Spatio-temporal Dynamics and Attribution Analysis of Vegetation in Gansu Province

doi:10.16258/j.cnki.1674-5906.2024.08.001

Ecology and Environment ›› 2024, Vol. 33 ›› Issue (8): 1163-1173.DOI: 10.16258/j.cnki.1674-5906.2024.08.001

• Research Article [Ecology] • Next Articles

Spatio-temporal Dynamics and Attribution Analysis of Vegetation in Gansu Province

DAI Xiaoai¹^,²^,³(), MA Jiaxin², TANG Yiling², LI Weile¹

1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, P. R. China
2. School of Geography and Planning, Chengdu University of Technology, Chengdu 610059, P. R. China
3. Digital Hu Huanyong Line Research Institute, Chengdu University of Technology, Chengdu 610059, P. R. China

Received:2024-04-29 Online:2024-08-18 Published:2024-09-25

甘肃省植被时空动态变化及其归因分析

戴晓爱¹^,²^,³(), 马佳欣², 唐艺菱², 李为乐¹

1.成都理工大学/地质灾害防治与地质环境保护国家重点实验室，四川成都 610059
2.成都理工大学地理与规划学院，四川成都 610059
3.成都理工大学数字胡焕庸线研究院，四川成都 610059

作者简介:戴晓爱（1979年生），女，教授，博士，主要从事生态环境监测与评价、生态系统服务等方面的教学和研究工作。E-mail: daixiaoa@cdut.edu.cn
基金资助:
国家自然科学基金项目(42271405);四川省重点研发项目(2023YFS0435);地质灾害防治与地质环境保护国家重点实验室自主研究课题(SKLGP2022Z007);成都理工大学研究生质量工程项目(2023YKC201);教育部产学合作协同育人项目(220802313174310);教育部产学合作协同育人项目(22073776112419);四川省线上线下混合式一流课程《地图学》(YLKC01430);雅鲁藏布江中游自然资源西藏自治区野外科学观测研究站开放基金项目(2024YJZKF005)

Abstract

Abstract:

Gansu Province, as a crucial component of the national ecological security barrier in Western China, has experienced significant vegetation cover changes in recent years that are directly related to ecosystem restoration and environmental protection. Fractional Vegetation Cover (FVC) data from 2000 to 2020 were used, with 16 influencing factors selected from both natural and anthropogenic aspects, including climate, topography, soil, and human activity. Trend analysis, geodetection, and partial least squares structural equation modeling (PLS-SEM) were applied to explore the dynamics and drivers of vegetation cover changes across different arid and humid regions and temporal periods in Gansu Province. The results indicate that: 1) from 2000 to 2020, the Fractional Vegetation Cover (FVC) in Gansu Province exhibited a continuous overall improvement, with a significant increase in areas with high vegetation cover. Notably, the most pronounced improvements were observed in the Longdong Plateau, Longzhong Plateau, and the southern areas of the Shule River. 2) This period also highlighted significant spatial variations in vegetation cover across zones with different moisture conditions. Arid and semi-arid areas showed relatively slower vegetation improvements than other areas, which were heavily influenced by fluctuations in precipitation and land-use practices. In contrast, the semi-humid and humid zones represented significant improvements in vegetation cover, benefiting from favorable climatic conditions, proactive ecological projects, and increased in soil organic carbon content. 3) Precipitation and land use changes emerged as the primary explanatory variables for FVC, and reforestation projects actively contributed to the increase in vegetation cover. However, unsustainable land use and urbanization processes have led to vegetation degradation. Over time, the influence of climate on FVC has become increasingly positive, whereas the negative impact of unreasonable human activities on FVC remains relatively stable and significant, partially offsetting the positive effects of grain for green programs. In addition, the effect of soil organic carbon content on FVC declined significantly and was indirectly positively affected by climate. 4) Utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) framework, this study effectively delineates the driving roles of natural and anthropogenic factors on vegetation cover and explores the strengths and pathways of these interactions between natural factors, anthropogenic factors, and vegetation cover. Comprehensive analysis of vegetation cover changes in Gansu Province not only enhances the understanding of regional vegetation dynamics, but also provides scientific support for ecological restoration and environmental management initiatives.

