Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia

Abstract

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.

SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan

“…Although the RDF is known as a strong classifier due to its "bagged decision tree" nature, which can split data on a subset of features, as mentioned above, the main reason for the low overall accuracy in RDF were the increase in the number of damage rates (response variables) and lack of enough training data. In general, we can say that, when we use several different datasets its performance and accuracy may reduce [35]. On the other hand, SVM provides better results when multiple datasets and smaller training set is available [35].…”

“…In general, we can say that, when we use several different datasets its performance and accuracy may reduce [35]. On the other hand, SVM provides better results when multiple datasets and smaller training set is available [35]. Table 4 shows some previous studies in which SVM and RDF algorithms were used for damage/land classification.…”

Spatial–temporal variations in deforestation hotspots in Sumatra and Kalimantan from 2001–2018

“…Land‐use change data were not used in this research. Kruskal–Wallis nonparametric test was also conducted to check the statistically significant differences between the variables used for explaining the variation in hotspots (Singh et al., 2019).…”

Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery

“…MLAs have also been implemented in satellite data to map fires, examine spectral properties, accurately delineate the area affected by the fire [79], analyze fire severity [72], and carry out precision analysis of the product [43,61]. Some of the most common MLAs for classifying and mapping burned areas include support vector machines (SVM), kNN, and Random Forest (RF) [80,81]. RF, for example, allows for integrating data from different scales and sources, which explains its wide use in many mapping applications based on satellite images [72].…”