Abstract
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.
Highlights
The recent agricultural practice of using plastic coverings is one of the most important differences between traditional agriculture and intensive agriculture
The fewer results of classifiers (SVM-L and the Maximum Likelihood Classifier (MLC) using different feature sets) were taken as examples to display the differences between classifiers and feature sets (Figure 4)
We compare the accuracy among the seven feature sets: the spectral features alone (S), original multi-bands textural features alone (T3 ), pan-sharpened multi-bands textural features alone (TF ), panchromatic band textural features alone (TP ), and combined spectral and original multi-bands textural features (S + T3 ), combined spectral and pan-sharpened multi-bands textural features (S + TF ), and combined spectral and panchromatic band textural features (S + the panchromatic band (TP) )
Summary
The recent agricultural practice of using plastic coverings is one of the most important differences between traditional agriculture and intensive agriculture. The farmland covered by plastic film has increased considerably because such mulching has multiple functions in agricultural production: improving hydrothermal conditions, promoting crop growth, increasing crop yields, and mitigating the effects of drought and Remote Sens. In China, the area of plastic-mulched farmland accounts for over 90% of total plastic-covered farmland. China has the largest area of plastic-mulched farmland in the world and that area has been growing rapidly; the 0.12 million ha covered by plastic film in 1981 rocketed to 19.79 million ha in 2011 [3] and increased to 25 million ha in 2013 [4]. The extensive use of plastic film in agriculture has been accompanied by a series of negative impacts on the climate, the eco-environment and the soil micro-environment
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