Land Cover Maps

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 ESA Worldcover V2 global map for 2021 (Source: ESA)


Product Category

  • Land use
  • Natural Disaster
  • Climate Change
  • Coast Management 
  • Marine
  • Earth's Surface Motion

Financial Domains

  • Green finance

User requirements 

UN11: Realistic assessment of accessibility to assets.

UN27: Need to assess historical trends and baseline of natural assets.

UN38: Need for trustworthy time series of reliable data on assets.

UN39: Need to assess the potential impact of business activities or investments on ecosystems and biodiversity.

UN40: Need to monitor the risk of sea level rise threatening coastal property, infrastructure, and supply chains.

UN43: Need to monitor changing precipitation patterns and flood risk in the vicinity of vulnerable assets.

Description

Land cover maps are geographical representations that depict the various types of surfaces and features present on the Earth's surface, categorizing them into different categories based on the physical and biological characteristics of the terrain. Common land cover classes include forests, agricultural land, urban areas, water bodies, wetlands, barren land, and more. These maps are typically represented through colour-coded legends or thematic symbols that make it easy to visualize and interpret the distribution of land cover across a specific geographic area.

Land cover change maps: From time series land cover maps, it is possible to provide land cover maps which are important for many applications.


Spatial Coverage Target

Asset Level

Data Throughput

Rapid tasking 

Data availability

  • High
  • High

PRODUCT SPECIFICATIONS

Main processing steps

There are many freely available land cover maps with different spatial resolutions, temporal coverages, and number of land cover classes. The highest freely available spatial resolution of land cover is 10 m and is provided by ESA. However, for some applications there might be a need to generate land cover maps at very high resolution, these maps can be generated by supervised machine learning algorithms. These models should be trained using ground truth land cover data.

Input data sources

Optical:  Sentinel2, VHR imagery based on the availability like Pleiades 1A/1B & NEO, WorldView2&3, and SPOT6/7.

Radar: Sentinel-1, VHR images from different sources like ICEYE, Capella space, Umbra, and TerraSAR-X.

Supporting data: ground truth land cover data.

Accessibility

Sentinel-1&2:  freely and publicly available from ESA.

VHR imagery: commercially available on demand from EO service providers.

Spatial resolution

Sentinel-2: 10 m

Optical VHR: ≤ 1 m

Sentinel-1: 20 m

SAR VHR: ≤ 3 m

Frequency (Temporal resolution)

Sentinel-1&2: 6 days

Optical and SAR VHR: Daily

Latency

< 1 Day

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: 80-90%

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

n Lack of ground truth data

n Cloud presence

n Limited spectral resolution for some optical VHR imagery.

n Seasonal variability

n Topographic effects

n In some cases, pixels may represent a mix of multiple land cover classes

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential

Similar Products

Name of the Product:

n ESA WorldCover (link)

n Copernicus Land cover classification gridded maps from 1992 to present (link)

n Corine Land Cover (CLC) (link)

Spatial resolution:

n ESA WorldCover: 10 m

n Copernicus Land cover classification gridded maps from 1992 to present: 300 m

n Corine Land Cover (CLC): 100 m

Frequency (Temporal resolution): Annual

Temporal coverage:

n ESA WorldCover: 2020, and 2021

n Copernicus Land cover classification gridded maps from 1992 to present: 1992 to present.

n Corine Land Cover (CLC): 1990, 2000, 2006, 2012, and 2018

Geographical scale coverage:

n ESA WorldCover: Globally

n Copernicus Land cover classification gridded maps from 1992 to present: Globally.

n Corine Land Cover (CLC): Europe

Delivery / output format: GeoTIFF (Raster), Shape files (Vector)

Accessibility: Freely and publicly available from ESA



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P17: Land Cover Maps

Download the gap analysis product sheet

Maturity score

Mean: 3.00

STD: 0.00

Constraints and limitations

·  Missing information on seasonal variability.

·  In some cases, pixels may represent a mix of multiple land cover classes.

Relevant user needs

UN11: Realistic assessment of accessibility to assets.

UN27: Need to assess historical trends and baseline of natural assets.

UN38: Need for trustworthy time series of reliable data on assets.

UN39: Need to assess the potential impact of business activities or investments on ecosystems and biodiversity.

UN40: Need to monitor the risk of sea level rise threatening coastal property, infrastructure, and supply chains.

UN43: Need to monitor changing precipitation patterns and flood risk in the vicinity of vulnerable assets.

R&D gaps

The product is highly responsive to UNS.

Potential improvements drivers

No crucial improvements were provided as it highly responds to the user's needs

Utilisation level review

Utilisation score

Mean: 2.86

STD: 0.64

No utilisation

Low utilisation

·  The product is already satisfying the technical and usability requirements.

·  The product is already being used by sovereign bond investors. Also, sectors such as asset management may be interested in using these maps in combination with asset-level data, but as they are already limited on the asset-level data (incompleteness, inaccurate locations), trying to do a detailed analysis wouldn't add much value.

Medium utilisation

·  Greater knowledge about the capability of the product.

·  Higher cost of using the best available commercial EO product. The existing stock of maps and mapping tools (including LiDAR and other aerial photography) is considered good enough in many cases. Organizations may lack the budget to motivate using this EO product to replace these existing resources and methods.

High utilisation

Critical gaps related to relevant user needs

Guideline gap

UN11: Realistic assessment of accessibility to assets

UN27: Need to assess historical trends and baseline of natural assets.

Utilisation gap

UN38: Need for trustworthy time series of reliable data on assets.

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