Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. Research Framework
2.3.2. Assessment of EQ
- Retrieval of the RSEI
- 2.
- Acquisition of the RSEI
2.3.3. Assessment of the EFI
2.3.4. Landscape Pattern Analysis Based on MSPA
2.3.5. Selection of Ecological Sources
2.3.6. Construction of Resistance Surface
2.3.7. Circuit Theory
3. Results
3.1. Distribution of the RSEI
3.2. Distribution of the EFI
3.3. Analysis of Landscape Patterns Based on MSPA
3.4. Ecological Source Identification and Resistance Surface Construction
3.5. Distribution of Ecological Corridors
3.6. Identification of Pinch Areas and Barriers
3.7. Construction of the Ecological Network
4. Discussion
4.1. Effectiveness of the Approach Based on the RSEI, EFI, and MSPA
4.2. Ecological Network Construction of Panzhou City
4.3. Insights for the Development of Panzhou City
4.4. Limitations and Directions for Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. Assignment of the Fsic Indicator in WR
Soil Texture | Value | Soil Texture | Value |
---|---|---|---|
Clay (heavy) | 1/13 | Sandy clay | 8/13 |
Silty Clay | 2/13 | Loam | 9/13 |
Clay (light) | 3/13 | Sandy Clay Loam | 10/13 |
Silty Clay Loam | 4/13 | Sandy Loam | 11/13 |
Clay Loam | 5/13 | Loamy Sand | 12/13 |
Silt | 6/13 | Sand | 13/13 |
Silt Loam | 7/13 |
Appendix A.2. Calculation Method of Ac
Land Cover Types | Farmland | Forestland | Grassland | Waterbody | Construction Land |
---|---|---|---|---|---|
C | 0.228 | 0.052 | 0.112 | 0 | 0 |
Appendix A.3. Calculation Method of Qxj
Threat Factors | Maximum Influence Distance (km) | Weight | Decay Linear Correlation |
---|---|---|---|
Paddy field | 1 | 0.4 | Exponential |
Arid field | 3 | 0.5 | Exponential |
Urban construction land | 9 | 1 | Exponential |
Rural residential land | 7 | 0.8 | Exponential |
Other construction land | 5 | 0.6 | Exponential |
Land Cover Types | Habitat Suitability | PF | AF | UCL | RRL | OCL |
---|---|---|---|---|---|---|
Paddy field | 0.4 | 0 | 1 | 0.4 | 0.35 | 0.35 |
Arid field | 0.3 | 1 | 0 | 0.35 | 0.3 | 0.3 |
Forestland | 1 | 0.5 | 0.6 | 0.9 | 0.8 | 0.8 |
Shrub land | 0.9 | 0.4 | 0.5 | 0.8 | 0.7 | 0.7 |
Wood land | 0.8 | 0.5 | 0.6 | 0.7 | 0.6 | 0.6 |
Other forestland | 0.7 | 0.5 | 0.6 | 0.6 | 0.5 | 0.5 |
Highly covered grassland | 0.8 | 0.4 | 0.45 | 0.6 | 0.55 | 0.5 |
Moderately covered grassland | 0.7 | 0.45 | 0.5 | 0.55 | 0.5 | 0.5 |
Low-covered grassland | 0.6 | 0.5 | 0.55 | 0.5 | 0.4 | 0.45 |
River and canals | 0.9 | 0.45 | 0.5 | 0.8 | 0.7 | 0.6 |
Lake | 0.7 | 0.65 | 0.7 | 0.75 | 0.55 | 0.2 |
Urban construction land | 0 | 0 | 0 | 0 | 0 | 0 |
Rural residential land | 0 | 0 | 0 | 0 | 0 | 0 |
Other construction land | 0 | 0 | 0 | 0 | 0 | 0 |
Appendix A.4. The Ecological Meaning of Spatial Pattern Classes in MSPA
Pattern Class | Ecological Meaning |
---|---|
Core | Large habitat patches that can serve as source areas and provide habitats or migration places for wildlife |
Islet | Small patches that are weakly connected to each other, providing a place for species to spread and communicate and promoting the flow of matter and energy |
Perforation | Transition zone between the core area and the nongreen landscape area: the edge of the internal patch, which has edge effects |
Edge | Transition zone between the core area and the nongreen landscape area; has an edge effect and protects the ecological process of the core area |
