Last updated:

Forest Monitor — Change Detection at Landscape Scale

Continuous forest monitoring for conservation finance, carbon programs, and supply-chain risk: deforestation alerts, canopy disturbance signals, and audit-ready map products built on multi-sensor Earth observation.

Request Forest Monitor Briefing
Forest monitoring and deforestation change detection from satellites

Why Continuous Forest Intelligence Matters

Forest loss and degradation can move faster than annual reporting cycles. Wall-to-wall satellite monitoring reduces blind spots between field visits and helps teams prioritize ground response where it matters most.

Kumi Analytics applies consistent classification and change logic so portfolio and project teams can compare regions, track trends, and evidence interventions with transparent assumptions.

Forest Monitor capabilities

Deforestation & Disturbance Alerts

Near real-time style alerting workflows for clearing and major canopy change, tuned to tropical cloud constraints where needed.

Canopy Change & Degradation Signals

Track subtle canopy changes that can precede full clearance—supporting degradation narratives and early intervention.

Wall-to-Wall Coverage

Consistent regional and project-scale layers so you are not limited to sparse plot networks for spatial risk.

Carbon & Conservation Alignment

Outputs structured to sit alongside biomass and landcover intelligence for NbS and forest carbon workflows.

Reporting & Evidence Bundles

Map products and summaries suitable for internal governance, investor reporting, and external technical review.

Human-in-the-Loop QA

Optional review checkpoints for high-stakes decisions—combining automation with analyst validation where required.

Multi-Sensor Monitoring Built for Operations

Forest cover monitoring with optical and SAR satellite data

Optical + SAR Where Clouds Block the View

Operational forest monitoring in the humid tropics requires more than single-sensor optical time series. We combine SAR and optical stacks to stabilize detections across seasons:

  • SAR-supported persistence checks for clearing signatures in cloud-prone AOIs
  • Optical composites and indices for canopy condition and seasonal phenology context
  • Explicit commission controls and confidence language for operational consumers

Portfolio-Ready Consistency

Standard legends and change semantics across AOIs reduce reconciliation when you are monitoring many sites or concessions in parallel.

Cadence Matched to Risk

From weekly monitoring windows during high-risk seasons to quarterly governance snapshots—aligned to how teams actually run programs.

Benefits for Forest Programs

Earlier Response Windows

Detection-oriented outputs help operations teams route field verification sooner—reducing irreversible loss.

Defensible Change Evidence

Transparent methods and map bundles improve reviewer confidence versus ad hoc screen captures.

One Stack with Kumi Analytics

Forest Monitor complements KACSAT, landcover, and blue carbon analytics for end-to-end environmental intelligence.

Operationalize Forest Monitoring

Share your AOI list, risk priorities, and reporting cadence—we will propose a monitoring configuration and delivery format that fits your team.

Request Forest Monitor Briefing

Frequently asked questions

Forest Monitor is positioned as a continuous monitoring and alerting layer for landscape-scale forest change, while KACSAT is our deeper analytical toolkit for biomass, sequestration, and project-specific MRV workflows. Many customers use both together.
Latency depends on sensor revisit, compositing strategy, cloud cover, and QA rules. We set expectations up front per AOI and season so operational teams know what “near real-time” means in practice for their geography.
Yes. Vector AOIs can drive summary statistics, ranked risk lists, and parcel-level evidence exports for governance workflows.
We combine multi-date persistence checks, sensor fusion, and conservative thresholds—then document residual error modes so users understand what automation can and cannot claim.