基于地理加权回归模型的能源“金三角”地区植被时空演变及主导因素分析

doi:10.11934/j.issn.1673-4831.2018.08.005

摘要/Abstract

摘要：

宁陕蒙接壤地区的能源"金三角"为我国提供了丰富的能源，其地表植被状况与我国的生态恢复与重建密切相关。应用RS和GIS技术，以归一化植被指数产品（MODIS NDVI）为数据源，借助逐像元趋势分析法研究了2005-2015年间的植被动态变化；基于规则网格构建地理加权回归模型（GWR），探索了高程、坡度、土壤黏粒含量、多年平均气温、多年平均降水、距煤矿区距离及距道路距离7个因子对植被变化的影响及其空间非平稳性。结果表明：（1）时间上，2005-2015年研究区平均NDVI整体呈现波动上升趋势，增长率为0.083·（10 a）^-1（P<0.05）；空间上，NDVI呈现由东南向西北递减的分布格局；趋势上，NDVI变化呈增加趋势的区域（27.11%）远大于减少区域（0.64%），显著增加区域主要分布在榆林市东部。（2）与2005-2012年相比，2013-2015年归一化植被指数（NDVI）在全区显著减少，且具有更强的空间聚集性（Moran's I值为0.851），但变化程度具有空间异质性。（3）2个时段全区植被变化受气候等自然因素的影响较大，影响植被变化的主要因素在时间与空间上具有差异性，人类活动对植被变化具有双重作用。

关键词: 植被覆盖, NDVI指数, 地理加权回归, 空间非平稳性

Abstract:

The "Energy Golden Triangle" area provides abundant energy for China, and its surface vegetation condition is closely related to the ecological restoration and reconstruction. The dynamic change of vegetation of the border regions of the three provincial areas (Ningxia, Shaanxi and Inner Mongolia) from 2005 to 2015 was studied with the aid of a pixel-by-pixel trend analysis method using RS and GIS technology and taking the normalized vegetation index product (MODIS NDVI) as the data source. Geo-weighted regression model is constructed based on a regular grid to explore the effect of 7 factors (elevation, slope, soil clay content, multi-year mean temperature, year average precipitation, distance to coal mine area and distance to road) on vegetation change and its spatial nonstationarity. The results show that the annual average of NDVI of the research area displayed a fluctuating upward trend with a linear tendency being 0.083·(10 a)^-1 (P<0.05) during 2005-2015. As for spatial distribution, NDVI showed a decreasing trend from southeast to the northwest. The area with improved NDVI is larger than the degraded area, and accounting for 27.11% and 0.64% of the total study area, respectively. The significant rising area is mainly distributed in the eastern part of Yulin City. NDVI decreased significantly in the whole area, and had stronger spatial clustering (Moran's I is 0.851) from 2013 to 2015, but the degree of variation was spatially heterogeneous, compared with that of 2005 to 2012 years. The change of vegetation in the two periods (2005-2012 and 2012-2015) of the whole region is greatly affected by the natural factors such as climate and so on. The main factors affecting vegetation change are different in time and space. Human activities have double effects on vegetation change.

Key words: vegetation cover, NDVI index, geographically weighted regression model, spatial non-stationary

中图分类号:

X173

李晶晶, 闫庆武, 胡苗苗. 基于地理加权回归模型的能源“金三角”地区植被时空演变及主导因素分析[J]. 生态与农村环境学报, 2018, 34(8): 700-708.

LI Jing-jing, YAN Qing-wu, HU Miao-miao. Spatial-Temporal Evolution of Vegetation and Dominant Factors in “Energy Golden Triangle” Region Based on Geographically Weighted Regression Model[J]. Journal of Ecology and Rural Environment, 2018, 34(8): 700-708.

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