Monitoring plastic-mulched farmland using Landsat-8 OLI imagery

Abstract

In the past decades, the area of plastic-mulched cultivation has grown rapidly both on the global and regional scope, due to its remarkable efficiency of increasing crop production. However, the rapid expansion has brought a series of ecological and environmental problems in many regions, such as the vast amount of residues from the plastic-mulched farmland bring about aesthetic pollution and adverse environmental impacts on water, air and soil of the agricultural ecosystem and the terrestrial ecosystem. It is a serious problem that will do great damage to the sustainability of agriculture. In order to alleviate problems and to find solutions, the development of efficient methods to map plastic-mulched farmland as accurately as possible is of great significance to the policy-makers and scientists. In this paper, the optical remotely sensed data, Landsat-8 OLI, have been used in monitoring the plastic-mulched farmland in a study area located in Jizhou city, Hebei Province, China, where the plastic mulching is extensively practiced in farming and interlaced with uncovered farmland. The spectral and texture features of different ground objects have been statistically analyzed on the OLI imagery in the light of the training samples. Then the spectral and texture features that carry a certain degree of explanation for plastic-mulched farmland were selected for classification. The training and testing samples (bigger than 4 pixels × 4 pixels of panchromatic fused OLI imagery) were collected from Google Earth images, and were amended by comparing to false color composite Landsat bands 7 (SWIR), 5 (NIR) and 4 (RED). At the same time, the samples were purified according the J-M distance method. The Support Vector Machines (SVM) has been used as classifier to extract the plastic-mulched farmland based on the spectral features alone, on the texture features alone and on the combined spectral and texture features respectively. The classification result was validated using confusion matrix. The overall classification accuracy for plastic-mulched farmland based on spectra features alone, on texture features alone and on combined spectral and texture features are 89.45%, 94.68% and 94.73%, respectively. Their respective Kappa coefficients are 0.85, 0.92 and 0.92. And the producer's accuracy and user's accuracy of plastic-mulched farmland are 87.30% and 63.96% on spectral features, 83.03%, and 81.37% on texture features, 83.39% and 81.37% on the combined features, respectively. The commission and omission errors for plastic-mulched farmland are 36.04% and 12.70% on spectral features, 18.63% and 16.97% on texture features, 18.63% and 16.61% on the combined features. This study shows that the plastic-mulched farmland can be extracted effectively using SVM classifier based on the Landsat-8 imagery.