Global tropical forest cover change assessment with medium spatial satellite imagery using a systematic sample grid – data, methods and first results

At the Joint Research Centre (JRC) of the European Commission, a methodology has been developed to monitor the pan-tropical forest cover with remote sensing data for the years 1990-2000-2005 in Latin America, Southeast Asia and Africa on the basis of over 4000 sample units sample units with a dimension of 20 km by 20 km located at every full latitude and longitude degree confluence. From the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) instruments, images with low cloud impact from the epochs around the years 1990, 2000 and 2005 were selected and subsets covering the sample units were cut-out, pre-processed, segmented and classified in five different land cover classes in order to build global and regional statistics on tropical forest cover change. The data was validated in three steps, internal correction of wrongly classified objects, external (national or regional) expert validation and internal harmonization of the data. In this paper, the data collection and the workflow of the forest cover change assessment for the epochs 1990 and 2000 is presented. Parts of the results for the Brazilian Amazon have been validated by comparing with interpretations of corresponding samples carried out by the Instituto Nacional de Pesquisas Espaciais (INPE), showing a very high correlation. Further, the figure produced by INPE through the PRODES program on gross deforestation for the years 1990-2000 was compared to the figure calculated on basis of the JRC results for the respective area, where the JRC estimate that was ca. 10% higher than the INPE estimate.JRC.DDG.H.3-Global environement monitorin

Global tropical forest cover change assessment with medium spatial stellite imagery using a systematic sample grid - data, methods and first results.

At the Joint Research Centre (JRC) of the European Commission, a methodology has been developed to monitor the pan-tropical forest cover with remote sensing data for the years 1990-2000-2005 in Latin America, Southeast Asia and Africa on the basis of over 4000 sample units sample units with a dimension of 20 km by 20 km located at every full latitude and longitude degree confluence. From the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) instruments, images with low cloud impact from the epochs around the years 1990, 2000 and 2005 were selected and subsets covering the sample units were cut-out, pre-processed, segmented and classified in five different land cover classes in order to build global and regional statistics on tropical forest cover change. The data was validated in three steps, internal correction of wrongly classified objects, external (national or regional) expert validation and internal harmonization of the data. In this paper, the data collection and the workflow of the forest cover change assessment for the epochs 1990 and 2000 is presented. Parts of the results for the Brazilian Amazon have been validated by comparing with interpretations of corresponding samples carried out by the Instituto Nacional de Pesquisas Espaciais (INPE), showing a very high correlation. Further, the figure produced by INPE through the PRODES program on gross deforestation for the years 1990-2000 was compared to the figure calculated on basis of the JRC results for the respective area, where the JRC estimate that was ca. 10% higher than the INPE estimate

A long-term perspective on deforestation rates in the Brazilian Amazon

Monitoring tropical forest cover is central to biodiversity preservation, terrestrial carbon stocks, essential ecosystem and climate functions, and ultimately, sustainable economic development. The Amazon forest is the Earth’s largest rainforest, and despite intensive studies on current deforestation rates, relatively little is known as to how these compare to historic (pre 1985) deforestation rates. We quantified land cover change between 1975 and 2014 in the so-called Arc of Deforestation of the Brazilian Amazon, covering the southern stretch of the Amazon forest and part of the Cerrado biome. We applied a consistent method that made use of data from Landsat sensors: Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). We acquired suitable images from the US Geological Survey (USGS) for five epochs: 1975, 1990, 2000, 2010, and 2014. We then performed land cover analysis for each epoch using a systematic sample of 156 sites, each one covering 10 km × 10 km, located at the confluence point of integer degree latitudes and longitudes. An object-based classification of the images was performed with five land cover classes: tree cover, tree cover mosaic, other wooded land, other land cover, and water. The automatic classification results were corrected by visual interpretation, and, when available, by comparison with higher resolution imagery. Our results show a decrease of forest cover of 24.2% in the last 40 years in the Brazilian Arc of Deforestation, with an average yearly net forest cover change rate of -0.71% for the 39 years considered

User Manual for the JRC Land Cover/Use Change Validation Tool

The JRC TREES-3 project aims at estimating forest cover changes at continental and regional levels for the Tropical belt for the periods 1990-2000 and 2000-(2005)-2010 based on a systematic sample of forest cover change maps. An operational system has been developed for the processing and change assessment of a large data set of multi-temporal medium resolution imagery (sample units of 20 km x 20 km size analysed from with Landsat imagery). The main task is to assess as accurately as possible for each sample unit the forest cover and forest cover change between two dates. The analysis includes a crucial final step of visual verification and final assignment of land cover labels which is carried out by forestry national officers or remote sensing experts from tropical countries. The visual interpretation is conducted interdependently on two-date imagery to verify and to adjust the labels pre-assigned to each segment for the different dates. A dedicated stand-alone application has been developed for this purpose. The application is a graphical user interface, called the JRC Land Cover/Use Change Validation Tool. The aim of this tool is to provide a user-friendly interface, with an optimised set of commands to navigate through and assess a given dataset of satellite imagery and land cover maps, and to correct easily the land-cover labels as appropriate. The present technical document, entitled ¿User Manual for the JRC Land Cover Change Validation Tool¿ describes the steps for the installation of the tool on a personal computer, as well as the detailed features of this dedicated graphical user interface. The authors welcome feedbacks from potential users of the tool, in particular reporting of any potential software issue or providing suggestions for improvements of future versions of the tool.JRC.DDG.H.3-Global environement monitorin

