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Title: Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments
Keywords: Random forests
Brazilian biomes
Issue Date: Aug-2019
Publisher: Elsevier
Citation: SILVEIRA, E. M. O. et al. Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments. Forest Ecology and Management, [S.l.], v. 445, p. 96-109, Aug. 2019.
Abstract: Mapping aboveground biomass (AGB) is a challenge in heterogeneous environments, such as the Brazilian savannas and tropical forests located in Minas Gerais state (MG), Brazil. The factors linked to AGB stocks vary in climate, soil characteristics, and stand-level structural attributes over short distances, making generalization of AGB difficult over regional-scales. We offer the hypothesis that stratification into vegetation types at the plot level plus a regression kriging technique, can reduce the variability of factors controlling AGB, helping to select the appropriate predictor variables and result in an ability to produce reliable models and maps. To do so, we incorporate remotely sensed data (Landsat and MODerate resolution Imaging Spectroradiometer-MODIS), spatio-environmental variables, and forest inventory data to develop spatial-explicit maps of AGB across three important Brazilian biomes (savanna, Atlantic forest, and semi-arid woodland). We modelled and predicted the spatial distribution of AGB of six individual vegetation types of savanna-forest biomes (shrub savanna, woodland savanna, densely wooded savanna, deciduous forest, semi-deciduous forest and rain forest), utilizing a random forests (RF) algorithm plus residual kriging, selecting the lowest number of variables that offer the best predictive performance. The stratified models notably improved the AGB prediction by reducing the mean absolute error – MAE (%) and the root-mean-square error – RMSE (Mg/ha) for all vegetation types, mainly for shrub savanna (MAE reduced from 82.69 to 54.73%). The AGB spatial distribution is governed mainly by precipitation and seasonality. The south and east of MG presented high values of AGB due to the predominance of semi-deciduous trees and rain forest conditions within Atlantic forest biome (total of 491,456,607 Mg), with a higher amount rain over the year, lower temperatures, and lower precipitation seasonality. Rain forests have the largest mean AGB per area (157.71 Mg/ha) while semi-deciduous forests hold the largest AGB stocks in the state (583,176,472 Mg). Shrub savannas, located in the central, northwest and north regions of MG (lower amount of rain, higher temperatures and strong seasonality), accounted the lowest amount of AGB in both total AGB (27,906,281 Mg) and AGB per area (18.80 Mg/ha). Our study demonstrates that stratification can reduce variability and improve estimates by developing individual models and selecting optimal predictor variables dependent on the characteristics of specific vegetation types. The methods demonstrated and the resultant maps and estimates improve the quality of regional biomass estimates needed to understand and mitigate climate change, enabling researchers to refine estimates of greenhouse gas emissions.
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