No abstract

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.

No abstract

Abstract:Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector Machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data.