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

• Published in: Journal of arid land 2014-02, Vol.6 (1), p.80-96
• Main Authors: AGUIRRE-SALADO, Carlos A, TREVINO-GARZA, Eduardo J, AGUIRRE-CALDERON, Oscar A, JIMENEZ, Javier, GONZALEZ-TAGLE, Marco A, VALDEZ-LAZALDE, Jose R, SANCHEZ, Guillermo, HAAPANEN, Reija, AGUIRRE-SALADO, Alejandro I, MIRANDA, Liliana
• Format: Article
• Language: English
• ISSN: 1674-6767
• 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 Kappa -nearest neighbor (kNN). We compared four variations of distance metrics of the kNN for the spatially-explic it 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+).