Weiss, D.J.、P.M. Atkinson, S. Bhatt, B. Mappin、S.I. Hay 和 P.W. Gething（2014 年）一种用于填补大陆级遥感时间序列数据缺口的有效方法。ISPRS Journal of Photogrammetry and Remote Sensing,
98, 106-118.
[null,null,[],[[["\u003cp\u003eThe dataset provides annual landcover data from 2001 to 2013, derived from the MODIS MCD12Q1 product.\u003c/p\u003e\n"],["\u003cp\u003eIt offers fractional coverage percentages for 17 IGBP landcover classes at a 5km resolution.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset was created by the Oxford Malaria Atlas Project and is available through Google Earth Engine.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access the dataset using the \u003ccode\u003eee.ImageCollection("Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual")\u003c/code\u003e code snippet.\u003c/p\u003e\n"],["\u003cp\u003eThe data is licensed under CC-BY-NC-SA-4.0.\u003c/p\u003e\n"]]],["The Oxford Malaria Atlas Project provides an annual fractional landcover dataset from 2001 to 2013, derived from the MODIS IGBP layer (MCD12Q1). It transforms 500-meter resolution categorical data into fractional percentages (0-100) for 17 landcover classes, including forests, shrublands, savannas, croplands, and urban areas. Each pixel also contains a dominant land cover classification, and the dataset has a 5000-meter pixel size. Data is accessed through Google Earth Engine.\n"],null,["# Oxford MAP: Malaria Atlas Project Fractional International Geosphere-Biosphere Programme Landcover\n\nDataset Availability\n: 2001-01-01T00:00:00Z--2013-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [Oxford Malaria Atlas Project](https://malariaatlas.org/)\n\nCadence\n: 1 Year\n\nTags\n:\n [landcover](/earth-engine/datasets/tags/landcover) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) [map](/earth-engine/datasets/tags/map) [oxford](/earth-engine/datasets/tags/oxford) \nigbp \n\n#### Description\n\nThe underlying dataset for this landcover product is the IGBP layer found\nwithin the MODIS annual landcover product (MCD12Q1). This data was\nconverted from its categorical format, which has a ≈500 meter resolution,\nto a fractional product indicating the integer percentage (0-100) of the\noutput pixel covered by each of the 17 landcover classes (1 per band).\n\nThis dataset was produced by Harry Gibson and Daniel Weiss of the\nMalaria Atlas Project (Big Data Institute, University of Oxford,\nUnited Kingdom, \u003chttps://malariaatlas.org/\u003e).\n\n### Bands\n\n\n**Pixel Size**\n\n5000 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|--------------------------------------|-------|-----|-----|------------|--------------------------------------------------|\n| `Overall_Class` | | 0 | 17 | meters | Dominant class of each resulting pixel |\n| `Water` | % | 0 | 100 | meters | Percentage of water |\n| `Evergreen_Needleleaf_Forest` | % | 0 | 100 | meters | Percentage of evergreen needleleaf forest |\n| `Evergreen_Broadleaf_Forest` | % | 0 | 100 | meters | Percentage of evergreen broadleaf forest |\n| `Deciduous_Needleleaf_Forest` | % | 0 | 100 | meters | Percentage of deciduous needleleaf forest |\n| `Deciduous_Broadleaf_Forest` | % | 0 | 100 | meters | Percentage of deciduous broadleaf forest |\n| `Mixed_Forest` | % | 0 | 100 | meters | Percentage of mixed forest |\n| `Closed_Shrublands` | % | 0 | 100 | meters | Percentage of closed shrublands |\n| `Open_Shrublands` | % | 0 | 100 | meters | Percentage of open shrublands |\n| `Woody_Savannas` | % | 0 | 100 | meters | Percentage of woody savannas |\n| `Savannas` | % | 0 | 100 | meters | Percentage of savannas |\n| `Grasslands` | % | 0 | 100 | meters | Percentage of grasslands |\n| `Permanent_Wetlands` | % | 0 | 100 | meters | Percentage of permanent wetlands |\n| `Croplands` | % | 0 | 100 | meters | Percentage of croplands |\n| `Urban_And_Built_Up` | % | 0 | 100 | meters | Percentage of urban and built up |\n| `Cropland_Natural_Vegetation_Mosaic` | % | 0 | 100 | meters | Percentage of cropland natural vegetation mosaic |\n| `Snow_And_Ice` | % | 0 | 100 | meters | Percentage of snow and ice |\n| `Barren_Or_Sparsely_Populated` | % | 0 | 100 | meters | Percentage of barren or sparsely populated |\n| `Unclassified` | % | 0 | 100 | meters | Percentage of unclassified |\n| `No_Data` | % | 0 | 100 | meters | Percentage of no data |\n\n**Overall_Class Class Table**\n\n| Value | Color | Description |\n|-------|---------|------------------------------------|\n| 0 | #032f7e | Water |\n| 1 | #02740b | Evergreen_Needleleaf_Fores |\n| 2 | #02740b | Evergreen_Broadleaf_Forest |\n| 3 | #8cf502 | Deciduous_Needleleaf_Forest |\n| 4 | #8cf502 | Deciduous_Broadleaf_Forest |\n| 5 | #a4da01 | Mixed_Forest |\n| 6 | #ffbd05 | Closed_Shrublands |\n| 7 | #ffbd05 | Open_Shrublands |\n| 8 | #7a5a02 | Woody_Savannas |\n| 9 | #f0ff0f | Savannas |\n| 10 | #869b36 | Grasslands |\n| 11 | #6091b4 | Permanent_Wetlands |\n| 12 | #ff4e4e | Croplands |\n| 13 | #999999 | Urban_and_Built-up |\n| 14 | #ff4e4e | Cropland_Natural_Vegetation_Mosaic |\n| 15 | #ffffff | Snow_and_Ice |\n| 16 | #feffc0 | Barren_Or_Sparsely_Vegetated |\n| 17 | #020202 | Unclassified |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html)\n\n### Citations\n\nCitations:\n\n- Weiss, D.J., P.M. Atkinson, S. Bhatt, B. Mappin, S.I. Hay \\& P.W. Gething\n (2014) An effective approach for gap-filling continental scale remotely\n sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing,\n 98, 106-118.\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset =\n ee.ImageCollection('Oxford/MAP/IGBP_Fractional_Landcover_5km_Annual')\n .filter(ee.Filter.date('2012-01-01', '2012-12-31'));\nvar landcover = dataset.select('Overall_Class');\nvar landcoverVis = {\n min: 1.0,\n max: 19.0,\n palette: [\n '032f7e', '02740b', '02740b', '8cf502', '8cf502', 'a4da01', 'ffbd05',\n 'ffbd05', '7a5a02', 'f0ff0f', '869b36', '6091b4', '999999', 'ff4e4e',\n 'ff4e4e', 'ffffff', 'feffc0', '020202', '020202'\n ],\n};\nMap.setCenter(-88.6, 26.4, 1);\nMap.addLayer(landcover, landcoverVis, 'Landcover');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Oxford/Oxford_MAP_IGBP_Fractional_Landcover_5km_Annual) \n[Oxford MAP: Malaria Atlas Project Fractional International Geosphere-Biosphere Programme Landcover](/earth-engine/datasets/catalog/Oxford_MAP_IGBP_Fractional_Landcover_5km_Annual) \nThe underlying dataset for this landcover product is the IGBP layer found within the MODIS annual landcover product (MCD12Q1). This data was converted from its categorical format, which has a ≈500 meter resolution, to a fractional product indicating the integer percentage (0-100) of the output pixel covered by each of ... \nOxford/MAP/IGBP_Fractional_Landcover_5km_Annual, landcover,landuse-landcover,map,oxford \n2001-01-01T00:00:00Z/2013-01-01T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://malariaatlas.org/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/Oxford_MAP_IGBP_Fractional_Landcover_5km_Annual)"]]
