Project Objectives & Scientific Context
"Developing high-resolution spatial intelligence to transition forest monitoring from retrospective detection to proactive risk prediction."
Scientific Rationale
Deforestation monitoring has historically been limited by a reactive paradigm—analyzing forest loss only after disturbance signals are detected by satellite sensors. This research introduces a predictive framework that integrates multi-source geospatial data to model the underlying environmental and anthropogenic pressures that precede actual forest loss.
By resampling diverse inputs—ranging from multispectral indices to topographic slope—into a unified 1 km analytical grid, the pipeline identifies spatial "hotspots" where the probability of near-term deforestation is statistically significant.
Empirical Pilot: Gia Lai Province
Gia Lai Province provides a robust empirical setting for testing risk-modeling architectures. The region exhibits high heterogeneity in forest types and land-use intensity, offering a comprehensive dataset for cross-district validation.
K’Bang District
A critical site for modeling risks to primary forest cover and existing protected area boundaries.
Mang Yang District
Enables the assessment of model sensitivity to infrastructure-driven disturbances and rapid land conversion.
Advancing Open Geospatial Intelligence
The full research methodology, including feature importance rankings and model hyperparameter settings, is documented transparently in our technical publication.