Bridge | Connecting corridor of the adjacent core area; provides the necessary pathways for species diffusion and energy exchange between adjacent patches of core areas |
Loop | Connects corridors inside the same core area to provide access to species diffusion and energy exchange within the core patch |
Branch | Only one side is connected to an edge, bridge, loop, or perforation |
Appendix A.5. Calculation Method of Resistance Factors and the Coefficient of Variation Method
Appendix A.5.1. Vegetation Coverage
Appendix A.5.2. Water and Soil Loss Sensitivity Index
Appendix A.5.3. Rocky Desertification Sensitivity Index
Appendix A.5.4. Coefficient of Variation Weight Method
Appendix B
References
- Bloom, D.E.; Canning, D.; Fink, G. Urbanization and the Wealth of Nations. Science 2008, 319, 772–775. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, Z.; Gan, X.; Dai, W.; Huang, Y. Construction of an Ecological Security Pattern and the Evaluation of Corridor Priority Based on ESV and the “Importance-Connectivity” Index: A Case Study of Sichuan Province, China. Sustainability 2022, 14, 3985. [Google Scholar] [CrossRef]
- Li, Z.-T.; Li, M.; Xia, B.-C. Spatio-temporal dynamics of ecological security pattern of the Pearl River Delta urban agglomeration based on LUCC simulation. Ecol. Indic. 2020, 114, 106319. [Google Scholar] [CrossRef]
- Zhang, Y.J.; Song, W.; Fu, S.; Yang, D.Z. Decoupling of Land Use Intensity and Ecological Environment in Gansu Province, China. Sustainability 2020, 12, 2779. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Fang, F.; Li, Y. Key issues of land use in China and implications for policy making. Land Use Policy 2014, 40, 6–12. [Google Scholar] [CrossRef]
- Wang, Z.; Shi, P.; Zhang, X.; Tong, H.; Zhang, W.; Liu, Y. Research on Landscape Pattern Construction and Ecological Restoration of Jiuquan City Based on Ecological Security Evaluation. Sustainability 2021, 13, 5732. [Google Scholar] [CrossRef]
- Peng, J.; Lyu, D.-n.; Dong, J.-q.; Liu, Y.-x.; Liu, Q.-y.; Li, B. Processes coupling and spatial integration: Characterizing ecological restoration of territorial space in view of landscape ecology. J. Nat. Resour. 2020, 35, 3–13. [Google Scholar] [CrossRef]
- Chen, C.; Shi, L.; Lu, Y.; Yang, S.; Liu, S. The Optimization of Urban Ecological Network Planning Based on the Minimum Cumulative Resistance Model and Granularity Reverse Method: A Case Study of Haikou, China. IEEE Access 2020, 8, 43592–43605. [Google Scholar] [CrossRef]
- Chen, D.-c.; Shi, Z.-k.; Wang, Z.-j.; Yu, C. Ecological Network Construction and Spatial Conflict Identification Around Taihu Lake Area in Suzhou City. J. Ecol. Rural. Environ. 2020, 36, 778–787. [Google Scholar] [CrossRef]
- Zhang, J.; Jiang, F.; Cai, Z.; Dai, Y.; Liu, D.; Song, P.; Hou, Y.; Gao, H.; Zhang, T. Resistance-Based Connectivity Model to Construct Corridors of the Przewalski’s Gazelle (Procapra Przewalskii) in Fragmented Landscape. Sustainability 2021, 13, 1656. [Google Scholar] [CrossRef]
- Aminzadeh, B.; Khansefid, M. A case study of urban ecological networks and a sustainable city: Tehran’s metropolitan area. Urban Ecosyst. 2010, 13, 23–36. [Google Scholar] [CrossRef]
