Atlas of Global Surface Water Dynamics

It is impossible to overstate the importance of freshwater in our daily lives – for proof, try going without it for any length of time. Surface waterbodies (lakes, ponds, rivers, creeks, estuaries… it doesn't matter what name they go under) are particularly important because they come into direct contact with us and our biophysical environment. But our knowledge concerning where and when waterbodies might be found was, until recently, surprisingly sparse. The paucity of information was because trying to map a moving target is actually very difficult – and waterbodies undeniably move, in both geographical space and time. By 2013 the U.S. Geological Survey and NASA were making petabyte scale archives of satellite imagery freely available, archives that covered the entire planet's surface and stretched back decades. Other's such as the European Commission / European Space Agency Copernicus programme were also putting full free and open data access policies into place, and Google's Earth Engine had become a mature, powerful cloud-based platform for processing very large geospatial datasets. Back in 2013 a small team working at the European Commission's Joint Research Centre were looking at ways satellite imagery could be used to capture surface waterbody dynamics, and create new maps that accurately incorporated time dimensions. Concurrently the Google Earth Engine team were focussing their massive computational capabilities on major issues facing humanity, such as deforestation, food security, climate change - and water management. The two teams came together in a partnership based not on financial transactions but on a mutual exchange of complementary capabilities, and devoted thousands of person hours and thousands of CPU years into turning petabytes of Landsat satellite imagery into unique, validated surface water maps, first published in 2016, and made available to everyone through a dedicated web portal, the Global Surface Water Explorer. Since then satellites have continued to image the Earth, surface water has continued to change and the JRC Goole Earth Engine partnership has continued to work on improving our knowledge of surface water dynamics and making sure this knowledge benefits as many people as possible. This Atlas is part of the outreach; it is not a guide to the Global Surface Water Explorer, it is not a Google Earth Engine tutorial (though if it inspires you to visit either of these resources then it has achieved one of its objectives), but it is a stand-alone window into how people and nature affect, and are affected by the 4.46 million km2 of the Earth's landmass that have been under water at some time over the past 35 years.JRC.D.5-Food Securit

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.JRC.H.3-Forest Resources and Climat

On the Contribution of Remote Sensing to DOPA, a Digital Observatory for Protected Areas

The Digital Observatory for Protected Areas (DOPA) is a biodiversity information system currently developed as a set of interoperable web services at the Joint Research Centre of the European Commission in collaboration with other international organizations, including GBIF, UNEP-WCMC, Birdlife International and RSPB. DOPA is not only designed to assess the state and pressure of Protected Areas (PAs) and to prioritize them accordingly, in order to support decision making and fund allocation processes, but it is also conceived as a monitoring and modeling service. To capture the dynamics of spatio-temporal changes in habitats and anthropogenic pressure on PAs, the automatic collection and processing of remote sensing data are processes at the heart of the system. In particular, DOPA uses information from EumetCAST and SpotVGT to compute environmental trends and detect anomalies every 10 days. Anthropogenic threats are also currently assessed through the analysis of agricultural pressure, population growth and habitat fragmentation around the protected areas. Fire activity in sub-Saharan protected areas which is derived from the MODIS fire products (active fires and burned areas) provide further support to park managers as well as to experts working for conservation and natural resource management. The purpose of this paper is to highlight the variety of uses of remote sensing data by the DOPA, the integration with other data sources, the practical implementation according to an architecture grounded in international initiatives such as GEOSS, GSDI and INSPIRE, and applications in monitoring and in ecological forecasting through e-Habitat, DOPAs¿ habitat modeling service.JRC.H.3-Global environement monitorin