Manual de utilização de ferramenta do Centro Comum de Investigação para validação das mudanças da cobertura vegetal e do uso da terra

O projeto TREES-3 do CCI tem como objetivo avaliar mudanças da cobertura vegetal na região tropical que ocorreram entre 1990 e 2000, e entre 2000-(2005)-2010. Para isto, foram processadas e avaliadas mudanças da cobertura vegetal em uma grande quantidade de imagens de satélite multi-temporais de resolução espacial média (unidades amostrais de 20 km x 20 km de imagens Landsat). Desta forma, o projeto TREES-3 busca avaliar para cada uma das unidades amostrais a cobertura florestal e as mudanças da cobertura vegetal ocorrida num quinquénio ou década com a mais alta precisão possível. A análise da mudança da cobertura vegetal e do uso da terra inclui também uma etapa de validação visual da classificação das imagens de satélite para atribuir as classes definitivas. Para esta etapa, o CCI desenvolveu uma ferramenta computacional chamada ‘‘Ferramenta do CCI para validação das mudanças da cobertura vegetal e do uso da terra’’. Esta ferramenta é utilizada por agentes florestais nacionais ou especialistas em sensoriamento remoto provenientes de países tropicais. Nesta ferramenta, a interpretação visual das imagens de satélite é efetuada de maneira simultânea utilizando imagens de dois períodos diferentes. Desta forma, é possível verificar e ajustar classes de uso da terra que foram previamente definidas. Neste trabalho, a FAO colabora com o CCI no âmbito do projeto de levantamento por sensoriamento remoto para avaliação dos recursos florestais mundiais (FRA). O CCI agregou na ferramenta computacional uma função que permite atribuir classes de uso da terra que fazem parte da classificação utilizada pela FAO. O presente documento, intitulado ‘‘Manual de utilização de ferramenta do Centro Comum de Investigação para validação das mudanças da cobertura vegetal e do uso da terra”, explica o procedimento para instalação da ferramenta e descreve as características da interface gráfica do usuário.JRC.H.3-Forest Resources and Climat

Manuel d'utilisation de l'outil du CCR pour la validation des changements du couvert végétal / de l'utilisation des terres

Le projet TREES-3 du CCR a pour objectif d¿estimer les changements dans le couvert forestier aux échelles continentales et régionales dans les régions tropicales qui sont survenus au cours des années 1990 à 2000 et 2000 à (2005)-2010 sur la base d¿un échantillon systématique de cartes révélant les changements du couvert forestier. Un système opérationnel a été mis au point pour traiter et évaluer les changements dans un grand nombre de sites à partir d¿images multi-temporelles de moyenne résolution spatiale (unités d¿échantillonnage de 20 km x 20 km analysées à partir d¿images Landsat). L¿objectif principal est d¿évaluer le plus précisément possible, pour chaque unité d¿échantillonnage, le couvert forestier et le changement dans celui-ci entre deux dates. L¿analyse comprend une étape ultime d¿une importance cruciale qui consiste à vérifier visuellement et à attribuer l¿identification finale des couverts végétaux. Cette dernière étape est confiée aux soins d¿agents forestiers nationaux ou d¿experts en télédétection, issus de pays tropicaux. L¿interprétation visuelle s¿effectue de manière interdépendante à partir d¿images pris à deux dates différentes afin de vérifier et d¿ajuster les classes de végétation préalablement attribuées à chaque segment aux différentes dates. Une application autonome a été spécialement conçue à cette fin. Dénommée «Outil du CCR pour la validation des changements du couvert végétal», cette application est une interface utilisateur graphique conviviale dont la série optimisée de commandes permet, d¿une part, de naviguer à des fins d¿évaluation dans un ensemble d¿images satellitaires et de cartes représentant le couvert végétal et, d¿autre part, de corriger aisément, le cas échéant, les classes de couvert végétal. La FAO collabore avec le CCR à ce travail dans le cadre de l¿enquête par télédétection qui est menée à bien au titre de l¿évaluation des ressources forestières mondiales (FRA). Le CCR a ajouté à l¿outil une fonctionnalité qui permet aussi d¿étiqueter les classes d¿utilisation des terres qui relèvent de la classification utilisée par la FAO. Le présent document, intitulé «Manuel d¿utilisation de l¿outil du CCR pour la validation des changements du couvert végétal / de l¿utilisation des terres», explique la procédure à suivre pour installer le logiciel sur un ordinateur personnel et décrit en détail les caractéristiques de cette interface utilisateur graphique spécifique. Les auteurs remercient d¿ores et déjà les utilisateurs potentiels de l¿outil de bien vouloir leur faire part de leurs commentaires et en particulier de les tenir informés de tout problème logiciel éventuel ou de leur faire parvenir toute suggestion d¿améliorations pour les futures versions de l¿outil.JRC.DDG.H.3-Global environement monitorin

