Remote Sensing Applications in Forest Monitoring
Summary
Remote sensing has become a cornerstone of contemporary forest monitoring by offering systematic, large-scale and repeatable observations of forest structure, composition and dynamics. Optical satellite platforms such as Landsat and Sentinel provide decades-long time series for mapping forest cover change, disturbance and recovery, while radar systems penetrate cloud and smoke to deliver wall-to-wall assessments of biomass and canopy moisture. Airborne and spaceborne LiDAR yield three-dimensional measurements of canopy height and vertical structure, enabling refined estimates of aboveground biomass, carbon stocks and habitat complexity. Hyperspectral sensors further enhance species discrimination and stress detection through narrowband reflectance signatures. Advances in data fusion and machine learning now allow synergies across sensor types and temporal frequencies, from daily high-resolution cube-satellite imagery to annual national inventories. These developments underpin global efforts to quantify deforestation and afforestation, monitor selective logging and forest degradation, assess post-fire recovery rates, and support climate-mitigation frameworks including REDD+. The capacity to generate cloud-free composited imagery, derive spectral indices sensitive to greenness and moisture, and integrate structural metrics has revolutionised the mapping of forest health and carbon dynamics. Together, these tools inform sustainable forest management, biodiversity conservation and policy decisions at local to international scales.
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Remote Sensing Applications in Forest Monitoring publication trend
The graph below shows the total number of articles in remote sensing applications in forest monitoring across all publications each year (not limited to Nature Index journals).
Technical terms
Optical remote sensing: Observation of surface reflectance in visible and infrared wavelengths for land cover and vegetation analysis.
LiDAR (Light Detection and Ranging): Active sensing technology emitting laser pulses to measure three-dimensional forest structure and canopy height.
Pixel-based composite: Multi-temporal image product selecting the most suitable pixel observations across dates to create cloud-free, gap-free mosaics.
Random Forest: Ensemble machine-learning algorithm using multiple decision trees for robust regression or classification of remote-sensing data.
XGBoost: Gradient-boosted decision-tree framework optimised for speed and predictive performance in classification and regression tasks.
References
- An assessment approach for pixel-based image composites. ISPRS Journal of Photogrammetry and Remote Sensing (2023).
- Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning. GIScience & Remote Sensing (2024).
- Field-independent carbon mapping and quantification in forest plantation through remote sensing. European Journal of Remote Sensing (2024).
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