- Pierik, M.E.; Dell’Acqua, M.; Confalonieri, R.; Bocchi, S.; Gomarasca, S. Designing ecological corridors in a fragmented landscape: A fuzzy approach to circuit connectivity analysis. Ecol. Indic. 2016, 67, 807–820. [Google Scholar] [CrossRef]
- Hepcan, Ç.C.; Özkan, M.B. Establishing ecological networks for habitat conservation in the case of Çeşme–Urla Peninsula, Turkey. Environ. Monit. Assess. 2011, 174, 157–170. [Google Scholar] [CrossRef]
- Hüse, B.; Szabó, S.; Deák, B.; Tóthmérész, B. Mapping an ecological network of green habitat patches and their role in maintaining urban biodiversity in and around Debrecen city (Eastern Hungary). Land Use Policy 2016, 57, 574–581. [Google Scholar] [CrossRef]
- Ings, T.C.; Montoya, J.M.; Bascompte, J.; Blüthgen, N.; Brown, L.; Dormann, C.F.; Edwards, F.; Figueroa, D.; Jacob, U.; Jones, J.J.; et al. Review: Ecological networks—Beyond food webs. J. Anim. Ecol. 2009, 78, 253–269. [Google Scholar] [CrossRef] [PubMed]
- McRae, B.H.; Dickson, B.G.; Keitt, T.H.; Shah, V.B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 2008, 89, 2712–2724. [Google Scholar] [CrossRef]
- Peng, J.; Zhao, H.; Liu, Y. Urban ecological corridors construction: A review. Acta Ecol. Sin. 2017, 37, 23–30. [Google Scholar] [CrossRef]
- Kong, F.; Yin, H.; Nakagoshi, N.; Zong, Y. Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Landsc. Urban Plan. 2010, 95, 16–27. [Google Scholar] [CrossRef]
- Cunha, N.S.; Magalhães, M.R. Methodology for mapping the national ecological network to mainland Portugal: A planning tool towards a green infrastructure. Ecol. Indic. 2019, 104, 802–818. [Google Scholar] [CrossRef]
- De Montis, A.; Ganciu, A.; Cabras, M.; Bardi, A.; Peddio, V.; Caschili, S.; Massa, P.; Cocco, C.; Mulas, M. Resilient ecological networks: A comparative approach. Land Use Policy 2019, 89, 104207. [Google Scholar] [CrossRef]
- Weber, T.; Sloan, A.; Wolf, J. Maryland’s Green Infrastructure Assessment: Development of a comprehensive approach to land conservation. Landsc. Urban Plan. 2006, 77, 94–110. [Google Scholar] [CrossRef]
- Elbakidze, M.; Angelstam, P.; Yamelynets, T.; Dawson, L.; Gebrehiwot, M.; Stryamets, N.; Johansson, K.-E.; Garrido, P.; Naumov, V.; Manton, M. A bottom-up approach to map land covers as potential green infrastructure hubs for human well-being in rural settings: A case study from Sweden. Landsc. Urban Plan. 2017, 168, 72–83. [Google Scholar] [CrossRef]
- Huang, L.-Y.; Liu, S.-H.; Fang, Y.; Zou, L. Construction of Wuhan’s ecological security pattern under the “quality-risk-requirement” framework. J. Appl. Ecol. 2019, 30, 615–626. [Google Scholar] [CrossRef]
- Sun, J.; Huang, J.; Wang, Q.; Zhou, H. A method of delineating ecological red lines based on gray relational analysis and the minimum cumulative resistance model: A case study of Shawan District, China. Environ. Res. Commun. 2022, 4, 045009. [Google Scholar] [CrossRef]
- Tang, Q.; Li, J.; Tang, T.; Liao, P.; Wang, D. Construction of a Forest Ecological Network Based on the Forest Ecological Suitability Index and the Morphological Spatial Pattern Method: A Case Study of Jindong Forest Farm in Hunan Province. Sustainability 2022, 14, 3082. [Google Scholar] [CrossRef]
- Keitt, T.H.; Urban, D.L.; Milne, B.T. Detecting Critical Scales in Fragmented Landscapes. Conserv. Ecol. 1997, 1. Available online: http://www.jstor.org/stable/26271642 (accessed on 31 May 2022). [CrossRef] [Green Version]
