Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA)

被引:132
作者
Chu, Lin [1 ,2 ]
Sun, Tiancheng [1 ,2 ]
Wang, Tianwei [1 ,2 ]
Li, Zhaoxia [1 ,2 ]
Cai, Chongfa [1 ,2 ]
机构
[1] Huazhong Agr Univ, Soil & Water Conservat Res Ctr, Key Lab Arable Land Conservat Middle & Lower Reac, Minist Agr, Wuhan 430070, Hubei, Peoples R China
[2] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
landscape pattern; InVEST model; habitat quality; CA-Markov model; logistic regression analysis; Three Gorges Reservoir Area (TGRA); LAND-USE CHANGE; LOGISTIC-REGRESSION; ECOSYSTEM SERVICES; LANDSLIDE SUSCEPTIBILITY; PROTECTED AREAS; URBAN-GROWTH; RIVER DELTA; GIS; BIODIVERSITY; CONSERVATION;
D O I
10.3390/su10113854
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The spatial pattern of landscape has great influence on the biodiversity provided by ecosystem. Understanding the impact of landscape pattern dynamics on habitat quality is significant in regional biodiversity conservation, ensuring ecological security guarantee, and maintaining the ecological environmental sustainability. Here, combining CA-Markov and InVEST model, we investigated the evolution of landscape pattern and habitat quality, and presented an explanation for variability of biodiversity linked to landscape pattern in Hubei section of Three Gorges Reservoir Area (TGRA). The spatial-temporal evolution characteristic of landscape pattern from 1990 to 2010 were analyzed by Markov chain. Then, the spatial pattern of habitat quality and its variation in three phases were computed by InVEST model. The driving force for landscape variation was explored by using Logistic regression analysis. Next, the CA-Markov model was used to simulate the future landscape pattern in 2020. Finally, future habitat quality maps were obtained by InVEST model predicted landscape maps. The results concluded that, the overall landscape pattern has changed slightly from 1990 to 2010. Woodland, waters and construction land had the greatest variations in proportion among the landscape types. The area of woodland has been decreasing gradually below the average elevation of 140 m, and the area of waters and construction land increased sharply. Logistics regression results indicated that terrain and climate were the most influencing natural factors compared with human factors. The Kappa coefficient reached 0.92, indicating that CA-Markov model had a good performance in future landscape prediction by adding nighttime light data as restriction factor. The biodiversity has been declining over the past 20 years due to the habitat degradation and landscape pattern variation. Overall, the maximum values of habitat degradation index were 0.1188, 0.1194 and 0.1195 respectively, showing a continuously increasing trend from 1990 to 2010. Main urban areas of Yichang city and its surrounding areas has higher habitat degradation index. The average values of habitat quality index of the whole region were 0.8563, 0.8529 and 0.8515 respectively, showing a continuously decreasing trend. The lower habitat quality index mainly located in the urban land as well as the main and tributary banks of the Yangtze River. Under the business as usual scenario, habitat quality continued to maintain the variation trend of the previous decade, showing a reducing habitat quality index and an increasing area of artificial surface. Under the ecological protection scenario, the variation of habitat quality in this scenario represented reverse trend to the previous decade, exhibiting an increase of habitat quality index and an increasing area of woodland and grassland. Construction of Three Gorges Dam, impoundment of Three Gorges Reservoir (TGR), resettlement of Three Gorges Project and urbanization were the most explanatory driving forces for landscape variation and degradation of habitat quality. The research may be useful for understanding the impact of landscape pattern dynamics on biodiversity, and provide scientific basis for optimizing regional natural environment, as well as effective decision-making support to local government for landscape planning and biodiversity conservation.
引用
收藏
页数:28
相关论文
共 80 条
[1]   Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach [J].
Adhikari, Sanchayeeta ;
Southworth, Jane .
REMOTE SENSING, 2012, 4 (10) :3215-3243
[2]   Tropical deforestation in Madagascar: analysis using hierarchical spatially explicit, Bayesian regression models [J].
Agarwal, DK ;
Silander, JA ;
Gelfand, AE ;
Dewar, RE ;
Mickelson, JG .
ECOLOGICAL MODELLING, 2005, 185 (01) :105-131
[3]   Providing insights on habitat connectivity for male brown bears: A combination of habitat suitability and landscape graph-based models [J].
Almpanidou, Vasiliki ;
Mazaris, Antonios D. ;
Mertzanis, Yorgos ;
Avraam, Ioannis ;
Antoniou, Ioannis ;
Pantis, John D. ;
Sgardelis, Stefanos P. .
ECOLOGICAL MODELLING, 2014, 286 :37-44
[4]  
[Anonymous], EXPANDING PARTNERSHI
[5]   Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy [J].
Atkinson, PM ;
Massari, R .
COMPUTERS & GEOSCIENCES, 1998, 24 (04) :373-385
[6]   The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J].
Ayalew, L ;
Yamagishi, H .
GEOMORPHOLOGY, 2005, 65 (1-2) :15-31
[7]   Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation [J].
David J. B. Baldwin ;
Kevin Weaver ;
Frank Schnekenburger ;
Ajith H. Perera .
Landscape Ecology, 2004, 19 (3) :255-271
[8]  
Batty M., 1999, Computers, Environment and Urban Systems, V23, P205, DOI 10.1016/S0198-9715(99)00015-0
[9]   The use of probabilistic habitat suitability models for biodiversity action planning [J].
Bayliss, JL ;
Simonite, V ;
Thompson, S .
AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2005, 108 (03) :228-250
[10]  
Bolliger J, 2007, IDENTIFYING QUANTIFY, P177