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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
Wang, Haozhou1,2; Han, Dong; Mu, Yue3; Jiang, Lina; Yao, Xueling; Bai, Yongfei4; Lu, Qi; Wang, Feng
2019
发表期刊AGRICULTURAL AND FOREST METEOROLOGY
ISSN0168-1923
卷号278
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.
关键词Dryland vegetation Machine learning Decision tree model Digital orthophoto map Otindag sandy land Semi-arid ecosystem Classification and regression tree (CART)
学科领域Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
DOI10.1016/j.agrformet.2019.107665
收录类别SCI
语种英语
WOS关键词SEMIARID ECOSYSTEMS ; TROPICAL FORESTS ; UAV ; QUANTIFICATION ; BIODIVERSITY ; VARIABILITY ; IMAGES
WOS研究方向Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS记录号WOS:000500196400021
出版者ELSEVIER
文献子类Article
出版地AMSTERDAM
EISSN1873-2240
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]
作者邮箱wangfeng@caf.ac.cn
引用统计
被引频次:17[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符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 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]. 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).
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