten sind gut geeignet, um die Dynamik dichter Zeitreihen zu beschreiben und zu quantifizieren. Dies ist mit weniger dichten Zeitreihen nicht möglich. Da diese Diskontinuitäten im Untersuchungsgebiet üblicherweise abrupter Natur sind, ist die Methode gut geeignet, um Kahlschläge zu erfassen. Die hier dargelegte neue Methode detektiert Waldbedeckungsveränderungen mit einzigartiger zeitlicher Auflösung und hoher Genauigkeit (93% Gesamtgenauigkeit). Die vorliegende Arbeit trägt zu einem verbesserten Verständnis bi-temporaler Veränderungsdetektion bei, indem Bildartefakte berücksichtigt werden, die infolge der Flexibilität moderner Sensoren entstehen können. Die dargestellte Möglichkeit, Daten zu analysieren, die von unterschiedlichen Sensoren stammen und die unter ungünstigen Bedingungen aufgenommen wurden, wird zukünftig bei der Erfassung von schnellen Veränderungen an Bedeutung gewinnen, z. B. bei Katastropheneinsätzen. Ein weiterer Beitrag der vorliegenden Arbeit liegt in der optimierten Anwendung von Fernerkundungszeitreihen zur Verbesserung von Verständnis, Genauigkeit und Verlässlichkeit der Waldveränderungsdetektion. Des Weiteren zeigt die Arbeit den Nutzen und die Notwendigkeit der Fortführung von Satellitendaten mit mittlerer Auflösung (z. B. Landsat) für das Waldmanagement. Konstellationen kürzlich gestarteter (z. B. Landsat 8 OLI) und zukünftiger Sensoren (z. B. Sentinel-2) werden neue Möglichkeiten zur Anwendung und Optimierung der hier vorgestellten Methoden bieten

Técnicas de detección de cambios mediante teledetección para el desarrollo sostenible y la desertificación

[ES] A lo largo de los últimos años ha ido aumentando el interés por disponer de información del uso y la cobertura del suelo y su cambio en el tiempo. Con la aparición de las imágenes de satélite y la teledetección, ahora se puede obtener y supervisar esta información de la Tierra de forma sistemática. El enfoque de esta Tesis consiste en desarrollar técnicas de detección de cambio mediante el análisis semiautomático de imágenes ópticas multitemporales y de microondas disponibles de forma abierta, con especial énfasis en la detección de desertificación en el norte de Argelia. En primer lugar, se emplea la técnica Change Vector Analysis y se estudian sus resultados en dos áreas diferentes con objeto de validar esta metodología de detección de cambios. Para ello, se realizan clasificaciones supervisadas por píxel, habiendo seleccionado las clases adecuadas por cada información de la escena. En esta fase, se comprueban los resultados obtenidos con diferentes tipos de clasificadores. Así, el clasificador Maximum Likelihood Classifier es el que proporciona una mejor precisión global, igual a 90,71%, en los escenarios bajo test. La evaluación de la calidad se realiza mediante matrices de confusión y sus parámetros derivados, tales como la precisión global y el coeficiente kappa. La fase de búsqueda del umbral óptimo es el punto crítico en esta metodología de detección de cambios. Una posibilidad de establecer el umbral nos lo da el método clásico Double-Window Flexible Pace Search. Los resultados de la discriminación del tipo de cambio se muestran mediante las matrices de transición e índices de cambios, y en formato gráfico mediante mapa de cambios. En segundo lugar, se estudia la detección de cambios aplicada a la desertificación en Argelia mediante datos ópticos. Se desarrolla una metodología basada en la comparación posterior a la clasificación para monitorizar de forma simple la degradación de la tierra. Este método de detección de cambio es el que proporciona los mejores resultados con una precisión global del 95,15%, tras compararlo con la detección con vectores y considerar diferentes parámetros en ambos métodos. En este caso, el clasificador basado en objetos y la técnica Support Vector Machine es el que proporciona los mejores resultados con un 92,91% en termino de precisión global y un valor del coeficiente kappa igual a 0,91, después de comparar las matrices de confusión y sus parámetros derivados. Consecuentemente, se diseña un método de detección de cambios y se evalúa la evolución del cambio en la ciudad de Biskra (Argelia) durante un período de veinticinco años. Los resultados se disponen en formato estadístico (matrices de transición e índices de