Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel

作者：Dario Schulz, He Yin, Bernhard Tischbein, Sarah Verleysdonk, Rabani Adamou, Navneet Kumar · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2021 · DOI：10.1016/j.isprsjprs.2021.06.005 · 被引用次数：128 · 研究领域：Remote Sensing in Agriculture、Remote-Sensing Image Classification、Land Use and Ecosystem Services

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accu...