Manual del usuario para la herramienta del CCI de validación del cambio en la cobertura vegetal/ocupación del suelo

El proyecto TREES-3 del CCI tiene por objeto estimar los cambios en la cobertura forestal a nivel continental y regional para el cinturón tropical y para los períodos 1990-2000 y 2000-(2005)-2010 basándose en una muestra sistemática de los mapas de cambios en la cobertura forestal. Se ha desarrollado un sistema para el tratamiento y evaluación de los cambios en la cobertura vegetal a partir de un amplio conjunto de datos de imágenes de resolución media multitemporales (unidades de muestra de 20 km x 20 km analizadas a partir de imágenes del satélite Landsat). La principal tarea es evaluar, de la manera más exacta posible y para cada unidad de muestra, la cobertura forestal y el cambio en esta entre dos fechas. El análisis incluye un paso final crucial consistente en la verificación visual y la asignación final de etiquetas de cobertura vegetal, efectuado por funcionarios nacionales responsables de los bosques o expertos en teledetección de los países tropicales. La interpretación visual se lleva a cabo de manera interdependiente en imágenes de dos fechas a fin de verificar y ajustar las etiquetas preasignadas a cada segmento para las diferentes fechas. Con esta finalidad se ha desarrollado una aplicación dedicada autónoma. La aplicación es una interfaz gráfica de usuario denominada «herramienta del CCI de validación del cambio en la cobertura vegetal», cuya finalidad es proporcionar una interfaz de fácil manejo con un conjunto optimizado de órdenes para navegar por un conjunto de datos de imágenes de satélite y mapas de la cobertura vegetal, evaluarlos y corregir fácilmente las etiquetas de ocupación del suelo según corresponda. En esta tarea la FAO está colaborando con el CCI en el marco del “Global Forest Resource Assessment (FRA) Remote Sensing Survey”. El CCI añadió funcionalidades a esta herramienta para permitir el etiquetado de clases de ocupación del suelo que forman parte de la clasificación FRA. El presente documento técnico, titulado «Manual de instrucciones para la herramienta del CCI de validación del cambio en la cobertura vegetal/ocupación del suelo» (JRC Land Cover/Use Change Validation Tool) describe el procedimiento de instalación de la herramienta en un ordenador personal, así como las características detalladas de la interfaz gráfica de usuario. Los autores agradecen las aportaciones de los usuarios de la herramienta, especialmente la información respecto a cualquier problema de software o las sugerencias para la mejora de futuras versiones.JRC.H.3-Forest Resources and Climat

Klassning av fjällbjörkskog enligt FAO:s definition av skogsmark med hjälp av flygburen laserskanning

Sweden’s forestry legislation was updated in 2010 and a new definition of forest land was introduced. This definition was adapted to the one used by the Food and Agriculture Organization of the United Nations (FAO) for international statistics on the state of the world's forests. It is in short based on the lands ability to grow forest that reaches 5 meters, 10 % canopy closure and has a continuous distribution, according to FAO at least 0.5 hectares. A country-wide laser scanning is now carried out for the production of a new national elevation model; the laser data also provides information on forest height and density. The mountain birch forest makes up much of the border with other land types, and to map the distribution of forest land here would be of interest. A distribution map could provide information such as how much forest land that lies within protected areas. In this study, laser data was used to classify the forest in the Abisko area, using reference data from sample plots. From the point cloud obtained from the laser scanning, different types of metrics were calculated and used to classify the forest. Classification results were evaluated by cross-validation, suggesting an overall accuracy of 92% and a kappa coefficient of 0.85. Despite the high accuracy, there were problems associated with a somewhat small sample of ground reference data. In order to separate forest that meets the requirements of forest land from forest which is high but not dense enough, more reference data would be needed. Steep and stony terrain also caused some problems, where the edges and rocks in some areas were mistaken for vegetation. The methods and problems that emerged from this study can be important experiences for potential future mapping of the forest land in the Swedish mountains

Triennial Report: 2009-2011

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 4 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 18 Postdoctoral Fellows [Page] 21 GSE Ph.D Program [Page] 30 Ph.D. Students [Page] 31 Ph.D. Fellowships [Page] 44 Recent Ph.D. Graduates [Page] 45 Center Scholars Program and Masters Students [Page] 51 Research Staff [Page] 52 Administrative and Information Technology Staff [Page] 55 Computer Resources [Page] 58 Research Funding [Page] 60 Looking Forward [Page] 61 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2009-2011 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC