Abstract
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.
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Data availability
The data that support the findings of this study are available from DGRE-DRDO. Still, restrictions apply to the availability of these data, which were used under license for current research, and so are not publicly available. Datasets were available for the concerned work only. Data are, however, available from the authors upon reasonable request and with permission of the Director, DGRE-DRDO, and with the approval of the competent authority.
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We thank the Director, DGRE-DRDO, Chandigarh, and the Project Director of the WISDOM project for providing us with the necessary resources and support to conduct this research.
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Adhikari, A., Garg, R.D., Pundir, S.K. et al. Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties. Environ Monit Assess 195, 605 (2023). https://doi.org/10.1007/s10661-023-11115-x
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DOI: https://doi.org/10.1007/s10661-023-11115-x