Identification and Quantification of Tree Species in Open Mixed Forests using High Resolution QuickBird Satellite Imagery

S. Arockiaraj, Amit Kumar, Najmul Hoda, A. Jeyaseelan
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引用次数: 5

Abstract

Present study deals with identification and quantification of tree species within an open mixed forest in parts of Ranchi district Jharkhand, India using high resolution QuickBird satellite data using image processing and GIS techniques. A high resolution QuickBird satellite image was used for shadow enhancement and tree crown area extraction. The First Principal Component of QuickBird satellite images was employed to enhance the shadowed area and subsequently shadow and non-shadow area were classified using ISODATA. The satellite image was used for crown area extraction with standard deviation of NDVI value and the crowns were classified into five classes using Maximum Likelihood supervised algorithm. Result shows that barring few limitation, the high resolution QuickBird image provides rapid and accurate results in terms of identification and quantification of tree species in conjugation with field verification and attained 88% of classification accuracy. It reduces the time required for obtaining inventory data in open mixed forest. Results also showed that total 5,522 trees of various species were present in the study area and dominated by Shorea robusta (80.48%) followed by Ziziphus mauritiana (16.26%), unknown tree (1.81%), Ficus religiosa (0.98%) and Mangifera indica (0.47%). The demography patterns of the locals mainly tribal (89.9%) exhibited their direct as well as indirect dependency on mixed forests resources for their subsistence and livelihood. The study necessitate towards the effective implication of policies to raise the standard of living of tribal people in the region.
基于高分辨率QuickBird卫星影像的开放混交林树种识别与定量研究
本研究利用高分辨率QuickBird卫星数据,利用图像处理和GIS技术,对印度贾坎德邦兰契地区部分开放混交林的树种进行了鉴定和量化。采用高分辨率QuickBird卫星图像进行阴影增强和树冠面积提取。利用QuickBird卫星图像的第一主成分对阴影区域进行增强,然后利用ISODATA对阴影和非阴影区域进行分类。利用卫星影像提取冠面积,提取NDVI值的标准差,并采用最大似然监督算法将冠分为5类。结果表明,高分辨率QuickBird图像结合野外验证,能够快速准确地对树种进行鉴定和定量,分类准确率达到88%。它减少了在开放混交林中获取清查数据所需的时间。结果表明,研究区共有各种乔木5522棵,以黄竹(80.48%)居多,其次为毛里求斯紫竹(16.26%)、未知乔木(1.81%)、榕(0.98%)和芒果(0.47%)。当地人口以部落为主(89.9%),对混交林资源有直接或间接的依赖。这项研究需要对提高该地区部落人民生活水平的政策的有效影响进行研究。
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