Classifying Plant Communities in the North American Coastal Plain With PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual

IF 3.7 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Journal of Geophysical Research: Biogeosciences Pub Date : 2024-09-03 DOI:10.1029/2024JG008217
Jennifer A. Rogers, Kevin M. Robertson, Todd J. Hawbaker, Daniel J. Sousa
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Abstract

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak-enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end-members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR-transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR-based classifications across all four landscapes. The MR-based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near-infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

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利用 PRISMA 星载高光谱成像和光谱混合残差对北美沿海平原的植物群落进行分类
近年来,绘制陆地生物多样性地图的工作主要局限于在分米尺度上使用宽带多光谱遥感,而利用高光谱图像则可以大大加强这一工作。混合残差(MR)光谱预处理转换可帮助解读高光谱图像。MR 将光谱混合分析的优势与去除连续波的吸收峰增强特性相结合。MR 将每个像素描述为估算光谱连续面的通用末端成员的线性组合,由此计算每个波长的残差,并将其作为附加信息源处理。利用高光谱应用任务前兆(PRISMA)图像,我们测试了经 MR 转换的反射率与未经转换的表面反射率(SR)相比,在北美沿海平原的四种地貌中使用地面实况和随机森林分类绘制植物关联和土地覆盖图的能力。我们使用前向逐步选择算法为每种分类选择波段,然后在 SR 和 MR 之间进行比较。与基于 SR 的分类相比,我们的 MR 分类在所有四种地貌中区分土地覆被的平衡精度平均高出 5%。基于 MR 的分类将所有地貌的数据整合到一个统一的模型中,涵盖了所有 21 种土地覆被类型,经过三次迭代,平均平衡精度达到 76%。一般来说,MR 对近红外区域的利用程度高于 SR,同时不强调绿色峰值。根据我们的结果,MR 提高了绘制陆地生物多样性地图的准确性,很可能会扩展到其他当前和计划中的卫星高光谱任务。
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来源期刊
Journal of Geophysical Research: Biogeosciences
Journal of Geophysical Research: Biogeosciences Earth and Planetary Sciences-Paleontology
CiteScore
6.60
自引率
5.40%
发文量
242
期刊介绍: JGR-Biogeosciences focuses on biogeosciences of the Earth system in the past, present, and future and the extension of this research to planetary studies. The emerging field of biogeosciences spans the intellectual interface between biology and the geosciences and attempts to understand the functions of the Earth system across multiple spatial and temporal scales. Studies in biogeosciences may use multiple lines of evidence drawn from diverse fields to gain a holistic understanding of terrestrial, freshwater, and marine ecosystems and extreme environments. Specific topics within the scope of the section include process-based theoretical, experimental, and field studies of biogeochemistry, biogeophysics, atmosphere-, land-, and ocean-ecosystem interactions, biomineralization, life in extreme environments, astrobiology, microbial processes, geomicrobiology, and evolutionary geobiology
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