Abstract
Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. In many tropical developing countries, this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB. Using National Forest Inventory (NFI) data for a ~1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was ~36.8 tonnes per hectare (i.e., 57 % of the mean). However, the sigmoidal model was approximately 30 % more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total AGB for the study area at 64,526,209 tonnes (+/− 477,730), with a confidence interval 20 times more precise than a simple design-based estimate. Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.
Highlights
Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels
We addressed the following specific questions: 1) Which definition of canopy cover (CC) is more useful as a single predictor variable to estimate AGB, the hemispherical view or the vertical projection view? 2) Does AGB estimation accuracy improve if CC is combined with other predictor variables? 3) What model-based AGB estimation method performs best in terms of fit and validation statistics? and 4) How do model-based methods compare to a simple design-based estimate of the sample mean? This research is intended as a case study to help inform development of forest monitoring programs which apply National Forest Inventory (NFI) data to estimation of AGB at sub-national or national scales
In predicting AGB, clear differences are apparent in the usefulness of CC as the percentage of the sky blocked by tree crowns over a hemispherical view (^ CC HEMI ) versus CC as the vertical projection of tree crowns onto the ground (^ CC VERT )
Summary
Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. Tropical forest resources are being exploited and converted to other uses at rates that seem to outpace the capacity for regrowth (Shearman et al 2012; Brink et al 2014; Sawe et al 2014; Suberu et al 2014) This is especially true in the miombo ecoregion of southern Africa (Cabral et al 2010; Mayes et al 2015), where forest productivity is marginal (Frost 1996). In this context, forest monitoring programs are required to provide information on forest resources for: i) planning use and management activities at local levels (Stringer et al 2012); and ii) designing effective policies and measures at national levels. Integrating ground plot data from a forest inventory with environmental and/or remotely-sensed predictor variables to predict AGB has shown increasing utility for estimating AGB (Moisen et al 2006; McRoberts et al 2010; Lu et al 2016; GOFC-GOLD 2015)
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