基于Google Earth Engine的漓江流域生态环境质量评价及驱动因子分析

魏玺; 阳大智; 蔡湘文; 邵亚; 唐湘玲

doi:10.12357/cjea.20230633

基于Google Earth Engine的漓江流域生态环境质量评价及驱动因子分析

魏玺¹,
阳大智^{2, 3},
蔡湘文^1, ,,
邵亚¹,
唐湘玲¹

1.
桂林理工大学地球科学学院　桂林　541006
2.
中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室　北京　100101
3.
中国科学院大学资源与环境学院　北京　100049

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中图分类号: X171.1
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出版历程
- 收稿日期: 2023-10-29
- 录用日期: 2024-04-29
- 网络出版日期: 2024-05-30
- 刊出日期: 2024-09-09

Ecological environmental quality evaluation and driving factor analysis of the Lijiang River Basin, based on Google Earth Engine

1.
College of Earth Sciences, Guilin University of Technology, Guilin 541006, China
2.
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: This study was supported by the Guangxi Natural Science Foundation (2020GXNSFAA297266), Doctoral Research Foundation of Guilin University of Technology (GUTQDJJ2007059) and Guangxi Hidden Metallic Mineral Exploration Key Laboratory.

More Information

Corresponding author:
CAI Xiangwen: E-mail: glut2004042@163.com

摘要

摘要:
评价生态环境质量并分析其变化原因, 对区域生态管理具有重要意义。本文基于Google Earth Engine (GEE)平台, 计算1991年、2001年、2011年和2021年4个时期漓江流域的遥感生态指数, 利用空间自相关分析漓江流域生态环境质量的时空变化, 并运用地理探测器进一步定量解析影响生态环境质量的因素及其交互影响。结果表明: 1) 1991—2021年, 漓江流域生态环境质量得到明显改善; 生态环境质量较好和好的区域面积占比增加19.69% (3406.57 km²), 差和较差区域面积占比减少10.76% (1860.36 km²)。2)从空间上看, 漓江流域生态环境质量呈中部低四周高的格局, 其中, 桂林市区、平乐县、灵川县生态环境质量较差, 永福县、恭城瑶族自治县(东部和西北部)等高海拔地区生态环境质量有所改善。3) 1991—2021年, 漓江流域生态环境质量在空间上存在显著正相关关系, 高-高集聚区以林地和草地为主, 生态环境质量较好; 低-低集聚区以耕地和建设用地为主, 生态环境质量较差。4)年平均降水量和年平均气温对漓江流域生态环境的影响最大, 且两者与其他因素相互作用影响力也最大。本研究旨在加强对漓江流域生态环境演变的认识, 并为该地区生态环境相关决策和管理提供科学指导。
- 生态环境质量 /
- 遥感生态指数 /
- 驱动因子 /
- Google Earth Engine /
- 漓江流域
Abstract:
For regional ecological management, it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes. Using the Google Earth Engine (GEE) platform, the remote sensing ecological index (RSEI) was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991, 2001, 2011, and 2021. Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin. Furthermore, geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality. The results verified that: 1) From 1991 to 2021, the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement. The area with good and excellent ecological environmental quality in proportion increased by 19.69% (3406.57 km²), while the area with fair and poor ecological environmental quality in proportion decreased by 10.76% (1860.36 km²). 2) Spatially, the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery. Specifically, poor ecological environmental quality characterized the Guilin urban area, Pingle County, and Lingchuan County. 3) From 1991 to 2021, a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin. Areas with high-high agglomeration were predominantly forests and grasslands, indicating good ecological environmental quality, whereas areas with low-low agglomeration were dominated by cultivated land and construction land, indicating poor ecological environmental quality. 4) Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin, and their interactions with other factors had the great influence. This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.
- Ecological environmental quality /
- Remote sensing ecological index /
- Driving factor /
- Google Earth Engine /
- Lijiang River Basin

HTML全文

Figure 1. Geographical location and administrative divisions of Lijiang River Basin

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Figure 2. Spatial distribution of ecological environmental quality grades based on remote sensing ecological index (RSEI) in the Lijiang River Basin from 1991 to 2021

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Figure 3. Spatial Moran scatter maps of remote sensing ecological index (RSEI) in the Lijiang River Basin from 1991 to 2021

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Figure 4. Local Indicators of Spatial Association (LISA) cluster diagram of ecological environmental quality in the Lijiang River Basin from 1991 to 2021

Value in the brackets denotes the number of grids.

