2000—2020年珠江流域NDVI动态变化及影响因素研究

doi:10.16258/j.cnki.1674-5906.2022.07.003

生态环境学报 ›› 2022, Vol. 31 ›› Issue (7): 1306-1316.DOI: 10.16258/j.cnki.1674-5906.2022.07.003

2000—2020年珠江流域NDVI动态变化及影响因素研究

陈文裕¹^,²(), 夏丽华¹^,²^,^*(), 徐国良¹^,², 余世钦¹^,², 陈行¹^,², 陈金凤¹^,²

1.广州大学地理科学与遥感学院,广东广州 510006
2.广东省农村水环境面源污染综合治理工程技术研究中心,广东广州 510006

收稿日期:2022-03-07 出版日期:2022-07-18 发布日期:2022-08-31
通讯作者: ^*夏丽华（1964年生）,女,教授,主要研究方向是海岸带土地利用变化与景观生态等。E-mail: xialihua@gzhu.edu.cn
作者简介:陈文裕（1996年生）,男,硕士研究生,主要研究方向为GIS空间分析与遥感应用。E-mail: 2112001050@e.gzhu.edu.cn
基金资助:
国家自然科学基金项目(42071061);国家自然科学基金面上项目(42071235);广东省科技计划项目(2015A020216021)

Dynamic Variation of NDVI and Its Influencing Factors in the Pearl River Basin from 2000 to 2020

CHEN Wenyu¹^,²(), XIA Lihua¹^,²^,^*(), XU Guoliang¹^,², YU Shiqin¹^,², CHEN Hang¹^,², CHEN Jinfeng¹^,²

1. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, P. R. China
2. Rural Non-point Source Pollution Comprehensive Management Technology Center of Guangdong Province, Guangzhou 510006, P. R. China

Received:2022-03-07 Online:2022-07-18 Published:2022-08-31

摘要/Abstract

摘要：

研究植被的时空变化趋势及其对自然和人为因素的响应机制,对区域的植被恢复和生态保护具有重要意义。以珠江流域为例,基于Theil-Sen Median斜率估计和Mann-Kendall显著性检验探究NDVI时空演化特征,通过相关分析揭示NDVI与气候因素和人为因素的相关性,并使用地理探测器探究珠江流域NDVI空间分异的主要影响因素。结果表明,（1）2000—2020年珠江流域NDVI整体呈上升趋势,2000—2004年、2005—2009年和2014—2018年是NDVI快速增长的3个时期;4个子流域NDVI变化趋势均以增长为主,但不同子流域内NDVI的空间分布和增长速率存在差异,东江流域的NDVI均值最高,北江流域的NDVI上升速率最高,而珠江三角洲流域的NDVI均值最低且增长速率较低。（2）在研究时段内,珠江流域的林地面积有所减少,建设用地面积显著增加。由其他土地覆盖类型转换为林地、草地和耕地的区域内,NDVI呈上升趋势的面积占比分别为95.37%、85.31%和90.75%;而在转换为建设用地的区域内,NDVI则以下降趋势为主,土地覆盖类型的转换对NDVI变化的影响存在差异。（3）NDVI与平均气温和降水量均以正相关为主,表明在珠江流域气候因素对NDVI的影响以正向促进作用为主;NDVI与夜间灯光强度以正相关为主,与人口密度则是以负相关为主;NDVI与人为因素呈显著正相关的区域主要分布于珠三角城市群以及各大城市的外围地区,呈负相关的区域主要分布于珠三角城市群以及各大城市的城区。（4）从单一因子来看,土地覆盖类型、人口密度和夜间灯光强度对NDVI空间分异的解释力度较高;影响因子交互作用结果均表现为双因子增强和非线性增强;在不同子流域中,土地覆盖∩夜间灯光强度和土地覆盖∩人口密度的解释力均较高。研究结果可为制定珠江流域植被资源管理方案提供依据。

关键词: 珠江流域, NDVI, 时空演变, 相关分析, 地理探测器, 影响因素

Abstract:

For regional ecological protection and vegetation restoration, it is of great importance to study the spatio-temporal pattern of vegetation cover and its responding mechanism to natural and anthropogenic factors. Taking the Pearl River basin as an example, this study showed the spatio-temporal variation characteristics of NDVI using Theil-Sen Median analysis and Mann-Kendall significance test. The study revealed the relationship between NDVI and climate factors and anthropogenic factors through correlation analysis, and explored the key factors contributing to the spatial differentiation of NDVI using geographic detectors model. The results showed that the NDVI of the Pearl River basin was on the rise from 2000 to 2020. 2000-2004, 2005-2009, and 2014-2018 were three periods of rapid growth. Overall, NDVI in the four sub-basins of Pearl River basin increased in the studied period. However, there were differences in the spatial distribution and growth rate of NDVI among the four sub-basins. Specifically, Dongjiang River basin had the highest mean NDVI, Beijiang River basin had the highest rising rate of NDVI, while Pearl River Delta basin had the lowest mean NDVI and lowest growth rate. (2) During the study period, the area of forest in the Pearl River basin decreased, while the area of construction land increased significantly. In the areas transformed from other land cover types to forest, grassland and cropland, the proportions of NDVI on the rise were 95.37%, 85.31% and 90.75%, respectively. In the areas transformed from other land cover types to construction land, NDVI was mainly in a downward trend. Thus, the land cover change had different impacts on NDVI. (3) NDVI was mainly positively correlated with temperature and precipitation, indicating that the influence of climate factors on NDVI in the Pearl River basin was positive. In most regions, NDVI increased with increasing nighttime light intensity, but decreased with increasing population density. The areas with significant negative correlation between NDVI and anthropogenic factors were mainly distributed in Pearl River Delta urban agglomeration and big cities, while the areas with positive correlation were mainly distributed in the periphery of these regions. (4) The land cover type, population density and nighttime light intensity contributed most to spatial variation of NDVI. The interaction of the two influencing factors showed mutual and non-linear enhancement. In all sub-basins, the interaction between land cover type and nighttime light intensity, and the interaction between land cover type and population density showed higher explanatory powers. The research results can provide a basis for formulating comprehensive vegetation resource management in the Pearl River basin.

