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The need for accurate information on the state and evolution of tropical forest types at regional and continental scales is widely recognized, particularly to analyze the forest diversity and dynamics, to assess degradation and deforestation processes and to better manage these natural resources. Here we document the approach that was developed by JRC to map and monitor the extent of moist tropical forests and their changes (degradation, deforestation and regrowth) over the last three decades (1990-2020) at fine spatial resolution (30 m × 30 m). The approach is based on the analysis of each valid observation from the Landsat archive and allows to capture disturbances with a short-duration appearance on satellite imagery such as selective logging, fires, and severe weather events (hurricanes, dryness). This new approach allows characterizing the sequential dynamics of forest cover changes by providing transition stages from the initial observation period to the most recent year (2019 for this report). For the first time at the pantropical scale the occurrence and extent of forest degradation can be documented on an annual basis in addition to the monitoring of deforestation. After a short introduction (chapter 1), this technical report describes the study area (chapter 2), the input data (chapter 3), the method that has been developed (chapter 4), and the outcomes of this study (chapter 5). A discussion is also provided regarding the specificities and added value of the outcomes (chapter 6), and the known limitations and future expected improvements (chapter 7). This new pan-tropical scale deforestation and forest degradation monitoring system will contribute to the EU Observatory on deforestation, forest degradation, changes in the world’s forest cover, and associated drivers, which is an action being implemented in the framework of the Communication from the Commission to step up EU action to protect and restore the World’s forests (COM(2019) 352)., JRC.D.1-Bio-economy

This report summarizes contents and outputs of three national workshops on ‘Tools and Methods for Monitoring Deforestation and Forest Degradation by Remote Sensing’, organized in 2016 in Cambodia, Laos and Vietnam. The main objective of the workshops was to improve the technical capabilities of ReCaREDD partner institutions in monitoring forest change from satellite remote sensing, addressing particularly processes of forest degradation. The workshops consisted of (i) an introduction to SENTINEL-2 imagery, (ii) intense training on the JRC IMPACT software tool for satellite image processing and monitoring of land cover change, and (iii) practical sessions on potential approaches for monitoring forest degradation., JRC.D.1-Bio-economy

The recent availability of time series of Sentinel-2 imagery represents a significant technological step in the use of Earth Observation (EO) data for forest cover monitoring. The 5 days revisiting time (S-2 A and S-2B satellites) and the 10m spatial resolution imply the need for much larger storage and processing resources than ever. To facilitate the use of Sentinel-2 imagery by national forestry services in the tropics (in particular in relation to REDD+ activities), the JRC has developed the Sentinel-2 web platform, an online Web system which is aimed at browsing, exploring and processing the full dataset of Sentinel-2 imagery available over theTropical belt., JRC.D.1-Bio-economy

A workshop was held at RCMRD, the Regional Centre for Mapping of Resources for Development, Nairobi 19th -23rd October 2015 under the aegis of the ReCaREDD project, which is funded by DG DEVCO. Both RCMRD and their partner ICPAC have a long standing collaborations with the JRC are the main actors in the East Africa MESA project. The workshop brought together a group of experts from partner countries of the East Africa to take part in a capacity building and information sharing workshop dedicated to reducing barriers for forest degradation monitoring in the tropics., JRC.D.1-Bio-economy

Did you ever try to produce a reliable land cover map from Earth Observation data? How many steps are involved and how many different tools do you need? Did you succeed in a reasonable amount of time? IMPACT toolbox offers a combination of elements of remote sensing, photo interpretation and processing technologies in a portable and stand-alone GIS environment. Image extraction, layerstack, radiometric calibration, normalization, mosaicking, automatic classification and segmentation could be easily performed while a user-friendly environment offers map editing and validation features. Based on open sources technologies, IMPACT relies on GeoExt, OpenLayers and Mapserver for the graphical user interface and raster/vector rendering; EO data processing is carried out by GDAL and Python. Single-date or multi-date segmentation is performed throughout Baazt segmenter libraries (Inpe's TerraAIDA Operators)., JRC.H.3-Forest Resources and Climate

