全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Forests  2011 

Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR

DOI: 10.3390/f2030707

Keywords: forest cover changes, “weights of evidence”, Markov-cellular automata model, business-as-usual scenario, pessimistic scenario, optimistic scenario, reference scenarios, Lao PDR, REDD/REDD+

Full-Text   Cite this paper   Add to My Lib

Abstract:

Taking Luangprabang province in Lao Peoples’s Democratic Republic (PDR) as an example, we simulated future forest cover changes under the business-as-usual (BAU), pessimistic and optimistic scenarios based on the Markov-cellular automata (MCA) model. We computed transition probabilities from satellite-derived forest cover maps (1993 and 2000) using the Markov chains, while the “weights of evidence” technique was used to generate transition potential maps. The initial forest cover map (1993), the transition potential maps and the 1993–2000 transition probabilities were used to calibrate the model. Forest cover simulations were then performed from 1993 to 2007 at an annual time-step. The simulated forest cover map for 2007 was compared to the observed (actual) forest cover map for 2007 in order to test the accuracy of the model. Following the successful calibration and validation, future forest cover changes were simulated up to 2014 under different scenarios. The MCA simulations under the BAU and pessimistic scenarios projected that current forest areas would decrease, whereas unstocked forest areas would increase in the future. Conversely, the optimistic scenario projected that current forest areas would increase in the future if strict forestry laws enforcing conservation in protected forest areas are implemented. The three simulation scenarios provide a very good case study for simulating future forest cover changes at the subnational level (Luangprabang province). Thus, the future simulated forest cover changes can possibly be used as a guideline to set reference scenarios as well as undertake REDD/REDD+ preparedness activities within the study area.

References

[1]  De Gryze, S. Cashing in carbon credits: Can GIS cost-effectively measure forest gains. Geoworld 2009, 22, 25–27.
[2]  Climate Change 2007: Synthesis Report; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2007. Available online: http://www.ipcc.ch/ (accessed on 3 August 2009).
[3]  Sustainable Forest Management and Conservation of Tropical Rainforests; FAO: Rome, Italy, 1995.
[4]  Realising REDD+: National Strategy and Policy Options; Angelsen, A., Brockhaus, M., Kanninen, M., Sills, E., Sunderlin, W.D., Wertz-Kanounnikoff, S., Eds.; CIFOR: Bogor, Indonesia, 2009.
[5]  Angelsen, A. REDD models and baselines. Int. For. Rev. 2008, 10, 465–475.
[6]  Terrestrial Carbon Group. How to Include Terrestrial Carbon in Developing Nations in the Overall Climate Change Solution. The Terrestrial Carbon Group's Policy Paper, 2008. Available online: http://www.terrestrialcarbon.org/site/DefaultSite/filesystem/documents/Terrestrial%20 Carbon%20Group%20080808.pdf (accessed on 14 January 2011).
[7]  Brown, S.; Hall, M.; Andrasko, K.; Ruiz, F.; Marzoli, W.; Guerrero, G.; Masera, O.; Dushku, A.; De Jong, B.; Cornell, J. Baselines for land-use change in the tropics: Application to avoided deforestation projects. Mitigat. Adaptat. Strateg. Glob. Change 2007, 12, 1001–1026.
[8]  Soares-Filho, B.S.; Nepsta, D.C.; Curran, L.M.; Cerqueira, G.C.; Garcia, R.A.; Ramos, C.A.; Voll, E.; McDonald, A.; Lefebvre, P.; Schlesinger, P. Modelling conservation in the Amazon basin. Nature 2006, 23, 520–523.
[9]  Sciotti, R. Demographic and ecological factors in FAO tropical deforestation modelling. In World Forests from Deforestation to Transition; Palo, M., Vanhanen, H., Eds.; Kluwer Academic Publishing: Dordrecht, The Netherlands, 2000.
[10]  Soares-Filho, B.S.; Alencar, A.; Nepstads, D.; Cerqueira, G.; Diaz, M.C.V.; Rivero, S.; Solorzanos, L.; Voll, E. Simulating the response of land-cover changes to road paving and governance along a major Amazonian highway: The SANTAREM-Cuiaba corridor. Glob. Change Biol. 2004, 10, 745–764.
[11]  GOFC-GOLD 2009. A Source Book of Methods and Procedures for Monitoring and Reporting Anthropogenic Greenhouse Gas Emissions and Removals Caused by Deforestation, Gains and Losses of Carbon stocks in Forests Remaining Forests, and Forestations. GOFC-GOLD Report version COP15–1; GOFC-GOLD, Natural Resources Canada: Edmonton, Alberta, Canada, 2009.
[12]  Veldkamp, T.; Lambin, E.F. Predicting land-use change. Agr. Ecosyst. Environ. 2001, 85, 1–6.
[13]  Teixerira, A.M.G.; Soares-Filho, B.S.; Freitas, S.R.; Metger, J.P. Modeling landscape dynamics in an Atlantic rainforest region: Implications for conservation. Forest Ecol. Man. 2009, 257, 1219–1230.
[14]  Messina, J.; Walsh, S. 2.5 D morphogenesis: Modeling landuse and landcover dynamics in the Ecuadorian Amazon. Plant Ecol. 2001, 156, 75–88.
[15]  Wada, Y.; Rajan, K.S.; Shibasaki, R. Modeling the spatial distribution of shifting cultivation in Luangprabang, Lao PDR. Environ. Plan. B Plan. Design 2007, 34, 261–278.
[16]  Walsh, S.J.; Entwisle, B.; Rindfuss, R.R.; Page, P.H. Spatial simulation modelling of land use/land cover change scenarios in northeastern Thailand: A cellular automata approach. J. Land Use Sci. 2006, 1, 5–28.
[17]  Lambin, E.F.; Turner, B.L.; Geist, H.J.; Agbola, S.B.; Angelsen, A.; Bruce, J.W.; Coomes, O.T.; Dirzo, R.; Fischer, G.; Folke, C.; et al. The causes of land-use and land-cover change: Moving beyond the myths. Glob. Environ. change 2001, 11, 261–269.
[18]  Lao Department of Statistics. Lao PDR Statistical Year Book-2008; Department of Statistics: Vientiane, Lao PRD, 2009.
[19]  FAO. Forest Resource Assessment 2005. Country Report- Lao PDR. WP 182; FAO: Rome, Italy, 2005.
[20]  Asia Air Survey. Progress Report on the Study on the Strengthening of Methodological and Technological Approaches for Reducing Deforestation and Forest Degradation within the REDD Implementation Framework: Application in Lao PDR; Asia Air Survey: Shin Yurigaoka, Japan, 2010.
[21]  Hosseinali, F.; Alesheikh, A.A. Weighting spatial information in GIS for copper mining exploration. Am. J. Appl. Sci. 2008, 5, 1187–1198.
[22]  Weng, Q. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J. Environ. Manage. 2002, 64, 273–284.
[23]  Petit, C.; Scudder, T.; Lambin, E. Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia. Int. J. Rem. Sens. 2001, 22, 3435–3456.
