基于RSEI和ANN-CA-Markov模型的伊宁市生态环境质量动态监测及预测研究

doi:10.12118/j.issn.1000-6060.2022.446

干旱区地理 ›› 2023, Vol. 46 ›› Issue (6): 911-921.doi: 10.12118/j.issn.1000-6060.2022.446

基于RSEI和ANN-CA-Markov模型的伊宁市生态环境质量动态监测及预测研究

陈勉为^1,²(),冯丹^1,²(),张仕凯^1,²,江雨^1,²,张新兰^1,²

1.伊犁师范大学化学与环境科学学院，新疆伊宁 835000
2.伊犁师范大学污染物化学与环境治理重点实验室，新疆伊宁 835000

收稿日期:2022-09-07 修回日期:2022-10-18 出版日期:2023-06-25 发布日期:2023-07-24
通讯作者: 冯丹（1984-），女，教授，主要从事污染物防治与生态修复等方面的研究. E-mail: 282625430@qq.com
作者简介:陈勉为（1995-），男，讲师，主要从事生态环境遥感监测技术等方面的研究. E-mail: mianweichen@163.com
基金资助:
伊犁师范大学校级一般项目(2022YSYB018);新疆维吾尔自治区自然科学基金项目(2021D01C461)

Dynamic monitoring and prediction of eco-environmental quality in Yining City based on RSEI and ANN-CA-Markov model

CHEN Mianwei^1,²(),FENG Dan^1,²(),ZHANG Shikai^1,²,JIANG Yu^1,²,ZHANG Xinlan^1,²

1. School of Chemistry and Environmental Science, Yili Normal University, Yining 835000, Xinjiang, China
2. Key Laboratory of Pollutant Chemistry and Environmental Treatment, Yili Normal University, Yining 835000, Xinjiang, China

Received:2022-09-07 Revised:2022-10-18 Online:2023-06-25 Published:2023-07-24
Contact: Dan FENG

摘要/Abstract

摘要：

伊宁市位于中国新疆西北边陲伊犁河谷内，土地肥沃，水资源和生物资源丰富，具有发展农、林、牧业的优越自然条件，但由于城市化进程过快，导致生态遭受破坏，生态环境质量不断下降，因此依托遥感生态指数（Remote-sensing ecological index，RSEI）及ANN-CA-Markov模型，科学、合理地利用Landsat TM5/OLI-TIRS8遥感数据对开展伊宁市2006—2021年生态环境动态评价及预测具有重要意义。结果表明：（1）绿度和湿度对伊宁市生态水平具有正面影响，干度和热度对伊宁市生态水平具有负面影响，影响伊宁市生态环境质量的主要因素依次为绿度、热度、干度、湿度，符合伊犁河谷地区所表现出来的生态状况。（2）伊宁市RSEI平均值为0.451，总体处于中等水平，生态环境质量变化呈现两极逐渐缩小的趋势，但RSEI指标中等区域及较差区域的面积正在逐年增大，总体生态环境呈现稳中向差的发展趋势。（3）预计2026年和2031年伊宁市北坡地区生态得到一定程度改善，结合《新疆伊宁市城市2018—2035年总体规划》，城区在未来生态环境质量依旧保持在中等水平，城市将继续向外扩张，可耕地面积将继续减少。

关键词: 遥感生态指数, Landsat卫星, 生态环境质量预测, ANN-CA-Markov模型, 伊宁市

Abstract:

Yining City, located at the northwest border of Xinjiang in the Ili River Valley, China, has fertile land, abundant water and biological resources, and favorable natural conditions for the development of agriculture, forestry, and animal husbandry. However, due to the rapid urbanization process, the ecology has been damaged, and the quality of the ecological environment has declined. Therefore, it is of great significance to scientifically and reasonably utilize Landsat TM5/OLI-TIRS8 remote sensing data to conduct a dynamic evaluation and prediction of the ecological environment in Yining City from 2006 to 2021 based on the remote-sensing ecological index (RSEI) and ANN-CA-Markov model. The results show that: (1) Greenness and humidity have a positive impact on the ecological level of Yining City, while dryness and heat have a negative impact. The main factors affecting the ecological environment quality of Yining City are, in order, greenness, heat, dryness, and humidity, which are consistent with the ecological conditions exhibited in the Ili River Valley region. (2) The average RSEI indicator of Yining City is 0.451, which is at a moderate level overall. The ecological environment quality change shows a trend of gradually narrowing extremes, but the areas with medium and poor RSEI indices are increasing year by year, and the overall ecological environment presents a stable-to-worse development trend. (3) It is predicted that by 2026 and 2031, the ecological environment in the northern slope region of Yining City will improve to a certain extent. In combination with the overall urban planning of Yining City, Xinjiang, from 2018 to 2035, the ecological environment quality in the urban area will still remain at a medium level in the future, and the city will continue to expand outward, resulting in a continuous reduction in arable land area.

