Cet ensemble de données contient des informations sur la variation annuelle de la superficie imperméable mondiale de 1985 à 2018, avec une résolution de 30 mètres. Le passage d'une surface perméable à une surface imperméable a été déterminé à l'aide d'une approche combinée de classification supervisée et de vérification de la cohérence temporelle. Les pixels imperméables sont définis comme étant imperméables à plus de 50 %. L'année de la transition (de perméable à imperméable) peut être identifiée à partir de la valeur du pixel, qui va de 34 (année 1985) à 1 (année 2018). Par exemple, la surface imperméable en 1990 peut être révélée comme la valeur de pixel supérieure à 29 (voir le tableau de correspondance). Cet ensemble de données est cohérent dans le temps, suivant la conversion de l'imperméabilité (par exemple, non urbaine) à l'imperméabilité (par exemple, urbaine) de manière monotone. Pour en savoir plus sur l'approche de cartographie et l'évaluation, consultez Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 (Gong et al. 2020).
Bracelets
Taille des pixels 30 mètres
Bandes de fréquences
Nom
Min
Max
Taille des pixels
Description
change_year_index
1*
34*
mètres
Année de la transition de l'état perméable à l'état imperméable. De 34 (année 1985) à 1 (année 2018)
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., ... & Zhou, Y. (2020).
Cartes annuelles de la zone imperméable artificielle mondiale (GAIA) entre 1985 et 2018.
Remote Sensing of Environment, 236, 111510.
Cet ensemble de données contient des informations sur les variations annuelles de la superficie imperméable mondiale de 1985 à 2018, avec une résolution de 30 mètres. Le passage d'une surface perméable à une surface imperméable a été déterminé à l'aide d'une approche combinée de classification supervisée et de vérification de la cohérence temporelle. Les pixels imperméables sont définis comme étant imperméables à plus de 50 %. L'année de la transition…
[null,null,[],[[["\u003cp\u003eThe FROM-GLC GAIA dataset provides annual change information of global impervious surface area at a 30m resolution from 1985 to 2018.\u003c/p\u003e\n"],["\u003cp\u003eIt identifies the year of transition from pervious (e.g., non-urban) to impervious (e.g., urban) surfaces using a combined approach of supervised classification and temporal consistency checking.\u003c/p\u003e\n"],["\u003cp\u003ePixel values represent the year of change, ranging from 34 (1985) to 1 (2018), allowing for the analysis of impervious surface expansion over time.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is freely available for research, education, and non-profit use under a Creative Commons Attribution 4.0 International License.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access and analyze this dataset using the Google Earth Engine platform.\u003c/p\u003e\n"]]],[],null,["# Tsinghua FROM-GLC Year of Change to Impervious Surface\n\nDataset Availability\n: 1985-01-01T00:00:00Z--2018-12-31T00:00:00Z\n\nDataset Provider\n:\n\n\n [Tsinghua University](http://data.ess.tsinghua.edu.cn/)\n\nTags\n:\n [built](/earth-engine/datasets/tags/built) [population](/earth-engine/datasets/tags/population) [tsinghua](/earth-engine/datasets/tags/tsinghua) [urban](/earth-engine/datasets/tags/urban) \n development \nimpervious \n\n#### Description\n\nThis dataset contains annual change information of global impervious surface area from 1985 to\n2018 at a 30m resolution. Change from pervious to impervious was determined using a combined\napproach of supervised classification and temporal consistency checking. Impervious pixels are\ndefined as above 50% impervious. The year of the transition (from pervious to