Best in Class Data to Monitor the World's Land

Land & Carbon Lab partnered with the Global Land Analysis & Discovery (GLAD) lab at University of Maryland to produce the first ever high-resolution map of global land cover change over the last 20 years. The map distinguishes eight general land cover classes: Forest, Cropland, Built-Up Area, Water, Bare Ground, Short Vegetation, Snow/Ice, and Wetlands. Data can be visualized and downloaded on Resource Watch.

Geographic coverage: Global 

Resolution: 30x30 meters 

Temporal coverage: 2000-2020 

Data Partner: University of Maryland

Dynamic World is a near-real-time global 10-meter land use/land cover mapping dataset created by Google and the World Resources Institute by using deep learning artificial intelligence methods and large training datasets. The dataset covers nine land cover classes and is based on Sentinel-2 10-meter resolution data. This high-resolution data helps us better understand seasonal and intra-annual land cover changes. The dataset is updated every few seconds with each new Sentinel-2 scene, and can be visualized on Resource Watch and accessed in Google Earth Engine.

Geographic coverage: Global

Resolution: 10x10 meters

Temporal coverage: 2016-present

Data Partner: Google, World Resources Institute

A groundbreaking new land surface disturbance monitoring system developed by NASA’s OPERA project and the University of Maryland detects disturbances to any kind of vegetation cover, including forests, grasses, shrubs and even crops, occurring anywhere on Earth in near real-time — Learn more.

Using harmonized data from the NASA/USGS Landsat and ESA Sentinel-2 satellites, the new system, known as OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2, or DIST-ALERT, can identify and continuously monitor vegetation in 30-m pixels across the globe.  

This new system will fuel future alert products that Land & Carbon Lab and the University of Maryland will develop for many possible applications, such as tracking agricultural expansion through the conversion of natural lands, detecting degradation dynamics such as logging, and monitoring short-lived vegetation changes such as from drought and fire. 

Geographic coverage: Global 

Resolution: 30x30 meters 

Temporal coverage: Sub-weekly

Data Producer: NASA's Jet Propulsion Laboratory (JPL) and the University of Maryland

Funding: The OPERA project is funded by NASA to address remote sensing needs identified under the Satellite Needs Working Group.   

Pastures and rangelands are the single most extensive land use on Earth and we need to understand the productivity of these areas and how they are changing over time to meet global climate goals. Yet these areas rarely have dedicated monitoring efforts and there is currently no good understanding of the their location, area, and condition.

A new Land & Carbon Lab research consortium will aim to produce recurrent and high-resolution (30-meter) maps for pasture areas and productivity from 2000 onwards. The project uses ensemble machine learning algorithms, visual interpretations, crowdsourcing, and earth observation data fusion to produce per-pixel probabilities of land use and land cover, livestock density, short vegetation height, and gross primary productivity (GPP). The resulting products will contribute to better understanding land use conversion, food production, land productivity, and impacts for biodiversity and climate change at a global scale. 

Geographic coverage: Global 

Resolution: 30x30 meters 

Temporal coverage: 2000-2020 

Data Partners: OpenGeoHub, LAPIG, IIASA, World Resources Institute

Status: Under development

The International Food Policy Research Institute’s Spatial Production Allocation Model (SPAM) provides globally consistent maps of crop areas and yields that are pivotal for understanding how crop types and their production vary both within and between countries.

Initially introduced in 2008, this innovative product has since undergone multiple updates. The latest version, developed with support from Land & Carbon Lab, features data from 2020 and represents a significant leap forward. It encompasses global maps of crop areas and yields for 46 crops and crop groups, mapped at a 10-kilometer resolution.

The result is globally consistent data that provides critical information for understanding drivers of deforestation and conversion of natural lands, crop-related emissions, water use and agricultural production worldwide to help achieve sustainable supply chains and food security.   

Geographic coverage: Global 

Resolution: 10-kilometers

Temporal coverage: 2000, 2005, 2010, 2020

Data Partner: The International Food Policy Research Institute (IFPRI)