Abstract
The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha−1 and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.
Highlights
Forests play an important role in mitigating climate change by fixing about 30% of fossil fuel CO2(C) emissions [1,2,3]
We found that the LiDAR-based above-ground biomass (AGB) varied from values near 0 to the highest values of 302 ± 25.7 and 492 ± 17 Mg ha−1 on the temperate forest landscape due to changes in forest structure and stand age, mainly attributed to forest management practices, that produced a mosaic of different age classes, tree heights and densities, and biomasses (Figures 3 and 4)
The analysis showed that spatial heterogeneity in AGB increased with increasing stand structural complexity and age in the managed forests, varying from 0 to 302–492 Mg ha−1
Summary
Forests play an important role in mitigating climate change by fixing about 30% of fossil fuel CO2(C) emissions [1,2,3]. Managed forests are critically important to assisting with reducing atmospheric CO2 concentration through initiatives like Reducing Emission from Deforestation and Forest Degradation, which includes the conservation and sustainable management of forests and enhancement of forest carbon stocks (REDD+) [6]. Incomplete information regarding the spatial variability (spatial distribution) of forest AGB introduces substantial uncertainty about current estimates of the carbon stocks present in managed forests and the global carbon budget [8,9]. It is important to improve our knowledge of the spatial variability of forest biomass and its associated uncertainty in order to: (a) improve estimates of carbon stocks and understand carbon cycles [10]; (b) support future climate mitigation actions [11]; and (c) support sustainable forest management [12]
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