From Anopheles to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge

When working on vector borne diseases, decision makers and researchers often face a lack of specific high quality data. However the results/decisions can be critical as they can impact on the lives of many people. This chapter reviews the challenges posed by spatial surveillance of anopheles-borne diseases with particular attention for malaria surveillance. These challenges will mainly reside in the difficulty of getting the appropriate raw data and the large spectrum of multidisciplinary expertise. Raw data include anopheles attributes. Design of sampling strategies is a compromise between the best sampling size for analysis, optimal sampling in space or time and cost-related factors. On the other hand, raw environmental factors from remote sensing products are increasingly available and used but ready to use information on temperature mainly available in Africa and resolution too coarse for detection of water bodies. Moreover the quality and interpretation of final product is dependent of image pre-processing which should be understood by the final user. Those include production of the pixels which do not totally represent environmental value at location, compositing which summarize several images into one to eliminate clouds contamination and production of land cover which represent environmental value at the time of original images capture, develop mosaic classes to gather pixel difficult to discriminate and propose land cover classes not always adapted to the anopheles species habitat. Remote sensing however provides a unique source of information which would not be available otherwise. Modelling techniques are then discussed as well as initiatives to help transfer results and expertise to health professionals in countries.JRC.H.4-Monitoring Agricultural Resource

State and evolution of the African rainforests between 1990 and 2010

This paper presents a 2005 map of Africa’s rainforests with new levels of spatial and thematic detail, being derived from 250m resolution MODIS data, and having an overall accuracy of 84%. A systematic sample of Landsat images (with supplemental data from equivalent platforms to fill sample gaps) is used to produce a consistent assessment of deforestation between 1990, 2000 and 2010 for West Africa, Central Africa and Madagascar. Net deforestation is estimated at 0.28% yr-1 for the period 1990-2000 and 0.14% yr-1 for the period 2000-2010. West Africa and Madagascar exhibit a much higher deforestation rate than the Congo Basin. Based on a simple analysis of the variance over the Congo Basin, we show that expanding agriculture and increasing fuelwood demands are key drivers of deforestation while well-controlled timber exploitation programmes have little or no direct influence on forest-cover reduction at present. Rural and urban population concentrations and fluxes are identified as strong underlying causes of deforestation in this study.JRC.H.5-Land Resources Managemen

Long-term monitoring of tropical moist forest extent (from 1990 to 2019): Description of the dataset

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-econom

An introduction to the Digital Observatory for Protected Areas (DOPA) and the DOPA Explorer (Beta)

The Digital Observatory for Protected Areas (DOPA) is conceived around a set of interacting Critical Biodiversity Informatics Infrastructures (databases, web modelling services, broadcasting services, ...) hosted at different institutions, including the Joint Research Centre of the European Commission, the World Conservation Monitoring Centre (UNEP-WCMC), the International Union for Conservation of Nature (IUCN), the Global Biodiversity Information Facility (GBIF) and BirdLife International. The current services of DOPA provide to a large variety of end-users, ranging from park managers, funding agencies to researchers, with means to assess, monitor and possibly forecast the state and pressure of protected areas at the local, national and global scales. With an introduction to the DOPA, the readers will find here a user manual of the beta version of DOPA Explorer, a first web based assessment tool where information on 9 000 protected areas covering almost 90% of the global protected surface has been processed automatically to generate a set of indicators on ecosystems, climate, phenology, species, ecosystem services and pressures. DOPA Explorer can so help identify the protected areas with most unique ecosystems and species and assess the pressures they are exposed to because of human development. Ecological data derived from and near real-time earth observations are also made available for the African continent. Inversely, DOPA Explorer indirectly highlights the protected areas for which the information is incomplete.JRC.H.5-Land Resources Managemen

Monitoring African surface water dynamic using medium resolution daily data allows anomalies detection in nearly real time

This paper proposes to use a water detection methodology based on a colorimetric approach to develop a near real time system allowing to monitor and to detect anomalies at a fine time resolution and in a systematic way The algorithm was calibrated over Africa using daily reflectance MODIS data from 2003 to 2011. The proposed approach has 3 major outputs updatable in near real time: (1) a permanent water mask (2) a every 10-days surface water map consolidated with time series and (3) an anomalies detection using 10 years of detection reanalysis. Three validation approaches are developed to deal with the large coverage and the high temporal resolution. The methodology is generic and could be applied to other extent and sensors

Monitoring African surface water dynamic using medium resolution daily data allows anomalies detection in nearly real time

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