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Figure 5. Interactive detection results of impact factors of ecological environmental quality in the Lijiang River Basin from 1991 to 2021

AAP: annual average precipitation; NDVI: normalized difference vegetation index; SL: slope; ST: soil type; NLI: nighttime lights intensity; DTRA: distance to roads; AAT: annual average temperature; LUT: land use type; DEM: Digital Elevation Model; DTRV: distance to rivers.

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Table 1 Calculation of the indexes

Index	Calculation
NDVI	$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=\left({B}_{\mathrm{N}\mathrm{I}\mathrm{R}}-{B}_{\text{Red}}\right)/\left({B}_{\mathrm{N}\mathrm{I}\mathrm{R}}+{B}_{\text{Red}}\right)$
WET	${\mathrm{W}\mathrm{E}\mathrm{T}}_{\text{TM}}=0.0315\times {B}_{\text{Blue}}+0.2021\times {B}_{\text{Green}}+0.3102\times {B}_{\text{Red}}+0.1594\times {B}_{\text{NIR}}-0.6806\times {B}_{\text{SWIR1}}-0.6109\times {B}_{\text{SWIR}2}$ ${\mathrm{W}\mathrm{E}\mathrm{T}}_{\text{OLI}}=0.1511\times {B}_{\text{Blue}}+0.1973\times {B}_{\text{Green}}+0.3283\times {B}_{\text{Red}}+0.3407\times {B}_{\mathrm{N}\mathrm{I}\mathrm{R}}-0.7117\times {B}_{\text{SWIR}1}-0.4559\times {B}_{\text{SWIR}2}$
LST	$\mathrm{L}\mathrm{S}\mathrm{T}={B}_{\mathrm{T}}/\left[1+\left(\lambda \times {B}_{\mathrm{T}}/\rho \right)\times\mathrm{l}\mathrm{n}\varepsilon \right]-237.15$
NDBSI	$\mathrm{N}\mathrm{D}\mathrm{B}\mathrm{S}\mathrm{I}=(\mathrm{S}\mathrm{I}+\mathrm{I}\mathrm{B}\mathrm{I})/2$ $\mathrm{S}\mathrm{I}=\left[\left({B}_{\text{SWIR}1}+{B}_{\text{Red}}\right)-\left({B}_{\text{Blue}}+{B}_{\mathrm{N}\mathrm{I}\mathrm{R}}\right)\right]/\left[\left({B}_{\text{SWIR}1}+{B}_{\text{Red}}\right)+\left({B}_{\text{Blue}}+{B}_{\mathrm{N}\mathrm{I}\mathrm{R}}\right)\right]$ $\mathrm{I}\mathrm{B}\mathrm{I}=2\times B_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}/\left(B_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}+B_{\text{SWIR}1}\right)]/\left[2\times B_{\text{SWIR}1}/\left(B_{\text{SWIR}1}+B_{\mathrm{N}\mathrm{I}\mathrm{R}}\right)+B_{\mathrm{N}\mathrm{I}\mathrm{R}}/\left(B_{\text{NIR}}+B_{\text{Red}}\right)+B_{\text{Green}}/\left(B_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}+B_{\text{SWIR}1}\right)]\right.$
NDVI: Normalized Difference Vegetation Index; WET_TM: wetness sourced from Landsat TM; WET_OLI: wetness sourced from Landsat OLI; LST: land surface temperature; NDBSI: Normalized Differential Building-soil Index; SI: bare soil index; IBI: index-based built-up index; B_T: the sensor temperature; λ: the center wavelength of the thermal infrared band, λ_TM = 11.435 μm and λ_OLI = 10.9 μm; $\rho$ : 1.438×10⁻² mK; ε: the surface emissivity. B_NIR, B_Red, B_Blue, B_Green, B_SWIR1, and B_SWIR2 represent the reflectance values of the near band, red band, blue band, green band, middle infrared band 1, and middle infrared band 2, respectively. The equations and parameter values were obtained from the literatures (He et al., 2023; Xiong et al., 2021; Zhou and Wang, 2020).

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Table 2 Interactive mode of geographic detectors

Criterion	Interaction
q(X1∩X2) < min[q(X1), q(X2)]	Nonlinear attenuation
min[q(X1), q(X2)] < q(X1∩X2) < max[q(X1), q(X2)]	Single factor nonlinear attenuation
q(X1∩X2) > max[q(X1), q(X2)]	Two-factor enhancement
q(X1∩X2) = q(X1) + q(X2)	Independent
q(X1∩X2) > q(X1) + q(X2)	Nonlinear enhancement
q(X1) and q(X2) represent the explanatory power of independent variables X1 and X2, respectively.

