基于地理探测器的宁夏草地植被覆被时空分异及驱动因子

doi:10.13866/j.azr.2023.08.13

摘要/Abstract

摘要：

以2000—2019年SPOT/VEGETATION的NDVI时间序列数据集为数据源，采用年均值法、Theil-Sen Median倾斜度分析和Mann-Kendall检验方法，研究宁夏草地植被覆被的时空分布及变化特征，利用Hurst指数方法分析草地植被覆被的可持续性特征和未来发展趋势。同时，基于地理探测器量化了平均降水量、海拔、地区生产总值（GDP）等13个因子对其时空分布的影响。结果表明：（1） 2000—2019年间，宁夏草地植被年均NDVI呈波动增加趋势，增速为0.005?a^-1，区域波动差异性较大；空间上呈现南高北低的分布特征，极高植被覆被和高植被覆被区域集中分布在固原市六盘山地区以及沿黄河灌溉地带；（2） 20 a间植被覆被状况显著改善，总体变化趋势向好，但仍有59.341%的草地未来可能存在持续退化或由改善向退化转变的潜在风险；（3）草地植被分布响应最敏感的环境因子是降水，交互解释力整体最强的是气候与土壤；影响草地植被分布和变化特征的因子之间交互作用的主要表现形式为相互增强或非线性增强关系，因子之间不存在独立关系。

关键词: NDVI, 趋势分析, 时空变化, 驱动力, 草地, 宁夏

Abstract:

This study aims to examine the spatiotemporal variation characteristics of grassland vegetation cover at the regional scale and analyze its driving factors. The findings will provide a scientific reference and decision-making basis for the scientific formulation of protection and restoration models, treatment measures, and the sustainable management of the grassland ecosystem in Ningxia, which are crucial for maintaining the balance of the regional grassland ecosystem and promoting ecological protection and high-quality development in the Yellow River Basin. In this study, the NDVI time series dataset of SPOT/VEGETATION (2000-2019) was used as the data source. The annual mean method, Theil-Sen Median trend analysis, and Mann-Kendall test were employed to study the spatiotemporal distribution and variation characteristics of grassland vegetation cover in Ningxia. Furthermore, the Hurst index method was used to analyze the sustainability characteristics and future development trends of grassland vegetation cover. Simultaneously, the influence of 13 factors, such as average precipitation, altitude, and gross domestic product, on the spatiotemporal distribution was quantified based on the geographical detectors approach. The results show that from 2000 to 2019, the average annual NDVI of vegetation in Ningxia grassland showed a fluctuating growth trend, with a growth rate of 0.005 per year. The regional fluctuation was quite different, with extremely high and high vegetation cover areas concentrated in the Liupan Mountains and the irrigation area along the Yellow River. Overall, the NDVI change showed a low to medium fluctuation trend, and the regional fluctuation was quite different. The vegetation cover condition improved significantly over the 20-year period, with a small degradation area and a favorable overall change trend. However, 59.341% of the grasslands are projected to face potential risks of continuous degradation or transformation from improvement to degradation in the future. The most sensitive environmental factor influencing grassland vegetation distribution response was precipitation, and climate and soil had the strongest interaction explanatory power overall. The relationship between the factors affecting the distribution and variation characteristics of grassland vegetation primarily manifested as mutual reinforcement or nonlinear enhancement, with no independent relationship between the factors. This study provides a scientific reference and decision-making basis for the sustainable management of the grassland ecosystem in Ningxia.

Key words: NDVI, trend analysis, spatiotemporal variation, driving factor, grassland, Ningxia

文妙霞, 何学高, 刘欢, 张婧, 罗晨, 贾丰铭, 王义贵, 胡云云. 基于地理探测器的宁夏草地植被覆被时空分异及驱动因子[J]. 干旱区研究, 2023, 40(8): 1322-1332.

WEN Miaoxia, HE Xuegao, LIU Huan, ZHANG Jing, LUO Chen, JIA Fengming, WANG Yigui, HU Yunyun. Analysis of the spatiotemporal variation characteristics and driving factors of grassland vegetation cover in Ningxia based on geographical detectors[J]. Arid Zone Research, 2023, 40(8): 1322-1332.

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数据集	数据来源	空间分辨率	数据预处理
NDVI数据集	中国科学院资源环境科学数据中心(https://www.resdc.cn/)	1 km	-
中国气象背景数据集		500 m	-
中国1∶1000000地貌类型空间分布数据		1 km	-
中国土壤类型空间分布数据		1 km	-
（数字高程模型）DEM		90 m	利用ArcGIS 10.8获取坡度和坡向数据，统一范围大小和空间分辨率
GDP密度	宁夏数据网站 (http://nxdata.com.cn/)	县域尺度	在ArcGIS 10.8中，插值到县域尺度
人口密度	宁夏数据网站 (http://nxdata.com.cn/)	县域尺度	在ArcGIS 10.8中，插值到县域尺度
草地矢量数据	2021年宁夏林草资源数据库	-	按细化地类提取宁夏回族自治区范围内的草地图斑，融合生成草地范围矢量数据

气候因素	地表因素	人类活动因素
平均降水量（X₁）	海拔（X₇）	GDP（X₁₂）
湿润指数(X₂)	土壤类型（X₈）	人口密度（X₁₃）
干燥度(X₃)	地貌（X₉）
年均温（X₄）	坡度（X₁₀）
≥0 ℃积温（X₅）	坡向（X₁₁）
≥10 ℃积温（X₆）

交互作用类型	判断准则
非线性减弱	q(X₁∩X₂)<Min[q(X₁), q(X₂)]
单因子非线性减弱	Min[q(X₁), q(X₂)]<q(X₁∩X₂)<Max[q(X₁), q(X₂)]
双因子增强	q(X₁∩X₂)>Max[q(X₁), q(X₂)]
非线性增强	q(X₁∩X₂)>q(X₁)+q(X₂)
独立	q(X₁∩X₂)=q(X₁)+q(X₂)

	植被类型	2000年
	植被类型	极低植被覆被	低植被覆被	中植被覆被	高植被覆被	极高植被覆被	合计
2019年	极低植被覆被	15.746	0.040	0.007	0.010		15.803
	低植被覆被	29.591	0.297	0.008			29.896
	中植被覆被	28.450	1.291	0.071		0.022	29.834
	高植被覆被	9.923	4.493	0.692	0.086	0.021	15.215
	极高植被覆被	0.304	3.093	3.477	1.735	0.643	9.252
合计		84.014	9.214	4.255	1.831	0.686	100.000

发展方向	结果	面积/km²	占比/%
良性方向	持续性&轻微改善	1554.343	7.560
	持续性&显著改善	5694.847	27.699
	反持续性&轻微退化	1009.142	4.909
	反持续性&显著退化	17.953	0.087
恶性方向	持续性&轻微退化	565.989	2.753
	持续性&显著退化	32.126	0.161
	反持续性&轻微改善	3562.232	17.326
	反持续性&显著改善	8039.117	39.101
不确定	反持续性&不变	51.024	0.248
稳定	持续性&不变	33.071	0.156