Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

• Published in: Journal of arid land Vol. 6; no. 1; pp. 80 - 96
• Main Author: Carlos A AGUIRRE-SALADO Eduardo J TREVIO-GARZA Oscar A AGUIRRE-CALDERóN Javier JIMNEZ-PREZ Marco A GONZLEZ-TAGLE José R VALDZ-LAZALDE Guillermo SNCHEZ-DíAZ Reija HAAPANEN Alejandro I AGUIRRE-SALADO Liliana MIRANDA-ARAGóN
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014; Faculty of Forest Sciences, Autonomous University of Nuevo Leon, Linares 67700, Mexico%Forestry Program, Postgraduate College, Montecillo 56230, Mexico%Faculty of Engineering, Autonomous University of San Luis Potosi, San Luis Potosí 78290, Mexico%Haapanen Forest Consulting, K?rjenkoskentie 64810, Finland%Faculty of Agronomy and Veterinary, Autonomous University of San Luis Potosí, San Luis Potosí 78321, Mexico
• Subjects: and; BRDF-adjusted; Deforestation; Degradation; Earth and Environmental Science; Emission; forest; from; Geography; inventory; k-nearest; Mahalanobis; MODIS; most; neighbor; Physical Geography; Plant Ecology; policy; Reducing; reflectance; similar; Sustainable Development; the; MODIS BRDF-adjusted reflectance; forest inventory; nearest neighbor; Mahalanobis; the policy of Reducing Emission from Deforestation and Forest Degradation; most similar neighbor; k-nearest neighbor
• ISSN: 1674-6767; 2194-7783
• DOI: 10.1007/s40333-013-0191-x

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+).