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Table 3 Principal component analysis (PCA) of remote sensing ecological index (RSEI) in the Lijiang River Basin

Year	Principal component	NDBSI	LST	NDVI	WET	Eigenvalue	Contribution rate (%)
1991	PC1	−0.239	−0.611	0.661	0.364	0.032	67.84
	PC2	0.204	−0.696	−0.211	−0.655	0.008	17.02
	PC3	0.352	−0.351	−0.562	0.662	0.001	14.73
	PC4	−0.882	−0.134	−0.451	0.015	0.007	0.41
2001	PC1	−0.259	−0.446	0.715	0.471	0.022	61.50
	PC2	0.369	−0.806	−0.448	0.119	0.008	20.96
	PC3	−0.145	−0.36	0.297	−0.872	0.006	16.92
	PC4	−0.881	−0.147	−0.447	0.055	0.000	0.62
2011	PC1	−0.256	−0.545	0.678	0.421	0.025	62.08
	PC2	0.242	−0.816	−0.294	−0.435	0.009	21.60
	PC3	−0.296	0.154	0.506	−0.795	0.006	15.87
	PC4	−0.888	−0.116	−0.444	0.025	0.000	0.44
2021	PC1	−0.284	−0.51	0.639	0.501	0.031	65.41
	PC2	−0.238	−0.219	0.394	−0.861	0.009	19.59
	PC3	−0.381	−0.219	0.394	−0.860	0.007	14.57
	PC4	−0.847	−0.137	−0.513	0.034	0.000	0.43
Normalized Difference Vegetation Index (NDVI), wetness index (WET), Normalized Differential Building-soil Index (NDBSI), and land surface temperature (LST) represent greenness, wetness, dryness, and heat, respectively.

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Table 4 Areas and proportions of grades of remote sensing ecological index (RSEI) in the Lijiang River Basin from 1991 to 2021

Year	Poor		Fair		Moderate		Good		Excellent
Year	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)
1991	1349.57	7.80	3472.40	20.08	4845.60	28.02	5097.09	29.47	2531.49	14.64
2001	808.18	4.67	2215.50	12.81	4240.12	24.51	5788.10	33.46	4244.20	24.54
2011	801.87	4.64	1842.14	10.65	4114.36	23.79	6279.50	36.31	4258.23	24.62
2021	891.10	5.15	2070.51	11.97	3299.34	19.08	5369.00	31.04	5666.17	32.76

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Table 5 Areas and proportions of various ecological environmental quality grades in Lijiang River Basin from 1991 to 2021

Year	Significant deterioration		Mild deterioration		Unchanged		Slight improvement		Remarkable improvement
Year	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)	Area (km²)	Proportion (%)
1991−2001	82.92	0.53	2065.07	13.25	6441.27	41.34	6855.37	43.99	138.25	0.89
2001−2011	243.76	1.41	3723.56	21.46	8424.82	48.56	4834.21	27.87	121.93	0.70
2011−2021	215.57	1.25	4653.86	26.96	7032.86	40.75	5186.61	30.05	171.74	0.99

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Table 6 Detection results of impact factors of ecological environmental quality in the Lijiang River Basin from 1991 to 2021

Year	AAP	NDVI	SL	ST	NLI	DTRA	AAT	LUT	DEM	DTRV
1991	0.92	0.82	0.63	0.81	0.25	0.90	0.94	0.59	0.89	0.92
2001	0.93	0.82	0.61	0.86	0.33	0.84	0.91	0.53	0.88	0.92
2011	0.89	0.75	0.59	0.86	0.56	0.84	0.91	0.55	0.86	0.89
2021	0.91	0.94	0.58	0.87	0.58	0.85	0.96	0.63	0.85	0.88
AAP: annual average precipitation; NDVI: normalized difference vegetation index; SL: slope; ST: soil type; NLI: nighttime lights intensity; DTRA: distance to roads; AAT: annual average temperature; LUT: land use type; DEM: Digital Elevation Model; DTRV: distance to rivers. Value in the table is q-value.

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基于Google Earth Engine的漓江流域生态环境质量评价及驱动因子分析

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Ecological environmental quality evaluation and driving factor analysis of the Lijiang River Basin, based on Google Earth Engine

Corresponding author: CAI Xiangwen: E-mail: glut2004042@163.com

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Corresponding author:
CAI Xiangwen: E-mail: glut2004042@163.com