Estimating the spatial distribution of woody biomass suitable for charcoal making from remote sensing and geostatistics in central Mexico

Energy for Sustainable Development

Volumn 17, Issue 2, 2013, Pages 177-188

  Castillo Santiago, Miguel Ángel ^a   Ghilardi, Adrián ^b,c   Oyama, Ken ^d   Hernández Stefanoni, José Luis ^e   Torres, Ignacio ^d   Flamenco Sandoval, Alejandro ^f   Fernández, Ana ^b   Mas, Jean François ^b  

^a DEPARTAMENTO DE CONSERVACIÓN DE LA BIODIVERSIDAD   (Mexico)

^b INSTITUTO DE MATEMÁTICAS   (Mexico)

^c Yale University   (United States)

^d INSTITUTO DE INVESTIGACIONES EN ECOSISTEMAS Y SUSTENTABILIDAD   (Mexico)

^e CENTRO DE INVESTIGACIÓN CIENTÍFICA DE YUCATÁN   (Mexico)

^f UNIVERSITY OF GUANAJUATO   (Mexico)

Author keywords

Aboveground biomass; Charcoal; Cuitzeo basin; Geostatistics; Quercus spp.; SPOT5 images

Indexed keywords

AUTOCORRELATION; BIOMASS; BRICKMAKING; CHARCOAL; COST EFFECTIVENESS; INTERPOLATION; REGRESSION ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL DISTRIBUTION; SPATIAL VARIABLES MEASUREMENT; SUSTAINABLE DEVELOPMENT;

ABOVE GROUND BIOMASS; CUITZEO BASIN; GEO-STATISTICS; QUERCUS SPP; SPOT5 IMAGES;

FORESTRY;

ABOVEGROUND BIOMASS; AUTOCORRELATION; CHARCOAL; DIGITAL ELEVATION MODEL; GEOSTATISTICS; KRIGING; MAPPING; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL DISTRIBUTION; SPOT; WOODY PLANT;

LAKE CUITZEO; MEXICO [NORTH AMERICA]; MICHOACAN;

QUERCUS;

EID: 84875602877     PISSN: 09730826     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.esd.2012.10.007     Document Type: Article

References (57)

1. Aguilar R., Ghilardi A., Vega E., Skutsch M., Oyama K. Sprouting productivity and allometric relationships of two oak species managed for traditional charcoal making in central Mexico. Biomass Bioenergy 2012, 36:192-207.
2. Ahrends A., Burgess N.D., Milledge S.A.H., Bulling M.T., Fisher B., Smart J.C.R., et al. Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city. Proc Natl Acad Sci U S A 2010, 107:14556-14561.
3. Arnold M., Köhlin G., Persson R., Shepherd G. Fuelwood revisited: what has changed in the last decade? Center for International Forestry Research (CIFOR) 2003, Bogor Barat, Indonesia.
4. Arnold J.E.M., Kohlin G., Persson R. Woodfuels, livelihoods, and policy interventions: changing perspectives. World Dev 2006, 34:596-611.
5. Avitabile V., Herold M., Henry M., Schmullius C. Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda. Carbon Balance Manage 2011, 6:7.
6. Cairns M.A., Olmsted I., Granados J., Argaez J. Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico's Yucatan Peninsula. For Ecol Manage 2003, 186:125-132.
7. Castillo-Santiago M.A., Ricker M., de Jong B.H.J. Estimation of tropical forest structure from SPOT-5 satellite images. Int J Remote Sens 2010, 31:2767-2782.
8. Chave J., Condit R., Aguilar S., Hernandez A., Lao S., Perez R. Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc Lond B Biol Sci 2004, 359:409-420.
9. Clark D.B., Clark D.A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For Ecol Manage 2000, 137:185-198.
10. Costanza R., Voinov A. Landscape simulation modeling: a spatially explicit, dynamic approach 2004, Springer, New York.
11. Deaton M.L., Winebrake J.J. Dynamic modeling of environmental systems 2000, Springer, New York.
12. Emrich W. Handbook of charcoal making: the traditional and industrial methods 1985, D. Reidel Publishing Company, Dordrecht, The Netherlands.
13. FAOStat ForestStat-forestry statistics 2010, FAO, Rome, [http://faostat.fao.org/].
14. Foody G.M., Cutler M.E., McMorrow J., Pelz D., Tangki H., Boyd D.S., et al. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol Biogeogr 2001, 10:379-387.
15. Foody G.M., Boyd D.S., Cutler M.E.J. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens Environ 2003, 85:463-474.
16. Fox J., Weisberg S. An R companion to applied regression 2011, Sage, Thousand Oaks CA. 2nd edition.
17. García E. Modificaciones al sistema de clasificación Climática de Köppen 2004, UNAM, México DF. 5th edition.
18. Goetz S.J., Baccini A., Laporte N.T., Johns T., Walker W., Kellndorfer J., et al. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance Manage 2009, 4:2.
19. Hansfort S.L., Mertz O. Challenging the woodfuel crisis in West African woodlands. Hum Ecol 2011, 39:583-595.
20. Haralick R.M., Shanmuga K., Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973, 36:610-621.
21. Hengl T., Heuvelink G.B.M., Rossiter D.G. About regression-kriging: from equations to case studies. Comput Geosci 2007, 33:1301-1315.
22. Hernandez-Stefanoni J.L., Gallardo-Cruz J.A., Meave J.A., Dupuy J.M. Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping. Ecol Indic 2011, 11:1046-1056.
23. Hofstad O. Woodland deforestation by charcoal supply to Dar es Salaam. J Environ Econ Manage 1997, 33:17-32.
24. IEA World energy statistics and balances (database) 2012, International Energy Agency, [Accessed on 09 October 2012]. 10.1787/data-00510-en.
25. IEA Energy for cooking in developing countries. World Energy Outlook 2006 2006, 419-445. International Energy Agency (IEA), Paris. OECD/IEA (Ed.).
26. Im J., Gleason C.J. A review of remote sensing of forest biomass and biofuel: options for small-area applications. GISci Remote Sens 2011, 48:141-170.
27. INEGI XIII Censo General de Población y Vivienda, 2010 2010, Instituto Nacional de Estadística, Geografía e Informática (INEGI), México DF.
28. INEGI Censo de Población y Vivienda 2010. Principales resultados por localidad (ITER), Michoacán de Ocampo 2011, Instituto Nacional de Estadística y Geografía (INEGI), México DF.
29. ITT FLAASH module user's guide, ENVI-FLAASH Version 4.3 2006, ITT Visual Information Solutions, Boulder, CO, USA.
30. Johnson M., Lam N., Brant S., Gray C., Pennise D. Modeling indoor air pollution from cookstove emissions in developing countries using a Monte Carlo single-box model. Atmos Environ 2011, 45:3237-3243.
31. Khundi F., Jagger P., Shively G., Sserunkuuma D. Income, poverty and charcoal production in Uganda. For Policy Econ 2011, 73:199-205.
32. Larsen W.A., McCleary S.J. The use of partial residual plots in regression analysis. Technometrics 1972, 14:781-790.
33. Lasserre B., Chirici G., Chiavetta U., Garfi V., Tognetti R., Drigo R., et al. Assessment of potential bioenergy from coppice forests trough the integration of remote sensing and field surveys. Biomass Bioenerg 2011, 35:716-724.
34. Legendre P. Spatial autocorrelation-trouble or new paradigm. Ecology 1993, 74:1659-1673.
35. Lillesand T.M., Kiefer R.W. Remote sensing and image interpretation 1994, 750. John Wiley & Sons, New York. 3rd edition.
36. López E., Bocco G., Mendoza M., Velázquez A., Aguirre J.R. Peasant emigration and land-use change at the watershed level: a GIS-based approach in Central Mexico. Agr Syst 2006, 90:62-78.
37. Lu D.S. The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 2006, 27:1297-1328.
38. Lu D., Batistella M. Exploring TM image texture and its relationships with biomass estimation in RondÔnia. Acta Amazonica 2005, 35:249-257.
39. Masera O., Arias T., Ghilardi A., Guerrero G., Patiño P. Estudio sobre la evolución nacional del consumo de leña y carbón vegetal en México 1990-2024 2010, Universidad Nacional Autónoma de México (UNAM), México DF.
40. Mendoza M., Bocco G., Bravo M., López G. Evaluación de la calidad espacial y temporal de estaciones meteorológicas. El caso de la Cuenca del Lago de Cuitzeo. Ciencia Nicolaíta 2005, 39:79-94.
41. Mendoza M., Bocco G., Bravo M., López M., Osterkamp W. Predicting water-surface fluctuation of continental lakes. A RS and GIS based approach in Central Mexico. Water Resour Manage 2006, 20:291-311.
42. Mendoza M.E., Granados E.L., Geneletti D., Perez-Salicrup D.R., Salinas V. Analysing land cover and land use change processes at watershed level A multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975-2003). Appl Geogr 2011, 31:237-250.
43. Murayama Y., Thapa R.B. Spatial analysis and modeling in geographical transformation process: GIS-based applications 2011, Springer, New York.
44. Mwampamba T.H. Has the woodfuel crisis returned? Urban charcoal consumption in Tanzania and its implications to present and future forest availability. Energy Policy 2007, 35:4221-4234.
45. Naughton-Treves L., Kammen D.M., Chapman C. Burning biodiversity: woody biomass use by commercial and subsistence groups in western Uganda's forests. Biol Conserv 2007, 134:232-241.
46. Paegelow M., Camacho-Olmedo M.T. Modelling environmental dynamics: advances in geomatic solutions 2008, Springer, New York. 1st ed.
47. Paradzayi C., Annegarn H.J. Estimating potential woody biomass in communal savanna woodlands from synthetic aperture radar (SAR). Int J Appl Geospat Res 2012, 3:53-62.
48. Parikka M. Global biomass fuel resources. Biomass Bioenerg 2004, 27:613-620.
49. Pebesma E.J. Multivariable geostatistics in S: the gstat package. Comput Geosci 2004, 30:683-691.
50. Pennise D.M., Smith K.R., Kithinji J.P., Rezende M.E., Raad T.J., Zhang J.F., et al. Emissions of greenhouse gases and other airborne pollutants from charcoal making in Kenya and Brazil. J Geophys Res Atmos 2001, 106:24143-24155.
51. R Development Core Team R: a language and environment for statistical computing 2012, R Foundation for Statistical computing, Vienna, Austria.
52. Ryan C.M., Hill T., Woollen E., Ghee C., Mitchard E., Cassells G., et al. Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery. Glob Chang Biol 2012, 18:243-257.
53. Sales M.H., Souza C.M., Kyriakidis P.C., Roberts D.A., Vidal E. Improving spatial distribution estimation of forest biomass with geostatistics: a case study for RondÔnia, Brazil. Ecol Model 2007, 205:221-230.
54. Stockholm Environment Institute Charcoal potential in southern Africa: final main report and country reports 2002, Stockholm Environment Institute (SEI), Stockholm, Sweden.
55. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 1979, 8:127-150.
56. von-Jonstorff H.J., Nagel O. Heat energy and fuels; pyrometry, combustion, analysis of fuels and manufacture of charcoal, coke and fuel gases (reprint of 1923 edition) 2010, 322. Nabu Press, Charleston, USA.
57. Zulu L.C. The forbidden fuel: charcoal, urban woodfuel demand and supply dynamics, community forest management and woodfuel policy in Malawi. Energy Policy 2010, 38:3717-3730.