Intergrating the Diversity Characteristics to Design a Quantitative Landscape Regionalization Framework: Case Study of Van Chan District, Yen Bai Province
Main Article Content
Abstract
Landscape regionalization plays an important role in delineating the heterogeneous characteristics of territory, and provide the spatial fundamental data for natural resource planning and environmental protection activities. The integrating of the diversity indices (landscape metrics) is expressed the change of landscape structure by the richness and evenness of land-use objectives. In this study, a quantitative landscape regionalization framework is designed from 03 group (attribute factor, driving factor, and diversity factor) of basic landscape unit. By using k-means clustering, the study is classified into 06 sub-regions of 68 watersheds in the administration boundary of Van Chan district, Yen Bai province. With the comparison of region numbers in statistical and practical dimensions, the optimal results are edited and determined 15 sub-regions for uncertainty reduction of landscape regionalization.
Keywords:
regionalization, quantitative modeling, landscape, diversity, cluster analysis, Van Chan.
References
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[3] M.G. Turner, Spatial and temporal analysis of landscape patterns, Landscape Ecology, Volume 4, Issue 1, 1990, pp. 21-30. https://doi.org/10.10 07/ BF02573948.
[4] G.P. Robertson, L.W. Burger, C.L. Kling, R.R. Lowrance, D.J. Mulla, Methods for Environmental Management Research at Landscape and Watershed Scales. Managing Agricultural Landscapes for Environmental Quality, Journal of Soil and Water Conservation Society, Ankeny, IA., 2007, 196 pages.
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[10] Y. Liu, B. Fu, Sh. Wang, W. Zhao, Global ecological regionalization: from biogeography to ecosystem services, Current Opinion in Environmental Sustainability, Volume 33, 2018, pp. 1-8. https://doi.org/10.1016/j.cosust.2018. 02.002.
[11] B.J. Amiri, G. Junfeng, N. Fohrer, F. Mueller, J. Adamowski, Regionalizing Flood Magnitudes using Landscape Structural Patterns of Catchments, Water Resources Management, Volume 32, Issue 7, 2018, pp. 2385-2403. https://doi.org/10.1007/s11269-018-1935-3.
[12] J.J. Starn, K. Belitz, Regionalization of groundwater residence time using metamodeling, Water Resources Research 54, 2018, pp. 6357- 6373. https://doi.org/10.1029/2017WR021531.
[13] M. Gao, X. Chen, J. Liu, Z. Zhang, Regionalization of annual runoff characteristics and its indication of co-dependence among hydro-climate–landscape factors in Jinghe River Basin, China, Stochastic Environmental Research and Risk Assessment, Volume 32, Issue 6, 2018, pp. 1613-1630. https://doi.org/10.1007/s00477-017-1494-9.
[14] W. Cao, S. Zhou, S. Wu, Land-use regionalization based on landscape pattern indices using rough set theory and catastrophe progression method, Environmental Earth Sciences, Volume 73, Issue 4, 2015, pp. 1611-1620. https://doi.org/10.1007/s 12665-014-3511-3.
[15] R. Wang, H. Yang, Landscape Regionalization for Highway Corridor Planning from Landscape Ecology Perspective: A Case Study of Shandong, China, 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), China, 2018, pp. 503-508.
[16] K. McGarigal, S. A. Cushman, E. Ene, FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst, 2012.
[17] W.W. Hargrove, F.M. Hoffman, Potential of multivariate quantitative methods for delineation and visualization of ecoregions, Environmental Management 34 (Suppl. 1), 2004, pp. S39-S60. https://doi.org/10.1007/s00267-003-1084-0.
[18] S. Ragettli, J. Zhou, H. Wang, Assessment of parameter regionalization methods for modeling flash floods in China, Geophysical Research Abstracts, Vol. 19, EGU2017-8018, 2017.
[19] G.H. Shah, A.N.S. Badana, C. Robb, W.C. Livingood, Cross-Jurisdictional Resource Sharing in Changing Public Health Landscape: Contributory Factors and Theoretical Explanations, Journal of Public Health Management and Practice, Volume 22, Number 2, 2016, pp. 110-119. https://doi.org/10.1097/PHH. 0000000000000368.
[20] J. Niesterowicz, T.F. Stepinski, On using landscape metrics for landscape similarity search, Ecological Indicators, Volume 64, 2016, pp. 20-30. https://doi.org/10.1016/j.ecolind.2015.12.027.
[21] O. Hall, W. Arnberg, A method for landscape regionalization based on fuzzy membership signatures, Landscape and Urban Planning 59, 2002, pp. 227-240. https://doi.org/10.1016/S0169-2046(02)00050-6.
[22] K.H. Riitters, J.D. Wickham, T.G. Wade, Evaluating anthropogenic risk of grassland and forest habitat degradation using landcover data. Landscape Online 13, 2009, pp. 1–14. https://doi.org/10.3097/LO.200913.
[23] D.R. Grafius, R. Corstanje, J.A. Harris, Linking ecosystem services, urban form and green space configuration using multivariate landscape metric analysis, Landscape Ecology, 2018, Volume 33, Issue 4, pp. 557–573. https://doi.org/10.1007/s 10980-018-0618-z.
[24] M.J. Todd, P.J. Wigington, E.A. Sproles, Hydrologic Landscape Classification to Estimate Bristol Bay, Alaska Watershed Hydrology. Journal of the American Water Resources Association (JAWRA) 53 (5), 2017, pp. 1008‐ 1031. https://doi.org/10.1111/1752-1688.12544.
[25] B. Choubin, K. Solaimani, M. Habibnejad Roshan, A. Malekian, Watershed classification by remote sensing indices: A fuzzy c-means clustering approach, Journal of Mountain Science, Volume 14, Issue 10, 2017, pp. 2053-2063. https://doi.org/ 10.1007/s11629-017-4357-4.
[26] L. Kaufman, P.J. Rousseeuw, Finding groups in data, Wiley, New York, 1990, 342 pages.
[27] M. Dale, M. Fortin, Spatial Analysis: A Guide for Ecologists, Cambridge University Press. 438 pages.