Evaluation and Prediction of Habitat Quality in Inner Mongolia Based on CA-Markov Model and Improved Remote Sensing Ecological Index

doi:10.11924/j.issn.1000-6850.casb2025-0634

Abstract

Abstract:

Inner Mongolia is located in the upper reaches of the North China region, and its ecological environment is sensitive and fragile. In recent years, due to climate change and rapid economic growth, ecological environment problems have become increasingly prominent. Conducting ecological environment quality assessment and trend prediction in Inner Mongolia is of great significance for guiding ecological environment protection work. This article is based on the GEE platform, introducing aerosols (AOD) and desertification difference index (DDI), and constructing an improved remote sensing ecological index ARSEI to dynamically monitor and predict the ecological environment quality of Inner Mongolia from 2000 to 2023. The spatial autocorrelation of Inner Mongolia's ecological environment quality is discussed using spatial autocorrelation, and the CA-Markov model is used to predict the future ecological environment quality of Inner Mongolia. The results show that: (1) this article introduces the aerosol AOD and desertification difference index DDI to construct the ARSEI index, with a PC1 contribution of over 87%, which can better concentrate the characteristics of various ecological indicators, with little difference from RSEI, and the grading results are basically consistent. It can more accurately evaluate the ecological environment quality of Inner Mongolia and has strong applicability. (2) The ecological environment quality in Inner Mongolia from 2000 to 2023 was mainly poor and moderate, showing a decreasing trend from east to west in spatial distribution. The Inner Mongolia region experienced severe degradation from 2000 to 2005, with a degradation area accounting for 21.18% and an improvement area accounting for 8.11%. Since then, the ecological environment quality has gradually improved. (3) The global Moran index of Inner Mongolia for six years has been above 0.606, and the spatial agglomeration characteristics within the region are obvious, mainly distributed in high-high and low-low patterns. The overall trend of improving the ecological environment quality center of each level in spatial distribution is from east to west and from north to south. (4) The ARSEI prediction indicates that the ecological quality deterioration in central and western Inner Mongolia is slightly higher than improvement potential in the future. Therefore, it is crucial to prioritize ecological restoration projects in the ecologically fragile areas of this region.

Key words: Inner Mongolia, improved remote sensing ecological index, habitat quality, spatial autocorrelation, CA-Markov model

ZHAO Yueji, JIANG Huyuan, ZHANG Yanbo, LI Dan, XIA Zeyu. Evaluation and Prediction of Habitat Quality in Inner Mongolia Based on CA-Markov Model and Improved Remote Sensing Ecological Index[J]. Chinese Agricultural Science Bulletin, 2026, 42(2): 171-183.

Figures/Tables 12

References 39

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指标	计算公式
植被指数/NDVI	$NDVI=({\rho }_{nir}-{\rho }_{red})/({\rho }_{nir}+{\rho }_{red})$
湿度分量/WET	$WET=0.1147{\rho }_{red}+0.2489{\rho }_{nir1}+0.2408{\rho }_{blue}+0.3132{\rho }_{green}^{}-0.3122{\rho }_{nir2}-0.6416{\rho }_{swir1}-0.5087{\rho }_{swir2}$
干度指数/NDBSI	NDBSI=(IBI+SI)/2 $IBI=\left[\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}-\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)\right]/\left[\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}+\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)\right]$ $SI=\left[\left({\rho }_{swir1}+{\rho }_{red}\right)-\left({\rho }_{nir1}+{\rho }_{blue}\right)\right]/\left[\left({\rho }_{swir1}+{\rho }_{red}\right)+\left({\rho }_{nir1}+{\rho }_{blue}\right)\right]$
地表温度/LST	LST=0.02DN-273.15
荒漠化差值指数/DDI	$A\mathrm{l}\mathrm{b}edo=0.16{\rho }_{red}+0.291{\rho }_{nir1}+0.243{\rho }_{blue}+0.116{\rho }_{green}+0.112{\rho }_{nir2}+0.081{\rho }_{swir2}-0.0015$ $A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{标}}=\left(A\mathrm{l}\mathrm{b}edo-A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)/\left(A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{a}\mathrm{x}}-A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)$ $NDV{I}_{\mathrm{标}}=\left(NDVI-NDV{I}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)/\left(NDV{I}_{\mathrm{m}\mathrm{a}\mathrm{x}}-NDV{I}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)$ $DDI=-\frac{1}{{k}_{i}}\times NDV{I}_{\mathrm{标}}-\mathrm{A}\mathrm{l}\mathrm{b}\mathrm{e}\mathrm{d}{\mathrm{o}}_{\mathrm{标}}$
气溶胶/AOD	来自MCD19A2产品Optical_Depth_047波段

