Sources de données
Les jeux de données qui alimentent l'Atlas mondial des écosystèmes. Ouvrez une source pour la consulter à son origine.
A dataset of land cover samples over the Tibetan PlateauThe Tibetan Plateau (TP) hosts a variety of vegetation types, ranging from broadleaved and needle-leaved forests at the lower altitudes and in mesic areas to alpine grassland at the higher altitudes and in xeric areas. Accurate and detailed mapping of the vegetation distribution on the TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets for the TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred to as TP_LC10-2022) by leveraging state-of-the-art remote-sensing approaches including Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and four machine learning models using the Google Earth Engine platform. Our TP_LC10-2022 dataset achieved an overall classification accuracy of 86.5 % with a kappa coefficient of 0.854. Upon comparing it with four existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree, which is ignored in existing land cover datasets, occupied 13.99 % of the TP region, and shrublands, which are characterized by distinct forms (deciduous shrublands and evergreen shrublands) that are largely determined by the topography and are missed in existing land cover datasets, occupied 4.63 % of the TP region. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in the TP. TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8214981 (Huang et al., 2023a) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023b), respectively. Additionally, the classification map can be viewed at https://cold-classifier.users.earthengine.app/view/tplc10-2022 (last access: 6 June 2024). | Wuhan University | 2024 | Creative Commons Attribution 4.0 International | |
A high-resolution map of Singapore’s terrestrial ecosystemsThe natural and semi-natural areas within cities provide important refuges for biodiversity, as well as many benefits to people. To study urban ecology and quantify the benefits of urban ecosystems, we need to understand the spatial extent and configuration of different types of vegetated cover within a city. It is challenging to map urban ecosystems because they are typically small and highly fragmented; thus requiring high resolution satellite images. This article describes a new high-resolution map of land cover for the tropical city-state of Singapore. We used images from WorldView and QuickBird satellites, and classified these images using random forest machine learning and supplementary datasets into 12 terrestrial land classes. Close to 50 % of Singapore’s land cover is vegetated while freshwater fills about 6 %, and the rest is bare or built up. The overall accuracy of the map was 79 % and the class-specific errors are described in detail. Tropical regions such as Singapore have a lot of cloud cover year-round, complicating the process of mapping using satellite imagery. The land cover map provided here will have applications for urban biodiversity studies, ecosystem service quantification, and natural capital assessment. | Natural Capital Singapore, Campus for Research Excellence and Technological Enterprise, National Parks Board Singapore | 2019 | CC BY 4.0 | |
A new satellite-derived dataset for marine aquaculture in the China's coastal regionChina has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. | Zhejiang University, University of Leeds, University of Hong Kong | 2020 | CC BY 4.0 | |
Abyssal plainsThese ecosystems on the very deep seafloors (3000-6000m) of all oceans support a low biomass but high diversity of small invertebrates and microbes, along with larger crustaceans, demersal fish and echinoderms like starfish. Tracks and burrows of larger organisms in fine sediments that may be up to thousands of metres thick, structure habitat for smaller invertebrates. The absence of light, scarcity of food, and extreme hydrostatic pressures limit the density and biomass of organisms as well as the interactions among them. Inaccessible and little known, exploration of these ecosystems continue to reveal large numbers of species new to science. | IUCN, University of New South Wales | 2021 | Creative Commons Attribution 4.0 International | |
Abyssopelagic ocean watersAt greater depths (~3,000-6,000m) than bathypelagic systems, these very deep open ocean ecosystems receive no light and rely solely on debris from upper layers for nutrients. Other resources such as oxygen are replenished via the ‘global ocean conveyer belt’ (thermohaline circulation) that starts when distant, polar surface waters cool and sink. There is a low diversity and low density of life, largely planktonic detritivores, along with some gelatinous invertebrates and scavenging or predatory fish like the anglerfish. Life histories body structures and physiological traits are adapted to the very high pressure and lack of light (e.g. non-visual sensory organs, specialised metabolic proteins, and low density body structures). | IUCN, University of New South Wales | 2021 | Creative Commons Attribution 4.0 International | |
Allen Coral AtlasThe Allen Coral Atlas is built by a dedicated team of scientists, technologists, and conservationists, using one-of-a-kind methodologies. The Allen Coral Atlas was conceived and funded by the late Paul Allen’s Vulcan Inc. and is managed by the Arizona State University Center for Global Discovery and Conservation Science. Along with partners from Planet, the Coral Reef Alliance, and the University of Queensland, the Atlas utilizes high-resolution satellite imagery and advanced analytics to map and monitor the world’s coral reefs in unprecedented detail. The partnership together identified the following methods for habitat map creation, dynamic monitoring, and other related coral reef products.
As part of the Allen Coral Atlas’s revisions to its habitat maps in 2022, a new reef extent product was generated for each mapping region. In context of this product, reef extent is defined as the location of shallow coral reef features that can generally be seen from satellites. It typically excludes areas of very deep and very turbid water. This is intended to provide a more generalized and inclusive layer that depicts the extent of the coral reef environment which is additional to the Atlas’ geomorphic zonation maps. The reef extent data product can be seen as a single layer on the Atlas and compared and analyzed alongside other datasets such as reef habitat maps and reef threat monitoring datasets.
