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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

7 Citations (Scopus)
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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.

Original languageEnglish
Article number14
JournalCarbon Balance and Management
Volume11
Issue number1
Number of pages10
ISSN1750-0680
DOIs
Publication statusPublished - 2016

Keywords

  • Compound Poisson distribution
  • Forest monitoring
  • REDD+
  • Spatial analysis
  • Tanzania

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