Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

Rachel R. Sleeter; William Acevedo; Christopher E. Soulard; Benjamin M. Sleeter; Rachel R. Sleeter; William Acevedo; Christopher E. Soulard; Benjamin M. Sleeter

doi:10.3934/environsci.2015.3.668

AIMS Environmental Science

2015, Volume 2, Issue 3: 668-693. doi: 10.3934/environsci.2015.3.668

Previous Article Next Article

Research article Special Issues

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

1.
U. S. Geological Survey, Eastern Geographic Science Center, Gig Harbor, WA, USA;
2.
U. S. Geological Survey, Western Geographic Science Center, Menlo Park, CA, USA;
3.
U. S. Geological Survey, Western Geographic Science Center, Tacoma, WA, USA

Received: 04 February 2015 Accepted: 18 June 2015 Published: 24 June 2015

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.
- state-and-transition modeling,
- land use and land cover,
- spatially-explicit modeling,
- scenarios
Citation: Rachel R. Sleeter, William Acevedo, Christopher E. Soulard, Benjamin M. Sleeter. Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model[J]. AIMS Environmental Science, 2015, 2(3): 668-693. doi: 10.3934/environsci.2015.3.668

Related Papers:

Abstract

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

References

[1]	Vitousek PM, Mooney HA, Lubchenco J, et al. (1997) Human domination of earth's ecosystems. Science 277: 494-499. doi: 10.1126/science.277.5325.494
[2]	DeFries RS, Foley JA, Asner GP (2004) Land-use choices: Balancing human needs and ecosystem function. Front Ecol Environ 2: 249-257.
[3]	IPCC (2000) In: Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. (Eds.), Land Use, Land-Use Change, and Forestry. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p. 377.
[4]	Foley JA, Defries R, Asner GP, et al. (2005) Global consequences of land use. Science 309: 570-574.
[5]	Pielke RA (2005) Land use and climate change. Science 310: 1625-1626. doi: 10.1126/science.1120529
[6]	Alcamo J (2008) Searching for the future of land: scenarios from the local to global scale. In: Alcamo J (Ed.), Environmental Futures: The Practice of Environmental Scenario Analysis. Elsevier, Amsterdam, The Netherlands.
[7]	USGCRP, The National Global Change Research Plan 2012-2021: A Strategic Plan for the U.S. Global Change Research Program. 132 pp. The U.S. Global Change Research Program Washington, D.C., 2012, Available from: http://downloads.globalchange.gov/strategic-plan/2012/usgcrp-strategic-plan-2012.pdf
[8]	Strengers B, Leemans R, Eickhout B, et al. (2004) The land-use projections and resulting emissions in the IPCC SRES scenarios as simulated by the IMAGE 2.2 model. GeoJournal 61: 381-393. doi: 10.1007/s10708-004-5054-8
[9]	National Research Council (2014) Advancing Land Change Modeling: Opportunities and Research Requirements. Washington, DC: The National Academies Press.
[10]	Soares-Filho BS, Nepstad DC, Curran LM, et al. (2006) Modeling conservation in the Amazon basin. Nature 440: 520-523.
[11]	Eastman JR (2007) The Land Change Modeler, a software extension for ArcGIS. Worcester, Mass.: Clark University.
[12]	Matthews RB, Gilbert NG, Roach A, et al. (2007) Agent-based land-use models: A review of applications. Landscape Ecol 22: 1447-1459.
[13]	Acosta-Michlik L, Espaldon V (2008) Assessing vulnerability of selected farming communities in the Philippines based on behavioural model of agent's adaptation to global environmental change. Glob Environ Chang 18: 554-563. doi: 10.1016/j.gloenvcha.2008.08.006
[14]	Brown DG, Page SE, Riolo R, et al. (2004) Agent based and analytical modeling to evaluate the effectiveness of greenbelts. Environ Modell Softw 19: 1097-1109.
[15]	Verburg PH (2002) Land use change modelling at the regional scale: The CLUE-S model. Environ Manage 30: 391-405. doi: 10.1007/s00267-002-2630-x
[16]	Burnham BO (1973) Markov intertemporal land use simulation model. South J Agr Econ 5: 253-258.
[17]	Baker WL (1989) A review of models of landscape change. Landscape Ecol 2: 111-133.
[18]	Turner M (1987) Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models. Landscape Ecol 1: 29-36.
[19]	Daniels CJ, Frid L (2011) Predicting landscape vegetation dynamics using state-and-transition simulation models, In Proceedings of the First Landscape State-and-Transition Simulation Modeling Conference, Portland, OR, USA, 14-16 June 2011; Kerns, B.K., Shlisky, A.J., Daniel, C.J., Eds.; U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 2012; pp.5-22.
[20]	Sleeter BM, Sohl T, Bouchard MA, et al. (2012) Scenarios of land use and land cover change in the conterminous U.S.: Utilizing the special report on emission scenarios at ecoregional scales. Glob Environ Chang 22: 896-914.
[21]	Omernik JM (1987) Ecoregions of the conterminous U.S. Ann Assoc Am Geogr 77: 118-125. doi: 10.1111/j.1467-8306.1987.tb00149.x
[22]	Sleeter BM, Liu J, Daniel C, et al. (2015) An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California. AIMS Environ Sci 2: 577-606.
[23]	Nakicenovic N, Swart R (Eds.) (2000) IPCC Special Report on Emission Scenarios; Cambridge University Press; Cambridge, UK; p. 570.
[24]	Sleeter BM, Sohl T, Loveland T, et al. (2013) Land-cover change in the conterminous United States from 1973 to 2000. Glob Environ Chang 23: 733-748. doi: 10.1016/j.gloenvcha.2013.03.006
