GCRI · Biodiversity Nexus
Forests and landscapes
Forests hold most of the world’s terrestrial species, store carbon, regulate water and sustain rural livelihoods in every tropical, temperate and boreal region. Their health depends on how much forest remains and also on how it is arranged: large intact blocks behave differently from fragments separated by farms and roads. This page sets out how forest loss, degradation and fragmentation are measured, how they propagate through landscapes, and what the evidence means for those who govern land use and supply chains.
How the work runs.
Ecosystem condition account
Forests and landscapes · Wetland complex · Accounting year 5
- Extent change▼ 2pct of openingWatch
- Condition index0.560 to 10.03Watch
- Species of concern19recorded
- Restoration under management353haNormal
Condition indicators
| Water level regime | 0.7 | 0.7 | 1.0 |
| Native vegetation cover | 0.5 | 0.4 | 1.0 |
| Invasive species pressure | 0.3 | 0.3 | 0.0 |
| Connectivity | 0.6 | 0.5 | 1.0 |
Condition index
Fig.Ecosystem condition account: example names and illustrative values, not a record or a live system.
Forests and landscapes: how the risk develops.
Forest health and landscape fragmentation. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Clearance and degradation
Clearing converts forest to other uses; degradation removes trees, structure and species while the land stays classed as forest. Selective logging, fire and fuelwood extraction often precede clearance, so degradation is an early signal of conversion.
Fragmentation and edge effects
Roads and clearings break forests into smaller patches with more edge. Edges are hotter, drier and windier, raising tree mortality and fire risk, and they isolate species that cannot cross open land.
Fire, drought and pests compound
Drought weakens trees, pests and pathogens spread through stressed stands, and dead fuel feeds larger fires. In some regions repeated fire can shift forest toward savanna or shrubland that does not return on its own.
Commodity and finance links
Most tropical clearance is driven by agriculture, including cattle, soy, oil palm, cocoa and wood fibre. Demand travels through traders, processors and lenders, so forest loss is a supply-chain and portfolio risk as well as a local one.
Habitat condition in the Amazon Basin.
Vegetation, land cover and active fires over the Amazon Basin, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Habitat condition, for this page
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds and overpass timing leave gaps)
- Source
- VIIRS NOAA-20 375 m (FIRMS) (NASA LANCE FIRMS)
- Provider
- NASA LANCE FIRMS
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds and overpass timing leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 500 m grid
- Uncertainty
- Satellite hot-spot detections for the day shown, including gas flares and industrial heat; not a confirmed fire, an incident report or a warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest 8-day period GIBS publishes)
- Source
- MODIS Aqua GPP (MYD17) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- 8-day; Satellite-driven model; latest 8-day period GIBS publishes
- Newest at build
- 2026-09-30
- Resolution
- about 500 m grid
- Uncertainty
- Model estimate of carbon taken up by plants over the 8-day period shown; an indicator of growth, not harvest or production.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Habitat condition, framed on the Amazon Basin.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Optional layers (6 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Gross primary production, 8-day total: MODIS Aqua GPP (MYD17), NASA LP DAAC · 30 Sep 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
Forests regulate water, store carbon and feed people.
| Domain | How it couples here | What to watch |
|---|---|---|
| Water | Forests influence rainfall recycling, infiltration and sediment. See catchment hydrology. | Forest loss in water source areas |
| Food | Agricultural expansion is the main driver of deforestation. See food trade and markets. | Commodity-driven forest loss |
| Energy | Fuel wood and charcoal for cooking; biomass for power. | Wood fuel share of household energy |
| Health | Forest fire smoke harms health far from the fire. | Smoke exposure days |
Forest change is measured from national inventories reported to FAO’s Global Forest Resources Assessment (2020 edition) and from satellite maps such as Hansen and colleagues (2013). The two differ because of definitions: tree cover loss is not deforestation, which requires a change of land use. The EU deforestation regulation (2023/1115) has had its application dates postponed by amendment; check the current text.
Model forests and landscapes as a system. Run the cascade.