This paper presents a method for mapping and assessing burned areas at a regional scale, using a systematic sample of medium spatial resolution satellite images (Landsat). The State of Mato Grosso, located in the Brazilian Amazon region, comprising an area of pproximately 903,366 km², was selected for this study. 77 sample sites (20km × 20km in size) located at each full degree confluence of latitude and longitude were analyzed. The results showed that 52,663 km² or approximately 5.8% of the State land cover was burned in 2010. Our method produced results comparable with PanAmazonia Project data and useful for evaluating burned area products based on coarse spatial resolution imagery like MODIS or SPOT-VEGETATION., JRC.H.3-Forest Resources and Climate

This article describes the methodology and results of a new JRC phenology-based classification algorithm able to generate accurate land cover maps in a fully automatic manner from Landsat 8 (L8) remote sensed data available since 12th April 2013 at no charge throughout the USGS website. A preliminary study aiming to bypass the single date classification inaccuracy (mainly due to seasonality) using long term MODIS time series as a “driver” to fill gaps between high resolution data, has been carried out. The high global acquisition frequency (~16 days) and distribution policy are making Landsat 8 product extremely suitable for near real time land cover mapping and monitoring. Five national parks in east Africa have been selected as study areas (Mahale Mountains, Mana Pools, West Lunga, Gorongosa, Tsimanampetsotsa); they are covering diverse eco-regions and vegetation types, from evergreen to deciduous. A buffer of 20 km around each park has been considered as well. Selected single date images were first preprocessed in order to convert raw DN values to top of atmosphere (TOA) reflectance and minimizes spectral differences caused by different acquisition time, sun elevation, sun-earth distance, and after processed by the algorithm to generate a thematic raster map with land cover classes. Is worth noting that the single date classification accuracy is closely related to the acquisition date of the image, the status of the vegetation and weather conditions such as cloud and shadows often present in tropical regions; here the need of developing a phenology based algorithm that considers the vegetation evolution and generates a more accurate land cover map including evergreen and deciduous discrimination on the basis of “frequency” rules. Land cover maps have been created for all parks and an exhaustive accuracy assessment has been carried out on Mahale Mountains and Tsimanampetsotsa. The combined overall accuracy of 82.8% demonstrates the high potentiality of this method and makes it usable at either local or regional scale., JRC.H.3-Forest Resources and Climate

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 Climate

Forest monitoring is crucial over tropical regions to assess the evolution of forest cover changes and give figures about deforestation. Based on a systematic sample of image extracts, a processing chain had been developed for producing deforestation estimates over the years 1990-2000-2005. Whereas this monitoring exercise was based on Landsat images, limitations in Landsat image availability for the year 2010 over Central Africa required alternative imagery. Given its high revisit period and characteristics close to the Landsat TM sensor, DMC images are considered in this paper to replace Landsat TM data gaps over Central Africa. However the classification module of the existing processing chain is based on Tasseled Caps (TC-based) analysis of Landsat TM imagery and needs to be adapted to such data in order to be sensor-independent. This adaptation is described in this paper. A sub-sample of the available image extracts has been used for the selection of the best object-based features through the analysis of existing Land-Cover maps for 1990 and 2000. The processing chain has been adapted for the production of Land-Cover change maps for year 2010. The resulting maps from the two methods, original TC-based classification and adapted Multi-Sensor approach, have been compared and evaluated. The overall accuracies of the 2010 Land-Cover classification results are 90% for the TC-based approach and 92% for the Multi-Sensor approach. When considering only objects for which label is changing between 2000 and 2010, the accuracies of the 2010 LC classifications are 45% and 72% for the TC-based and Multi-Sensor approaches respectively. These results show that, even with lower radiometric quality of DMC imagery the performance of the automated classification has been improved thanks to an appropriate selection of object-based features. As similar adaptation will be developed for other satellite imagery such as SPOT and Rapid-Eye in order to be sensor-independent, the future adaptation to Sentinel-2 data will be very easy using the same approach., JRC.H.3-Forest Resources and Climate

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 monitoring

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 monitoring