干旱区生态环境知识资源中心(Arid): Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties

Arid

DOI	10.1007/s10661-023-11115-x
	Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties
	Adhikari, Abhishek; Garg, Rahul Dev; Pundir, Sunil Kumar; Singhal, Anupam
通讯作者	Adhikari, A
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2023
卷号	195 期号:5
英文摘要	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.
英文关键词	Worldview-2 Crop mapping Random forest Image texture analysis
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000983758000004
WOS关键词	RANDOM FOREST CLASSIFIER ; FARMING SYSTEMS ; REMOTE ; ACCURACY ; DESERT
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396172
推荐引用方式 GB/T 7714	Adhikari, Abhishek,Garg, Rahul Dev,Pundir, Sunil Kumar,et al. Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties[J],2023,195(5).
APA	Adhikari, Abhishek,Garg, Rahul Dev,Pundir, Sunil Kumar,&Singhal, Anupam.(2023).Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties.ENVIRONMENTAL MONITORING AND ASSESSMENT,195(5).
MLA	Adhikari, Abhishek,et al."Delineation of agricultural fields in arid regions from Worldview-2 datasets based on image textural properties".ENVIRONMENTAL MONITORING AND ASSESSMENT 195.5(2023).