Retrieval of reed biomass based on multi-time remote sensing data: a case study on ShuangTai Estuary Nature Reserve, Panjin

Ailian Chen, Yu Wan, Jie Zhang, Yanhua Wu
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引用次数: 1

Abstract

Wetland plays an important role in improving the ecosystem around it. It's able to store carbon and slow down the global warming. Recently, however, there are a lot of evidences that wetlands are diminishing rapidly. As the primary producer of the many wetlands, reed has great ecological value, as well as economical and decorative value. It is significant to study reed. In this article, the feasibility of retrieving reed biomass based on multi-time remote sensing data has been proved. In ShuangTai Estuary Nature Reserve of Panjin, as reed grows mainly between May to September, some pieces of Landsat TM data of these months were collected, and Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI) are extracted from this multi-spectral data, and then Renormalized Vegetation Index (RDVI) is calculated through DVI and NDVI. With those Vegetation index and field data of reed biomass, the relationship between them is explored, which shows that reed biomass, including its stem biomass and leaf biomass, is poorly related to RDVI (R<0.5), but significantly related to NDVI.(R = 0.923). Moreover, NDVI has a similar growing trend with the reed leaf biomass, thus linear and quadratic models to calculate reed biomass from NDVI are derived and the better one is picked to produce thematic maps of reed biomass. Uncertainties while using the models are analyzed in the end.
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基于多时段遥感数据的芦苇生物量反演——以盘锦双台河口自然保护区为例
湿地在改善其周边生态系统方面发挥着重要作用。它能够储存碳,减缓全球变暖。然而,最近有很多证据表明湿地正在迅速减少。芦苇是许多湿地的主要生产者,具有很高的生态价值、经济价值和装饰价值。研究芦苇很有意义。本文验证了基于多时段遥感数据检索芦苇生物量的可行性。盘锦双台河口自然保护区芦苇生长主要集中在5 ~ 9月,采集了该月份的部分Landsat TM数据,提取多光谱数据的归一化植被差指数(NDVI)、差异植被指数(DVI),再通过DVI和NDVI计算再归一化植被指数(RDVI)。利用这些植被指数和芦苇生物量的野外数据,对两者之间的关系进行了探讨,结果表明,芦苇生物量包括茎生物量和叶生物量与RDVI的相关性较差(R<0.5),但与NDVI的相关性显著。(r = 0.923)。此外,NDVI与芦苇叶生物量具有相似的增长趋势,因此推导了利用NDVI计算芦苇生物量的线性和二次模型,并选择较优的模型制作芦苇生物量专题图。最后分析了使用模型时的不确定性。
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