Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features

Remote Sensing

Limin Wang¹  Zhongxin Chen¹  He Li¹  Hasituya¹  Wenbin Wu¹  Zhiwei Jiang² 

[1] Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12, Zhongguancun Nan Dajie, Beijing 100081, China;National Meteorological Information Center, China Meteorological Administration, No. 46, Zhongguancun Nan Dajie, Beijing 100081, China;

关键词: plastic-mulched farmland;    spectral features;    textural features;    support vector machine;    Landsat-8;    OLI imagery;   

DOI  :  10.3390/rs8040353

来源: DOAJ

【 摘 要 】

In recent decades, plastic-mulched farmland has expanded rapidly in China as well as in the rest of the world because it results in marked increases of crop production. However, plastic-mulched farmland significantly influences the environment and has so far been inadequately investigated. Accurately monitoring and mapping plastic-mulched farmland is crucial for agricultural production, environmental protection, resource management, and so on. Monitoring plastic-mulched farmland using moderate-resolution remote sensing data is technically challenging because of spatial mixing and spectral confusion with other ground objects. This paper proposed a new scheme that combines spectral and textural features for monitoring the plastic-mulched farmland and evaluates the performance of a Support Vector Machine (SVM) classifier with different kernel functions using Landsat-8 Operational Land Imager (OLI) imagery. The textural features were extracted from multi-bands OLI data using a Grey Level Co-occurrence Matrix (GLCM) algorithm. Then, six combined feature sets were developed for classification. The results indicated that Landsat-8 OLI data are well suitable for monitoring plastic-mulched farmland; the SVM classifier with a linear kernel function is superior both to other kernel functions and to two other widely used supervised classifiers: Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC). For the SVM classifier with a linear kernel function, the highest overall accuracy was derived from combined spectral and textural features in the 90° direction (94.14%, kappa 0.92), followed by the combined spectral and textural features in the 45° (93.84%, kappa 0.92), 135° (93.73%, kappa 0.92), 0° (93.71%, kappa 0.92) directions, and the spectral features alone (93.57%, kappa 0.91). Spectral features make a more significant contribution to monitoring the plastic-mulched farmland; adding textural features from medium resolution imagery provide only limited improvement in accuracy.

【 授权许可】

Unknown