Potential of Pleiades and Radarsat-2 Data for Mapping Plastic-Mulched Farmland Using Object-Based Image Analysis

摘要

The increasing area of Plastic-Mulched Farmland (PMF) is aggravating the conflict between agriculturaldevelopment and environmental protection. The spatial distribution of PMF requires aneffective and economic technique. However, most works are currently carried out on pixel-levelalgorithms, which leads inevitably to mixed spectral errors. In this connection, PMF has beenmapped with Pleiades and Radarsat-2 data combining object-based image analysis (OBIA) andRandom Forest (RF). At first, through visual interpretation, the outcomes of various segmentingscenarios were used to select the optimum segmentation parameters. The spectral characteristics,textural and geometric features were then extracted and tailored to the best PMF mappingfunction subset. Finally, we map the PMF using the optimized object-level feature subset basedon RF. The results show that the ability of Pleiades data to map PMF in Northern China ishigher than that of Radarsat-2. The overall mapping accuracy achieved is 90.27%. In general,the precision and reliability of the mapping are the product of extensive structural data andobject-level features that can reduce the reliance on spectral data.