The underlying reef extent product is a raster at 5m pixel resolution, matching the geomorphic and benthic map products. The raster combines three sources:
1. The extent of the Allen Coral Atlas’s 12 geomorphic zones
2. The extent of our own machine learning-based coral reef habitat layer, originally developed for the Global Coral Reef Monitoring Network’s 2020 Report on the Status of Coral Reefs of the World
3. A third extent layer that: Filled in holes greater than 400 pixels (0.64Ha) and applied a morphological filter (circle kernel of 5 pixels; 25m) to smooth the reef boundaries and regain missing slope/beach features..
For the purposes of the data product made visible on the Allen Coral Atlas, these three sources are combined into one single-colored layer.
The reef extent layer more inclusively depicts the shallow coral reef environment than our more detailed benthic or geomorphic habitat maps. It includes reef features that were unmappable to geomorphic/benthic level, including deeper reef structures, reef habitat in more turbid water, deep or very steep reef slope areas, and very shallow intertidal areas at the land-sea interface. Known limitations are that some areas of supra-tidal beach and vegetation are included, which may not strictly be coral reef environments. Overall, the reef extent product is still conservative, and we expect that the area of reef erroneously included at the land-sea interface is greatly outweighed by the areas of reef still missed at both the shallow and deep margins of the product. | Allen Coral Atlas Partnership, University of New South Wales, University of Queensland, James Cook University, Australian Institute of Marine Science, Coral Reef Alliance, Arizona State University, National Geographic Society | 2022 | CC BY 4.0 | |
Antarctic Ecosystem Inventory: Ecosystem Typology v1.0This is Antarctica’s first comprehensive ecosystem map of ice-free lands. The data comprise a spatially explicit 3-tiered hierarchical ecosystem classification with nine Major Environment Types (tier 1), 33 Habitat Complexes (tier 2) and 269 Bioregional Ecosystem Types (tier 3). These Bioregional Ecosystem Types are aligned with ‘level 4’ of the IUCN Global Ecosystem Typology (Keith et al. 2022).
The spatial data are available in raster format (TIF) at 100 m resolution in the Polar Stereographic Projected Coordinate System (GCS_WGS_1984) for all known ice-free areas south from latitude -57.330551 decimal degrees South (pdf map shows extent of ice-free areas in relation to terrestrial ice and ice shelves). A value attribute table (VAT) provides text fields containing codes and full names for each unit in each level of the classification hierarchy and the spatial extent of tier 3 units in hectares.
Methods of development, source data and uses of the inventory are detailed by Tóth et al. (2024a). Descriptive profiles for tier 1 and 2 units are available in Tóth et al. (2024b). | University of New South Wales, Australian Antarctic Division, Monash University, Natural Environment Research Council, University of Johannesburg, Millennium Institute Biodiversity of Antarctic and Subantarctic Ecosystems (BASE), University of Pretoria, James Cook University, University of Wollongong, Queensland University of Technology, South Australian Museum, University of Adelaide | 2024 | Creative Commons Attribution 4.0 International | |
Aquaculture Land Cover DataAquaculture Land Cover Data Download
All files are in raster format and distributed as compressed GeoTIFF files at a 15m resolution. Each zipped archive contains a
text file describing the metadata including the spatial referencing system and the legend categories. For information on the
procedures used and accuracy/skill of the mapping, please see the Reports section. The data are copyright © 2024 by Clark Labs,
Clark University and is licensed under a Creative Commons Attribution 4.0 International License. You are free to use these data
for non-commercial purposes only. All other applications require permission. | Clark University | 2024 | CC BY 4.0 | |
Australian saltmarsh and sparsely vegetated saltmarsh map version 1.0Saltmarshes are one of Australia's most widespread coastal ecosystems, yet their spatial extent around the country remains largely unquantified. Recent advances in cloud-based geospatial platforms have enabled the development of an analysis pipeline to monitor saltmarsh distribution at a continental scale. Our remote sensing pipeline builds on work to develop the first integrated global maps of tidal flat, saltmarsh and mangrove ecosystems.
We are first compiling a large set of occurrence records to train our remote sensing classification models (at least 10,000 point records across Australia's coastline). Our training set includes field data acquired from Australia's saltmarsh research community, published papers and records developed from high-resolution image interpretation conducted at JCU's Global Ecology Lab.
Our remote sensing classification approach uses up to 100 nationwide remote sensing-derived covariate layers to support a suite of machine-learning classification models. The classification models are tasked with estimating the coastal ecosystem type of every 30-m pixel that occurs around Australia's coastal zone. This mapping framework will be implemented as a collaborative effort between JCU, UNSW and Digital Earth Australia.