- An, Y.; Liu, S.; Sun, Y.; Shi, F.; Beazley, R. Construction and optimization of an ecological network based on morphological spatial pattern analysis and circuit theory. Landsc. Ecol. 2021, 36, 2059–2076. [Google Scholar] [CrossRef]
- Huang, L.; Wang, J.; Fang, Y.; Zhai, T.; Cheng, H. An integrated approach towards spatial identification of restored and conserved priority areas of ecological network for implementation planning in metropolitan region. Sustain. Cities Soc. 2021, 69, 102865. [Google Scholar] [CrossRef]
- Peng, J.; Zhao, S.; Dong, J.; Liu, Y.; Meersmans, J.; Li, H.; Wu, J. Applying ant colony algorithm to identify ecological security patterns in megacities. Environ. Model. Softw. 2019, 117, 214–222. [Google Scholar] [CrossRef] [Green Version]
- He, P.; Chen, K. Analysis of Blue Infrastructure Network Pattern in the Hanjiang Ecological Economic Zone in China. Water 2022, 14, 1234. [Google Scholar] [CrossRef]
- Peng, J.; Pan, Y.; Liu, Y.; Zhao, H.; Wang, Y. Linking ecological degradation risk to identify ecological security patterns in a rapidly urbanizing landscape. Habitat Int. 2018, 71, 110–124. [Google Scholar] [CrossRef]
- Zhang, Y.-Z.; Jiang, Z.-Y.; Li, Y.-Y.; Yang, Z.-G.; Wang, X.-H.; Li, X.-B. Construction and Optimization of an Urban Ecological Security Pattern Based on Habitat Quality Assessment and the Minimum Cumulative Resistance Model in Shenzhen City, China. Forests 2021, 12, 847. [Google Scholar] [CrossRef]
- Gurrutxaga, M.; Lozano, P.J.; del Barrio, G. GIS-based approach for incorporating the connectivity of ecological networks into regional planning. J. Nat. Conserv. 2010, 18, 318–326. [Google Scholar] [CrossRef]
- Zhao, H.; Jiang, X.; Gu, B.; Wang, K. Evaluation and Functional Zoning of the Ecological Environment in Urban Space&mdash: A Case Study of Taizhou, China. Sustainability 2022, 14, 6619. [Google Scholar] [CrossRef]
- MacDonald, A.J.; Larsen, A.E.; Plantinga, A.J. Missing the people for the trees: Identifying coupled natural–human system feedbacks driving the ecology of Lyme disease. J. Appl. Ecol. 2019, 56, 354–364. [Google Scholar] [CrossRef]
- Belote, R.T.; Dietz, M.S.; McRae, B.H.; Theobald, D.M.; McClure, M.L.; Irwin, G.H.; McKinley, P.S.; Gage, J.A.; Aplet, G.H. Identifying Corridors among Large Protected Areas in the United States. PLoS ONE 2016, 11, e0154223. [Google Scholar] [CrossRef] [Green Version]
- Yu, K. Security patterns and surface model in landscape ecological planning. Landsc. Urban Plan. 1996, 36, 1–17. [Google Scholar] [CrossRef]
- Peng, J.; Wang, A.; Liu, Y.X.; Ma, J.; Wu, J.S. Research progress and prospect on measuring urban ecological land demand. Acta Geogr. Sin. 2015, 70, 333–346. [Google Scholar] [CrossRef]
- Dai, L.; Liu, Y.; Luo, X.Y. Integrating the MCR and DOI models to construct an ecological security network for the urban agglomeration around Poyang Lake, China. Sci. Total Environ. 2021, 754, 141868. [Google Scholar] [CrossRef]
- Knaapen, J.P.; Scheffer, M.; Harms, B. Estimating habitat isolation in landscape planning. Landsc. Urban Plan. 1992, 23, 1–16. [Google Scholar] [CrossRef]
- Xiao, S.; Wu, W.; Guo, J.; Ou, M.; Pueppke, S.G.; Ou, W.; Tao, Y. An evaluation framework for designing ecological security patterns and prioritizing ecological corridors: Application in Jiangsu Province, China. Landsc. Ecol. 2020, 35, 2517–2534. [Google Scholar] [CrossRef]