impervious) can\nbe identified from the pixel value, ranging from 34 (year: 1985) to 1 (year: 2018). For\nexample, the impervious surface in 1990 can be revealed as the pixel value greater than 29\n(see the lookup table). This dataset is temporally consistent, following the conversion from\npervious (e.g., non-urban) to impervious (e.g., urban) monotonically. For more information\nabout the mapping approach and assessment, see\n[Annual maps of global artificial impervious area (GAIA) between 1985 and 2018\n(Gong et al. 2020)](https://doi.org/10.1016/j.rse.2019.111510).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|---------------------|-----|------|------------|-------------------------------------------------------------------------------------------------|\n| `change_year_index` | 1\\* | 34\\* | meters | Year of the transition from from pervious to impervious. From 34 (year: 1985) to 1 (year: 2018) |\n\n\\* estimated min or max value\n\n**change_year_index Class Table**\n\n| Value | Color | Description |\n|-------|---------|-------------|\n| 1 | #014352 | 2018 |\n| 2 | #1a492c | 2017 |\n| 3 | #071ec4 | 2016 |\n| 4 | #b5ca36 | 2015 |\n| 5 | #729eac | 2014 |\n| 6 | #8ea5de | 2013 |\n| 7 | #818991 | 2012 |\n| 8 | #62a3c3 | 2011 |\n| 9 | #ccf4fe | 2010 |\n| 10 | #74f0b9 | 2009 |\n| 11 | #32bc55 | 2008 |\n| 12 | #c72144 | 2007 |\n| 13 | #56613b | 2006 |\n| 14 | #c14683 | 2005 |\n| 15 | #c31c25 | 2004 |\n| 16 | #5f6253 | 2003 |\n| 17 | #11bf85 | 2002 |\n| 18 | #a61b26 | 2001 |\n| 19 | #99fbc5 | 2000 |\n| 20 | #188aaa | 1999 |\n| 21 | #c2d7f1 | 1998 |\n| 22 | #b7d9d8 | 1997 |\n| 23 | #856f96 | 1996 |\n| 24 | #109c6b | 1995 |\n| 25 | #2de3f4 | 1994 |\n| 26 | #9a777d | 1993 |\n| 27 | #151796 | 1992 |\n| 28 | #c033d8 | 1991 |\n| 29 | #510037 | 1990 |\n| 30 | #640c21 | 1989 |\n| 31 | #31a191 | 1988 |\n| 32 | #223ab0 | 1987 |\n| 33 | #b692ac | 1986 |\n| 34 | #2de3f4 | 1985 |\n\n### Terms of Use\n\n**Terms of Use**\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License.\n\u003chttps://creativecommons.org/licenses/by/4.0/\u003e\n\n### Citations\n\nCitations:\n\n- Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., ... \\& Zhou, Y. (2020).\n Annual maps of global artificial impervious area (GAIA) between 1985 and 2018.\n Remote Sensing of Environment, 236, 111510.\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 = ee.Image('Tsinghua/FROM-GLC/GAIA/v10');\n\nvar visualization = {\n bands: ['change_year_index'],\n min: 0,\n max: 34,\n palette: [\n '014352', '1a492c', '071ec4', 'b5ca36', '729eac', '8ea5de',\n '818991', '62a3c3', 'ccf4fe', '74f0b9', '32bc55', 'c72144',\n '56613b', 'c14683', 'c31c25', '5f6253', '11bf85', 'a61b26',\n '99fbc5', '188aaa', 'c2d7f1', 'b7d9d8', '856f96', '109c6b',\n '2de3f4', '9a777d', '151796', 'c033d8', '510037', '640c21',\n '31a191', '223ab0', 'b692ac', '2de3f4',\n ]\n};\n\nMap.setCenter(-37.62, 25.8, 2);\n\nMap.addLayer(dataset, visualization, 'Change year index');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/Tsinghua/Tsinghua_FROM-GLC_GAIA_v10) \n[Tsinghua FROM-GLC Year of Change to Impervious Surface](/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10) \nThis dataset contains annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution. Change from pervious to impervious was determined using a combined approach of supervised classification and temporal consistency checking. Impervious pixels are defined as above 50% impervious. The year of the transition ... \nTsinghua/FROM-GLC/GAIA/v10, built,population,tsinghua,urban \n1985-01-01T00:00:00Z/2018-12-31T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/http://data.ess.tsinghua.edu.cn/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10)"]]
