Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region

Sangui Yi; Jihua Zhou; Liming Lai; Hui Du; Qinglin Sun; Liu Yang; Xin Liu; Benben Liu; Yuanrun Zheng

doi:10.7717/peerj.9839

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region

PeerJ. 2020 Sep 2:8:e9839. doi: 10.7717/peerj.9839. eCollection 2020.

Authors

Sangui Yi^{1

2}, Jihua Zhou¹, Liming Lai¹, Hui Du¹, Qinglin Sun^{1

2}, Liu Yang^{1

2}, Xin Liu^{1

2}, Benben Liu^{1

2}, Yuanrun Zheng¹

Affiliations

¹ Key Laboratory of Plant Resources, West China Subalpine Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, China.
² University of Chinese Academy of Sciences, Beijing, China.

Abstract

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.

Keywords: Important predictor variable; Jing-Jin-Ji region; Vegetation classification unit; Vegetation distribution model.

Grants and funding

This work was funded by the National Key R & D Program of China (2018YFC0506903). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.