- Saltelli, A.; Tarantola, S.; Campolongo, F.; Ratto, M. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models; Wiley: Chichester, UK, 2004. [Google Scholar]
- Dimov, I.; Todorov, V.; Sabelfeld, K. A study of highly efficient stochastic sequences for multidimensional sensitivity analysis. Monte Carlo Methods Appl. 2022, 28, 1–12. [Google Scholar] [CrossRef]
- Yue, H.; Liu, Y.; Li, Y.; Lu, Y. Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index. IEEE Access 2019, 7, 51295–51311. [Google Scholar] [CrossRef]
- Willis, K.S. Remote sensing change detection for ecological monitoring in United States protected areas. Biol. Conserv. 2015, 182, 233–242. [Google Scholar] [CrossRef]
- Xu, H.; Wang, M.; Shi, T.; Guan, H.; Fang, C.; Lin, Z. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol. Indic. 2018, 93, 730–740. [Google Scholar] [CrossRef]
- Barbosa, C.C.D.; Atkinson, P.M.; Dearing, J.A. Remote sensing of ecosystem services: A systematic review. Ecol. Indic. 2015, 52, 430–443. [Google Scholar] [CrossRef]
- Gupta, K.; Kumar, P.; Pathan, S.K.; Sharma, K.P. Urban Neighborhood Green Index—A measure of green spaces in urban areas. Landsc. Urban Plan. 2012, 105, 325–335. [Google Scholar] [CrossRef]
- Yang, Z.Y.; Witharana, C.; Hurd, J.; Wang, K.; Hao, R.M.; Tong, S.Q. Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA. Environ. Earth Sci. 2020, 79, 1–13. [Google Scholar] [CrossRef]
- Zhang, H.; Li, J.; Tian, P.; Pu, R.; Cao, L. Construction of ecological security patterns and ecological restoration zones in the city of Ningbo, China. J. Geogr. Sci. 2022, 32, 663–681. [Google Scholar] [CrossRef]
- Hu, X.; Xu, H. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecol. Indic. 2018, 89, 11–21. [Google Scholar] [CrossRef]
- Essa, W.; Verbeiren, B.; van der Kwast, J.; Van de Voorde, T.; Batelaan, O. Evaluation of the DisTrad thermal sharpening methodology for urban areas. Int. J. Appl. Earth Obs. Geoinf. 2012, 19, 163–172. [Google Scholar] [CrossRef]
- Han, N.; Hu, K.; Yu, M.; Jia, P.; Zhang, Y. Incorporating Ecological Constraints into the Simulations of Tropical Urban Growth Boundaries: A Case Study of Sanya City on Hainan Island, China. Appl. Sci. 2022, 12, 6409. [Google Scholar] [CrossRef]
- Yang, X.; Bai, Y.; Che, L.; Qiao, F.; Xie, L. Incorporating ecological constraints into urban growth boundaries: A case study of ecologically fragile areas in the Upper Yellow River. Ecol. Indic. 2021, 124, 107436. [Google Scholar] [CrossRef]
- Zou, C.; Wang, L.; Liu, J. Classification and management of ecological protection redlines in China. Biodivers. Sci. 2015, 23, 716–724. [Google Scholar] [CrossRef] [Green Version]
- Ministry of Environmental Protection of the People’s Repulic of China (MEP); National Development and Reform Commission of the People’s Repulic of China (NDRC). Guidelines for the Delimitation of the Red Line of Ecological Protection; MEP and NDRC: Beijing, China, 2017.
- Williams, J.; Renard, K.; Dyke, P. EPIC: A new method for assessing erosion’s effect on soil productivity. J. Soil Water Conserv. 1983, 38, 381–383. [Google Scholar] [CrossRef]
- SL773-2018; Guidelines for Measurement and Estimation of Soil Erosion in Production and Construction Projects. Ministry of Water Resources of the People’s Repulic of China (MWR): Beijing, China, 2018.