指标	计算公式
植被指数/NDVI	$NDVI=({\rho }_{nir}-{\rho }_{red})/({\rho }_{nir}+{\rho }_{red})$
湿度分量/WET	$WET=0.1147{\rho }_{red}+0.2489{\rho }_{nir1}+0.2408{\rho }_{blue}+0.3132{\rho }_{green}^{}-0.3122{\rho }_{nir2}-0.6416{\rho }_{swir1}-0.5087{\rho }_{swir2}$
干度指数/NDBSI	NDBSI=(IBI+SI)/2 $IBI=\left[\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}-\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)\right]/\left[\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}+\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)\right]$ $SI=\left[\left({\rho }_{swir1}+{\rho }_{red}\right)-\left({\rho }_{nir1}+{\rho }_{blue}\right)\right]/\left[\left({\rho }_{swir1}+{\rho }_{red}\right)+\left({\rho }_{nir1}+{\rho }_{blue}\right)\right]$
地表温度/LST	LST=0.02DN-273.15
荒漠化差值指数/DDI	$A\mathrm{l}\mathrm{b}edo=0.16{\rho }_{red}+0.291{\rho }_{nir1}+0.243{\rho }_{blue}+0.116{\rho }_{green}+0.112{\rho }_{nir2}+0.081{\rho }_{swir2}-0.0015$ $A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{标}}=\left(A\mathrm{l}\mathrm{b}edo-A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)/\left(A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{a}\mathrm{x}}-A\mathrm{l}\mathrm{b}ed{o}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)$ $NDV{I}_{\mathrm{标}}=\left(NDVI-NDV{I}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)/\left(NDV{I}_{\mathrm{m}\mathrm{a}\mathrm{x}}-NDV{I}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)$ $DDI=-\frac{1}{{k}_{i}}\times NDV{I}_{\mathrm{标}}-\mathrm{A}\mathrm{l}\mathrm{b}\mathrm{e}\mathrm{d}{\mathrm{o}}_{\mathrm{标}}$
气溶胶/AOD	来自MCD19A2产品Optical_Depth_047波段