Key words: FVC, PLS-SEM, driving factors, arid and humid regions, geodetector, anthropogenic activity

摘要：

甘肃省作为中国西部生态安全屏障的重要组成部分，其植被覆盖变化直接关系中国西部的生态安全。采用2000-2020年植被覆盖度（FVC）数据，并从自然和人文影响方面选取16个影响因子，涵盖气候、地形地貌、土壤、人类活动，利用趋势分析法、地理探测器模型和偏最小二乘结构方程建模（PLS-SEM），从甘肃省不同干湿区、不同时间节点的角度探究其植被覆盖的变化特征及驱动力。结果表明，1）2000-2020年，甘肃省FVC整体持续改善，高植被覆盖度显著增加，其中陇东高原、陇中高原和疏勒河南部地区改善最为明显。2）不同干湿区的植被覆盖变化差异显著。干旱和半干旱区植被改善较慢，受降水量波动和土地利用方式影响大；而半湿润和湿润区植被改善明显，得益于良好的气候条件、生态工程的推动以及土壤有机碳含量的增加。3）降水和土地利用对FVC解释力最大，“退耕还林工程”对FVC的增加起到积极作用，但不合理的土地利用和城市化等也引起植被退化。随着时间推移，气候对FVC的影响一直在积极增加，不合理人类活动对FVC的负面影响相对稳定且较大，部分抵消了退耕还林的正面效果。此外，土壤有机碳含量对FVC的影响显著下降，且间接受到气候的正向影响。4）利用PLS-SEM模型，可有效分离自然与人为因素对植被覆盖的驱动作用，也可探究因素间的作用强度与途径。该研究增强了对区域植被动态变化规律的理解，也为生态恢复和环境管理提供了科学支持。

关键词: 植被覆盖度, 偏最小二乘结构方程, 驱动因子, 干湿区, 地理探测器, 人类活动

CLC Number:

X171.4

DAI Xiaoai, MA Jiaxin, TANG Yiling, LI Weile. Spatio-temporal Dynamics and Attribution Analysis of Vegetation in Gansu Province[J]. Ecology and Environment, 2024, 33(8): 1163-1173.

戴晓爱, 马佳欣, 唐艺菱, 李为乐. 甘肃省植被时空动态变化及其归因分析[J]. 生态环境学报, 2024, 33(8): 1163-1173.

Figures/Tables 13

References 33

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数据	缩写	数据源	加工方法	时间	分辨率
降雨量	Pre	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn)	由逐月降水数据处理后得	2000‒2020	1 km
平均气温	Tem	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn)	由逐月降水数据处理后得	2000‒2020	1 km
空气湿度	Hum	中国地面气候资料日值数据集V3.0处理生成	将逐日csv文件展点后投影, 采用反距离权重法对其进行插值	2000‒2020
太阳辐射	SR	中国区域地面气象要素驱动数据集		2000‒2020	0.1°
风速	WS	国家环境信息中心(NCEI)	将逐日csv文件展点后投影, 采用反距离权重法对其进行插值	2000‒2020
CO₂浓度	CO₂	中国二氧化碳排放数据1997‒2017 (https://figshare.com/articles/ dataset/County-level_CO₂_emissions_in_China/12887213)		2000、2005、 2010、2015
CO₂浓度	CO₂	中国城市温室气体工作组 (CCG)		2020
海拔	Alt	美国太空总署 (NASA) 和国防部国家测绘局 (NIMA) 联合测量 (http://srtm.csi.cgiar.org/srtmdata)			90 m
坡度	SLP		ArcGIS Pro 3.0中坡度工具提取
坡向	ASP		ArcGIS Pro 3.0中坡向工具提取
土壤类型	Soilt	世界土壤数据库 (https://www.rserforum.com)
土壤有机碳含量	SC	https://data.isric.org			250 m
到最近路的距离	DNR		ArcGIS Pro中使用邻近分析工具计算
人口密度	Popd	LandScan全球人口数据 (https://landscan.ornl.gov)		2000‒2020	1 km
GDP	GDP	《中国县域统计年鉴》		2000‒2020
土地利用	LUCC	中国区1990‒2020逐年30 m分辨率土地利用分类数据 (https://zenodo.org/record/5210928)		2000‒2020	30 m
夜间灯光密度	NL	中国长时间序列夜间灯光数据集 (2000‒2020) [J/DB/OL] (https://doi.org/10.3974/geodb.2022.06.01.V1.)		200‒2020	1 km
FVC	FVC	国家青藏高原科学数据中心(https://cstr.cn/18406.11.Terre.tpdc.300330)		2000‒2020	250 m
逐年造林数据		2000‒2020年统计年鉴		2000‒2020