Key words: Pearl River basin, normalized difference vegetation index, spatio-temporal variation, correlation analysis, geographical detector model, influencing factor

中图分类号:

Q948
X171.1

陈文裕, 夏丽华, 徐国良, 余世钦, 陈行, 陈金凤. 2000—2020年珠江流域NDVI动态变化及影响因素研究[J]. 生态环境学报, 2022, 31(7): 1306-1316.

CHEN Wenyu, XIA Lihua, XU Guoliang, YU Shiqin, CHEN Hang, CHEN Jinfeng. Dynamic Variation of NDVI and Its Influencing Factors in the Pearl River Basin from 2000 to 2020[J]. Ecology and Environment, 2022, 31(7): 1306-1316.

图/表 10

参考文献 43

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判断依据 Judgment basis	交互作用类型 Interaction type
q(X₁∩X₂)<Min(q(X₁), q(X₂))	非线性减弱
Min(q(X₁), q(X₂)) <q(X₁∩X₂)<Max(q(X₁), q(X₂))	单因子非线性减弱
q(X₁∩X₂)>Max(q(X₁), q(X₂))	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

判断依据 Judgment basis	交互作用类型 Interaction type
q(X₁∩X₂)<Min(q(X₁), q(X₂))	非线性减弱
Min(q(X₁), q(X₂)) <q(X₁∩X₂)<Max(q(X₁), q(X₂))	单因子非线性减弱
q(X₁∩X₂)>Max(q(X₁), q(X₂))	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

土地覆盖类型 Land cover type	2000年 Year 2000		2010年 Year 2010		2020年 Year 2020		2000—2010年 Year 2000‒2010	2010—2020年 Year 2010‒2020
土地覆盖类型 Land cover type	面积 Area/km²	面积占比 Area ratio/%	面积 Area/km²	面积占比 Area ratio/%	面积 Area/km²	面积占比 Area ratio/%	变化 Change/km²	变化 Change/km²
耕地 Cropland	1166312	26.46	117836	26.73	113177	25.68	1204	-4659
林地 Forest	264369	59.98	259521	58.88	254982	57.85	-4848	-4539
草地 Grass	44160	10.02	46775	10.61	45352	10.29	2615	-1423
水体 Water	8527	1.93	8535	1.94	9758	2.21	8	1223
建设用地 Construction land	7059	1.60	8088	1.83	17479	3.97	1029	9391
裸地 Bare land	26	0.01	19	0.01	26	0.01	-7	7

土地覆盖类型 Land cover type	2000年 Year 2000		2010年 Year 2010		2020年 Year 2020		2000—2010年 Year 2000‒2010	2010—2020年 Year 2010‒2020
土地覆盖类型 Land cover type	面积 Area/km²	面积占比 Area ratio/%	面积 Area/km²	面积占比 Area ratio/%	面积 Area/km²	面积占比 Area ratio/%	变化 Change/km²	变化 Change/km²
耕地 Cropland	1166312	26.46	117836	26.73	113177	25.68	1204	-4659
林地 Forest	264369	59.98	259521	58.88	254982	57.85	-4848	-4539
草地 Grass	44160	10.02	46775	10.61	45352	10.29	2615	-1423
水体 Water	8527	1.93	8535	1.94	9758	2.21	8	1223
建设用地 Construction land	7059	1.60	8088	1.83	17479	3.97	1029	9391
裸地 Bare land	26	0.01	19	0.01	26	0.01	-7	7

地区 Region	海拔 Altitude	坡度 Slope	坡向 Aspect	降水量 Precipitation	平均气温 Temperature	土地覆盖 Land cover	土壤类型 Soil type	夜间灯光强度 Nighttime light intensity	人口密度 Population density
东江流域 Dongjiang River basin	0.43*	0.29*	0.04	0.28*	0.47*	0.51*	0.21*	0.50*	0.48*
西江流域 Xijiang River basin	0.2*	0.12*	0.01	0.13*	0.10*	0.29*	0.08*	0.22*	0.28*
北江流域 Beijiang River basin	0.26*	0.18*	0.01	0.24*	0.15*	0.37*	0.15*	0.34*	0.40*
珠江三角洲流域 Pearl River Delta basin	0.59*	0.32*	0.01	0.41*	0.46*	0.59*	0.43*	0.64*	0.59*

2000—2020年珠江流域NDVI动态变化及影响因素研究

Dynamic Variation of NDVI and Its Influencing Factors in the Pearl River Basin from 2000 to 2020

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