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

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

“…Optical remote sensing based estimates of canopy density have been used to predict forest biophysical attributes such as height, growing stock volume, and aboveground biomass for forests in the US [11,36], for tropical forests in the Amazon [12], for boreal forests in Sweden and Central Siberia [81], as well as for an arid forest area in North Central Mexico [82]. A limitation of canopy density metrics as predictors of aboveground biomass is that they function well only as long as the canopies are not closed (i.e., primarily during early-successional stages of forest development).…”

A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data

“…However, several studies also used these techniques with k > 1 [36]. Some studies have compared kNN distance techniques [31,32,37]. Hudak, Crookston, Evans, Hall and Falkowski [31] compared several kNN distance techniques for imputing plot-level response variables (basal area and tree density) using airborne lidar data in small case study areas.…”

“…This can be divided into two groups: direct and indirect imputation methods. In the direct method, forest biomass variables are included in kNN models as response variables and are thus directly imputed based on their relationship with predictor variables [30,[37][38][39][40]. In the indirect method, however, kNN models are trained by response variables other than biomass [15,19,24].…”

Estimación de parámetros forestales en bosques de coníferas con técnicas de percepción remota

“…In Mexico, the use of satellite technology has focused on the detection of changes in the tree cover (Valdez-Lazalde et al, 2006;Aguirre-Salado et al, 2012;Gebhardt et al, 2014), and forest management monitoring has been directed primarily at such variables as basal area, biomass (Aguirre-Salado et al, 2014), and, secondly, at the estimation of the standing volume and certain important dasometric variables for forest management, such as diameter at breast heigh and total height.…”

“…Quickbird®, Geoeye®) posibilita nuevas opciones para la estimación de características biofísicas del arbolado de manera indirecta, lo cual minimiza el costo de hacerlo mediante inventarios tradicionales (Valdez-Lazalde et al, 2006). En México, la utilización de tecnología satelital se ha enfocado a la detección de cambios en la cobertura arbórea ( Valdez-Lazalde et al, 2006;Aguirre-Salado et al, 2012;Gebhardt et al, 2014), y el monitoreo aplicado al manejo forestal se ha encaminado, básicamente, a variables tales como área basal, biomasa área (Aguirre-Salado et al, 2014), y en segundo plano, las estimaciones de volumen en pie y algunas variables dasométricas importantes para el manejo forestal: diámetro normal y altura total. 9 En este trabajo se evaluó la capacidad de dos plataformas satelitales: SPOT (Satellite Pour l'Observation de la Terre, por sus siglas en francés) y Quickbird®, en la estimación de parámetros forestales de interés (i.e.…”