Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties

Barren lands are being transformed into agricultural fields with the growing demand for agriculture-based products. Hence, monitoring these regions for better planning and management is crucial. Surveying with high-resolution RS (remote sensing) satellites like Worldview-2 provides a faster and cheaper solution than conventional surveys. In the study, the arid region comprising cropland and barrenlands are efficiently and autonomously delineated using its spectral and textural properties using state-of-the-art random forest (RF) ensemble classifiers. The textural information window size is optimized and at a GLCM (gray-level co-occurrence matrix) window size of 13, a stable trend in classification accuracy was observed. A further rise in window sizes did not improve the classification accuracy; beyond GLCM 19, a decline in accuracy was observed. Comparing GLCM-13 RF with the no-GLCM RF classifier, the GLCM-based classifiers performed better; thus, the textural information assisted in removing isolated crop-classified outputs that are falsely predicted pixel groups. Still, it also obscured information about barren lands present within croplands. Delineation accuracy was 93.8 % for the no-GLCM RF classifier, whereas, for the GLCM-13 RF classifier, an accuracy of 97.3 % was observed. Thus, overall, a 3.5 % improvement in accuracy was observed while using the GLCM RF classifier with window size 13. The textural information with proper calibration over high-spatial resolution datasets improves crop delineation in the present study. Henceforth, a more accurate cropland identification will provide a better estimate of the actual cropland area in such an arid region, which will assist in formulating a better resource management policy.