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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
发表期刊PEERJ
ISSN2167-8359
卷号8
摘要Background. 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.
关键词Vegetation distribution model Vegetation classification unit Important predictor variable Jing-Jin-Ji region
学科领域Multidisciplinary Sciences
DOI10.7717/peerj.9839
收录类别SCI
语种英语
WOS关键词SUPPORT VECTOR MACHINES ; PLANT FUNCTIONAL TYPES ; RANDOM FORESTS ; DISTRIBUTION MODELS ; CLIMATE-CHANGE ; LANDSAT TM ; CLASSIFICATION ; IMAGERY
WOS研究方向Science & Technology - Other Topics
WOS记录号WOS:000565075100006
出版者PEERJ INC
文献子类Article
出版地LONDON
资助机构National Key R&D Program of China [2018YFC0506903]
作者邮箱zhengyr@ibcas.ac.cn
作品OA属性gold, Green Published
引用统计
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/21605
专题中科院北方资源植物重点实验室
作者单位1.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
推荐引用方式
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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