IB-CAS  > 中科院北方资源植物重点实验室
Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region
Yi, Sangui1; Zhou, Jihua; Lai, Liming; Du, Hui; Sun, Qinglin1; Yang, Liu1; Liu, Xin1; Liu, Benben1; Zheng, Yuanrun
2020
Source PublicationPEERJ
ISSN2167-8359
Volume8
AbstractBackground. Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance. Methods. We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China's most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment. Results. Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy. Conclusions. We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.
KeywordVegetation distribution model Vegetation classification unit Important predictor variable Jing-Jin-Ji region
Subject AreaMultidisciplinary Sciences
DOI10.7717/peerj.9839
Indexed BySCI
Language英语
WOS KeywordSUPPORT VECTOR MACHINES ; PLANT FUNCTIONAL TYPES ; RANDOM FORESTS ; DISTRIBUTION MODELS ; CLIMATE-CHANGE ; LANDSAT TM ; CLASSIFICATION ; IMAGERY
WOS Research AreaScience & Technology - Other Topics
WOS IDWOS:000565075100006
PublisherPEERJ INC
SubtypeArticle
Publication PlaceLONDON
Funding OrganizationNational Key R&D Program of China [2018YFC0506903]
Corresponding Author Emailzhengyr@ibcas.ac.cn
OAgold, Green Published
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ibcas.ac.cn/handle/2S10CLM1/21605
Collection中科院北方资源植物重点实验室
Affiliation1.Chinese Acad Sci, Inst Bot, Key Lab Plant Resources, West China Subalpine Bot Garden, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Yi, Sangui,Zhou, Jihua,Lai, Liming,et al. Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region[J]. PEERJ,2020,8.
APA Yi, Sangui.,Zhou, Jihua.,Lai, Liming.,Du, Hui.,Sun, Qinglin.,...&Zheng, Yuanrun.(2020).Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region.PEERJ,8.
MLA Yi, Sangui,et al."Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region".PEERJ 8(2020).
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