乌鲁木齐市生态环境遥感评价及驱动因子分析

doi:10.13866/j.azr.2021.05.31

摘要/Abstract

摘要：

生态环境质量评估对于区域社会经济的可持续发展至关重要。以新疆乌鲁木齐市为研究区,通过对2000年、2010年和2019年3期Landsat系列数据进行预处理,利用主成分分析法构建遥感生态指数（Remote Sensing Ecological Index, RSEI）,使用地理探测器中单因子分析和多因子交互作用分析法对RSEI的8个影响因子（人口密度、土地利用类型、海拔高度、经济密度、绿度、湿度、热度和干度）进行影响程度的定量探测,进而揭示出影响研究区生态环境质量的主导因素。结果表明：（1） 2000年、2010年和2019年乌鲁木齐市RSEI均值分别为0.21、0.21、0.23呈增加趋势,研究期内乌鲁木齐市生态变好的面积大于变差的面积,说明乌鲁木齐市的生态环境质量处于转好的状态;（2）近20 a研究区8个影响因子中单因子热度指标对RSEI的空间分异特征解释力最强;（3）研究区生态环境的空间分布特征是多种影响因子共同交互作用的结果,在考虑多因子交互作用情景下人口密度,绿度指标和干度指标是区域生态环境质量状况的主要影响因素和关键驱动力。评价区域生态环境质量,了解生态环境状况及掌握其变化规律,不仅有利于促进区域经济可持续发展,而且对于城市生态文明建设具有重要的现实意义和参考价值。

关键词: 乌鲁木齐市, 生态环境, 驱动因子, 遥感生态指数, 地理探测器

Abstract:

The ecological environment quality assessment is essential to the sustainable development of the regional social economy. This paper takes Urumqi, Xinjiang, as the research area, preprocessed the Landsat series data of 2000, 2010, and 2019. The principal component analysis method was used to construct the Remote Sensing Ecological Index (RSEI), and the single factor analysis and the multi-factor interaction analysis method were used in the geographic detector to influence the eight factors of RSEI (population density, land use type, altitude, economics density, green degree, humidity, heat, and dryness). The quantitative detection of the degree of influence was conducted, after which the dominant factors affecting the ecological environment quality of the study area were revealed. The results show that: (1) The average RSEI values of Urumqi in 2000, 2010, and 2019 were 0.21, 0.21, and 0.23, respectively, showing an increasing trend. During the study period, Urumqi's ecological improvement area was greater than the area that deteriorated, indicating the ecological environment quality of Urumqi. In a state of improvement; (2) In the past 20 years, among the eight impact factors in the study area, the single factor heat index has the strongest explanatory power for the spatial differentiation characteristics of RSEI; (3) The spatial distribution characteristics of the ecological environment in the study area are the result of the mutual interaction of multiple influencing factors. Considering the interaction of multiple factors, population density, greenness index, and dryness index are the main influencing factors and key driving forces of the quality of regional ecological environment. Evaluating the quality of the regional ecological environment, understanding the status of the ecological environment and mastering its changing laws do not only aid the promotion of sustainable development of the regional economy but also has important practical significance and reference value for constructing urban ecological civilization.

Key words: Urumqi City, ecological environment, driving factors, remote sensing ecological index, geographic detector

排日海·合力力,昝梅,阿里木江·卡斯木. 乌鲁木齐市生态环境遥感评价及驱动因子分析[J]. 干旱区研究, 2021, 38(5): 1484-1496.

Pariha Helili,ZAN Mei,Alimjan Kasim. Remote sensing evaluation of ecological environment in Urumqi City and analysis of driving factors[J]. Arid Zone Research, 2021, 38(5): 1484-1496.

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主成分	2000年		2010年		2019年
主成分	特征值	贡献率/%	特征值	贡献率/%	特征值	贡献率/%
PC1	0.1475	82.69	0.0629	89.02	0.0622	88.71
PC2	0.0276	11.68	0.0062	7.89	0.0057	5.47
PC3	0.0025	4.56	0.0013	2.61	0.0032	4.43
PC4	0.0008	1.13	0.0002	0.48	0.0004	1.13

年份	统计值	NDVI	WET	LST	NDBSI	RSEI
2000	最小值	-1.00	-0.02	-20.78	-0.90	0.00
	最大值	1.00	0.02	63.64	0.37	1.00
	均值	-0.02	0.01	8.93	0.02	0.21
	标准差	0.06	0.00	13.86	0.09	0.30
2010	最小值	-0.76	-0.03	-18.48	-0.82	0.00
	最大值	0.52	0.02	54.31	0.50	1.00
	均值	-0.03	0.00	10.07	0.02	0.21
	标准差	0.10	0.00	17.25	0.07	0.32
2019	最小值	-1.00	-0.03	-16.21	-0.81	0.00
	最大值	0.60	0.02	54.24	0.72	1.00
	均值	0.03	0.01	8.87	-0.11	0.23
	标准差	0.06	0.09	15.20	0.18	0.33

变化	2000—2010年		2010—2019年		2000—2019年
变化	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
恶化	0.001	0.00	0.00	0.00	0	0.00
变差	156.37	1.14	1104.65	8.03	56.91	0.41
不变	9058.09	65.75	7184.73	52.21	10621	77.10
变好	4435.82	32.20	5470.91	39.76	3097.87	22.49
优化	125.88	0.91	0.02	0.00	0.37	0.00

探测因子	2000年			2010年			2019年
探测因子	q值	P值	q排序	q值	P值	q排序	q值	P值	q排序
绿度指标	0.179	0	7	0.349	0	3	0.095	0	5
湿度指标	0.526	0	2	0.168	0	6	0.326	0	3
热度指标	0.857	0	1	0.706	0	1	0.653	0	1
干度指标	0.252	0	4	0.368	0	2	0.046	0	8
人口密度	0.07	1	8	0.067	0	8	0.166	0	4
土地利用	0.235	0	5	0.077	0	7	0.091	0	6
海拔	0.2	0	6	0.345	0	4	0.52	0	2
经济密度	0.263	0	3	0.199	0	5	0.072	0	7

年份	交互因子	q(X1∩X2)	q(X1)+q(X2)	C	影响模式
2000	NDVI∩NDBSI	0.604	0.431	0.173	非线性增强
	人口密度∩NDBSI	0.482	0.322	0.160	非线性增强
	人口密度∩NDVI	0.369	0.249	0.120	非线性增强
	DEM∩经济密度	0.576	0.463	0.113	非线性增强
2010	土地利用∩经济密度	0.336	0.276	0.060	非线性增强
	人口密度∩NDBSI	0.489	0.435	0.054	非线性增强
	人口密度∩WET	0.265	0.235	0.030	非线性增强
	人口密度∩NDVI	0.430	0.416	0.014	非线性增强
2019	人口密度∩NDVI	0.324	0.261	0.063	非线性增强
	人口密度∩NDBSI	0.248	0.212	0.036	非线性增强
	NDVI∩WET	0.439	0.421	0.018	非线性增强
	人口密度∩经济密度	0.247	0.238	0.009	非线性增强