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Banana orchard inventory using IRS LISS sensors

机译:香蕉果园库存使用IRS Liss传感器

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摘要

Banana is one of the major crops of India with increasing export potential. It is important to estimate the production and acreage of the crop. Thus, the present study was carried out to evolve a suitable methodology for estimating banana acreage. Area estimation methodology was devised around the fact that unlike other crops, the time of plantation of banana is different for different farmers as per their local practices or conditions. Thus in order to capture the peak signatures, biowindow of 6 months was considered, its NDVI pattern studied and the optimum two months were considered when banana could be distinguished from other competing crops. The final area of banana for the particular growing cycle was computed by integrating the areas of these two months using LISS Ⅲ data with spatial resolution of 23m. Estimated banana acreage in the three districts were 11857Ha, 15202ha and 11373Ha for Bharuch, Anand and Vadodara respectively with corresponding accuracy of 91.8%, 90% and 88.16%. Study further compared the use of LISS IV data of 5.8m spatial resolution for estimation of banana using object based as well as per-pixel classification and the results were compared with statistical reports for both the approaches. In the current paper we depict the various methodologies to accurately estimate the banana acreage.
机译:香蕉是印度的主要作物之一,增加出口潜力。重要的是估计作物的生产和种植面积。因此,进行本研究以演变为估计香蕉种植面积的合适方法。面积估计方法围绕着与其他作物不同,由于其本地实践或条件,香蕉种植​​的时间与不同的农民不同。因此,为了捕获峰值签名,考虑了6个月的生物indow,研究了其NDVI模式,当香蕉可以与其他竞争作物区分开时,考虑了最佳两个月。通过将这两个月的区域与空间分辨率的空间分辨率集成了这两个月的区域来计算特定生长周期的香蕉的最终区域。三个地区的估计香蕉种植面积为11857Ha,15202Ha和113733A,分别具有91.8%,90%和88.16%的相应准确性。进一步研究了利用基于物体的对象和每像素分类来估计5.8M空间分辨率的Liss IV数据的使用,并将结果与​​两种方法进行了比较。在目前的论文中,我们描绘了准确地估计香蕉种植面积的各种方法。

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