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

TY - JOUR

T1 - Indirect approach for estimation of forest degradation in non-intact dry forest

T2 - modelling biomass loss with Tweedie distributions

AU - Dons, Klaus

AU - Bhattarai, Sushma

AU - Meilby, Henrik

AU - Smith-Hall, Carsten

AU - Panduro, Toke Emil

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Compound Poisson distribution

KW - Forest monitoring

KW - REDD+

KW - Spatial analysis

KW - Tanzania

U2 - 10.1186/s13021-016-0051-z

DO - 10.1186/s13021-016-0051-z

M3 - Journal article

C2 - 27429643

AN - SCOPUS:84976897960

SN - 1750-0680

VL - 11

JO - Carbon Balance and Management

JF - Carbon Balance and Management

IS - 1

M1 - 14

ER -