基于GEE的干旱区县域生态环境质量时空变化及驱动力分析——以阿拉善左旗为例

doi:10.13866/j.azr.2025.02.15

Abstract

Abstract:

Objective, accurate, and timely evaluation of the spatiotemporal changes and driving forces of ecological environment quality is of great significance for the formulation of ecological protection plans and policies. Taking Alxa Left Banner as an example, this study constructs a Remote Sensing-based Ecological Index (RSEI) for arid regions based on the Google Earth Engine (GEE) platform and analyzes the spatiotemporal changes and driving forces of RSEI over the past 30 years (1991-2021). The study shows that: (1) Over the past 30 years, the RSEI of Alxa Left Banner has shown a fluctuating upward trend, with the maximum RSEI in 2012 (0.360) and the minimum in 2007 (0.264). (2) Over the past 30 years, the area of ecological improvement (RSEI>0.2, 3.15%) in Alxa Left Banner is larger than the area of degradation (RSEI<-0.2, 2.48%), with the largest area showing no change (-0.2<RSEI<0.2, 94.37%). Regions with poorer RSEI are mainly distributed in bare land areas, while the RSEI of forest, grassland, farmland, and impervious surface areas has gradually improved. (3) From 1991 to 2021, the Global Moran’s I index ranged between 0.600 and 0.650, indicating a high degree of clustering. (4) According to the results of the linear mixed-effects model, human activities account for 89% of the changes in RSEI, while climate change accounts for 11%. In summary, over the past 30 years, the overall ecological environment quality in the Alxa Desert area has gradually improved, with significant improvements in the northern edge of the Tengger Desert, primarily due to human activities, especially the aerial seeding afforestation projects.

Key words: remote sensing-based ecological index, GEE, spatial autocorrelation, arid region

LI Qi, DANG Guofeng, YU Tengfei, ZHANG Lang, CHEN Weiyu. Spatial-temporal variation and driving forces analysis of ecological environment quality in arid counties based on GEE: A case study of Alxa Left Banner[J].Arid Zone Research, 2025, 42(2): 360-371.

Figures/Tables 13

Fig. 1

Tab. 1

Fig. 2

Tab. 2

Calculation formulas for various indicators"

指标	计算方法	描述
NDVI	$N D V I = ρ N I R - ρ R e d ρ N I R + ρ N I R$	$ρ B l u e$ 、 $ρ G r e e n$ 、 $ρ R e d$ 、 $ρ N I R$ 、 $ρ S W I R 1$ 、 $ρ S W I R 2$ 分别表示Landsat TM、 ETM+、OLI影像的蓝色、绿色、红色、近红外、短波红外1和短波红外2波段的反射率。
WET	$W E T T M = 0.0315 ρ B l u e + 0.2021 ρ G r e e n + 0.3102 ρ R e d + 0.1594 ρ N I R - 0.6806 ρ S W I R 1 - 0.6109 ρ S W I R 2$ $W E T E T M + = 0.0315 ρ B l u e + 0.2021 ρ G r e e n + 0.0926 ρ R e d + 0.0656 ρ N I R - 0.7629 ρ S W I R 1 - 0.5388 ρ S W I R 2$ $W E T O L I = 0.1511 ρ B l u e + 0.1972 ρ G r e e n + 0.3283 ρ R e d + 0.3407 ρ N I R - 0.7117 ρ S W I R 1 - 0.4559 ρ S W I R 2$
SI	$S I = ρ B l u e + ρ R e d$
NDBSI	$I B I = 2 ρ S W I R 1 ρ S W I R 1 + ρ N I R - ρ N I R / ρ N I R + ρ R e d + ρ G r e e n / ρ G r e e n + ρ S W I R 1 2 ρ S W I R 1 ρ S W I R 1 + ρ N I R + ρ N I R / ρ N I R + ρ R e d + ρ G r e e n / ρ G r e e n + ρ S W I R 1$ $B S I = ρ S W I R 1 + ρ R e d - ρ B l u e + ρ N I R ρ S W I R 1 + ρ R e d + ρ B l u e + ρ N I R$ $N D B S I = I B I + S I 2$

Tab. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab. 3

Tab. 4

Tab. 5

Fig. 7

Fig. 8

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年份	数据集	卫星	使用波段	月/云/空间分辨率
1991—2011	LANDSAT/LT05/C02/T1_L2	Landsat 5/TM	SR_B（1，2，3，4，5，7）ST_B6	7—9月 <20% 30 m
2012	LANDSAT/LE07/C02/T1_L2	Landsat 7/ETM+	SR_B（1，2，3，4，5，7）ST_B6
2013—2021	LANDSAT/LC08/C02/T1_L2	Landsat 8/OLI/TIRS	SR_B（2，3，4，5，6，7）ST_B10

1991年	2008年
1991年	劣	差	中	良	优
劣	20837.54	6388.98	772.57	77.57	23.20
差	11384.72	26602.14	3675.01	493.89	208.30
中	62.32	3309.34	6598.99	1120.84	200.68
良	1.76	30.54	1475.00	2024.40	585.54
优	0.35	3.33	23.16	645.20	1776.98

2008年	2020年
2008年	劣	差	中	良	优
劣	20395.57	11355.27	481.44	31.82	17.81
差	6178.71	24840.16	5029.51	186.36	81.75
中	390.01	3061.34	7854.05	1125.48	92.59
良	38.79	184.52	1279.73	2543.47	303.50
优	9.15	38.84	119.60	917.33	1686.13

年份	土地利用	显著变差 (-1.0~-0.6)	变差 (-0.6~-0.2)	不变 (-0.2~0.2)	变好 (0.2~0.6)	显著变好 (0.6~1.0)
1991—2008	耕地	0.00	0.02	0.51	0.41	0.07
	林地	0.00	0.00	0.97	0.03	0.00
	草地	0.00	0.05	0.87	0.08	0.00
	裸地	0.00	0.05	0.91	0.04	0.00
	不透水面	0.00	0.20	5.43	0.45	0.03
2008—2020	耕地	0.01	0.07	0.80	0.11	0.01
	林地	0.00	0.00	0.96	0.04	0.00
	草地	0.00	0.05	0.89	0.06	0.00
	裸地	0.00	0.04	0.95	0.01	0.00
	不透水面	0.01	0.13	0.80	0.06	0.00

Spatial-temporal variation and driving forces analysis of ecological environment quality in arid counties based on GEE: A case study of Alxa Left Banner

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