Deforestation maps: Global stats and ways GIS tools track forest loss

When forests disappear, the effects ripple across biodiversity habitats, protected areas, and the policies designed to manage them.

But deforestation is difficult to track in real time. It often happens across large, remote landscapes, and distributing data through traditional legacy workflows can lead to delayed reports with fragmented information. A static forest map shows past changes, but you need a way to understand where risk is building now.

Deforestation maps help you close that gap. In this guide, we’ll explain where major deforestation occurs and how modern geographic information systems (GIS) support analysis and action.

What’s a deforestation map?

A deforestation map is a spatial view of where a forest area or tree cover shifts over time. It shows how forest loss causes on-ground changes, and how it relates to nearby land use and protected areas.

Researchers can combine historic and current data to analyze patterns across different time periods. Continuously monitoring forest conditions makes it easier to assess risk before loss spreads further.

These maps let you visualize change, so you can make environmental decisions with geographic context. For example, a conservation team can use a deforestation map to see whether forest loss is moving toward a national park. They then deploy ranger patrols and camera traps to catch illegal activity. 

What data is used in global deforestation maps?

Deforestation mapping has several layers of spatial data, including:

• Satellite imagery provides visual and spectral evidence of forest cover and the ways land changes over time.
• Remote sensing turns information from satellites and sensors into measurable information about the Earth’s surface. For forest monitoring, you can compare tree cover and vegetation health across large areas that are difficult to inspect from the ground.
• Land cover data classifies what exists in a given area. The context these datasets provide tell you whether a cleared forest was converted into cropland or caused by a natural disaster.
• Climate and environmental data offer insights into the conditions around forest change. Elements like drought, rainfall patterns, and soil moisture influence where degradation is more likely to occur or where recovery is harder after tree cover loss.
• Administrative boundary data connects forest change to ownership and conservation status, so you know who’s responsible for land management. It lets you see if deforestation overlaps with a protected area or government zone.

To obtain and use this information, public datasets and surveys are a great starting point. Global Forest Watch (GFW) provides access to global forest monitoring data, while the Hansen Global Forest Change dataset is popular for analyzing tree cover loss and growth over time.

Where is deforestation happening? Global deforestation statistics and trends

Deforestation isn’t evenly distributed. Most long-term, human-caused forest removal occurs in the tropics, where forests store large amounts of carbon and support high levels of biodiversity.

According to the GFW and World Resources Institute (WRI), tropical primary rainforest loss fell 36% in 2025. But the world still lost 4.3 million hectares of tropical primary forests, and loss remained 46% higher than it was a decade earlier.

Global tree cover loss also remains high. WRI reports that fires accounted for 42% of the 25.5 million hectares of worldwide tree cover loss in 2025. A few regional hotspots include:

• Latin America: Brazil still had the largest absolute area of tropical primary forest loss in 2025, even after the country saw a 42% reduction in primary forest loss. Agriculture is a major long-term pressure, especially with soy and cattle expansion. This map visualizes deforestation in the Amazon Rainforest, with layers for features like protected areas, timelines, and illegal mining operations.

• Congo Basin: In the Democratic Republic of the Congo, WRI reports that non-fire forest loss reached its highest level on record in 2025. This was driven largely by small-scale shifting cultivation, firewood and charcoal production, mining, and conflict-related displacement. 

• Southeast Asia: Forest loss in parts of Southeast Asia is due to agriculture, mining, and commodity supply chains. Indonesia and Malaysia maintained relatively low or stable rates of primary forest loss in 2025, but pressure continues to rise from projects like rubber and oil palm production. In deforestation mapping, supply chain data lets researchers see links between sourcing regions rather than broad national trends. This allows teams to target conservation actions specifically.
• Boreal forests: WRI reports that fires drove a growing share of global tree cover loss in 2025, with severe impacts across northern temperate regions like Canada and Alaska. Some boreal fires are part of natural forest cycles, but repeated or unusually severe fires can release large amounts of carbon, damaging peatlands to near-unrecoverable conditions.