年份	生态因子	PC1	PC2	PC3	PC4	PC5	PC6
2000	NDVI	0.6468	-0.2549	0.4299	0.4233	-0.3861	0.0608
	WET	0.0382	-0.0315	-0.2853	-0.1201	-0.2189	0.9240
	LST	-0.1301	-0.5165	-0.5905	0.5908	0.0984	-0.0945
	NDBSI	-0.1593	-0.3893	-0.1513	-0.4521	-0.7184	-0.2824
	DDI	-0.6092	-0.4406	0.6019	0.1040	0.0908	0.2310
	AOD	-0.4084	-0.5671	-0.0033	-0.4921	0.5187	0.0217
	贡献率/%	87.18	6.55	4.94	3.24	2.65	0.45
2005	NDVI	0.5758	-0.3188	0.4875	0.4383	-0.3676	0.0437
	WET	0.0165	0.1569	0.2551	0.1526	0.3041	-0.8912
	LST	-0.0665	-0.4828	-0.4917	-0.1383	-0.5549	-0.4400
	NDBSI	-0.5721	0.4846	-0.0049	-0.6012	-0.2591	-0.0969
	DDI	-0.4768	-0.1583	0.6745	-0.4664	-0.2733	-0.0167
	AOD	-0.3303	-0.6171	0.0208	-0.4318	0.5680	0.0233
	贡献率/%	89.88	5.51	4.33	2.18	1.98	0.12
2010	NDVI	0.5679	-0.3539	0.4568	0.4302	-0.3964	0.0364
	WET	0.2230	0.5130	0.4221	0.2830	0.6548	-0.0010
	LST	-0.3617	0.4453	0.1203	-0.5625	-0.3057	0.4965
	NDBSI	-0.4759	-0.1010	0.7675	-0.3478	-0.1035	-0.2064
	DDI	-0.3818	-0.2273	0.0971	0.2647	0.1325	0.8399
	AOD	-0.3532	0.5927	-0.0148	0.4769	-0.5407	-0.0634
	贡献率/%	90.37	4.94	3.18	2.37	1.01	0.12
2015	NDVI	0.6041	-0.3368	0.4323	0.4585	-0.3528	-0.0079
	WET	0.1934	0.5910	0.5527	0.0676	0.5350	-0.1305
	LST	-0.0925	-0.0357	-0.5248	0.7052	0.4655	0.0266
	NDBSI	-0.5943	-0.4595	0.3794	0.2275	0.1919	-0.4508
	DDI	-0.2820	0.1357	-0.2959	-0.1206	-0.1462	-0.8824
	AOD	-0.3955	0.5535	0.0267	0.4707	-0.5608	-0.0216
	贡献率/%	89.39	4.19	2.86	1.96	1.45	0.15
2020	NDVI	0.5554	-0.3012	0.5142	0.5028	-0.2891	-0.0015
	WET	0.1911	-0.2998	-0.8408	0.3652	-0.1804	-0.0271
	LST	-0.6855	0.6009	-0.1497	-0.0268	0.3783	-0.0534
	NDBSI	-0.3462	-0.0507	-0.0727	-0.6962	-0.6223	0.0179
	DDI	-0.0064	-0.1037	0.0304	-0.0637	0.0441	-0.9911
	AOD	-0.2554	-0.6670	0.0118	-0.3526	0.5929	0.1176
	贡献率/%	87.09	5.91	2.97	2.48	1.22	0.33
2023	NDVI	0.5999	-0.3132	0.4245	0.4745	-0.3677	0.0369
	WET	0.0249	-0.2920	-0.6964	0.5787	0.1227	-0.2813
	LST	-0.1668	-0.4656	-0.2783	-0.4051	-0.7157	0.0411
	NDBSI	-0.6611	-0.4259	-0.5028	-0.2062	0.1031	-0.2750
	DDI	-0.2336	0.1638	0.0673	-0.2516	-0.0975	-0.9172
	AOD	-0.3464	-0.6258	-0.0051	-0.4123	0.5634	0.0293
	贡献率/%	90.45	4.83	3.95	1.95	1.05	0.07

