干旱区生态环境知识资源中心(Arid): Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform

Arid

DOI	10.1016/j.agrformet.2019.107665
	Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform
	Wang, Haozhou 1,2,3; Han, Dong 1; Mu, Yue 1,4; Jiang, Lina 1; Yao, Xueling 1; Bai, Yongfei 5; Lu, Qi1 ; Wang, Feng 1
通讯作者	Wang, Feng
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
ISSN	0168-1923
EISSN	1873-2240
出版年	2019
卷号	278
英文摘要	The change of fraction vegetation cover (FVC) is the key ecological index for vegetation dynamics of dryland ecosystem. However, it is difficult to directly map woody vegetation and herbaceous vegetation in the dryland from the satellite images due to the mixture of their distribution at small scale. Emerging UAV remote sensing provides a good opportunity to capture and quantify the distribution of the sparse vegetation in the drylands ecosystem. In this study, we proposed a new method to classify woody vegetation and herbaceous vegetation and calculate their FVC based on the high-resolution orthomosaic generated from UAV images by the machine learning algorithm of classification and regression tree (CART). This proposed method was validated and evaluated by visual interpretation, the detailed ground measurement dataset of 4832 trees and 18,798 shrubs and three popular machine learning algorithms of Support Vector Machine(SVM), Random Forest(RF), Gradient Boosting Decision Tree(GBDT). The overall assessments showed good overall accuracy (0.78), average accuracy (0.76), and the Kappa coefficient (0.64). The FVC of woody vegetation calculated from orthomosaic agreed well with that estimated from ground measurements. Both group of FVC have a stable linear relationship over different spatial scales. The proposed method showed higher efficiency of 166%, 111% and 290% than SVM, RF, GBDT respectively. A new optimized model was developed to reduce the workload of vegetation investigation and to design more efficient sampling strategies. The proposed method was incorporated into an interactive web-based software UAV-High Resolution imagery Analysis Platform (UAV-HiRAP, http://www.uav-hirap.org). Our study demonstrates that UAV-HiRAP combined with UAV platform can be a powerful tool to classify woody vegetation and herbaceous vegetation and calculate their FVC for sparse vegetation in the drylands. The new optimization model will inspire researchers to design more effective sampling strategies for future field investigation.
英文关键词	Dryland vegetation Machine learning Decision tree model Digital orthophoto map Otindag sandy land Semi-arid ecosystem Classification and regression tree (CART)
类型	Article
语种	英语
国家	Peoples R China ; Canada
收录类别	SCI-E
WOS记录号	WOS:000500196400021
WOS关键词	SEMIARID ECOSYSTEMS ; TROPICAL FORESTS ; UAV ; QUANTIFICATION ; BIODIVERSITY ; VARIABILITY ; IMAGES
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
EI主题词	2019-11-15
来源机构	中国科学院植物研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310883
作者单位	1.Chinese Acad Forestry, Inst Desertificat Studies, Beijing 100091, Peoples R China; 2.Naing Forestry Univ, Coll Biol & Environm, Nanjing 210037, Jiangsu, Peoples R China; 3.Univ New Brunswick, Fac Forestry & Environm Management, Fredericton, NB E3B 5A3, Canada; 4.Nanjing Agr Univ, Plant Phen Res Ctr, 1 Weigang, Nanjing 210095, Jiangsu, Peoples R China; 5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Wang, Haozhou,Han, Dong,Mu, Yue,et al. Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform[J]. 中国科学院植物研究所,2019,278.
APA	Wang, Haozhou.,Han, Dong.,Mu, Yue.,Jiang, Lina.,Yao, Xueling.,...&Wang, Feng.(2019).Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform.AGRICULTURAL AND FOREST METEOROLOGY,278.
MLA	Wang, Haozhou,et al."Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform".AGRICULTURAL AND FOREST METEOROLOGY 278(2019).