{"id":12853,"date":"2016-12-04T07:27:47","date_gmt":"2016-12-04T13:27:47","guid":{"rendered":"http:\/\/gisgeography.com\/?p=12853"},"modified":"2025-03-31T19:54:22","modified_gmt":"2025-04-01T00:54:22","slug":"free-global-land-cover-land-use-data","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/free-global-land-cover-land-use-data\/","title":{"rendered":"10 Best Free Land Cover\/Land Use Data"},"content":{"rendered":"\n
\"Global<\/figure>\n\n\n\n
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What are the top data sources for world land cover?<\/h2>\n\n\n\n

Over the span of your life, how much has the Earth changed? Quite a bit, but you probably just can’t put a number to it.<\/p>\n\n\n\n

Thanks to global land cover<\/strong> and satellite sensors like MODIS, AVHRR, and Enhanced Thematic Mapper, we can finally shed some light on our changing planet.<\/p>\n\n\n\n

But what global land cover data sets exist? What are the best?<\/p>\n\n\n\n

What is their accuracy? How fine is its spatial resolution?<\/p>\n<\/div><\/div>\n\n\n\n

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Seeing is Believing<\/h2>\n\n\n\n

To help you follow along and get a visual understanding of the accuracy of the land cover classification, we produce screenshots of New York. Some of the key features to keep in mind are:<\/p>\n\n\n\n

CENTRAL PARK<\/strong>: The square green space in Manhattan, the Jacqueline Kennedy Onassis Reservoir, and the Upper Bay that divides New York and New Jersey.<\/p>\n\n\n\n

FIRE ISLAND<\/strong>: The thin barrier island off the southern shore protects the beach fronts of Long Island, New York.<\/p>\n\n\n\n

LONG ISLAND<\/strong>: The islands in the northeast corner of Long Island like Plum Island, Great Gull Island, and Little Gull Island.<\/p>\n\n\n\n

Here is New York in the eyes of Sentinel-2<\/a> pinpointing the features above.<\/p>\n\n\n\n

\"New<\/figure>\n\n\n\n

How well are these features classified in the land cover maps? It’s not an easy task to classify the globe. <\/p>\n\n\n\n

Read below for a summary of the various land cover classification schemes available for free at your fingertips.<\/p>\n<\/div><\/div>\n\n\n\n

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1. Esri Land Cover 10m<\/h3>\n\n\n\n
\"LC<\/figure>\n\n\n\n

The Esri Land Cover (10m)<\/a> is really the first solid effort to achieve a 10-meter resolution land cover using Sentinel-2. Although the spatial resolution is much greater than the other land cover products in the list, it still struggles in certain aspects<\/strong>.<\/p>\n\n\n\n

But first, the Esri Land Cover dataset consists of 5 billion human-labeled pixels<\/strong> and is the result of training a deep learning model. This 10-meter resolution data source is open source and is available on the Living Atlas of the World<\/a> by Esri.<\/p>\n\n\n\n

The classes include built areas, water, trees, grass, crops, flooded vegetation, scrub\/shrub, bare ground, snow\/ice, and clouds. Overall, it’s the best global land cover available today because it uses the highest resolution spatial open data source<\/strong>. However, be cautious with the accuracy of roads, agriculture, and several of the other classes.<\/p>\n<\/div><\/div>\n\n\n\n

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2. Global Land Survey (GLS)<\/h3>\n\n\n\n
\"New<\/figure>\n\n\n\n

At 30-meter resolution, this land cover is one of the finest available. The University of Maryland teamed up with the USGS to lace together its circa 2010 tree cover, bare ground, and persistent surface water. <\/p>\n\n\n\n

Using Landsat 7 ETM+ data, its most impressive attribute is its tree cover canopy<\/strong> described as a percentage per output grid cell. This canopy cover is used to weigh in global forest extent, loss, and gain from 2000 to 2012 such as this Global Forest Change web map<\/a>. <\/p>\n\n\n\n

Studies have shown that the overall accuracy of the Global Land Survey (GLS) static forest cover is 91% with forest cover change at >88%<\/a>. Echoing the accuracy of this paper, Central Park and the outer islands are clearly visible. The lack of forested areas on Fire Island is consistent and logical. Things are looking good.<\/p>\n\n\n\n

READ MORE:<\/strong> 4 Global Forest Maps to See the Forest for the Trees<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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3. Climate Change Initiative (CCI) Land Cover V2<\/h3>\n\n\n\n
\"New<\/figure>\n\n\n\n

At 300 meter resolution, ENVISAT MERIS sensor is the biggest contributor to the 3 epoch land cover maps (1998-02, 2003-07, and 2008-12) of the CCI Land Cover V2<\/a>.<\/p>\n\n\n\n

By area proportions, it’s been cited to reach 73% accuracy<\/a> for the 23-class land cover map. Additionally, the European Space Agency has created the ESA\/CCI Land Cover viewer<\/a> to dynamically view the land cover<\/p>\n\n\n\n

Visually, you can see how it captures the thin barrier on the southern shore of Long Island. Despite missing most of the central park area (4km x 0.8km), it classifies the northeast islands impeccably.<\/p>\n<\/div><\/div>\n\n\n\n

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4. OSM Land Use Data<\/h3>\n\n\n\n
\"New<\/figure>\n\n\n\n

Every single land cover classification on this list uses image classification algorithms<\/a>. But what happens if you were to use the data produced by the ginormous global community known as OpenStreetMap?<\/p>\n\n\n\n

You get one ridiculously accurate land use map<\/strong>.<\/p>\n\n\n\n

For example, the white square below represents a single-pixel of ENVISAT MERIS. If you were to select all the buildings from OpenStreetMap, you would count 157 buildings altogether.<\/p>\n\n\n

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\"OSM<\/figure>\n<\/div>\n\n\n

On top of that, it tells you if it’s residential, commercial, industrial, or any other type.<\/p>\n\n\n\n

In theory, you can create a solid land cover map with OSM data. The only downsides are the following:<\/p>\n\n\n\n