Journal of the Korean Geographical Society (대한지리학회지)

The Korean Geographical Society (대한지리학회)
권호 표지

Optimal Spatial Scale for Land Use Change Modelling : A Case Study in a Savanna Landscape in Northern Ghana

지표피복변화 연구에서 최적의 공간스케일의 문제 : 가나 북부지역의 사바나 지역을 사례로

  • Published : 2005.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.

토지이용 및 지표피복변화 (Land Use and land Cover Changes, LUCC)는 지구환경변화의 원인으로 중요한 연구대상이 되고 있다. LUCC는 복잡한 사회적, 경제적, 정치적 상호작용속에서 다양한 시$\cdot$공간적 스케일에서 발생하게 된다. 따라서 LUCC를 모델화하기 위해서는 LUCC를 야기시키는 원인(driving forces)과 제한요인(constraints)들의 시$\cdot$공간적인 다양성을 이해하는 작업이 선행되어야 한다. 특히, 특정 지역에서 나타나는 LUCC의 동인을 파악하기 위해서는 스케일에 따른 그 특성의 변화를 이해하는 것이 급선무이다. 이 연구는 가나(Ghana) 북부지역의 사바나 지역을 대상으로 지난 15년간 나타난 지표피복변화의 공간적인 다양성을 파악한 뒤, 공간적 스케일을 달리하면서 나타나는 LUCC의 원인을 분석하였다. 이 과정을 통해 사바나 지역에서 LUCC 과정을 모형화하기 위한 최적의 공간적인 스케일을 규명하고자 하였다. 연구지역은 지난 15년간 인구증가의 결과로 농업생산활동이 급격하게 증가한 지역이다. 연구지역에서 나타나는 지표피복변화의 정도는 LANDSAT 위성영상에서 추출한 NDVI들을 다변량 통계분석기법을 이용하여 정량화하였다. 그리고 지표피복변화의 원인을 스케일별로 파악하기 위한 도구로 다축척 계층분석기법(multi-scale hierarchical adaptive model)을 개발$\cdot$제안하였다. 개발된 기법은 지표피복의 변화정도와 원인이 될 수 있는 공간변수들간의 상관성을 공간적인 스케일을 달리하면서 순차적으로 계산해낼 수 있는 기법이다. 이 연구에서 지표피복변화의 원인으로는 '도로에서부터의 거리', 하천으로부터의 거리', '지형특성' 의 세가지 변수를 사용하였다. 지표피복 변화정도와 위의 세가지 변수들간의 상관관계는 공간적인 범위가 10$\times$10km 이하인 경우에 높게 나타났다. 하지만 공간범위가 그 이상이 될 경우에는 그 내부에서 나타나는 다양성으로 인해 통계적인 상관성이 현격하게 낮아지는 것을 관찰할 수 있었다. 이러한 결과는 지역 및 국가 단위의 환경변화모델에서 모델의 공간적인 구성범위가 일정한 수준을 넘으면, 그 내부에서 발생하고 있는 다양성이 급격하게 증가하여 지표피복변화의 원인과 결과를 정확하게 파악하기 힘들게 된다는 것을 의미한다. 10$\times$10km의 공간적인 범위는 농업생산이 위주가 되는 사바나 지역에서는 주로 개별 마을이 차지하고 있는 공간적인 범위와 대체적으로 일치한다. 따라서 사바나 지역에서 나타나는 지표피복변화의 다양성을 고려하면서 보다 정확하게 모형화하기 위해서는 마을단위에서 나타나는 지표피복변화과정이 최소의 모델단위가 되어야 함을 시사한다.

