气候变化领域集成服务门户(CCPortal): Understanding limits of species identification using simulated imaging spectroscopy

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DOI	10.1016/j.rse.2021.112405
	Understanding limits of species identification using simulated imaging spectroscopy
	van Leeuwen M.; Frye H.A.; Wilson A.M.
发表日期	2021
ISSN	00344257
卷号	259
英文摘要	Imaging spectroscopy is a powerful tool for mapping and monitoring the spatial distribution of species compositions. Most spectroscopy studies rely on extensive field campaigns to assess classification accuracies against ground-truth samples. Their findings are correlative and not based on first principles and by design they often do not provide for an end-to-end traceability of error budgets. In this study, we present a simulation approach for modeling canopy-level reflectance using three-dimensional plant models arranged on a synthetic landscape. This type of simulation offers an important avenue for understanding the interplay between instrument parameters and landscape characteristics for different ecosystems. The simulation approach was applied using species-level leaf reflectance data from the biodiversity hotspot in the Greater Cape Floristic Region (GCFR) of South Africa. We simulated 140 random realizations, each of 20x20m in size, encompassing 10 to 70 species to understand how changing the diversity and image resolution (pixel size) affected spectral mixing and the ability to discern species within the plot. Overall, across all resolutions and species mixtures, the mean classification accuracy was 0.690±0.184 (mean F1-score±standard deviation across realizations), however, this varied widely from 0.812±0.057 at a pixel size of 60 cm for landscapes with 30 species to 0.196±0.106 for landscapes with 70 species and a pixel size of 240 cm. As expected, classification accuracy decreased with increasing spatial grain from 0.763±0.064 at 15 cm resolution to only 0.414±0.214 at 240 cm. Classification accuracies across different levels of species richness remained fairly constant except in the coarsest grain size (240 cm). This suggests an increasing challenge in species identification as grain sizes become coarser than the constituent organisms. In summary, this approach can facilitate detailed simulation of reflectance while exploring various ecological scenarios such as changing species composition or the impacts of environmental variability such as drought. © 2021 The Authors
英文关键词	Biodiversity; Canopy; Greater Cape Floristic Region; Hyperspectral; Path tracing; Remote sensing; Simulation; Spectral mixing
语种	英语
scopus关键词	Biodiversity; Budget control; Ecology; Grain size and shape; Image resolution; Mixing; Plants (botany); Reflection; Remote sensing; Canopy; Classification accuracy; Great cape floristic region; HyperSpectral; Path tracing; Pixel size; Remote-sensing; Simulation; Species identification; Spectral mixing; Pixels; accuracy assessment; biodiversity; Cape Floristic Region; drought; environmental gradient; identification method; image resolution; spectroscopy; South Africa
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178869
作者单位	University at Buffalo, Department of Geography, 120 Wilkeson Quadrangle, Buffalo, NY 14261, United States; University of Connecticut, Department of Ecology and Evolutionary Biology, 75 N. Eagleville Road, U-3043, Storrs, CT 06269, United States
推荐引用方式 GB/T 7714	van Leeuwen M.,Frye H.A.,Wilson A.M.. Understanding limits of species identification using simulated imaging spectroscopy[J],2021,259.
APA	van Leeuwen M.,Frye H.A.,&Wilson A.M..(2021).Understanding limits of species identification using simulated imaging spectroscopy.Remote Sensing of Environment,259.
MLA	van Leeuwen M.,et al."Understanding limits of species identification using simulated imaging spectroscopy".Remote Sensing of Environment 259(2021).