气候变化领域集成服务门户(CCPortal): Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering

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DOI	10.1016/j.rse.2021.112397
	Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering
	Xu X.; Zhou Z.; Tang Y.; Qu Y.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	Accurate tree density and location are important information for optimizing the management and production of forest. Combination of remote sensing techniques and local maximum (LM) filtering algorithm provides a feasible approach to individual tree crown detection, but still faces high error under complicate canopy structure. In this study, a revised LM (RLM) algorithm is presented and evaluated for identifying individual trees from four high spatial-resolution images. Instead of a moving window technique, the RLM algorithm finds crown center seeds by searching local maximal in the transects along row and column directions of the image. Each of final crown centers is then searched using a variable window centered at the crown center seed. Strategies for splitting and merging crowns are implemented in the RLM algorithm to reduce false detection. Result showed that accuracy of the RLM algorithm was more sensitive to its minimum crown length parameter (CLmin). The RLM algorithm driven by the CLmin estimates achieved high overall accuracies between 85% and 91% and low commission (9–14%) and omission errors (8–15%) for the four images. Splitting and merging strategies implemented in the RLM algorithm effectively reduced commission and omission errors. These results indicate that the RLM algorithm is a feasible method with well-defined parameters for automatically detecting individual trees with satisfactory detection accuracy. © 2021 Elsevier Inc.
英文关键词	Crown center detection; Local maxima; Splitting and merging; Transect data; Variable window size
语种	英语
scopus关键词	Errors; Forestry; Image resolution; Parameter estimation; Remote sensing; Crown center detection; Individual tree; Individual tree crown; LM algorithm; Local maximum; Local maximum filtering; Omission errors; Splitting and merging; Transect data; Variable window size; Merging; accuracy assessment; algorithm; canopy architecture; detection method; estimation method; forest management; image analysis; individual variation; parameter estimation; remote sensing; satellite imagery; spatial resolution
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/178873
作者单位	State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin'an, Zhejiang 311300, China; Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization, Zhejiang A & F University, Lin'an, Zhejiang 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Lin'an, Zhejiang 311300, China
推荐引用方式 GB/T 7714	Xu X.,Zhou Z.,Tang Y.,et al. Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering[J],2021,258.
APA	Xu X.,Zhou Z.,Tang Y.,&Qu Y..(2021).Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering.Remote Sensing of Environment,258.
MLA	Xu X.,et al."Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering".Remote Sensing of Environment 258(2021).