- Richard Sharp, R.C.-K.; Wood, S.; Guerry, A.; Tallis, H.; Ricketts, T.; Nelson, E.; Ennaanay, D.; Wolny, S.; Olwero, N.; Vigerstol, K.; et al. VEST 3.2.0 User‘s Guide; Natural Capital, Project; Stanford University: Stanford, CA, USA; University of Minnesota: Twin Cities, MN, USA; Nature Conservancy: Arlington County, VA, USA; World Wild Life Fund: Stanford, CA, USA, 2015. [Google Scholar]
- Vogt, P.; Ferrari, J.R.; Lookingbill, T.R.; Gardner, R.H.; Riitters, K.H.; Ostapowicz, K. Mapping functional connectivity. Ecol. Indic. 2009, 9, 64–71. [Google Scholar] [CrossRef]
- Riitters, K.H.; Vogt, P.; Soille, P.; Kozak, J.; Estreguil, C. Neutral model analysis of landscape patterns from mathematical morphology. Landsc. Ecol. 2007, 22, 1033–1043. [Google Scholar] [CrossRef]
- Soille, P.; Vogt, P. Morphological segmentation of binary patterns. Pattern Recognit. Lett. 2009, 30, 456–459. [Google Scholar] [CrossRef]
- Li, F.; Ye, Y.; Song, B.; Wang, R. Evaluation of urban suitable ecological land based on the minimum cumulative resistance model: A case study from Changzhou, China. Ecol. Model. 2015, 318, 194–203. [Google Scholar] [CrossRef]
- Ye, H.; Yang, Z.; Xu, X. Ecological Corridors Analysis Based on MSPA and MCR Model—A Case Study of the Tomur World Natural Heritage Region. Sustainability 2020, 12, 959. [Google Scholar] [CrossRef] [Green Version]
- Foltête, J.-C.; Girardet, X.; Clauzel, C. A methodological framework for the use of landscape graphs in land-use planning. Landsc. Urban Plan. 2014, 124, 140–150. [Google Scholar] [CrossRef]
- Tannier, C.; Bourgeois, M.; Houot, H.; Foltête, J.-C. Impact of urban developments on the functional connectivity of forested habitats: A joint contribution of advanced urban models and landscape graphs. Land Use Policy 2016, 52, 76–91. [Google Scholar] [CrossRef]
- Gutman, G.; Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens. 1998, 19, 1533–1543. [Google Scholar] [CrossRef]
- Yang, Y.; Song, G.; Lu, S. Study on the ecological protection redline (EPR) demarcation process and the ecosystem service value (ESV) of the EPR zone: A case study on the city of Qiqihaer in China. Ecol. Indic. 2020, 109, 105754. [Google Scholar] [CrossRef]
- Sun, Y.; Liang, X.; Xiao, C. Assessing the influence of land use on groundwater pollution based on coefficient of variation weight method: A case study of Shuangliao City. Environ. Sci. Pollut. Res. 2019, 26, 34964–34976. [Google Scholar] [CrossRef]
- McRae, B.H.; Hall, S.A.; Beier, P.; Theobald, D.M. Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits. PLoS ONE 2012, 7, e52604. [Google Scholar] [CrossRef]
- Kavanagh, B.M.D. User Guide: Linkage Pathways Tool of the Linkage Mapper Toolbox. 2017. Available online: http://www.circuitscape.org/linkagemapper (accessed on 31 May 2022).
- Kavanagh, P.; Newlands, N.; Christensen, V.; Pauly, D. Automated parameter optimization for Ecopath ecosystem models. Ecol. Model. 2004, 172, 141–149. [Google Scholar] [CrossRef]
- Fang, Y.; Wang, J.; Huang, L.Y.; Zhai, T.L. Determining and identifying key areas of ecosystempreservation and restoration for territorial spatial planning based on ecological security patterns: A case study of Yantai city. J. Nat. Resour. 2020, 35, 190–203. [Google Scholar] [CrossRef]
- Pan, S.; Liang, J.; Chen, W.; Li, J.; Liu, Z. Gray Forecast of Ecosystem Services Value and Its Driving Forces in Karst Areas of China: A Case Study in Guizhou Province, China. Int. J. Environ. Res. Public Health 2021, 18, 12404. [Google Scholar] [CrossRef]
