Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region.
Published in MDPI Remote Sensing, 2020
In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model.
Recommended citation: Poortinga, Ate, Aekkapol Aekakkararungroj, Kritsana Kityuttachai, Quyen Nguyen, Biplov Bhandari, Nyein Soe Thwal, Hannah Priestley et al. "Predictive analytics for identifying land cover change hotspots in the mekong region." Remote Sensing 12, no. 9 (2020): 1472.
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