cambio) y en formato gráfico mediante mapas de distribución de cambios, obteniendo excelentes resultados con un bajo coste en tiempo de operador humano. Finalmente, teniendo en cuenta la creciente disponibilidad de imágenes de microondas, se realiza un estudio añadiendo imágenes radar a los datos ópticos en la metodología previamente seleccionada de detección de desertificación. Después de evaluar diferentes configuraciones para introducir la nueva información en la cadena de procesado, se escoge la integración de la imagen radar en polarización vertical-vertical sin filtrado Speckle después de la fase de segmentación. Esta nueva estrategia, empleando imágenes ópticas y de radar, introduce una mejora significativa sobre los resultados anteriormente obtenidos, con 97,05% de precisión global y 0,96 del coeficiente kappa, ya que las propiedades de la arena seca en la imagen radar hacen que sea más fácilmente identificada. Este nuevo método semiautomático integrando distintos tipos de imágenes reduce el trabajo del analista y produce un informe de detección de cambios fácil de interpretar. La utilidad de este tipo de informe reside en ayudar a las autoridades gubernamentales argelin[CA] Al llarg dels últims anys ha anat augmentant l'interés per disposar d'informació de l'ús i la cobertura del sòl i el seu canvi en el temps. Amb l'aparició de les imatges de satèl·lit i la teledetecció, ara es pot obtindre i supervisar aquesta información de la Terra de forma sistemàtica. L'enfocament d'aquesta Tesi consisteix a desenvolupar tècniques de detecció de canvi mitjançant l'anàlisi semiautomática d'imatges òptiques multitemporales i de microones disponibles de forma oberta, amb especial èmfasi en la detecció de desertificació en el nord d'Algèria. En primer lloc, s'empra la tècnica Change Vector Analysis i s'estudien els seus resultats en dues àrees diferents a fi de validar aquesta metodologia de detecció de canvis. Per a això, es realitzen classificacions supervisades per píxel, havent seleccionat les classes adequades per cada informació de l'escena. En aquesta fase, es comproven els resultats obtinguts amb diferents tipus de classificadors. Així, el classificador Maximum Likelihood Classifier és el que proporciona una millor precisió global del 90,71% en els escenaris sota test. L'avaluació de la qualitat es realitza mitjançant matrius de confusió i els seus paràmetres derivats, tals com la precisió global i el coeficient kappa. La fase de cerca del llindar òptim és el punt crític en aquesta metodologia de detecció de canvis. Una possibilitat d'establiment de llindar ens ho dóna el mètode clàssic Double-Window Flexible Pace Search. Els resultats de la discriminació del tipus de canvi es mostren mitjançant les matrius de transició i índexs de canvis, i en format gràfic mitjançant mapa de canvis. En segon lloc, s'estudia la detecció de canvis aplicada a la desertificació a Algèria mitjançant dades òptiques. Es desenvolupa una metodologia basada en la comparació posterior a la classificació per a monitorar de forma simple la degradació de la terra. Aquest mètode de detecció de canvi és el que proporciona els millors resultats amb una precisió global del 95,15% després de comparar-ho amb la detecció amb vectors i considerar diferents paràmetres en tots dos mètodes. En aquest cas, el classificador basat en objectes i la tècnica Support Vector Machine és el que proporciona els millors resultats amb una precisió global igual a 92,91% i un coeficient kappa de 0,91, després de comparar les matrius de confusió i els seus paràmetres derivats. Conseqüentment, es dissenya un mètode de detecció de canvis i s'avalua l'evolució del canvi a la ciutat de Biskra (Algèria) durant un període de vint-i-cinc anys. Els resultats es disposen en format estadístic (matrius de transició i índexs de canvi) i en format gràfic mitjançant mapes de distribució de canvis, obtenint excel·lents resultats amb un baix cost en temps d'operador humà. Finalment, tenint en compte la creixent disponibilitat d'imatges de microones, es realitza un estudi afegint imatges radar a les dades òptiques en la metodologia prèviament seleccionada de detecció de desertificació. Després d'avaluar diferents configuracions per a introduir la nova informació en la cadena de processament, es tria la integració