- Xu, H. A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 2008, 29, 4269–4276. [Google Scholar] [CrossRef]
- Zhang, R.; Zhang, Q.; Zhang, L.; Zhong, Q.; Liu, J.; Wang, Z. Identification and extraction of a current urban ecological network in Minhang District of Shanghai based on an optimization method. Ecol. Indic. 2022, 136, 108647. [Google Scholar] [CrossRef]
- Serret, H.; Raymond, R.; Foltête, J.-C.; Clergeau, P.; Simon, L.; Machon, N. Potential contributions of green spaces at business sites to the ecological network in an urban agglomeration: The case of the Ile-de-France region, France. Landsc. Urban Plan. 2014, 131, 27–35. [Google Scholar] [CrossRef]
- Todorov, V.; Dimov, I. Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling. Mathematics 2022, 10, 2146. [Google Scholar] [CrossRef]
- Todorov, V.; Dimov, I.; Ostromsky, T.; Apostolov, S.; Georgieva, R.; Dimitrov, Y.; Zlatev, Z. Advanced stochastic approaches for Sobol’ sensitivity indices evaluation. Neural Comput. Appl. 2021, 33, 1999–2014. [Google Scholar] [CrossRef]
- -Zhai, T.; Huang, L. Linking MSPA and Circuit Theory to Identify the Spatial Range of Ecological Networks and Its Priority Areas for Conservation and Restoration in Urban Agglomeration. Front. Ecol. Evol. 2022, 10, 828979. [Google Scholar] [CrossRef]
- Yang, L.; Jiao, H. Spatiotemporal Changes in Ecosystem Services Value and Its Driving Factors in the Karst Region of China. Sustainability 2022, 14, 6695. [Google Scholar] [CrossRef]
- Qiu, S.; Peng, J.; Dong, J.; Wang, X.; Ding, Z.; Zhang, H.; Mao, Q.; Liu, H.; A Quine, T.; Meersmans, J. Understanding the relationships between ecosystem services and associated social-ecological drivers in a karst region: A case study of Guizhou Province, China. Prog. Phys. Geogr. Earth Environ. 2021, 45, 98–114. [Google Scholar] [CrossRef]
- Wei, S.; Pan, J.; Liu, X. Landscape ecological safety assessment and landscape pattern optimization in arid inland river basin: Take Ganzhou District as an example. Hum. Ecol. Risk Assess. Int. J. 2020, 26, 782–806. [Google Scholar] [CrossRef]
- Vergnes, A.; Kerbiriou, C.; Clergeau, P. Ecological corridors also operate in an urban matrix: A test case with garden shrews. Urban Ecosyst. 2013, 16, 511–525. [Google Scholar] [CrossRef]
- Rouget, M.; Cowling, R.M.; Lombard, A.T.; Knight, A.T.; Kerley, G.I.H. Designing Large-Scale Conservation Corridors for Pattern and Process. Conserv. Biol. 2006, 20, 549–561. [Google Scholar] [CrossRef]
- Parks, S.A.; Mckelvey, K.S.; Schwartz, M.K. Effects of Weighting Schemes on the Identification of Wildlife Corridors Generated with Least-Cost Methods. Conserv. Biol. 2013, 27, 145–154. [Google Scholar] [CrossRef] [PubMed]
- Ariken, M.; Zhang, F.; Liu, K.; Fang, C.; Kung, H.-T. Coupling coordination analysis of urbanization and eco-environment in Yanqi Basin based on multi-source remote sensing data. Ecol. Indic. 2020, 114, 106331. [Google Scholar] [CrossRef]
- Qureshi, S.; Alavipanah, S.K.; Konyushkova, M.; Mijani, N.; Fathololomi, S.; Firozjaei, M.K.; Homaee, M.; Hamzeh, S.; Kakroodi, A.A. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sens. 2020, 12, 2989. [Google Scholar] [CrossRef]
- Huang, G. Evaluation of Ecosystem Services in Karst Basin Based on InVEST Model: A Case Study the Zunyi Section of the Middle Reaches of the Wujiang River Basin in Guizhou. Master’s Thesis, Guizhou Normal University, Guiyang, China, 2020. [Google Scholar]