年份	生态因子	PC1	PC2	PC3	PC4	PC5	PC6
2000	NDVI	0.6468	-0.2549	0.4299	0.4233	-0.3861	0.0608
	WET	0.0382	-0.0315	-0.2853	-0.1201	-0.2189	0.9240
	LST	-0.1301	-0.5165	-0.5905	0.5908	0.0984	-0.0945
	NDBSI	-0.1593	-0.3893	-0.1513	-0.4521	-0.7184	-0.2824
	DDI	-0.6092	-0.4406	0.6019	0.1040	0.0908	0.2310
	AOD	-0.4084	-0.5671	-0.0033	-0.4921	0.5187	0.0217
	贡献率/%	87.18	6.55	4.94	3.24	2.65	0.45
2005	NDVI	0.5758	-0.3188	0.4875	0.4383	-0.3676	0.0437
	WET	0.0165	0.1569	0.2551	0.1526	0.3041	-0.8912
	LST	-0.0665	-0.4828	-0.4917	-0.1383	-0.5549	-0.4400
	NDBSI	-0.5721	0.4846	-0.0049	-0.6012	-0.2591	-0.0969
	DDI	-0.4768	-0.1583	0.6745	-0.4664	-0.2733	-0.0167
	AOD	-0.3303	-0.6171	0.0208	-0.4318	0.5680	0.0233
	贡献率/%	89.88	5.51	4.33	2.18	1.98	0.12
2010	NDVI	0.5679	-0.3539	0.4568	0.4302	-0.3964	0.0364
	WET	0.2230	0.5130	0.4221	0.2830	0.6548	-0.0010
	LST	-0.3617	0.4453	0.1203	-0.5625	-0.3057	0.4965
	NDBSI	-0.4759	-0.1010	0.7675	-0.3478	-0.1035	-0.2064
	DDI	-0.3818	-0.2273	0.0971	0.2647	0.1325	0.8399
	AOD	-0.3532	0.5927	-0.0148	0.4769	-0.5407	-0.0634
	贡献率/%	90.37	4.94	3.18	2.37	1.01	0.12
2015	NDVI	0.6041	-0.3368	0.4323	0.4585	-0.3528	-0.0079
	WET	0.1934	0.5910	0.5527	0.0676	0.5350	-0.1305
	LST	-0.0925	-0.0357	-0.5248	0.7052	0.4655	0.0266
	NDBSI	-0.5943	-0.4595	0.3794	0.2275	0.1919	-0.4508
	DDI	-0.2820	0.1357	-0.2959	-0.1206	-0.1462	-0.8824
	AOD	-0.3955	0.5535	0.0267	0.4707	-0.5608	-0.0216
	贡献率/%	89.39	4.19	2.86	1.96	1.45	0.15
2020	NDVI	0.5554	-0.3012	0.5142	0.5028	-0.2891	-0.0015
	WET	0.1911	-0.2998	-0.8408	0.3652	-0.1804	-0.0271
	LST	-0.6855	0.6009	-0.1497	-0.0268	0.3783	-0.0534
	NDBSI	-0.3462	-0.0507	-0.0727	-0.6962	-0.6223	0.0179
	DDI	-0.0064	-0.1037	0.0304	-0.0637	0.0441	-0.9911
	AOD	-0.2554	-0.6670	0.0118	-0.3526	0.5929	0.1176
	贡献率/%	87.09	5.91	2.97	2.48	1.22	0.33
2023	NDVI	0.5999	-0.3132	0.4245	0.4745	-0.3677	0.0369
	WET	0.0249	-0.2920	-0.6964	0.5787	0.1227	-0.2813
	LST	-0.1668	-0.4656	-0.2783	-0.4051	-0.7157	0.0411
	NDBSI	-0.6611	-0.4259	-0.5028	-0.2062	0.1031	-0.2750
	DDI	-0.2336	0.1638	0.0673	-0.2516	-0.0975	-0.9172
	AOD	-0.3464	-0.6258	-0.0051	-0.4123	0.5634	0.0293
	贡献率/%	90.45	4.83	3.95	1.95	1.05	0.07

指标		2000年	2005年	2010年	2015年	2020年	2023年
RSEI	数值	0.4356	0.4101	0.4175	0.4266	0.4179	0.4507
RSEI	等级	中等	中等	中等	中等	中等	中等
ARSEI	数值	0.4382	0.3949	0.3911	0.4278	0.4334	0.4583
ARSEI	等级	中等	差	差	中等	中等	中等
差值		0.0026	0.0161	0.0264	0.0012	0.0155	0.0076
差值百分比/%		0.5969	4.0769	6.7502	0.2813	3.7090	1.6863