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

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

SUBMITTER: Yi S 

PROVIDER: S-EPMC7474518 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature