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

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• 作者：Carlos A. Aguirre-Salado (1) (2)
  Eduardo J. Trevi?o-Garza (1)
  Oscar A. Aguirre-Calderón (1)
  Javier Jiménez-Pérez (1)
  Marco A. González-Tagle (1)
  José R. Valdéz-Lazalde (3)
  Guillermo Sánchez-Díaz (2)
  Reija Haapanen (4)
  Alejandro I. Aguirre-Salado (3)
  Liliana Miranda-Aragón (5)
  
• 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
• 刊名：Journal of Arid Land
• 出版年：2014
• 出版时间：February 2014
• 年：2014
• 卷：6
• 期：1
• 页码：80-96
• 全文大小：688 KB
• 作者单位：Carlos A. Aguirre-Salado (1) (2)
  Eduardo J. Trevi?o-Garza (1)
  Oscar A. Aguirre-Calderón (1)
  Javier Jiménez-Pérez (1)
  Marco A. González-Tagle (1)
  José R. Valdéz-Lazalde (3)
  Guillermo Sánchez-Díaz (2)
  Reija Haapanen (4)
  Alejandro I. Aguirre-Salado (3)
  Liliana Miranda-Aragón (5)
  
  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, K?rjenkoskentie, 64810, Finland
  5. Faculty of Agronomy and Veterinary, Autonomous University of San Luis Potosí, San Luis Potosí, 78321, Mexico
  

文摘

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+).