干旱区生态环境知识资源中心(Arid): 中蒙俄经济走廊荒漠化时空格局变化及其驱动因子

Arid

	中蒙俄经济走廊荒漠化时空格局变化及其驱动因子
其他题名	Spatio-temporal pattern change of desertification and its driving factors analysis in China-Mongolia-Russia economic corridor
	范泽孟; 李赛博
来源期刊	生态学报
ISSN	1000-0933
出版年	2020
卷号	40 期号:13 页码:4252-4263
中文摘要	针对如何定量揭示中蒙俄经济走廊荒漠化的时空变化规律及其驱动因子的影响作用,在构建荒漠化程度判别标准和指标体系的基础上,基于Google Earth Engine云计算平台,在对分类回归树(CART)、支持向量机(SVM)、随机森林(RF)和AlbedoNDVI 4个模型精度进行对比分析的基础上,选取精度最高的CART模型实现中蒙俄经济走廊区域20002015年的荒漠化的时空变化分析。另外,在拓展线性趋势法的基础上,构建荒漠化驱动因子定量评估模型,实现气候变化和人类活动对荒漠化驱动效应的定量测度。结果表明:1)CART模型在中蒙俄经济走廊区域荒漠化信息提取精度最高,总体精度和Kappa系数分别85%和0.754;2)极重度荒漠化区域主要分布在中国内蒙古自治区西部,蒙古国的南戈壁盟、东戈壁盟和中戈壁盟;3)20002015年间,中蒙俄经济走廊荒漠化总体呈扩张趋势,但极重度、重度和中度荒漠化面积呈缩减趋势。其中,中国的荒漠化面积呈明显缩减趋势,荒漠化程度净改善面积为15.18万km~2,而蒙古国和俄罗斯的荒漠化现象在加重。另外,中蒙俄经济走廊荒漠化恢复区域的气候变化驱动贡献率为68.8%;而荒漠化退化区域的人类活动驱动贡献率为69.68%,表明中蒙俄经济走廊荒漠化程度加重区域的驱动作用主要来自于人类活动。
英文摘要	To explicitly understand the spatio-temporal pattern change of desertification and its driving mechanism in ChinaMongolia-Russia economic corridor proposed by the Belt and Road Initiative, the classification and regression tree (CART), support vector machine (SVM), random forest (RF) and Albedo-NDVI model were compared to better monitor desertification on Google Earth Engine platform. Furthermore, the climate and human factors were introduced into the linear trend method to quantitatively measure the driving effect of each factor on the desertification process. The results show that 1) the classification regression tree model combined with multi-source data can better monitor desertification in the study area. The overall accuracy and kappa coefficient are 85% and 0.754, respectively. 2) From 2000 to 2015, although the area of land degradation and strong degradation was 17,800 km~2more than the area recovered and significantly restored, the area of extremely severe, severe and moderate desertification was decreased by 73,100 km~2, 43,200 km~2 and 13,900 km~2, respectively. In addition, the desertification area in China has recovered significantly, with a net restoration area of 151,800 km~2. The desertification in Mongolia and Russia is worsening. 3) During the study period, climate change not only played a major role in desertification restoration in the study area, but also played a major role in desertification restoration in various countries. The regions with the main driving force of climate change, accounted for 68.8% of the total desert restoration areas in the study area. In areas where desertification is increasing, human activity is driving more than climate change, accounted for 69.68% of the total desert restoration areas in the China-Mongolia-Russia economic corridor.
中文关键词	中蒙俄经济走廊 ; 荒漠化 ; 时空格局 ; 驱动因子
英文关键词	Google Earth Engine China-Mongolia-Russia economic corridor desertification spatio-temporal pattern driving mechanism Google Earth Engine
类型	Article
语种	中文
收录类别	CSCD
WOS研究方向	Environmental Sciences & Ecology
CSCD记录号	CSCD:6770674
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/336723
作者单位	范泽孟, 中国科学院地理学与资源研究所;;中国科学院大学资源与环境学院;;江苏省地理信息资源开发与利用协同创新中心, 资源与环境信息系统国家重点实验室;;;;江苏省地理信息资源开发与利用协同创新中心, ;;;;南京, 北京;;北京;; 100101;;100049;;210023, 中国.; 李赛博, 中国科学院地理学与资源研究所;;中国科学院大学资源与环境学院, 资源与环境信息系统国家重点实验室;;, ;;, 北京;;北京 100101;;100049, 中国.
推荐引用方式 GB/T 7714	范泽孟,李赛博. 中蒙俄经济走廊荒漠化时空格局变化及其驱动因子[J],2020,40(13):4252-4263.
APA	范泽孟,&李赛博.(2020).中蒙俄经济走廊荒漠化时空格局变化及其驱动因子.生态学报,40(13),4252-4263.
MLA	范泽孟,et al."中蒙俄经济走廊荒漠化时空格局变化及其驱动因子".生态学报 40.13(2020):4252-4263.