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Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform

文献类型：期刊论文

作者Wang, Haozhou2,3; Han, Dong; Mu, Yue4; Jiang, Lina; Yao, Xueling; Bai, Yongfei5; Lu, Qi; Wang, Feng
刊名AGRICULTURAL AND FOREST METEOROLOGY
出版日期2019
卷号278
关键词Dryland vegetation Machine learning Decision tree model Digital orthophoto map Otindag sandy land Semi-arid ecosystem Classification and regression tree (CART)
ISSN号0168-1923
DOI10.1016/j.agrformet.2019.107665
文献子类Article
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.
学科主题Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
出版地AMSTERDAM
电子版国际标准刊号1873-2240
WOS关键词SEMIARID ECOSYSTEMS ; TROPICAL FORESTS ; UAV ; QUANTIFICATION ; BIODIVERSITY ; VARIABILITY ; IMAGES
WOS研究方向Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000500196400021
出版者ELSEVIER
资助机构National Key Research and Development Program of China [2017YFC0503804, 2016YFC0500801] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31570710] ; Chinese Academy of Forestry Science Funds for Distinguished Young Scholar [CAFYBB2017QC007]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/19499]  
专题植被与环境变化国家重点实验室
作者单位1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
2.Chinese Acad Forestry, Inst Desertificat Studies, Beijing 100091, Peoples R China
3.Naing Forestry Univ, Coll Biol & Environm, Nanjing 210037, Jiangsu, Peoples R China
4.Univ New Brunswick, Fac Forestry & Environm Management, Fredericton, NB E3B 5A3, Canada
5.Nanjing Agr Univ, Plant Phen Res Ctr, 1 Weigang, Nanjing 210095, Jiangsu, 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]. AGRICULTURAL AND FOREST METEOROLOGY,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).

入库方式： OAI收割

来源：植物研究所

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