纳米比亚干旱生态系统草种的光谱成像

Paul Bantelmann, D. Wyss, Elizabeth Twitileni Pius, Martin Kappas
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引用次数: 0

摘要

整个非洲大陆的草原都面临着气候变化和人类活动的压力,尤其是在干旱的生态系统中。从遥感的角度来看,这些生态系统并没有得到太多的科学关注,尤其是在纳米比亚。为了填补这一知识空白,利用新一代空间成像光谱仪等设备采用了各种遥感方法。因此,本研究提供了第一种方法,旨在绘制和评估两个私人自然保护区内的草地分布情况,即纳米布兰德自然保护区(NRNR)和纳米布自然保护区(PNNR)以及纳米布沙海边缘的周边农田。多传感器方法利用混合调谐匹配滤波(MTMF),并结合实地收集的光谱信息来分析草原。这项研究包括对哨兵-2 和 PlanetScope 的多光谱数据、环境制图和分析计划(EnMAP)和 PRecursore IperSpettrale della Missione Applicativa(PRISMA)的高光谱数据进行传感器比较,以及根据基于平滑滤波器的强度调制超锐化方法(SFIM-HS)从哨兵-2 和 EnMAP 图像中得出的附加数据融合产品。此外,还建立了一个独特的采集野外光谱库,并对物种间光谱分离性和物种内光谱同质性进行了分析。该光谱库展示了新近发表的各个物种的光谱。由于初始条件干燥,计算出的单个草种的光谱可分离性有限,因此仅有一个平均端元可用于部分解混合。卫星对比的验证结果表明,数据融合产品(归一化植被指数(NDVI)的 R2 = 0.51;土壤调整植被指数(SAVI)的 R2 = 0.66)比多光谱或高光谱数据(所有 R2 均小于 0.35)更适合绘制干旱草原地图。为进一步研究干旱草原的时空动态并协助大索苏斯维莱-纳米布景观的保护工作,以配合联合国恢复十年活动,需要开展更多研究并讨论可能的方法调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral imaging of grass species in arid ecosystems of Namibia
Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.
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