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. 2016 Jun 29;11(1):14.
doi: 10.1186/s13021-016-0051-z. eCollection 2016 Dec.

Indirect approach for estimation of forest degradation in non-intact dry forest: modelling biomass loss with Tweedie distributions

Klaus Dons  1 , Sushma Bhattarai  2 , Henrik Meilby  3 , Carsten Smith-Hall  3 , Toke Emil Panduro  3
Affiliations

Indirect approach for estimation of forest degradation in non-intact dry forest: modelling biomass loss with Tweedie distributions

Klaus Dons et al. Carbon Balance Manag. .

Abstract

Background: Implementation of REDD+ requires measurement and monitoring of carbon emissions from forest degradation in developing countries. Dry forests cover about 40 % of the total tropical forest area, are home to large populations, and hence often display high disturbance levels. They are susceptible to gradual but persistent degradation and monitoring needs to be low cost due to the low potential benefit from carbon accumulation per unit area. Indirect remote sensing approaches may provide estimates of subsistence wood extraction, but sampling of biomass loss produces zero-inflated continuous data that challenges conventional statistical approaches. We introduce the use of Tweedie Compound Poisson distributions from the exponential dispersion family with Generalized Linear Models (CPGLM) to predict biomass loss as a function of distance to nearest settlement in two forest areas in Tanzania.

Results: We found that distance to nearest settlement is a valid proxy variable for prediction of biomass loss from fuelwood collection (p < 0.001) and total subsistence wood extraction (p < 0.01). Biomass loss from commercial charcoal production did not follow a spatial pattern related to settlements.

Conclusions: Distance to nearest settlement seems promising as proxy variable for estimation of subsistence wood extraction in dry forests in Tanzania. Tweedie GLM provided valid parameters from the over-dispersed continuous biomass loss data with exact zeroes, and observations with zero biomass loss were successfully included in the model parameters.

Keywords: Compound Poisson distribution; Forest monitoring; REDD+; Spatial analysis; Tanzania.

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Figures

Fig. 1
Fig. 1
Quantile residuals of Tweedie GLM (left) and delta log-normal models (right). Predicted values of wood extraction by distance to settlement against quantile residuals. (Upper): Fuelwood, (middle): Total subsistence wood extraction, and (lower): Charcoal
Fig. 2
Fig. 2
Biomass loss models by type of wood extraction. Biomass loss by wood harvested for fuelwood (upper), total subsistence (middle), and charcoal (lower) plotted against distance to nearest settlement. Model prediction by Tweedie GLM (solid red line) with 95 % confidence intervals by Jackknife bootstrapping (dashed red lines)
Fig. 3
Fig. 3
Biomass loss and distance to nearest settlement. Biomass loss (Mg ha−1) from wood harvest for fuel and total subsistence at increasing distance intervals from nearest settlement (km)
Fig. 4
Fig. 4
Biomass loss across the study area. CPGLM-based prediction of biomass loss by total subsistence wood extraction applied to forests in the study areas using multiple ringbuffer vectors at 100 m intervals
Fig. 5
Fig. 5
Overview of study areas in Iringa Rural District in Tanzania. (Upper right): an overview map of Tanzania and the location of Iringa District, (upper middle): Iringa Rural District with delineation of the two study sites. (Lower left): The Idodi site including village administrative boundaries, settlements digitized from Google Earth™, forest and field plots. (Lower right): The Kiwele site
Fig. 6
Fig. 6
Residuals for the stem shape model. Residuals for the prediction of DBH from diameter measurements (Eq. 1). Measurement heights above ground are 5 cm (black circle) and 15 cm (red triangle). The standard error (SE) is marked by the horizontal black lines
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