基于遥感和GIS的印度贾巴尔普尔小麦种植面积估算

U. Rawat, Ankit Yadav, P. Pawar, Aniket Rajput, Devendra Vasht, S. Nema
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引用次数: 1

摘要

利用卫星图像对作物进行制图和分类是一项具有挑战性的任务,它可以最大限度地减少实地考察的复杂性。最近发射的Sentinel-2卫星具有13个光谱波段、较短的重访时间和3种不同分辨率(10 m、20 m和60 m)的测定,此外,图像的免费可用性使其成为植被制图的良好选择。本研究旨在利用印度中央邦贾巴尔普尔(Jabalpur)单一日期Sentinel-2图像对作物进行分类。采用无监督分类方法进行分类。本研究将Sentinel-2的近红外、红、绿、蓝4个光谱波段进行叠加分类。结果表明:小麦种植面积为83.07%;克/脉冲占14.64%;其他作物,2.28%。利用Sentinel-2影像进行分类的总体准确率为85.71%,总体Kappa Statistics为0.819%。因此,本研究发现,Sentinel-2在贾巴尔普尔农业区的遥感制图中具有很大的潜力。Rawat et al.;生物工程学报,39(2):88-94,2021;文章no.AJAEES。65961 89
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wheat Crop Acreage Estimation Based on Remote Sensing and GIS in Jabalpur (Madhya Pradesh, India)
Mapping and classification crop by using satellite images is a challenging task that can minimize the complexities of field visits. The recently launched Sentinel-2 satellite has thirteen spectral bands, short revisit time and determination at three different resolutions (10 m, 20 m and 60 m), besides that, the free availability of the images makes it a good choice for vegetation mapping. This study aims to classify crop, using single date Sentinel-2 imagery within the Jabalpur, state of Madhya Pradesh, India. The classification was performed by using Unsupervised Classification. In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 were stacked for the classification. The results show that the area of wheat crop corresponds to 83.07%; Gram/ Pulses, 14.64%; and other crop, 2.28%. The overall accuracy and overall Kappa Statistics of the classification using Sentinel-2 imagery are 85.71% and 0.819%, respectively. Therefore, this study has found that Sentinel-2 presented great potential in the mapping of the agriculture areas of Jabalpur by remote sensing. Original Research Article Rawat et al.; AJAEES, 39(2): 88-94, 2021; Article no.AJAEES.65961 89
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