[25]	EPA (U.S. Environmental Protection Agency), Primary distinguishing characteristics of Level III ecoregions of the continental United States. Environmental Protection Agency, 1999. Available from: http://www.epa.gov/wed/pages/ecoregions/level_iii.htm
[26]	Gallant AL, Loveland T, Sohl T, et al. (2004) Using an ecoregion framework to analyze land-cover and land-use dynamics. Environ Manage 34: S89-S110.
[27]	Soulard CE and Acevedo W, Multi-temporal harmonization of independent land-use/land-cover datasets for the conterminous U.S. American Geophysical Union, 2013. Available from: http://adsabs.harvard.edu/abs/2013AGUFM.B41E0448S
[28]	McConnell WJ, Moran EF (Eds.) (2001) Meeting in the middle: the challenge of meso-level integration. An international workshop on the harmonization of land-use and land-cover classification. LUCC Report Series No. 5. Anthropological Center for Training and Research on Global Environmental Change - Indiana University and LUCC International Project Office, Louvain-la-Neuve.
[29]	Jansen LJM, Groom GB, Carrai G (2008) Land-cover harmonisation and semantic similarity: some methodological issues. J Land Use Sci 3:131-160. doi: 10.1080/17474230802332076
[30]	Homer C, Dewitz J, Fry J, et al. (2007) Completion of the 2001 national land cover database for the conterminous U.S. Photogramm Eng Remote Sens 73: 337-341.
[31]	Vogelmann JE, Howard SM, Yang L, et al. (2001) Completion of the 1990's national land cover data set for the conterminous U.S. Photogramm Eng Remote Sens 67: 650-662.
[32]	Fry J, Xian G, Jin S, et al. (2011) Completion of the 2006 national land cover database for the conterminous U.S. Photogramm Eng Remote Sens 77:858-864.
[33]	Jin S, Yang L, Danielson P (2013) A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sens Environ 132: 159-175. doi: 10.1016/j.rse.2013.01.012
[34]	LANDFIRE: LANDFIRE Existing Vegetation Type layer. U.S. Department of Interior, Geological Survey, 2013 June last update. Available from: http://landfire.cr.usgs.gov/viewer/ (accessed 1 October 2012)
[35]	U.S Department of Agriculture, National Agricultural Statistics Service (2011) Cropland Data Layer. Available from http://nassgeodata.gmu.edu/CropScape/ (accessed on 1 October 2012).
[36]	U.S. Geological Survey, Gap Analysis Program (GAP). May 2011. National Land Cover, Version 2
[37]	Hansen MC, Potapov PV, Moore R, et al. (2013) High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342: 850-853. doi: 10.1126/science.1244693
[38]	LANDFIRE: LANDFIRE Disturbance (1999-2010). U.S. Department of Interior, Geological Survey, 2013 June last update. Available from: http://landfire.cr.usgs.gov/viewer/ (accessed on 1 October 2012).
[39]	Roy DP, Ju J, Kline K, et al. (2010) Web-enabled Landsat data (WELD): Landsat ETM+ composited mosaics of the conterminous U.S. Remote Sens Environ 114: 35-49. doi: 10.1016/j.rse.2009.08.011
[40]	Eidenshink J, Schwind B, Brewer K, et al. (2007) A project for monitoring trends in burn severity. Fire Ecol Spec Issue 3: 3-21.
[41]	Anderson JR, Hardy E, Roach JT, et al. (1976) A land use and land cover classification scheme for use with remote sensor data. U.S. Geological Survey, Reston, VA. USA, Professional Paper 964.
[42]	Pan Y, Chen JM, Birdsey R, et al. (2011) Age structure and disturbance legacy of North American forests. Biogeosciences 8: 715-732. doi: 10.5194/bg-8-715-2011
[43]	Masek JG, Huang R, Wolfe R, et al. (2008) North American forest disturbance mapped from a decadal Landsat record. Remote Sens Environ 112: 2914- 2926. doi: 10.1016/j.rse.2008.02.010
[44]	Huang C, Goward SN, Masek JG, et al. (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114: 183-198. doi: 10.1016/j.rse.2009.08.017
[45]	Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46: 234-240. doi: 10.2307/143141
[46]	U.S. Geological Survey GAP. Protected Areas Database of the United States (PAD-US), version 1.3 Combined Feature Class. 2012. Available from: http://gapanalysis.usgs.gov/padus/ (accessed on 13 November 2013).
[47]	Natural Resources Conservation Service. Soil Survey Geographic (SSURGO) Database. U.S. Department of Agriculture, 2011. Available from: http://soildatamart.nrcs.usda.gov (accessed on 22 November 2013).
[48]	Sleeter R, Gould M (2007) Geographic Information System Software to Remodel Population Data Using Dasymetric Mapping Methods; U.S. Geological Survey Techniques and Methods 11-C2; U.S. Geological Survey, Reston, VA. USA, p. 15.
[49]	Wilson BT, Woodall CW, DM Griffith (2013) Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance Manag 8: 1-15.
[50]	IMAGE team (2001) The IMAGE 2.2 implementation of the SRES scenarios: climate change scenarios resulting from runs with several GCMs. RIVM CD-ROM Publication 481508019, National Institute of Public Health and the Environment, Bilthoven.
[51]	Van Vuuren D, Edmonds JA, Kainuma M, et al. (2011) The Representative Concentration Pathways: An Overview. Climatic Change 109: 5-31. doi: 10.1007/s10584-011-0148-z
[52]	Wilson TS, Sleeter BM, Sleeter RR, et al. (2014) Land use threats and protected areas: a scenario-based, landscape level approach. Land 3: 362-389. doi: 10.3390/land3020362
[53]	Sleeter BM (2008) Late 20th century land change in the Central California Valley Ecoregion. California Geographer 48:27-60

Reader Comments

Your name:*

Email:*
© 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)