| Research Articles |
|
|
|
|
| Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico |
| Carlos A AGUIRRE-SALADO1,2*, Eduardo J TREVIÑO-GARZA1, Oscar A AGUIRRE-CALDERÓN1, Javier JIMÉNEZ-PÉREZ1, Marco A GONZÁLEZ-TAGLE1, José R VALDÉZ-LAZALDE3, Guillermo SÁNCHEZ-DÍAZ2, Reija HAAPANEN4, Alejandro I AGUIRRE-SALADO3, Liliana MIRANDA-ARAGÓN5 |
1 Faculty of Forest Sciences, Autonomous University of Nuevo Leon, Linares 67700, Mexico;
2 Faculty of Engineering, Autonomous University of San Luis Potosi, San Luis Potosí 78290, Mexico;
3 Forestry Program, Postgraduate College, Montecillo 56230, Mexico;
4 Haapanen Forest Consulting, Kärjenkoskentie 64810, Finland;
5 Faculty of Agronomy and Veterinary, Autonomous University of San Luis Potosí, San Luis Potosí 78321, Mexico |
|
|
|
Abstract As climate change negotiations progress, monitoring biomass and carbon stocks is becoming an important part of the current forest research. Therefore, national governments are interested in developing forest-monitoring strategies using geospatial technology. Among statistical methods for mapping biomass, there is a nonparametric approach called k-nearest neighbor (kNN). We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone. Satellite derived, climatic, and topographic predictor variables were combined with the Mexican National Forest Inventory (NFI) data to accomplish the purpose. Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique. The results indicate that the Most Similar Neighbor (MSN) approach maximizes the correlation between predictor and response variables (r=0.9). Our results are in agreement with those reported in the literature. These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation (REDD+).
|
|
Received: 19 November 2012
Published: 10 February 2014
|
|
Corresponding Authors:
Carlos A AGUIRRE-SALADO
|
| Cite this article:
Carlos A AGUIRRE-SALADO, Eduardo J TREVI?O-GARZA, Oscar A AGUIRRE-CALDERóN, Javier JIMéNEZ-PéREZ, Marco A GONZáLEZ-TAGLE, José R VALDéZ-LAZALDE, Guillermo SáNCHEZ-DíAZ, Reija HAAPANEN, et al.. Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico. Journal of Arid Land, 2014, 6(1): 80-96.
URL:
http://jal.xjegi.com/10.1007/s40333-013-0191-x OR http://jal.xjegi.com/Y2014/V6/I1/80
|
|
|
| Aguirre-Salado C A, Valdez-Lazalde J R, Ángeles-Pérez G, et al. 2009. Mapping aboveground tree carbon in managed Patula Pine forests in Hidalgo, Mexico. Agrociencia, 43: 209–220.Anaya J A, Chuvieco E, Palacios-Orueta A. 2009. Aboveground biomass assessment in Colombia: a remote sensing approach. Forest Ecology and Management, 257: 1237–1246.Baffeta F, Fattorini L, Franceschi S, et al. 2009. Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys. Remote Sensing of Environment, 113: 463–475.Barati S, Rayegan B, Saati M, et al. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Sciences, 14: 49–56.Bhaduri K, Das K, Votava P. 2010. Distributed anomaly detection using satellite data from multiple modalities. In: Srivastava A, Chawla N, Yu P, et al. Proceedings of the 2010 Conference on Intelligent Data Understanding CIDU 2010, California: NASA Ames Research Center, 109–123.Blackard J A, Finco M V, Helmer E H, et al. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment, 112: 1658–1677.Breidenbach J, Naesset E, Lien V, et al. 2010. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sensing of Environment, 114: 911–924.Breidenbach J, Naesset E, Gobakken T. 2012. Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data. Remote Sensing of Environment, 117: 358–365.Cairns M, Olmsted I, Granados J, et al. 2003. Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula. Forest Ecology and Management, 186: 125–132.Canisius F, Fernandes R, Chen J. 2010. Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use. Remote Sensing Environment, 114: 950–960.Chen H, Chen L, Albright T P. 2007. Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: a case of ragweed (Ambrosia artemisifolia L.) distribucion in China. Chinese Science Bulletin, 52(9): 1223–1230.Chirici G, Barbati A, Corona P, et al. 2008. Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems. Remote Sensing Environment, 112: 2686–2700.Colditz R, López-Saldaña G, Maeda P, et al. 2012. Generation and analysis of the 2005 land cover map for Mexico using 250 m MODIS data. Remote Sensing of Environment, 123: 541–552.Cole T, Ewel J. 2006. Allometric equations for four valuable tropical tree species. Forest Ecology and Management, 229: 351–360.Crookston N L, Finley A O. 2008. yaImpute: an R Package for kNN Imputation. Journal of Statistical Software, 23(10): 1–16.Cruz-Leyva I A, Valdez-Lazalde J R, Ángeles-Pérez G, et al. 2010. Spatial modeling of basal area and tree volume in managed Pinus patula and P. teocote forests in the ejido Atopixco, Hidalgo. Maderay Bosques, 16(3): 75–97.De Jong B, Anaya C, Masera O, et al. 2010. Greenhouse gas emissions between 1993 and 2002 from land-use change and forestry in Mexico. Forest Ecology and Management, 260(10): 1689–1701.De Leeuw J, Georgiadou Y, Kerle N, et al. 2010. The function of remote sensing in support of environmental policy. Remote Sensing, 2: 1731–1750.Etchevers-Barra J, Vargas-Hernández J, Acosta-Mireles M, et al. 2002. Aboveground biomass estimation by means of allometric relationships in six hardwood species in Oaxaca, Mexico. Agrociencia, 36: 725–736.FAO. 2009. State of The World’s Forests. Food and Agriculture Organization of the United Nations. Rome: Electronic Publishing Policy and Support Branch Communication Division, 152 [2010-08-15]. http://www.fao.org/docrep/011/i0350e/i0350e00.htm.Fox J. 1984. Linear Statistical Models and Related Methods: with Applications to Social Research. New York: John Wiley.Franco-López H, Alan R E, Bauer M E. 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment, 77: 251–274.Fuchs H, Magdon P, Klein C, et al. 2009. Estimating aboveground carbon in a catchment of the Siberian forest tundra: combining satellite imagery and field inventory. Remote Sensing of Environment, 113: 518–531.Gallaun H, Zanchi G, Nabuurs G J, et al. 2009. EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements. Forest Ecology and Management, 260(3): 252–261.Galvao L, Dos Santos J, Roberts D, et al. 2011. On intra-annual EVI variability in the dry season of tropical forest: a case study with MODIS and hyperspectral data. Remote Sensing of Environment, 115: 2350–2359.Gao B C. 1996. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58: 257–266.García-Villalpando J, Castillo-Morales A, Ramírez-Guzmán M E, et al. 2001. A comparison of Tukey, Duncan, Dunnet, HSU and Bechhofer procedures for selection of means. Agrociencia, 35: 79–86.Getirana A C V. 2010. Integrating spatial altimetry data into the automatic calibration of hydrological models. Journal of Hydrology, 387: 244–255.Gjertsen A K. 2007. Accuracy of forest mapping based on Landsat TM data and a kNN-based method. Remote Sensing of Environment, 110: 420–430.GOFC-GOLD. 2010. A sourcebook 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 forestation. Canada: GOFC-GOLD Project Office, Natural Resources, 203 [2010-02-10]. http://www.gofc-gold.uni-jena.de/redd/sourcebook.Hansen M C, DeFries R S, Townshend J R, et al. 2003. MOD44B: vegetation continuous fields collection 3, version 3.0.0. Earth Interactions, 7: 1–20.Hongoh V, Berrang-Ford L, Scott M E, et al. 2012. Expanding geographical distribution of the mosquito, Culex pipiens, in Canada under climate change. Applied Geography, 33: 53–62.Hudak A T, Crookston N L, Evans J E, et al. 2008. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112: 2232–2245.IMTA. 2006. Rapid Extractor for Climatic Information III, v. 1.0. Climatic information available in electronic format. Morelos, Mexico: Mexican Institute for Water Technology. [2010-02-15]. http://www. imta.gob.mx.INEGI. 2009. Land Use and Land Cover Map Series IV. Mexico: National Institute of Geography and Statistics of Mexico. [2010-03-15]. http://www.inegi.org.mx.Joseph M, Wang L, Wang F. 2012. Using Landsat imagery and census data for urban population density modeling in Port-au-Prince, Haiti. GIScience & Remote Sensing, 49(2): 228–250.Kajisa T, Murakami T, Mizoue N, et al. 2008. Estimation of stand volumes using the k-nearest neighbors method in Kyushu, Japan. Journal of Forest Research., 13: 249–254.Kaul M, Dadhwal V K, Mohren G M J. 2009. Land use change and net C flux in Indian forests. Forest Ecology and Management, 258: 100–108.Köhl M, Magnussen S S, Marchetti M. 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. New York: Springer, 373. Labrecque S, Fournier R, Luther J, et al. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, 226(1–3): 129–144.Lasserre B, Chirici G, Chiavetta U, et al. 2011. Assessment of potential bioenergy from coppice forests through the integration of remote sensing and field surveys. Biomass and Bioenergy, 35(1): 716–724.Loog M, Ginneken B, Duin R P. 2005. Dimensionality reduction of image features using the canonical contextual correlation projection. Pattern Recognition, 38(12): 2409–2418.McRoberts R E. 2008. Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. Remote Sensing Environment, 112: 2212–2221.McRoberts R E. 2009. A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes. Remote Sensing of Environment, 113: 532–545.McRoberts R E. 2012. Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management. 272: 3–12.Miranda-Aragón L, Treviño-Garza E J, Jiménez-Pérez J, et al. 2012. Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression. Journal of Forestry Research, 23(3): 345–354.Nakakaawa C A, Vedeld P O, Aune J B. 2011. Spatial and temporal land use and carbon stock changes in Uganda: implications for a future REDD strategy. Mitigation and Adaptation Strategies for Global Change, 16: 25–62.NASA. 2009. MODIS Product Table. Sioux Falls, South Dakota: NASA Land Processes Distributed Active Archive Center (LP DAAC). https://lpdaac.usgs. gov/lpdaac/products/modis_products_table.National Forestry Comission of Mexico. 2010. Manual of Procedures for Field Sampling. Jalisco, Mexico: National Forestry Comission of Mexico, 19 [2011-01-05]. http://www.cnf.gob.mx:8080/snif/portal/compnent/phocadownload/category/153-2012?download=781:manual-del-remuestreo-infys-2012.Návar J, Méndez E, Nájera A, et al. 2004. Biomass equations for shrub species of Tamaulipan thornscrub of northeastern Mexico. Journal of Arid Environments, 59: 657–674.Návar J. 2009. Biomass component equations for Latin American species and groups of species. Annals of Forest Science, 66(2): 208.Northup B, Sitzer S, Archer S, et al. 2005. Above-ground biomass and carbon and nitrogen content of woody species in a subtropical thornscrub parkland. Journal of Arid Environments, 62: 23–43.Nothdurf A, Saborowski J. 2009. Spatial prediction of forest stand variables. European Journal of Forest Research, 128: 241–251.Packalén P, Maltamo M. 2007. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing Environment, 109: 328–341.Powell S L, Cohen W B, Healey S P, et al. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment, 114: 1053–1068.Ramachandran B, Justice C, Abrams M. 2010. Land Remote Sensing and Global Environmental Change. NASA’s Earth Observing System and the Science of ASTER and MODIS. New York: Springer, 894.Reyes-Hernández H, Aguilar-Robledo M, Aguirre-Rivera J R, et al. 2006. Land cover and land use change in the Pujal-Coy project area, San Luis Potosí, Mexico, 1973–2000. Investigaciones Geográficas Boletín del Instituto de Geografía UNAM, 59: 26–42. Robles A, España J, Robles H. 2008. Biomass and forage, spatial distribution and abundance of sotol (Dasylirion spp.) in the Ejido El Jazmin, Mazapil, Zacatecas, Mexico. Revista Investigación Científica, 4(2): 1–9.Rock B, Vogelmann J, Williams D, et al. 1986. Remote detection of forest damage. Bioscience, 36: 439–445.Salis S, Assis M, Mattos P, et al. 2006. Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil’s Pan-tanal wetlands based on allometric correlations. Forest Ecology and Management, 228: 61–68.Sampaio E, Silva G. 2005. Biomass equations for Brazilian semiarid caatinga plants. Acta Botânica Brasílica, 19: 935–945.Sarker L, Nichol J. 2011. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115: 968–977.SAS Institute Inc. 2004. SAS/STAT 9.1 User’s Guide. Cary, NC, USA: SAS Publishing, 5121.Segura M, Kanninen M, Suarez D. 2006. Allometric models for estimating aboveground biomass of shade trees and coffee bushes grown together. Agroforestry Systems, 68: 143–150.SEMARNAT-INE. 2009. Fourth National Communication of Mexico to the United Nations Framework Convention on Climate Change. Mexico: Ministry of Environment and Natural Resources–National Institute of Ecology, 274 [2010-04-15]. http://www2.ine.gob.mx/ publicaciones/consultaPublicacion.html?id_pub=654.Silva-Arredondo F M, Návar-Chaidez J J. 2009. Estimating carbon expansion factors in temperate forest communities of northern Du-rango, Mexico. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 15(2): 155–163.Streck C, O'Sullivan R, Janson-Smith T. 2008. Climate Change and Forests: Emerging Policy and Market Opportunities. Baltimore: Brookings Institution Press, 346.Tian X, Su Z, Chen E, et al. 2012. Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area. International Journal of Applied Earth Observation, 14: 160–168.Tomppo E, Olsson H, Stahl G, et al. 2008. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment, 112: 1982–1999.Valdez-Tamez V, Rahim-Foroughbakhch P, Gláfiro-Alanís F. 2011. Relict distribution of cloud forest in Northeast Mexico. Ciencia UANL, 6(3): 360–365.Zhou P, Luukkanen O, Tokola T, et al. 2008. Effect of vegetation cover on soil erosion in a mountainous watershed. Catena, 75: 319–325. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
