基于随机森林模型的内陆干旱区植被指数变化与驱动力分析: 以北天山北坡中段为例
收稿日期: 2020-04-20
录用日期: 2020-06-02
网络出版日期: 2020-07-07
基金资助
国家自然科学基金(41671108);中国科学院国际合作局对外合作重点项目(131965KYSB20160004)
Analysis of vegetation index changes and driving forces in inland arid areas based on random forest model: a case study of the middle part of northern slope of the north Tianshan Mountains
Received date: 2020-04-20
Accepted date: 2020-06-02
Online published: 2020-07-07
Supported by
National Natural Science Foundation of China(41671108);International Partnership Program of Chinese Academy of Sciences(131965KYSB20160004)
全球变化背景下的干旱区植被变化受气候变化和人类活动双重影响。定量评价植被变化特征及其驱动机制, 对监测干旱区区域生态环境变化, 促进区域可持续发展有重要意义。由于复杂多样的人类活动难以量化, 有关这方面的研究多局限于植被对气候变化的响应, 而对人类活动影响考虑不足, 导致关于这方面的认识存在较大的偏差和不确定性。该文首先提出与土地利用相关的人类活动量化表征方法; 然后运用多元线性回归模型和随机森林模型中的较优模型, 分析气候变化和具体的人类活动对北天山北坡中段归一化植被指数(NDVI)的影响。主要结果: (1) 2000-2015年期间北天山北坡中段年NDVI总体呈增加趋势; 基于随机森林构建的NDVI与气候因子和人类活动的模型拟合精度明显优于多元线性回归模型, 其决定系数(R2)至少提高了24%; (2)研究期内与耕地有关的人类活动对北天山北坡中段NDVI分布及时空变化的影响呈增加的特征, 在2000-2015年期间人类活动对NDVI变化的贡献率为0.59, 超过了气候因子。该项研究为气候变化和人类活动对植被的影响研究提供了新思路, 也为干旱区生态环境保护和恢复提供了科学依据。
张文强, 罗格平, 郑宏伟, 王浩, HAMDI Rafiq, 何惠丽, 蔡鹏, 陈春波 . 基于随机森林模型的内陆干旱区植被指数变化与驱动力分析: 以北天山北坡中段为例[J]. 植物生态学报, 2020 , 44(11) : 1113 -1126 . DOI: 10.17521/cjpe.2020.0111
Aims In the context of global change, vegetation changes in arid areas in the context of global change are both affected by climate change and human activities. Quantifying the vegetation dynamics and their driving mechanism are essential for monitoring the ecological environment change in arid areas and for promoting the sustainable development. Because of the complexity of human activities, most researches are limited to the response of normalized differential vegetation index (NDVI) to climate change, while the impacts of human activities have not yet been comprehensively considered.
Methods Firstly, we proposed a quantification method to quantify the main human activities related to land use. Then, the contribution of climate change and human activities to the NDVI in the middle part of the northern slope of the north Tianshan Mountains was analyzed using the multiple linear regression model and random forest model.
Important findings We found that an overall upward trend was evident in NDVI variations from 2000 to 2015. The fitting accuracy of NDVI based on the random forest model was significantly better than the multiple linear regression model with an improved R2 of about 24%. The contribution of human activities related to arable land to NDVI change in the study area showed an increasing trend which was greater than climatic factors from 2000 to 2015. This study provides new insight into the effects of climate change and human activities on vegetation and a scientific basis for the protection and restoration of the ecological environment in the arid areas.
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