Keywords

References

  1. Abudulai, S., 1996, Perceptions of land rights, rural-urban land use dynamics and policy development, in: Managing land tenure and resource access in west Africa. Proceedings of a regional workshop held at Goree Senegal. Nov. 18-22 1996, 107-127
  2. Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P. and Schweik, C.M., 2002, A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice, CIPEC Collaborative Report No. l
  3. Aitkin, M., 1996, A general maximum likelihood analysis of over dispersion in generalised linear models, Statistics and Computing, 6, 251-261 https://doi.org/10.1007/BF00140869
  4. Badhwar, G. D. and Henderson, H.K., 1985, Application of thematic mapper data to com and soy-bean development stage estimation, Remote Sensing and Environments, 17, 197-201 https://doi.org/10.1016/0034-4257(85)90074-4
  5. Beven, K., 1995, Linking parameters across scales: subgrid parameterizations and scale dependent hydrological models, in Kalma, J.D. and Sivapalan, M. (eds), Scale Issues in Hydrological Modelling. John Wiley & Sons, Chichester, 263-282
  6. Bloschl, G. and Sivapalan, M., 1995, Scale issues in hydrological modeling: a review, in: Kalma, J.D. and Sivapalan, M. (eds), Scale Issues in Hydrological Modelling, John Wiley & Sons, Chichester, 9-48
  7. Bloschl, G., Grayson, R.B., and Sivapalan, M., 1995, On the representative elementary area (REA) concept and its utility for distributed rainfall-runoff modelling, in Kalma, J.D. and Sivapalan, M. (eds), Scale Issues in Hydrological Modelling, John Wiley & Sons, Chichester, 71-88
  8. Braihmoh, A.K., 2003, Modelling of land use change in the Volta basin of Ghana using GIS, Remote Sensing and Geostatistical techniques, Unpublished Ph.D. thesis, University of Bonn
  9. Brunsdon, C., Fotheringham, A.S. and Charlton, M.E., 1996, Geographically weighted regression: A method for exploring spatial nonstationarity, Geographical Analysis, 28, 281-298 https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
  10. Civco D.L., Huff, J.D., Wilson, E.H., Song, M. and Zhang, Z., 2002, A comparison of land use and land over change detection methods. Proceedings of 2002 ASPRS-ACSM Annulal Conference and FIG XXII Congress
  11. Clottery, V. and Kombiok, J.M., 2000, Land use types in Northern Region of Ghana, in The report on 'Workshop on Land use types in the Volta Basin: Tamale. November 2000. ZEF, Bonn. 13-15
  12. Flugel, W.A., 1995, Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the river Brl, Germany, in Kalma, J.D. and Sivapalan, M. (eds)., Scale Issues in Hydrological Modelling, John Wiley & Sons, Chichester, 181-194
  13. Fortheringham, A.S., 1997, Trends in quantitative methods I: stressing the local, Progresses in Human Geography, 21, 88-96 https://doi.org/10.1191/030913297676693207
  14. Foster S.A. and Gorr, W.L., 1986, Adaptive filter for estimating spatially varying parameters: application to modelling police hours spent in response to calls for service, Management Science, 32, 878-889 https://doi.org/10.1287/mnsc.32.7.878
  15. Geist, H.J. and Lambin, E.F., 2002, Proximate causes and underlying driving forces of tropical deforestration, Bioscience, 52, 143-150 https://doi.org/10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2
  16. Gibson, C., Ostrom, E. and Ahn, T-K., 1998, Scaling Issues in the Social Sciences, A Report for the International Human Dimensions Programme on Global Environmental Change, IHDP Working Paper No. 1. IHDP, Bonn
  17. Hoddinott, J., Adato, M., Besley T. and Haddad, L., 2001, Participation and poverty reduction: Issues, theory, and new evidence from South Africa, FCND Discussion Paper No. 98. International Food Policy Research Institute, Washington D.C
  18. Irwin, E.G. and Geoghegan, J., 2001, Theory, data, methods: developing spatially explicit economic models of land use change, Agriculture Ecosystems and Environment, 85, 7-23 https://doi.org/10.1016/S0167-8809(01)00200-6
  19. Kirkby, M.J., Imeson, A.C., Bergkamp, G. and Cammeraat, L.H., 1996, Scaling up process and models, Journal of Soil and Water Conseruatton, 15, 391-396
  20. Kok, K. and Veldkamp, A., 2001, Evaluating impact of spatial scales on land use pattern analysis in Central America, Agriculture, Ecosystems and Environment, 85, 205-221 https://doi.org/10.1016/S0167-8809(01)00185-2
  21. Lambin, E.F., Baulies, X., Bockstael, N., Fischer, G., Krug, T, Leemans, R., Moran, E.F., Rindfuss, R. R., Sato, Y., Skole, D., Turner II, B.L. and Vogel, C., 1999, Land-Use and Land-Cover Change Implementation Strategy, IGBP Report No. 48 and IHDP Report No. 10. Bonn
