This study develops a modelling framework by utilizing multi-sensor imagery for
classifying different forest and land use types in the Phnom Kulen National Park (PKNP)
in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data
and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with
three different machine learning (ML) regression techniques (Support Vector Machines
(SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML
methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band &
ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR
CHM data. The Landsat-ALOS combination produced more accurate classification
results (95% overall accuracy with SVM) compared to Landsat-only bands for all
ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights)
improved the overall accuracy to 98%. The research establishes that majority of PKNP
is dominated by cashew plantations and the nearly intact forests are concentrated in
the more inaccessible parts of the park. The findings demonstrate how different RS
datasets can be used in conjunction with different ML models to map forests that had
undergone varying levels of degradation and plantations.