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The Indochinese Peninsula, which contains two thirds of the world’s tropical forests, however, is one of the world’s most threatened habitat with some of the highest rates of deforestation and land use changes. Availability of higher resolution satellite data collected by the likes of Landsat 8 and Sentinel-1-2 has brought new opportunities for precise land cover monitoring in recent years. However, utilizing a massive volume of high spatial and temporal resolution data for ecological applications is challenging. One approach is to employ composite images generated from the multi-temporal satellite data. The research was conducted in two study sites located in the Indochinese Peninsula, Laos-Thailand and Vietnam-Cambodia, vulnerable to deforestation and land use changes. We assessed the potential of recently available composite images, such as Biophysical Image Composite (BIC), Forest Cover Composite (FCC), Enhanced Forest Cover Composite (EFCC), and Water Cover Composite (WCC) for the classification and mapping of land cover types. Three machine learning classifiers, k-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Random Forests (RF) were employed and the performance of composite images was evaluated quantitatively with the support of ground truth data. The overall accuracies (Kappa coefficient) obtained from the combination of composite images were 0.92 (0.89) and 0.90 (0.86) for Laos-Thailand, and Vietnam sites respectively. These results highlight effectiveness of the composite images for the classification and mapping of land cover types.

Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula