Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 6; p. 557
• Main Authors: Hasituya, Chen, Zhongxin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2017
• Subjects: Agricultural land; Algorithms; Crop yield; Crops; Environmental impact; indices features; Land cover; Land use; Landsat; Landsat satellites; Mapping; mapping plastic-mulched farmland; multi-temporal Landsat-8 imagery; Normalized difference vegetative index; Plastics; Remote sensing; Satellite imagery; Spectra; spectral feature; Support vector machines; textural feature; thermal feature
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9060557

Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer’s, and user’s accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively.