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

作　　者：Nguyen Thanh Hoan Ram C. Sharma Nguyen Van Dung Dang Xuan Tung Nguyen Thanh Hoan;Ram C. Sharma;Nguyen Van Dung;Dang Xuan Tung(Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam;Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam;Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan;Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam)

机构地区：[1]Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam [2]Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam [3]Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan [4]Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam

出　　处：《Journal of Geoscience and Environment Protection》2020年第9期24-32,共9页地球科学和环境保护期刊（英文）

摘　　要：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.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.

关 键 词：Satellite Remote Sensing Machine Learning Optical RADAR 

分 类 号：TB3[一般工业技术—材料科学与工程]