de la imatge radar en polarització vertical-vertical sense filtrat Speckle després de la fase de segmentació. Aquesta nova estrategia, emprant imatges òptiques i de radar, introdueix una millora significativa sobre els resultats anteriorment obtinguts, amb 97,05% de precisió global i 0,96 del coeficient kappa, ja que les propietats de l'arena seca en la imatge radar fan que siga més fàcilment identificada. Aquest nou mètode semiautomàtic integrant diferents tipus d'imatges redueix el treball de l'analista i produeix un informe de detecció de canvis fàcil d'interpretar. La utilitat d'aquest tipus d'informe resideix a ajudar les autoritats governamentals algerianes a prendre les accions adequades en la lluita contra la degradació de la Terra.[EN] Over the last few years, the interest on the use of land use, land cover and its change in the time has grown. With the appearance of satellite images and Remote Sensing techniques, this type of Earth information can be obtained in a systematic way. The main focus of this Thesis is to develop change detection techniques through semiautomatic analysis of freely available optical and microwave images, with special emphasis on the detection of desertification in the north of Algeria. Firstly, Change Vector Analysis is studied in two different zones in order to validate this change detection technique. For that purpose, supervised classification per pixel is employed with the selection of the appropriate classes for each scene information. In this step, comparison among different types of classifiers is done and Maximum Likelihood Classifier provides the better accuracy equal to 90,71%. Quality evaluation is given by matrices of confusion and its derived parameters, such as global accuracy and kappa coefficient. A critical point in change detection methodology is optimal threshold selection. One possibility for it is given by the classical method Double-Window Flexible Pace Search. The results of change detection are given by transition matrices, change indexes and change maps. Secondly, change detection applied to the issue of desertification in Algeria is studied using optical data. A methodology based on post classification comparison is developed to monitor the degradation of the Earth in a simple way. This method of change detection provides the best results with a value of 95,15% in overall accuracy, after the comparison with Change Vector Analysis and considering different processing parameters in both methods. In this case, the Support Vector Machine classifier based on objects is the one that provides the best results with a remarkable global accuracy of 92,91% and kappa coefficient equal to 0,91, after comparing the confusion matrices and their derived products. Consequently, a change detection method is designed and evaluated in the city of Biskra (Algeria) during a period of twenty-five years. The results are available in statistical format (transition matrices and change indexes) and in graphical format using change distribution maps. The excellent results are obtained with low operator time. Finally, taking into account the increasing availability of microwave images, the addition of radar images to the optical data in the previously selected desertification detection methodology is carried out. After evaluating different configurations, the integration of the radar image in vertical-vertical polarization without Speckle filtering after the segmentation step is chosen. This new strategy employing optical and radar images provides a significant improvement over previous results (with a value of 97,05% in global accuracy and 0,96 in kappa coefficient), since the properties of dry sand in the radar image make it more easily identifiable. This new semiautomatic method integrating different types of images reduces the analyst's work and produces an easily interpretable change detection report. The usefulness of this type of report lies in helping the Algerian government authorities to take appropriate actions to fight against land degradation.Azzouzi, SA. (2019). Técnicas de detección de cambios mediante teledetección para el desarrollo sostenible y la desertificación [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/117994TESI

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin