Hostname: page-component-77c89778f8-gvh9x Total loading time: 0 Render date: 2024-07-23T08:29:13.500Z Has data issue: false hasContentIssue false

Influence of carbon mapping and land change modelling on the prediction of carbon emissions from deforestation

Published online by Cambridge University Press:  15 June 2012

VICTOR HUGO GUTIERREZ-VELEZ*
Affiliation:
Department of Ecology, Evolution, and Environmental Biology, Columbia University, 10th floor Schermerhorn, 1200 Amsterdam Avenue, New York, NY 10027, USA Research Centre on Ecosystems and Global Change (Carbono and Bosques), Calle 51A # 72-23, Interior 601, Medellin, Colombia
ROBERT GILMORE PONTIUS JR
Affiliation:
Graduate School of Geography, Clark University, 950 Main Street, Worcester MA 01610-1477, USA
*
*Correspondence: Victor Hugo Gutierrez-Velez e-mail vhg2103@columbia.edu

Summary

The implementation of an international programme for reducing carbon emissions from deforestation and degradation (REDD) can help to mitigate climate change and bring numerous benefits to environmental conservation. Information on land change modelling and carbon mapping can contribute to quantify future carbon emissions from deforestation. However limitations in data availability and technical capabilities may constitute an obstacle for countries interested in participating in the REDD programme. This paper evaluates the influence of quantity and allocation of mapped carbon stocks and expected deforestation on the prediction of carbon emissions from deforestation. The paper introduces the conceptual space where quantity and allocation are involved in predicting carbon emissions, and then uses the concepts to predict carbon emissions in the Brazilian Amazon, using previously published information about carbon mapping and deforestation modelling. Results showed that variation in quantity of carbon among carbon maps was the most influential component of uncertainty, followed by quantity of predicted deforestation. Spatial allocation of carbon within carbon maps was less influential than quantity of carbon in the maps. For most of the carbon maps, spatial allocation of deforestation had a minor but variable effect on the prediction of carbon emissions relative to the other components. The influence of spatial carbon allocation reaches its maximum when 50% of the initial forest area is deforested. The method can be applied to other case studies to evaluate the interacting effects of quantity and allocation of carbon with future deforestation on the prediction of carbon emissions from deforestation.

Type
Papers
Copyright
Copyright © Foundation for Environmental Conservation 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alexandrov, G.A. (2011) Forest cover: setting targets for the future. Carbon Balance and Management 6:12.CrossRefGoogle ScholarPubMed
Asner, G.P., Powell, G.V.N., Mascaro, J., Knapp, D.E., Clark, J.K., Jacobson, J., Kennedy-Bowdoin, T., Balaji, A., Paez-Acosta, G., Victoria, E., Secada, L., Valqui, M. & Flint Highes, R. (2010) High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences 107: 1673816742.Google Scholar
Baddeley, A. & Turner, R. (2005) Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software 12: 142.Google Scholar
Brown, S. & Lugo, A. (1992) Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17: 818.Google Scholar
Brown, S., Hall, M., Andrasko, K., Ruiz, F., Marzoli, W. & Guerrero, G. (2007) Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitigation and Adaptation Strategies for Global Change 12: 10011026.Google Scholar
Busch, J., Godoy, F., Turner, W.R. & Harvey, C.A. (2011) Biodiversity co-benefits of reducing emissions from deforestation under alternative reference levels and levels of finance. Conservation Letters 4: 101115.Google Scholar
Castillo-Santiago, M., Hellier, A., Tipper, R. & De Jong, B. (2007) Carbon emissions from land-use change: an analysis of causal factors in Chiapas, Mexico. Mitigation and Adaptation Strategies for Global Change 12: 12131235.CrossRefGoogle Scholar
Cerbu, G. A., Swallow, B.M. & Thompson, D.Y. (2011) Locating REDD: a global survey and analysis of REDD readiness and demonstration activities. Environmental Science and Policy 14: 168180.CrossRefGoogle Scholar
Chave, J., Condit, R., Aguilar, S., Hernandez, A., Lao, S. & Perez, R. (2004) Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London B: Biological Sciences 359: 409420.Google Scholar
Chomitz, K.M. & Thomas, T.S. (2003) Determinants of land use in Amazonia: a fine-scale spatial analysis. American Journal of Agricultural Economics 85: 10161028.CrossRefGoogle Scholar
Corbera, E., Estrada, M., & Brown, K. (2010) Reducing greenhouse gas emissions from deforestation and forest degradation in developing countries: revisiting the assumptions. Climatic Change 100: 355388.CrossRefGoogle Scholar
da Fonseca, G.A.B., Rodriguez, C.M., Midgley, G., Busch, J., Hannah, L. & Mittermeier, R.A. (2007) No forest left behind. PLoS Biology 5: 16451646.CrossRefGoogle ScholarPubMed
DeFries, R., Hansen, M., Townshend, J., Janetos, A. & Loveland, T. (2000) A new global 1km data set of percent tree cover derived from remote sensing. Global Change Biology 6: 247254.CrossRefGoogle Scholar
De Jong, B., Hellier, A., Castillo-Santiago, M. & Tipper, R. (2005) Application of the ‘climafor’ approach to estimate baseline carbon emissions of a forest conservation project in the Selva Lacandona, Chiapas, Mexico. Mitigation and Adaptation Strategies for Global Change 10: 265278.CrossRefGoogle Scholar
Ebeling, J. & Yasué, M. (2008) Generating carbon finance through avoided deforestation and its potential to create climatic, conservation and human development benefits. Philosophical Transactions of the Royal Society B: Biological Sciences 363: 19171924.CrossRefGoogle ScholarPubMed
Estrada, M. (2011) Standards and methods available for estimating project-level REDD+ carbon benefits: reference guide for project developers. CIFOR, Bogor, Indonesia [www document]. URL http://www.cifor.org/nc/online-library/browse/view-publication/publication/3412.htmlGoogle Scholar
Fearnside, P.M. (1997) Greenhouse gases from deforestation in the Brazilian Amazonia: net committed emissions. Climatic Change 35: 321360.CrossRefGoogle Scholar
Fearnside, P.M. (2000) Global warming and tropical land-use change: greenhouse gas emissions from biomass burning, decomposition and soils in forest conversion, shifting cultivation and secondary vegetation. Climatic Change 46: 115158.CrossRefGoogle Scholar
Fedorko, E., Pontius, R.G. Jr, Aldrich, S., Claessens, L., Hopkinson, C. Jr & Wolheim, W. (2005) Spatial distribution of land type in regression models of pollutant loading. Journal of Spatial Hydrology 5: 6080.Google Scholar
Gibbs, H., Brown, S., Niles, J. & Foley, J. (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2: 13.Google Scholar
GOFC-GOLD (2010) Sourcebook COP 16 Version 1.GOFC-GOLD project office, Alberta, Canada [www document]. URL http://www.gofc-gold.uni-jena.de/redd/Google Scholar
Griscom, B., Shoch, D., Stanley, B., Cortez, R. & Virgilio, N. (2009) Sensitivity of amounts and distribution of tropical forest carbon credits depending on baseline rules. Environmental Science & Policy 12:897911.CrossRefGoogle Scholar
Harris, N., Petrova, S., Stolle, F. & Brown, S. (2008) Identifying optimal areas for REDD intervention: East Kalimantan, Indonesia as a case study. Environmental Research Letters 3: 035006.Google Scholar
Houghton, R.A., Lawrence, K.T., Hackler, J.L. & Brown, S. (2001) The spatial distribution of forest biomass in the Brazilian Amazon: A comparison of estimates. Global Change Biology 7: 731746.Google Scholar
Houghton, R.A. (2005) Aboveground forest biomass and the global carbon balance. Global Change Biology 11: 945958.Google Scholar
Huettner, M., Leemans, R., Kok, K. & Ebeling, J. (2009) A comparison of baseline methodologies for ‘Reducing Emissions from Deforestation and Degradation’. Carbon Balance and Management 4: 4.Google Scholar
INPE (2009) Monitoramento da Floresta Amazônica Brasileira por Satelite, Projeto PRODES., Sao Paulo, Brazil [www document]. URL http://www.obt.inpe.br/prodes/Google Scholar
Kim, O.S. (2010) An assessment of deforestation models for Reducing Emissions from Deforestation and Forest Degradation (REDD). Transactions in GIS 14: 631654.CrossRefGoogle Scholar
Kindermann, G., Obersteiner, M., Sohngen, B.et al. (2008) Global cost estimates of reducing carbon emissions through avoided deforestation. Proceedings of the National Academy of Sciences USA 105: 1030210307.Google Scholar
Kirby, K.R., Laurance, W.F., Albernaz, A.K., Schroth, G., Fearnside, P.M., Bergen, S., Venticinque, E.M. & da Costa, C. (2006) The future of deforestation in the Brazilian Amazon. Futures 38: 432453.CrossRefGoogle Scholar
Malhi, Y., Wood, D., Baker, T.R., Wright, J., Phillips, O.L., Cochrane, T., Meir, P., Chave, J., Almeida, S., Arroyo, L., Higuchi, N., Killeen, T.J., Laurance, S.G., Laurance, W.F., Lewis, S.L., Monteagudo, A., Neill, D.A., Nuñez, P., Nigel, V., Pitman, C.A., Quesada, C.A., Salomao, R., Natalino, J., Silva, M., Torres Lezama, A., Terborgh, J., Vàsquez MartÍnez, R., & Vinceti, B. (2006) The regional variation of aboveground live biomass in old-growth Amazonian forests. Global Change Biology 12: 11071138.CrossRefGoogle Scholar
Miles, L., Grainger, A. & Phillips, O. (2004) The impact of global climate change on tropical forest biodiversity in Amazonia. Global Ecology and Biogeography 13: 553565.Google Scholar
Nogueira, E.M., Fearnside, P.M., Nelson, B.W., Barbosa, R.I. & Keizer, E.W.H. (2008) Estimates of forest biomass in the Brazilian Amazon: New allometric equations and adjustments to biomass from wood-volume inventories. Forest Ecology and Management 256: 18531867.Google Scholar
Olander, L.P., Gibbs, H.K., Steininger, M., Swenson, J.J. & Murray, B.C. (2008) Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods. Environmental Research Letters 3: 11.Google Scholar
Olson, J., Watts, J. & Allison, L. (1983) Carbon in Live Vegetation of Major World Ecosystems. Technical Report TR004. US Department of Energy, Washington, DC, USA [www document]. URL http://www.osti.gov/energycitations/product.biblio.jsp?osti_id=5963568Google Scholar
Pan, Y., Hom, J., Birdsey, R. & McCullough, K. (2004) Impacts of rising nitrogen deposition on N exports from forests to surface waters in the Chesapeake Bay watershed. Environmental Management 33: S120S131.CrossRefGoogle Scholar
Parker, C. & Mitchell, A. (2009) Little REDD+ book: an updated guide to governmental and non-governmental proposals for reducing emissions from deforestation and degradation. Global Canopy Programme, Oxford, UK [www document]. URL http://www.globalcanopy.org/materials/little-redd-bookGoogle Scholar
Penman, J., Gytarsky, M., Hiraishil, T., Irving, W. & Krug, T. (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories: overview. In: IPCC Guidelines for National Greenhouse Gas Inventories, ed. Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K., pp. 112. IGES, Hayama, Japan [www document]. URL http://www.ipcc.ch/publications_and_data/publications_and_data_reports.shtml#.T4SFv46p2K4Google Scholar
Peterson, A.T., Ortega-Huerta, M.A., Bartley, J., Sanchez-Cordero, V., Soberon, J. & Buddemeier, R.H. (2002) Future projections for Mexican faunas under global climate change scenarios. Nature 416: 626629.Google Scholar
Pontius, R., Boersma, W., Castella, J.-C., Clarke, K., de Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C., McConnell, W., Mohd Sood, A., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T., Veldkamp, A., & Verburg, P. (2008 a) Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science 42: 1147.CrossRefGoogle Scholar
Pontius, R., Thontteh, O. & Chen, H. (2008 b) Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics 15: 111142.Google Scholar
Pontius, R.G. & Millones, M. (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing 32: 44074429.CrossRefGoogle Scholar
Potter, C.S. (1999) Terrestrial biomass and the effects of deforestation on the global carbon cycle. Bioscience 49: 769778.Google Scholar
Ramankutty, N., Gibbs, H.K., Achard, F., DeFries, R, Foley, J.A. & Houghton, R.A. (2007) Challenges to estimating carbon emissions from tropical deforestation. Global Change Biology 13: 5166.CrossRefGoogle Scholar
Saatchi, S.S., Houghton, R.A., Dos Santos-Alvalá, R.C., Soares, J.V. & Yu, Y. (2007) Distribution of aboveground live biomass in the Amazon basin. Global Change Biology 13: 816837.CrossRefGoogle Scholar
Sanchez-Azofeifa, G.A., Castro-Esau, K.L., Kurz, W.A. & Joyce, A. (2009) Monitoring carbon stocks in the tropics and the remote sensing operational limitations: from local to regional projects. Ecological Applications 19: 480494.CrossRefGoogle ScholarPubMed
Sangermano, F., Toledano, J. & Eastman, J. (2012) Land cover change in the Bolivian Amazon and its implications for REDD+ and endemic biodiversity. Landscape Ecology 27: 571584.CrossRefGoogle Scholar
Scheyvens, H. (2010) Developing national REDD-Plus systems: progress, challenges and ways forward. IGES, Kanagawa, Japan: 104 pp. [www document]. URL http://enviroscope.iges.or.jp/modules/envirolib/view.php?docid=3051Google Scholar
Sloan, S. & Pelletier, J. (2012) How accurately may we project tropical forest-cover change? A validation of a forward-looking baseline for REDD. Global Environmental Change (in press).CrossRefGoogle Scholar
Soares-Filho, B.S., Nepstad, D.C., Curran, L.M., Cerqueira, G.C., Garcia, R.A., Ramos, C.A., Voll, E., McDonald, A., Lefebvre, P. & Schlesinger, P. (2006) Modelling conservation in the Amazon basin. Nature 440: 520523.Google Scholar
Umemiya, C., Amano, M. & Wilamart, S. (2010) Assessing data availability for the development of REDD-plus national reference levels. Carbon Balance and Management 5: 6.CrossRefGoogle ScholarPubMed
UNFCCC (2009) Decision 4/CP.15. Methodological guidance for activities relating to reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries [www document]. URL http://unfccc.int/methods_and_science/lulucf/items/4267.phpGoogle Scholar
Venter, O., Laurance, W.F., Iwamura, T., Wilson, K.A., Fuller, R.A. & Possingham, H.P. (2009) Harnessing carbon payments to protect biodiversity. Science 326: 13681368.Google Scholar
Verchot, L.V. & Petkova, E. (2010) The state of REDD negotiations: consensus points, options for moving forward and research needs to support the process. UN-REDD, CIFOR, Bogor, Indonesia [www document]. URL http://www.cifor.org/nc/online-library/browse/view-publication/publication/2870.htmlGoogle Scholar
Verbist, B., Vangoidsenhoven, M., Dewulf, R. & Muys, B. (2011) Reducing emissions from deforestation and degradation (REDD). Klimos Working Paper-3. Klimos, Leuven, Belgium [www document]. URL http://www.biw.kuleuven.be/lbh/lbnl/forecoman/klimos/publications.htmlGoogle Scholar