- Vogiatzakis, I.N.; Stirpe, M.T.; Rickebusch, S.; Metzger, M.J.; Xu, G.; Rounsevell, M.D.A.; Bommarco, R.; Potts, S.G. Rapid assessment of historic, current and future habitat quality for biodiversity around UK Natura 2000 sites. Environ. Conserv. 2015, 42, 31–40. [Google Scholar] [CrossRef]
- McNeely, J.A. How Conservation Strategies Contribute to Sustainable Development. Environ. Conserv. 1990, 17, 9–13. [Google Scholar] [CrossRef] [Green Version]
Data Types | Data Sources | Resolution | Period |
---|---|---|---|
Landsat 8 OIL | The Geospatial Data Cloud (http://www.gscloud.cn, accessed on 2 September 2021) | 30 m | 2020 |
Digital Elevation Model | The Geospatial Data Cloud (http://www.gscloud.cn, accessed on 2 September 2021) | 30 m | 2020 |
Net Primary Productivity | Google Earth Engine, accessed on 8 September 2021 | 500 m | 2014–2020 |
Normalized Difference Vegetation Index | Google Earth Engine, accessed on 8 September 2021 | 30 m | 2020 |
Harmonized World Soil Database | The Food and Agriculture Organization of the United Nations (http://www.fao.org, accessed on 8 September 2021) | 1 km | |
Annual precipitation data at 1 km resolution in China | The National Earth System Science Data Center (http://www.geodata.cn, accessed on 8 September 2021) | 1 km | 2014–2020 |
Multi-period land-use land cover remote-sensing detection dataset in China | The Resource and Environment Science and Data Central (https://www.resdc.cn/, accessed on 19 September 2021) | 30 m | 2020 |
RC | Elevation | Slope | TR | VC | PD | LCT | WSLSI | RDSI |
---|---|---|---|---|---|---|---|---|
Weight | 0.078 | 0.102 | 0.108 | 0.102 | 0.283 | 0.108 | 0.088 | 0.131 |
1 | 752–1563 | 0–10.4 | 20–104 | 0.83–1 | 19.12–293.18 | Forestland | 0–0.25 | 0–0.15 |
250 | 1563–1751 | 10.4–17.83 | 104–151 | 0.64–0.83 | 293.18–822 | Waterbody | 0.25–0.36 | 0.15–0.29 |
500 | 1751–1931 | 17.83–25.87 | 151–206 | 0.44–0.64 | 822–1864.63 | Grassland | 0.36–0.49 | 0.29–0.43 |
750 | 1931–2178 | 25.87–36.09 | 206–287 | 0.19–0.44 | 1864.63–3970.46 | Farmland | 0.49–0.6 | 0.43–0.58 |
1000 | 2178–2867 | 36.09–75.17 | 287–670 | 0–0.19 | 3970.46–7997.61 | Construction land | 0.6–1 | 0.58–1 |
Indicators | PC1 | PC2 | PC3 | PC4 |
---|---|---|---|---|
NVDI | 0.448 | 0.570 | −0.550 | 0.415 |
WET | 0.555 | −0.147 | 0.663 | 0.481 |
NDBSI | −0.545 | −0.290 | −0.165 | 0.769 |
LST | −0.440 | 0.755 | 0.481 | 0.076 |
Eigenvalues | 0.158 | 0.035 | 0.025 | 0.002 |
Percent covariance eigenvalue (%) | 71.62% | 15.94% | 11.42% | 1.02% |
Categories | Area (km2) | Proportion (%) |
---|---|---|
Core | 1306.55 | 82.65 |
Islet | 0.05 | 0.00 |
Perforation | 33.25 | 2.10 |
Edge | 230.22 | 14.56 |
Loop | 0.19 | 0.01 |
Bridge | 1.79 | 0.11 |
Branch | 8.7 | 0.55 |
Value | Number | Total Area (km2) | Area Percentage |
---|---|---|---|
dPC ≥ 17.09 | 3 | 328.18 | 32.57% |
17.08 ≥ dPC ≥ 8.41 | 5 | 318.62 | 31.62% |
dPC ≤ 8.40 | 18 | 360.95 | 35.82% |
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Yang, L.; Suo, M.; Gao, S.; Jiao, H. Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province. Sustainability 2022, 14, 9136. https://doi.org/10.3390/su14159136
Yang L, Suo M, Gao S, Jiao H. Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province. Sustainability. 2022; 14(15):9136. https://doi.org/10.3390/su14159136
Chicago/Turabian StyleYang, Liu, Mengmeng Suo, Shunqian Gao, and Hongzan Jiao. 2022. "Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province" Sustainability 14, no. 15: 9136. https://doi.org/10.3390/su14159136