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

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Journal of Arid Land 2014年第1期6卷 80-96页

作者： 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 Faculty of Forest Sciences Autonomous University of Nuevo Leon Faculty of Engineering Autonomous University of San Luis Potosi Forestry Program Postgraduate College Haapanen Forest Consulting Faculty of Agronomy and Veterinary Autonomous University of San Luis Potosí

As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest ***,national governments are interested in developing forest-monitoring strategies using geospatial *** 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 *** derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the *** of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out *** 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 *** 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+).

关键词： k-nearest neighbor Mahalanobis most similar neighbor MODIS BRDF-adjusted reflectance forest inventory the policy of Reducing Emission from Deforestation and Forest Degradation

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