Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia
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2019
- handle: 10670/1.brna0w
- hal: hal-02324835
- doi: 10.7717/peerj.7841
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.7717/peerj.7841
Ce document est lié à :
info:eu-repo/grantAgreement//639828/EU/The Cambodian Archaeological Lidar Initiative: Exploring Resilience in the Engineered Landscapes of Early SE Asia/CALI
info:eu-repo/semantics/OpenAccess
Mots-clés
Data Science Forestry Spatial and Geographic Information Ecosystem Science Biodiversity Deforestation Plantations SE Asia Remote sensing Landsat ALOS PALSAR Machine learning LiDAR Support vector machines Tropical forestsSujets proches
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Minerva Singh et al., « Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia », HAL-SHS : archéologie, ID : 10.7717/peerj.7841
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Résumé
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.