[24]  Karlin, S.; Taylor, H.M. A First Course in Stochastic Processes; Academic Press: New York, NY, USA, 1975; p. 557.
[25]  Lambin, E.F. Modelling Deforestation Processes: A Review; Office for Official Publications of the European Community: New York, NY, USA, 1994; p. 128.
[26]  Moreno, N.; Wang, F.; Marceau, D.J. A geographic object-based approach in cellular automata modeling. Photogramm. Eng. Rem. Sens. 2010, 76, 183–191.
[27]  Tobler, W. Cellular geography. In Philosophy in Geography; Gale, S., Olsson, G., Eds.; D. Reidel Publishing Company: Dordrecht, The Netherlands, 1979; pp. 379–386.
[28]  Couclelis, H. Cellular worlds: A framework for modeling micro-macro dynamics. Environ. Plan. A 1985, 17, 585–596.
[29]  Engelen, G.; White, R.; Uljee, I.; Drazan, P. Using cellular automata for integrated modeling of socio-environmental systems. Environ. Monit. Assess 1995, 34, 203–214.
[30]  Wolfram, S. Cellular automata as models of complexity. Nature 1984, 311, 419–424.
[31]  White, R.; Engelen, G. Cellular automata as the basis of integrated dynamic regional modeling. Environ. Plan. B 1997, 24, 235–246.
[32]  Zhou, G.; Liebhold, A.M. Forecasting the spread of gypsy moth outbreaks using cellular transition models. Landsc. Ecol. 1995, 10, 177–186.
[33]  Li, H.; Reynolds, J.F. Modeling effects of spatial pattern, drought, and grazing on rates of rangeland degradation: A combined Markov and cellular automaton approach. In Scale in Remote Sensing and GIS; Quattrochi, D.A., Goodchild, M.F., Eds.; Lewis Publishers: Boca Raton, FL, USA, 1997; pp. 211–230.
[34]  Almeida, C.M.; Batty, M.; Monteiro, A.M.V.; Camara, G.; Soares-Filho, B.S.; Cerqueira, G.C.; Pennachin, C.L. Stochastic cellular automata modelling of urban land use dynamics: Empirical development and estimation. Comput. Environ. Urban Syst. 2003, 27, 481–509.
[35]  UFMG (Universidade Federal de Minas Gerais). Dinamica EGO; Centro de Sensoriamento Remoto/Universidade Federal de Minas Gerais: Belo Horizonte, Brazil, 2009. Available online: http://www.csr.ufmg.br/dinamica/ (accessed on 6 April 2010).
[36]  Almeida, C.M.; Monteiro, A.M.V.; Camara, G.; Soares-Filho, B.S.; Cerqueira, G.C.; Pennachin, C.L.; Batty, M. GIS and remote sensing as tools for the simulation of urban land-use change. Int. J. Rem. Sens. 2005, 26, 759–774.
[37]  Agterberg, F.P.; Cheng, Q. Conditional independence test for weights-of-evidence modeling. Natur. Resour. Res. 2002, 11, 249–255.
[38]  Soares-Filho, B.S.; Assuncao, R.M.; Pantuzzo, A. Modeling the spatial transition probabilities of landscape dynamics in an Amazonian colonization frontier. BioScience 2001, 51, 1039–1046.
[39]  Bonham-Carter, G.F.; Agterberg, F.P.; Wright, D.F. Integration of geological data sets for gold exploration in Nova Scotia. Photogram. Eng Remote. Sens. 1988, 54, 1585–1592.
[40]  Ford, A.; Clarke, K.C.; Raines, G. Modeling settlement patterns of the late classic Maya civilization with Bayesian methods and geographic information systems. Ann. Assn. Amer. Geogr. 2009, 93, 496–520.
[41]  Soares-Filho, B.S.; Cerqueira, G.C.; Pennachin, C.L. DINAMICA—A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol. Model. 2002, 154, 217–235.
[42]  McGarigal, K.; Cushman, S.A.; Neel, M.C.; Ene, E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps; University of Massachusetts: Massachusetts, MA,USA, 2002.
[43]  Paegelow, M.; Olmedo, M.T.C. Possibilities and limits of prospective GIS land cover modelling—A compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain). Int. J. Geogr. Inform. Sci. 2005, 19, 697–722.
[44]  Myint, S.W.; Wang, L. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Can. J. Rem. Sens. 2006, 32, 390–404.
[45]  Kamusoko, C.; Aniya, M.; Bongo, A.; Munyaradzi, M. Rural sustainability under threat in Zimbabwe—Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Appl. Geogr. 2009, 29, 435–447.
[46]  Pontius, R.G., Jr.; Walker, R.; Yao-Kumah, R.; Arima, E.; Aldrich, S.; Caldas, M.; Vergara, D. Accuracy assessment for a simulation model of Amazonian deforestation. Ann. Assn Amer. Geogr. 2007, 97, 677–695.
[47]  Verburg, P.H.; Schot, P.P.; Dijst, M.J.; Veldkamp, A. Land use change modelling: Current practice and research priorities. Geojournal 2004, 61, 309–324.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133