Common use cases and examples of deforestation maps

Deforestation maps help various industries layer environmental data, study relationships between them, and make an impact on the world. Here are a few applications:

• Environmental monitoring: People use deforestation maps to track where forest loss is spreading and how ecosystems change. For example, mapping tree cover loss patterns helps teams identify hotspots for field checks and restoration planning.
• Climate and carbon analysis: Forest loss releases stored carbon and reduces future carbon absorption capabilities. GFW estimates that global tree cover loss from 2001 to 2025 was associated with 230 gigatons of CO₂ emissions. Combining forest loss with layers like fire and land use lets analysts estimate which areas have the highest climate impact.
• Conservation planning: These teams use deforestation maps to see where natural forest and protected areas are under threat. If tree cover loss moves toward a sensitive area, they can intervene or plan restoration before the situation becomes irreversible.
• Land management and policy: Agencies compare forest change with property lines, zoning, and protected area boundaries. For instance, a team may monitor canopy removal to see how it impacts the watershed and soil erosion near regulated land.
• Supply chain monitoring: Companies and regulators use deforestation maps to check commodity sourcing regions. Under rules like the European Union Deforestation Regulation, these maps help with due diligence by flagging areas where recent forest loss overlaps with sourcing locations.

Common challenges in deforestation mapping

Here are a few challenges to consider when working with deforestation maps:

• Massive environmental datasets: Satellite imagery and raster data cover enormous areas at high resolution. The scale helps with forest monitoring, but it also makes data processing and sharing slow without the right infrastructure.
• Data inconsistency across sources: Forest datasets can have different resolutions, formats, and update schedules. For example, one layer might show coarse tree cover loss, while another displays high-definition boundaries. This results in analysis errors and misalignment.
• Delayed or static reporting: Traditional reporting workflows can lag behind on-ground realities. For instance, by the time you download datasets, build forest maps, and circulate reports, forest conditions may have already changed.
• Limited accessibility across teams: Deforestation analysis often involves technical experts, like researchers and field teams. If the mapping tools are too complex, non-specialists may have trouble exploring the map directly.

How GIS improves deforestation analysis

GIS platforms help organizations map, layer, and analyze location-related data. In deforestation mapping, they help people examine the surrounding context around a single forest-loss layer and see what changed in the area and why.

For example, instead of treating satellite imagery, climate data, and property boundaries as separate files, you can bring them into one map. This lets you analyze their relationships, viewing all datasets together or toggling layers on and off.

Modern GIS provides a way for countless industries to examine real-world events to predict outcomes and make proactive decisions. Here are a few ways GIS improves deforestation analysis:

• Monitoring change over time: Deforestation is a recurring event. You can use GIS to compare forest conditions across different periods to spot whether loss is slowing down or accelerating into new areas. For example, Economic R&D firm Beard Labs built a Felt map for their  African mangrove population study that compares the current mangrove count against the loss and gain.
• Identifying spatial patterns and hotspots: Instead of showing a broad area of total forest loss, GIS can reveal concentrated deforestation areas. For example, you can see a clearing along roads or around mining locations. With GIS, you can narrow down large datasets and turn them into priority areas for investigation.
• Supporting real-time environmental monitoring: GIS lets you refresh different layers and compare them with earlier baselines in real time, assisting faster and more accurate analysis. You can also share updated maps with non-specialists without late exports or rebuilding the analysis from scratch.
• Accelerating environmental analysis with AI: AI-assisted GIS workflows help you explore large datasets faster. Rather than parsing through spreadsheets, you can query AI to find deforestation hotspots and identify patterns. This straightforward approach makes spatial analysis accessible to both technical and non-technical teammates.

Monitor forest change in the field with Felt

When it comes to forest monitoring, proactive work lets teams stay ahead of environmental damage and safeguard communities and protected areas. These people need fast access to accurate spatial data and an easy way to compare and share evidence through a joint workflow.

Felt is an enterprise-grade GIS platform where you can work with location data in one collaborative map. Stream Cloud Optimized GeoTIFFs, access STAC catalogs, and auto-index S3 straight from cloud sources. Connect with object storage like Amazon S3, Azure Blob Storage, and Google Cloud Storage and warehouses like Databricks, Redshift, Postgres, Snowflake, and BigQuery. Then, share live maps with team members with a simple link. Everyone can contribute as the visuals update in real time.

With our Field App, these workflows extend onto the ground. Field crews can gather data, take photos, and upload surveys — even in remote areas — and sync updates once they’re back online.

Book a demo with our team, and start using Felt to visualize and analyze the real world.