利用大面积清查和遥感数据评估半干旱人类主导景观中的树木密度、树木覆盖率、物种多样性和生物量

C. Sudhakar Reddy, K. V. Satish
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引用次数: 0

摘要

在人类占主导地位的镶嵌景观中,必须开发估算物种多样性和生物量的方法,以最大限度地减少不确定性。高分辨率卫星图像提供的详细程度可精确绘制和监测森林外树木中的单棵树木和树木斑块。这项工作是同类研究中的首次,试图通过全面普查和极高分辨率遥感数据估算树木密度、树木覆盖率、物种多样性和生物量。这项研究比较了安得拉邦斯里-萨蒂亚-赛地区 900 公顷土地(当地景观)和 15,142 公顷土地(区域景观)上全部树木的普查情况。这项研究绘制了 47 054 个树木个体的分布图,覆盖了区域景观中 1.64% 的土地面积。根据重要性价值指数,最主要的树种是罗望子、芒果、椰子、糙叶槐和凤梨。估计的树木密度显示,在地区和地方景观中,每公顷分别约有 3 棵树。在 42 个登记的树种中,有 22 个是野生树种。分析表明,常绿树比落叶树多,占树木密度的 88%。当地景观的阿尔法多样性高达 H′ = 1.93。研究结果表明,一个地点的地面生物量最大为 40.61 吨/公顷,而其余地点的地面生物量相对较低。由于不涉及抽样,根据普查数据得出的估算值不受抽样误差的影响,因此结果精度较高。该研究采用的空间方法将实地数据收集与遥感技术的优势相结合,对农村地区的树木资源进行了详细评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Tree Density, Tree Cover, Species Diversity and Biomass in Semi-arid Human Dominated Landscape Using Large Area Inventory and Remote Sensing Data

It is essential to develop methods for estimating species diversity and biomass in human-dominated mosaic landscapes to minimise uncertainty. The level of detail provided by very high-resolution satellite imagery enables the precise mapping and monitoring of individual trees and tree patches in trees outside forests. This work is the first of its kind and attempts to estimate tree density, tree cover, species diversity, and biomass from a comprehensive survey and very high-resolution remote-sensing data. This research compared the census of the entire tree population over a 900-ha site (local landscape) and a 15,142-ha site (regional landscape) in the Sri Sathya Sai district of Andhra Pradesh. This study mapped 47,054 tree individuals that cover a land area of 1.64% in a regional landscape. The most dominant species based on the importance value index are Tamarindus indica, Mangifera indica, Cocos nucifera, Prosopis juliflora, and Pongamia pinnata. Estimated tree density indicates about 3 trees per hectare in regional and local landscapes, respectively. Among the 42 inventoried tree species, 22 were wild. Analysis shows evergreen trees are dominating over deciduous trees with 88% of tree density. Alpha diversity of the local landscape reaching up to H′ = 1.93. The findings show that the maximum above-ground biomass is 40.61 tonnes/ha at one site, while it is relatively low at the remaining sites. Since no sampling is involved, the estimates derived from census data are not subject to sampling error, leading to high precision in the results. The spatial approach used in the study combines field-based data collection with the advantages of remote sensing technology to provide a detailed assessment of tree resources in rural landscapes.

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