UMD Global Land Analysis and Discovery team

UMD Global Land Analysis and Discovery team The team is also interested in drivers and ecological
implications of land cover change, e.g. associated carbon and biodiversity losses.

UMD GLAD is focused on global land cover change mapping via automated satellite imagery processing and machine learning combined with statistically rigorous accuracy assessment and area estimation, including fieldworks and UAV reference data collection. University of Maryland Global Land Analysis and Discovery (UMD GLAD) team is focused on
global land cover change and agricultural mapping primarily using data mining of the Landsat archive
and automated imagery mass processing.

‼️ New publication from the UMD Global Land Analysis and Discovery team in Science Magazine!How much tree cover is being...
06/05/2026

‼️ New publication from the UMD Global Land Analysis and Discovery team in Science Magazine!

How much tree cover is being lost globally, and what is driving that loss?

In our new study, global extent and drivers of tree cover loss were quantified with high-resolution satellite data. We used high-resolution satellite imagery (3–10 m) for a global probability sample to quantify tree cover loss in 2018 at the scale of individual disturbances and identify the land uses that followed based on a three year observation period (2019-2021).

Key findings include:

🌳🌲🌴 An estimated 277,000 km² of tree cover was lost globally in 2018.

🚜🐂 🌾 Nearly one-third of this loss resulted from long-term conversion to other land uses, including pasture, cropland, and tree plantations.

🌲🌴🔥 The remainder was associated with tree plantation management, shifting agriculture, forestry operations, fire, and natural disturbances.

🌏🛰️🌎 Our analysis also reveals important limitations of existing global maps in capturing fine-scale land-use dynamics, particularly in regions dominated by shifting agriculture, and in correctly attributing intensive plantation management.

By providing a more detailed picture of the drivers of tree cover loss, this work helps improve our understanding of land-use change and supports efforts to reduce deforestation, greenhouse gas emissions, and forest-related impacts on biodiversity.

Read the paper: https://doi.org/10.1126/science.adz9042

UMD- Department of Geographical Sciences-Students & Alumni

We’re excited to see NASA spotlight the impact of DIST-ALERT in helping organizations monitor and respond to land and fo...
05/27/2026

We’re excited to see NASA spotlight the impact of DIST-ALERT in helping organizations monitor and respond to land and forest change around the world in near real time.

Developed through a collaboration between NASA OPERA and the UMD Global Land Analysis and Discovery team, DIST-ALERT leverages Harmonized Landsat and Sentinel-2 (HLS) data to provide rapid, global vegetation disturbance alerts every few days. The system is already supporting applications ranging from environmental compliance and sustainable supply chains to conservation and habitat protection.

The NASA feature highlights how DIST-ALERT is helping:
🌎 Detect unauthorized land clearing and construction in New England
🌲 Support sustainable forestry and supply chain transparency across the US, Canada and the EU
🐒 Protect critical chimpanzee habitat in East Africa

We’re proud to contribute to tools that turn Earth observation science into actionable information for decision-makers worldwide.

Read the full NASA story here: https://science.nasa.gov/missions/landsat/three-ways-that-a-new-land-monitoring-system-is-transforming-how-we-manage-forests/

DIST-ALERT, a global land change monitoring system, is revolutionizing forest management.

“Generally speaking, a good year is a good year,” said Matt Hansen, a professor at the University of Maryland and direct...
04/30/2026

“Generally speaking, a good year is a good year,” said Matt Hansen, a professor at the University of Maryland and director of the UMD Global Land Analysis and Discovery team, which contributed forest-loss data to the report. “But you need good years forever if you’re going to conserve the tropical rainforest.”

In 2025, the world razed less forest than any other year in the last decade. The bad news: global warming is making wildfires more frequent and intense.

🌍 The 2025 Global Tree Cover Loss Map update from the UMD Global Land Analysis and Discovery team in collaboration with ...
04/29/2026

🌍 The 2025 Global Tree Cover Loss Map update from the UMD Global Land Analysis and Discovery team in collaboration with Global Forest Watch at the World Resources Institute is now available!

This latest release provides valuable insights into global forest change, helping researchers, policymakers, and environmental practitioners better understand patterns of tree cover loss worldwide.

Explore and download the data here:

🌲 Global Tree Cover Loss:
https://glad.earthengine.app/view/global-forest-change

🔥 Global Tree Cover Loss Due to Fire:
https://glad.earthengine.app/view/global-forest-loss-due-to-fire

Access to timely, high-quality forest monitoring data is essential for supporting conservation efforts, climate action, and sustainable land management.

Take a look and explore the latest updates!



UMD- Department of Geographical Sciences-Students & Alumni

04/29/2026

Tropical primary rainforest loss dropped in 2025 after record-breaking loss in 2024. But forest loss remains high, and fires pose a growing threat.

2025 data from UMD Global Land Analysis and Discovery team is now available on Global Forest Watch.

Countries like Brazil, Colombia, Indonesia and Malaysia are showing it’s possible to quickly slow forest loss with stronger policies and enforcement.

However, climate-driven fires are a dangerous new normal, threatening to reverse this progress. Plus, demand for commodities like cattle, soy, palm oil and gold is driving forest loss in Latin America and Southeast Asia.

Read the full analysis here on World Resources Institute's living report on forests, the Global Forest Review 👉 https://bit.ly/4tEjQm7

🌎 Explore the data on Global Forest Watch: https://bit.ly/48suuUq
🌍 Learn more about how the data compares to other national estimates: https://bit.ly/3QFdAMe
🌏 Check out the data on WRI’s new innovative, AI-powered system: https://bit.ly/48sU4Zu

Our thanks to WRI Africa, WRI Brasil, WRI Indonesia, WRI Colombia and WRI Europe for their invaluable contributions to this analysis.

04/23/2026

🌍 New Publication from the UMD Global Land Analysis and Discovery team 🌱

We’re proud to congratulate lead authors Ahmad Khan and Peter Potapov, along with their colleagues from the University of Maryland and World Resources Institute, on the new paper in Remote Sensing of Environment: “Global annual cropland dynamics 2015–2024.”

This study presents the first operational, annual global cropland dataset at 30 m resolution, using Landsat Analysis Ready Data and advanced machine learning to map cropland extent and change from 2015 to 2024.

🔍 Key findings:

- Global cropland area increased by ~6% (2015–2024), continuing a longer-term rise of nearly 14% since 2003;

- Africa and South America led recent growth, with the largest national-scale increase observed in Brazil;

- About one-third of new cropland came from conversion of natural vegetation or irrigation expansion in drylands;

- Despite expansion, cropland per capita is declining, underscoring mounting pressure on global food systems.

By combining consistent satellite observations with scalable modeling, this work provides critical insights into how food production, land use, climate, and socio-economic forces are reshaping the Earth’s surface.

📊 Open data & tools:

Global annual cropland dataset (2015–2024): https://glad.umd.edu/dataset/annual-croplands

Landsat Analysis Ready Data (GLAD-ARD):
https://glad.umd.edu/ard

📄 Read the paper:
https://authors.elsevier.com/sd/article/S0034-4257(26)00208-7

This open-access dataset supports applications in food security monitoring, sustainable land management, and SDG reporting, and reflects the GLAD Lab’s commitment to transparent, operational Earth observation.

👏 Congratulations again to Ahmad, Peter, and the entire team on this impactful contribution!



UMD- Department of Geographical Sciences-Students & Alumni

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