气候变化领域集成服务门户(CCPortal): Monitoring grassland invasion by spotted knapweed (Centaurea maculosa) with RPAS-acquired multispectral imagery

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DOI	10.1016/j.rse.2020.112008
	Monitoring grassland invasion by spotted knapweed (Centaurea maculosa) with RPAS-acquired multispectral imagery
	Baron J.; Hill D.J.
发表日期	2020
ISSN	00344257
卷号	249
英文摘要	The ability to accurately detect and quantify the presence of invasive plants is integral in their management, treatment, and removal. Remotely piloted aircraft systems (RPASs) are becoming an important remote sensing tool for mapping invasive plants. Spotted knapweed (Centaurea maculosa) is highly invasive in North America. This study developed and evaluated a novel method for analysis of multispectral data to map the relative cover of spotted knapweed in a heterogeneous grassland community. The method developed in this work, termed metapixel-based image analysis, segments the image into a grid of metapixels for which grey level co-occurrence matrix (GLCM)-based statistics can be computed as descriptive features. Using RPAS-acquired multispectral imagery and plant species inventories performed on 1m2 quadrats, a random forest classifier was trained to predict the qualitative degree of spotted knapweed ground cover within each metapixel. The best mean cross-validation score achieved was 71.3% when describing relative ground cover of spotted knapweed, with an accuracy of 66.0% when applied to an independent validation dataset. Analysis of the performance of metapixel-based image analysis on this study site suggests that feature optimization, including feature subset selection, and the use of GLCM-based texture features is of critical importance for achieving an accurate classification. © 2020 Elsevier Inc.
英文关键词	Centaurea maculosa; Grey-level co-occurrence matrix; Invasive plant species; Random forest; Remotely piloted aircraft systems (RPAS); UAV
语种	英语
scopus关键词	Decision trees; Plants (botany); Remote sensing; Textures; Feature optimizations; Feature subset selection; Grey-level co-occurrence matrixes; Multi-spectral data; Multi-spectral imagery; Random forest classifier; Remote sensing tools; Remotely piloted aircraft; Image analysis; airborne survey; biological invasion; biomonitoring; grassland; image resolution; model validation; pixel; satellite imagery; spectral analysis; weed; North America; Centaurea maculosa
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/179170
作者单位	Department of Geography and Environmental Studies, Thompson Rivers University, Kamloops, BC, Canada
推荐引用方式 GB/T 7714	Baron J.,Hill D.J.. Monitoring grassland invasion by spotted knapweed (Centaurea maculosa) with RPAS-acquired multispectral imagery[J],2020,249.
APA	Baron J.,&Hill D.J..(2020).Monitoring grassland invasion by spotted knapweed (Centaurea maculosa) with RPAS-acquired multispectral imagery.Remote Sensing of Environment,249.
MLA	Baron J.,et al."Monitoring grassland invasion by spotted knapweed (Centaurea maculosa) with RPAS-acquired multispectral imagery".Remote Sensing of Environment 249(2020).