Remote Sensing, Vol. 8, Pages 353: Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features

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 Class