Assembled from the Nexus Standards area profile Biodiversity and nature risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Deforestation
- Biodiversity loss (set off downstream)
- Soil erosion (set off downstream)
- Land-system change boundary transgression (set off downstream)
- Amazon rainforest dieback (set off downstream)
- Slope slide (set off downstream)
- Flash flood (set off downstream)
- Zoonotic spillover event (set off downstream)
- Wildfire (set off downstream)
- Emerging arbovirus outbreak (set off downstream)
- Malaria resurgence (set off downstream)
Systems and couplings
- Biodiversity and nature risk standards
- Climate risk standards Feedback
- Health risk standards Cascade
- Food and agriculture risk standards Dependency
- Environmental degradation hazard standards Shared hazard records
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-5Figure, map and visualisation standard (with the UI/UX kit)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-N5Biodiversity and nature risk and resilienceNRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H27Hazard family: Biodiversity and species stress (NHS-1 eco.species)NRS-H26Hazard family: Land and wetland degradation (NHS-1 eco.land)NRS-H28Hazard family: Climate-state shifts (NHS-1 eco.climate-state)NRS-H64Hazard family: Ecosystem regime shifts (NHS-1 eco.regime-shift)
Observation
- GBIF occurrence API and downloads (listed for reference)
- World Database on Protected and Conserved Areas (listed for reference)
- Global Forest Watch tree cover loss (Hansen/UMD) and alerts (listed for reference)
- ESA WorldCover 10 m land cover (listed for reference)
- Copernicus Land Monitoring Service products (listed for reference)
- Red List Index
- Forest area as a proportion of total land area
- Proportion of important sites for terrestrial and freshwater biodiversity covered by protected areas
Example geography
- Pacific Island states: 150 hexagonal cells
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
Forest evidence and contributions.
Forest and landscape analyses are designed for the Observatory Signal series. Satellite-derived change carries the label estimated until checked with reference data. Useful contributions: national inventory plots, field validation of change and community forest records shared with consent.
The sector signature of forests and landscapes.
The sectors forests and landscapes touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Biodiversity and its partners
Biodiversity gives pollination and soils to food (0.6), watershed regulation to water (0.5) and zoonotic spillover and medicines to health (0.4).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Tree cover loss | hectares per year | annual | Landsat time-series classification | OBSERVED |
| Forest area | hectares and percent of land area | five-yearly | National forest inventories reported to FAO | Published statistics |
| Forest disturbance alerts | alerts per square kilometre per week | weekly | Optical and radar change detection | OBSERVED |
| Above-ground biomass | tonnes per hectare | periodic | Lidar, radar and inventory plots | ESTIMATED |
| Effective mesh size | square kilometres | periodic | Landscape metrics on forest maps | ESTIMATED |
| Burned area | hectares per month | monthly | Satellite burned-area products | OBSERVED |
| Forest loss by driver | hectares per year by driver class | annual | Driver classification of mapped loss | MODELLED |
How forests and landscapes is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Earth observation chain
How a satellite or sensor record becomes evidence on forests and landscapes, and where each step adds uncertainty.
- 01 · Acquireorbit, sensor and revisitUncertainty: gaps from cloud and orbit
- 02 · Calibrateradiometric and geometricUncertainty: sensor drift
- 03 · Correctatmosphere and terrainUncertainty: aerosol and slope assumptions
- 04 · Retrieve or classifythe variable or the classUncertainty: model and training error
- 05 · Validateagainst ground truthUncertainty: sample design and size
- 06 · Publishwith error bars and lineageUncertainty: stated, never hidden
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Global Forest Watch and Global Forest Change (University of Maryland) | Annual tree cover loss and gain mapped from Landsat | Free and open, attribution |
| FAO Global Forest Resources Assessment | Country-reported forest area, characteristics and management | Free and open, attribution |
| GLAD and RADD forest disturbance alerts | Near-real-time disturbance alerts from optical and radar imagery | Free and open, attribution |
| GEDI lidar and ESA Climate Change Initiative biomass | Forest structure and above-ground biomass estimates | Public domain; free and open |
| Intact Forest Landscapes | Mapped large unfragmented forest areas and their reduction over time | Free and open, attribution |
| Copernicus Sentinel-1 and Sentinel-2 | Radar and optical imagery for monitoring cloud-prone forests | Free and open, attribution |
| National forest inventories | Plot measurements of species, size, condition and growth | Terms per agency |
Who decides, and what the evidence serves.