数据	缩写	数据源	加工方法	时间	分辨率
降雨量	Pre	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn)	由逐月降水数据处理后得	2000‒2020	1 km
平均气温	Tem	国家青藏高原科学数据中心 (https://data.tpdc.ac.cn)	由逐月降水数据处理后得	2000‒2020	1 km
空气湿度	Hum	中国地面气候资料日值数据集V3.0处理生成	将逐日csv文件展点后投影, 采用反距离权重法对其进行插值	2000‒2020
太阳辐射	SR	中国区域地面气象要素驱动数据集		2000‒2020	0.1°
风速	WS	国家环境信息中心(NCEI)	将逐日csv文件展点后投影, 采用反距离权重法对其进行插值	2000‒2020
CO₂浓度	CO₂	中国二氧化碳排放数据1997‒2017 (https://figshare.com/articles/ dataset/County-level_CO₂_emissions_in_China/12887213)		2000、2005、 2010、2015
CO₂浓度	CO₂	中国城市温室气体工作组 (CCG)		2020
海拔	Alt	美国太空总署 (NASA) 和国防部国家测绘局 (NIMA) 联合测量 (http://srtm.csi.cgiar.org/srtmdata)			90 m
坡度	SLP		ArcGIS Pro 3.0中坡度工具提取
坡向	ASP		ArcGIS Pro 3.0中坡向工具提取
土壤类型	Soilt	世界土壤数据库 (https://www.rserforum.com)
土壤有机碳含量	SC	https://data.isric.org			250 m
到最近路的距离	DNR		ArcGIS Pro中使用邻近分析工具计算
人口密度	Popd	LandScan全球人口数据 (https://landscan.ornl.gov)		2000‒2020	1 km
GDP	GDP	《中国县域统计年鉴》		2000‒2020
土地利用	LUCC	中国区1990‒2020逐年30 m分辨率土地利用分类数据 (https://zenodo.org/record/5210928)		2000‒2020	30 m
夜间灯光密度	NL	中国长时间序列夜间灯光数据集 (2000‒2020) [J/DB/OL] (https://doi.org/10.3974/geodb.2022.06.01.V1.)		200‒2020	1 km
FVC	FVC	国家青藏高原科学数据中心(https://cstr.cn/18406.11.Terre.tpdc.300330)		2000‒2020	250 m
逐年造林数据		2000‒2020年统计年鉴		2000‒2020

趋势斜率	标准正态分布Z值	FVC趋势变化	面积占比/%
≥5×10⁻⁴	≥1.96	明显改善	61.31
≥5×10⁻⁴	−1.96‒1.96	轻微改善	22.90
1×10⁻³	−1.96‒1.96	稳定不变	10.78
<−5×10⁻⁴	−1.96‒1.96	轻微退化	4.24
<−5×10⁻⁴	<−19.96	严重退化	0.77

趋势斜率	标准正态分布Z值	FVC趋势变化	面积占比/%
≥5×10⁻⁴	≥1.96	明显改善	61.31
≥5×10⁻⁴	−1.96‒1.96	轻微改善	22.90
1×10⁻³	−1.96‒1.96	稳定不变	10.78
<−5×10⁻⁴	−1.96‒1.96	轻微退化	4.24
<−5×10⁻⁴	<−19.96	严重退化	0.77

干湿区	降水	气温	空气湿度	太阳辐射	风速	CO₂浓度	海拔	坡度	坡向	土壤类型	土壤有机碳含量	到最近道路距离	人口密度	GDP	土地利用
干旱区	0.452	0.151	0.300	0.133	0.007	0.124	0.235	0.091	0.027	0.467	0.284	0.230	0.082	0.247	0.777
半干旱区	0.866	0.251	0.849	0.476	0.639	0.372	0.091	0.102	0.015	0.500	0.546	0.313	‒	0.862	0.602
半湿润区	0.640	0.092	0.454	0.305	0.188	0.188	0.205	0.088	0.018	0.580	0.639	‒	0.002	0.105	0.277
湿润区	0.321	‒	‒	‒	‒	‒	‒	‒	‒	‒	0.302	‒	‒	‒	0.266

Spatio-temporal Dynamics and Attribution Analysis of Vegetation in Gansu Province

甘肃省植被时空动态变化及其归因分析

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指标	2000	2010	2020
有效样本数	32892	32873	32870
SRMR	0.069	0.075	0.053
d_ULS	0.169	0.203	0.101
d_G	0.217	0.210	0.188

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[8]	LI Jianhui, DANG Zheng, CHEN Lin. Spatial-temporal Characteristics of PM_2.5 and Its Influencing Factors in the Yellow River Jiziwan Metropolitan Area [J]. Ecology and Environment, 2023, 32(4): 697-705.
[9]	WANG Chengwu, LUO Junjie, TANG Honghu. Analysis on the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in the Taihang Mountains Based on InVEST Model [J]. Ecology and Environment, 2023, 32(2): 215-225.
[10]	XIAO Chengzhi, JI Yang, LI Jianzhong, ZHANG Zhi, BA Renji, CAO Yating. Spatial and Temporal Differentiation of Ecological Vulnerability and Interaction Effects of Driving Factors in the Upper reaches of the Minjiang River: A Case Study of the Zagunao River Basin [J]. Ecology and Environment, 2023, 32(10): 1760-1770.
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[14]	WANG Lixia, SHI Yuanli, ZHANG Hongwei, BI Xiaoling, SHEN Wenming, MA Wandong. Analysis of Vegetation Ecological Function Changes and Driving Factors in Farming-pastoral Ecotone in Northern China from 2000 to 2020 [J]. Ecology and Environment, 2021, 30(10): 1990-1998.
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