The dataset contains a national map of saltmarsh ecosystems produced via a classification of Landsat 8 images. Each pixel was classified into four map classes: saltmarsh, saltflats, other terrestrial (land), and permanent water. | James Cook University, University of New South Whales, Digital Earth Australia | 2023 | CC BY 4.0 DEED | |
Bathypelagic ocean watersThese deep (~1000-3000m depth), open-ocean ecosystems receive no sunlight and rely on detritus from upper layers for nutrients. Other resources such as oxygen are replenished via the ‘global ocean conveyer belt’ (thermohaline circulation) that starts when distant, polar surface waters cool and sink. With no primary producers, life is limited to groups like zooplankton, jellyfish, crustaceans, cephalopods and fish like the gulper eel. Common adaptations that enable animals to live under high pressure and no light include slow metabolism, long generation lengths and low density bodies. | IUCN, University of New South Wales | 2021 | Creative Commons Attribution 4.0 International | |
Bioclimatic and vegetational synopsis of Chile (2022 extent map)The concept of vegetation floor is defined here as "spaces characterized by a set of zooclimatically homogeneous conditions, which occupy a determined position along an elevation gradient, at a specific spatio-temporal scale. It synthesizes the response of the vegetation, in terms of its physiognomy and dominant species, to the influence of the mesoclimate, reflected through the definition of bioclimatic floors. A vegetation floor is typically characterized by a plant formation with specific dominant species and a bioclimatic floor under which such formations can be found. The space that is identified with a vegetation floor can be characterized, a posteriori, by its floristic composition, its dynamics and its internal heterogeneity. | Universität Bonn, Pontificia Universidad Católica de Chile, MapBiomas Chile | 2022 | Original license CC BY NC | |
Carta Degli Ecosistemi d’ItaliaA second version of the Ecosystems Map of Italy is presented that updates the one realized for the Mapping and Assessment of Ecosystems and their Services (MAES) process. The map represents a renewed reference for the implementation of biodiversity-related policies in the country, including the Red List of Ecosystems, Ecosystem Accounting under the UN System of integrated Environmental and Economic Accounts, EU Nature Restoration Law, and, more in general, for the support of planning initiatives aimed at climate change adaptation and recovery of degraded ecosystems in keeping with restoration ecology principles. The mapping approach recalled the earlier rationale, i.e. that current and potential vegetation are valuable proxies for outlining ecosystems, but basic information on land cover and characterization of types have been updated. Additionally, a detailed description of the mapping procedure is provided that may facilitate replication in time, validation processes and comparison with different maps. The crosswalk between the Italian ecosystem typology and other classification systems, already available for Corine Land Cover and EUNIS habitats, was therefore revised and complemented with respect to the IUCN Global Ecosystem Typology. Finally, future perspectives for a regular updating of the map and further improvement of its geometric and thematic detail are sketched. | Società Botanica Italiana, Sapienza University of Rome, Ministry of Environment and Energy Security Italy | 2021 | CC BY 4.0 | |
Circumpolar Arctic Land Cover for circa 2020 (CALC-2020)Circumpolar Arctic Land Cover for circa 2020 (CALC-2020) is a new baseline land cover product that maps the entire terrestrial Arctic (tree line nowthward) at 10m resolution. This dataset is derived from Sentinel-1/2 and ArcticDEM, using locally adaptive machine learning models trained by sample over 58,000 site locations. | Sun Yat-sen University | 2022 | CC BY 4.0 International | |
Cocoa plantations are associated with deforestation in Côte d’Ivoire and GhanaCôte d’Ivoire and Ghana, the world’s largest producers of cocoa, account for two thirds of the global cocoa production. In both countries, cocoa is the primary perennial crop, providing income to almost two million farmers. Yet precise maps of the area planted with cocoa are missing, hindering accurate quantification of expansion in protected areas, production and yields and limiting information available for improved sustainability governance. Here we combine cocoa plantation data with publicly available satellite imagery in a deep learning framework and create high-resolution maps of cocoa plantations for both countries, validated in situ. Our results suggest that cocoa cultivation is an underlying driver of over 37% of forest loss in protected areas in Côte d’Ivoire and over 13% in Ghana, and that official reports substantially underestimate the planted area (up to 40% in Ghana). These maps serve as a crucial building block to advance our understanding of conservation and economic development in cocoa-producing regions. | ETH Zurich | 2023 | Creative Commons Attribution 4.0 International License | |
Conjunto de datos vectoriales de uso del suelo y vegetación. Escala 1:250 000. Serie VII. Conjunto NacionalThis map contains information on Land Use and Vegetation obtained from the application of photointerpretation techniques with Geomedian images with a base year of 2018, generated from the LANDSAT satellite constellation in multispectral format. This interpretation is supported by field work. The Data Set contains the location, distribution and extent of different plant communities and uses, with their respective variants in vegetation types, agricultural uses, and relevant ecological information. Said digital geographic information contains data structured in vector form coded in accordance with the Dictionary of Vector Data on Land Use and Vegetation Scale 1:250,000 (version 3) applicable to the different ecological units (plant communities and anthropic uses) contained in the data set. | Instituto Nacional de Estadística y Geografía (INEGI) | 2021 | Ok to use for POC - check again for main Atlas |