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

Klaus Dons,Carsten Smith-Hall,Toke Emil Panduro,Sushma Bhattarai,Henrik Meilby

doi:10.1186/s13021-016-0051-z

Klaus Dons, Carsten Smith-Hall + Show 3 more

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https://doi.org/10.1186/s13021-016-0051-z

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Abstract

BackgroundImplementation 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.ResultsWe 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.ConclusionsDistance 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.

Highlights

Implementation of REDD+ requires measurement and monitoring of carbon emissions from forest degradation in developing countries
We assessed the accuracy of an indirect remote sensing approach to estimate commercial and subsistence wood extraction in dry tropical forests in Tanzania
We found that levels of fuelwood extraction as well as total subsistence wood harvest decrease significantly with increasing distance to nearest settlement

Summary

Introduction

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 often display high disturbance levels. They are susceptible to gradual but persistent degrada‐ tion and monitoring needs to be low cost due to the low potential benefit from carbon accumulation per unit area. The potential carbon benefit per unit area in dry forests is low, but countries may access benefits from REDD+ funding if they can quantitatively monitor significant degradation activities over large forest areas likely to be degraded [4]. We would expect that the level of subsistence degradation decreases with increasing distance to populated areas [13, 14]

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