  22. Lambin, E.F, Turner II, B.L., Geist, H.J., Agbola, S.B., Angelsen, A., Bruce, J.W., Coomes, O.T, Dirzo, R., Fischer, G., Folke, C., George, P.S., Homewood, K., Imbernon, J., Leemans, R., Li, X., Moran, E.F., Mortimore, M., Ramakrishnan, P.S., Richards, J.F., Skanes, H., Steffen, W., Stone, G.D., Svedin, U., Veldkamp, T.A., Vogel, C. and Xu, J., 2001, The Causes of Land-Use and Land-Cover Change: Moving Beyond the Myths, Global Environmental Change, 11, 261-269 https://doi.org/10.1016/S0959-3780(01)00007-3
  23. Lambin, E.F. and Strahler, A.H., 1994, Change vector analysis in multi temporal space: a tool to detect and categorise land-cover change processes using high temporal resolution satellite data, Remote Sensing of Environment, 48, 231-244 https://doi.org/10.1016/0034-4257(94)90144-9
  24. Lambin, E.F. and Ehrlich, D., 1997, Land-cover changes in Sub-saharian Africa (1982-1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale, Remote Sensing of Environment, 61, 181-200 https://doi.org/10.1016/S0034-4257(97)00001-1
  25. Mertens, B. and Lambin, E.F., 1977, Spatial modelling of deforestation in southern Cameroon, Applied Geography, 12, 143-162
  26. NASA. 2002. land cover changes may rival greenhous gases as cuase of climate change. in Top story. http://www.gsfc.nasa.gov/topstory/20020926landcover.html
  27. Nelson, A., 2001, Analysing data across geographic scales: Detecting levels of organisation within systems, Agriculture, Ecosystems and Environment, 85, 107-131 https://doi.org/10.1016/S0167-8809(01)00191-8
  28. Park, S.J. and Vlek, P.L.G., 2002, Environmental correlation of three-dimensional spatial soil variability: A comparison of three adaptive techniques, Geoderma, 109, 117-140 https://doi.org/10.1016/S0016-7061(02)00146-5
  29. Park, S.J. and Vlek, P.L.G., 2005, Detection of land use and cover using multi-temporal spatial information, Remote Sensing of Environment (in review)
  30. Park, S.J., McSweeney K. and Lowery B., 2001, Prediction of soils using a process based terrain characterisation, Geoderma, 103, 249-272 https://doi.org/10.1016/S0016-7061(01)00042-8
  31. Parker, D.C., Berger, T. and Manson, S. M., 2002, Agent-Based Models of Land-Use and Land-Cover Change, Report and Review of an International Workshop October 47, 2001, Irvine, California, USA. LUCC report series No.6., Bonn
  32. Redman, C, Grove, J.M. and Kuby, L., 2000, Toward a Unified Understanding of Human Ecosystems: Integrating Social Sciences into Long-Term Ecological Research, White Paper of the Social Science Committee of the LTER Network. Accessible at http://www.lternet.edu/research/pubs/informal/socsciwhtppr.htm
  33. Schulze, R., 2000, Transcending scales of space and time in impact studies of climate and climate change on agrohydrological responses, Agriculture, Ecosystems and Environment, 82, 185-212 https://doi.org/10.1016/S0167-8809(00)00226-7
  34. Singh, A., 1989, Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, 10, 989-1003 https://doi.org/10.1080/01431168908903939
  35. Turner II, B.L., Skole, D., Sanderson, S., Fischer, G., Fresco L. and Leemans, R., 1995, Land-Use and Land-Cover Change Science/Research Plan, IGBP Report No. 35/HDP Report No.7, Bonn
  36. Urban, D.S., Pierce G.K. and Lookingbill, T., 2002, Extending community ecology to landscapes. Ecoscience, 9, 200-202 https://doi.org/10.1080/11956860.2002.11682706
  37. Veldkamp, A. and Lambin, E.F., 2001, Predicting landuse change: Editorial, Agriculture, Ecosystems and Environment, 85, 1-6 https://doi.org/10.1016/S0167-8809(01)00199-2
  38. Verburg, P.H., Schot, P., Dijst, M. and Veldkamp, A., 2002, Land-Use Change Modeling: Current Practice and Research Priorities, GeoJournal (in press)
  39. Vescovi, F.D., Park, S.J. and Vlek, P.L.G., 2002, Detection of human-induced land cover changes in a savannahsavanna landscape in Ghana: I. Change detection and quantification, Proceedings of the EARSeL workshop 'Remote sensing for developing countries' 18-20 Sept. 2002., Bonn
  40. Vlek P.L.G., Berger, T., Iskandarani, M., Park, S.J. and van de Giesen, N., 2003, Integrative water research project: GLOWA-Volta. in Ehlers, E. (ed.) Earth System Science in the Antropcene. Emerging Issues and Problems, Springer Verlag
  41. Walsh, S.J., Evans, T.P., Welsh, W.F., Entwisle B. and Rindfuss, R.R., 1999, Scale dependent relationships between population and environment in Northeastern Thailand, Photogrammetric Engineering and Remote Sensing, 65, 97-105
  42. Wood, E.F., Sivapalan, M. Beven, K.J. and Band, L., 1988, Effects of spatial variability and scale with implications to hydrologic modelling, Journal of Hydrology, 102, 29-47 https://doi.org/10.1016/0022-1694(88)90090-X