Key words: RSEI, Landsat, eco-environmental quality prediction, ANN-CA-Markov model, Yining City

陈勉为, 冯丹, 张仕凯, 江雨, 张新兰. 基于RSEI和ANN-CA-Markov模型的伊宁市生态环境质量动态监测及预测研究[J]. 干旱区地理, 2023, 46(6): 911-921.

CHEN Mianwei, FENG Dan, ZHANG Shikai, JIANG Yu, ZHANG Xinlan. Dynamic monitoring and prediction of eco-environmental quality in Yining City based on RSEI and ANN-CA-Markov model[J]. Arid Land Geography, 2023, 46(6): 911-921.

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序号	传感器类型	日期（年-月-日）	云量/%
1	Landsat TM5	2006-09-06	1.00
2	Landsat TM5	2011-09-20	2.00
3	Landsat OLI-TIRS8	2016-09-01	0.40
4	Landsat OLI-TIRS8	2021-09-15	0.75

卫星	传感器类型	波段	名称	波长范围/μm	中心波长/μm
Landsat TM5	陆地成像仪TM	ρ_blue	蓝波段	0.450~0.520	0.485
		ρ_green	绿波段	0.520~0.600	0.560
		ρ_red	红波段	0.630~0.690	0.660
		ρ_NIR	近红外波段	0.760~0.900	0.830
		ρ_SWIR1	短波红外1	1.550~1.750	1.650
		ρ_SWIR2	短波红外2	10.400~12.500	11.475
	热红外传感器TIRS	ρ_TIRS1	热红外1	2.080~2.350	2.215
Landsat OLI-TIRS8	陆地成像仪OLI	ρ_coastal	海岸波段	0.433~0.453	0.443
		ρ_blue	蓝波段	0.450~0.515	0.482
		ρ_green	绿波段	0.525~0.600	0.562
		ρ_red	红波段	0.630~0.680	0.655
		ρ_NIR	近红外波段	0.845~0.885	0.865
		ρ_SWIR1	短波红外1	1.560~1.660	1.610
		ρ_SWIR2	短波红外2	2.100~2.300	2.200
		ρ_pan	全色波段	0.500~0.680	0.640
		ρ_cirrus	卷云波段	1.360~1.390	1.375
	热红外传感器TIRS	ρ_TIRS1	热红外1	10.60~11.19	10.900
	热红外传感器TIRS	ρ_TIRS2	热红外2	11.50~12.51	12.000

年份	分量指标	PC1	PC2	PC3	PC4
2006	NDVI	0.511	0.675	-0.514	0.138
	WET	0.496	-0.362	0.221	0.758
	NDSI	-0.488	-0.266	-0.726	0.405
	TEM	-0.505	0.585	0.399	0.493
	特征值	0.374	0.027	0.014	0.009
	贡献率/%	85.67	6.66	5.19	2.48
2011	NDVI	0.531	0.781	-0.372	0.180
	WET	0.468	-0.348	0.288	0.716
	NDSI	-0.482	-0.155	-0.665	0.550
	TEM	-0.515	0.496	0.581	0.390
	特征值	0.246	0.034	0.027	0.015
	贡献率/%	76.39	10.60	8.35	4.66
2016	NDVI	0.524	0.512	-0.582	0.386
	WET	0.464	0.660	0.229	0.536
	NDSI	-0.474	-0.309	-0.732	0.739
	TEM	-0.507	0.480	0.267	0.131
	特征值	0.258	0.032	0.029	0.004
	贡献率/%	79.67	10.00	9.02	1.31
2021	NDVI	0.515	0.810	-0.270	0.268
	WET	0.447	-0.392	0.349	0.677
	NDSI	-0.496	-0.130	-0.602	0.613
	TEM	-0.508	0.417	0.665	0.307
	特征值	0.297	0.037	0.020	0.008
	贡献率/%	82.17	10.14	5.61	2.08

年份	差		较差		中等		较好		好
年份	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
2006	161.10	23.89	113.32	16.80	161.14	23.89	152.03	22.54	86.84	12.88
2011	102.70	15.23	196.86	29.19	196.37	29.12	122.17	18.12	56.32	8.35
2016	126.63	18.78	203.17	30.13	162.34	24.07	125.77	18.65	56.51	8.38
2021	148.18	21.97	162.91	24.16	165.20	24.50	120.56	17.88	77.57	11.50

生态质量变化等级		面积
生态质量变化等级		2006—2011年	2011—2016年	2016—2021年	2006—2021年
变差	-4	1.16	2.36	4.67	0.30
	-3	16.52	9.23	22.45	9.43
	-2	49.95	43.58	43.27	50.55
	-1	110.77	173.13	112.34	142.02
不变	0	315.53	268.45	247.89	312.99
变好	+1	156.18	118.97	181.61	125.06
	+2	22.42	48.40	50.72	28.74
	+3	1.33	9.72	9.93	5.10
	+4	0.56	0.57	1.54	0.24

基于RSEI和ANN-CA-Markov模型的伊宁市生态环境质量动态监测及预测研究

Dynamic monitoring and prediction of eco-environmental quality in Yining City based on RSEI and ANN-CA-Markov model

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