Biodiversity Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Forestry and land-use ministries | Where to enforce, recognise tenure or restore, and how to report forest reference levels | Loss, degradation and driver maps with stated accuracy; inventory data |
| Competent authorities under deforestation-free supply rules | Whether a commodity plot was cleared or degraded after the regulatory cut-off date | Plot-level loss history with alert dates and map accuracy |
| Commodity buyers and lenders | Which sourcing regions and clients carry deforestation and conversion risk | Loss by driver and jurisdiction; fragmentation and fire trends |
| Indigenous and community forest governance bodies | How to monitor and defend their territories against incursion | Alerts and loss maps for their lands, held under their own data terms |
Outputs this topic produces
- Forest loss and degradation maps with accuracy stated
- Fragmentation and connectivity layers by landscape
- Commodity-driver attribution notes with uncertainty
- Evaluation guides for forest protection policies
What experts ask.
Forest and landscape ecologists work on questions that cross scales from the tree to the region.
- Where is forest being cleared or degraded, and what is driving it in each place?
- How healthy are remaining forests in the face of drought, heat, pests and changing fire regimes?
- How does fragmentation change edge effects, species movement and the risk of fire and invasion?
- Which corridors and stepping stones keep landscapes connected as species shift with climate?
- How can farmland, plantations and settlements be arranged to retain ecological function?
Area, arrangement and stress.
Clearing removes habitat outright, but degradation through selective logging, grazing, fire and road building often affects larger areas and is harder to see. Fragmentation splits what remains into smaller patches with more edge. Edges are hotter, drier and windier than interiors, and they let in invasive species, hunters and fire. Small patches support smaller populations that are more prone to local extinction, and isolated patches are less likely to be recolonised.
Climate change adds stress from within. Hotter droughts raise tree mortality, warmer winters let some pests survive and spread, and longer dry seasons lengthen fire seasons. In some regions these pressures combine with fragmentation to create a risk that forest gives way to more open vegetation, with consequences for regional rainfall, water supply and carbon storage.
How the evidence is built, and where it stops.
- 01
Satellite forest monitoring
Optical and radar imagery map clearing and canopy loss consistently over time. Degradation beneath the canopy and in dry forests is harder to detect.
- 02
Landscape metrics
Patch size, edge density and connectivity are computed with geospatial information systems. Metrics depend on map resolution and class definitions.
- 03
Forest inventories and plots
Permanent plots measure growth, mortality and species. They are the reference for satellite estimates but sparse in many regions.
- 04
Connectivity modelling
Circuit and least-cost models predict movement routes. They rest on assumed resistance values that are rarely validated.
How it is studied, and what the methods cannot say.
- 01Earth observation and remote sensingForest loss and disturbance alerts · Wetland and mangrove dynamicsApplied here
- 02Geospatial information systemsOverlap with sensitive biodiversity · Connectivity and corridor analysisApplied here
- 03Spatial statistics and geostatisticsBias-corrected distribution surfaces · Hotspots of change and threatApplied here
- 04Causal inference and impact evaluationProtected area effectiveness · Restoration and payment programme evaluationApplied here
- 05Ecology and biodiversity scienceSpecies distribution modelling · Population trend and viability analysisApplied here
- 06Complex systems and agent-based modellingEarly warning of regime shifts · Food-web and co-extinction networksApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Why do national forest statistics differ from satellite maps?
Is replanting the same as protecting old forest?
Can satellites detect selective logging?
Is planting trees the same as restoring forest?
Why does fragmentation matter if total forest area is stable?
Can satellite data identify who is clearing forest?
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