A Decision-Tree Classifier for Extracting Transparent Plastic-Mulched Landcover from Landsat-5 TM Images

The area of plastic-mulched landcover (PML), an important agricultural landscape, is increasing rapidly at a rate of 20% per year globally. However, the spatial and temporal distributions of PML have been poorly understood because of lack of effective technology to extract PML for large geographic areas. This paper presents a decision-tree classifier for extracting the transparent PML information from Landsat-5 TM images. The classifier was built with rules obtained from analyzing the spectral characteristics of transparent PML on Landsat-5 TM images, covering the study area of in Xinjiang, the largest PML-based cotton plantation provinces in China. Then, the classifier was applied at the study area for years 1998, 2007, and 2011. Results indicate that the classifier successfully extracted the PML from Landsat-5 TM images at overall accuracies of 97.82%, 85.27%, and 95.00% and Kappa coefficients of 0.9782, 0.80, and 0.93 for years 2011, 2007, and 1998, respectively. The results also imply that the decision-tree classifier is temporally stable and can be applied in different years. Visual comparison of the results with the high-spatial resolution images on Google Earth also shows that detected locations of PML are correct. The study shows that the classifier is an effective method for extracting PML for large geographic areas from Landsat-5 TM. Because of the long history of global coverages as well as free availability of Landsat-5 TM images, it is feasible to map the spatio-temporal dynamics of PML over large geographic areas with the technologies presented in this paper.