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

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          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.


          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.


          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.

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          Most cited references 31

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          Selective logging in the Brazilian Amazon.

          Amazon deforestation has been measured by remote sensing for three decades. In comparison, selective logging has been mostly invisible to satellites. We developed a large-scale, high-resolution, automated remote-sensing analysis of selective logging in the top five timber-producing states of the Brazilian Amazon. Logged areas ranged from 12,075 to 19,823 square kilometers per year (+/-14%) between 1999 and 2002, equivalent to 60 to 123% of previously reported deforestation area. Up to 1200 square kilometers per year of logging were observed on conservation lands. Each year, 27 million to 50 million cubic meters of wood were extracted, and a gross flux of approximately 0.1 billion metric tons of carbon was destined for release to the atmosphere by logging.
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            Detecting anthropogenic disturbance in tropical forests.

            Tropical forests are beleaguered by an array of threats driven by different scales of anthropogenic perturbations, which vary in the degree to which they can be detected by remote sensing. The extent of different patterns of cryptic disturbance often far exceeds the total area deforested, as shown by two recent studies on selective logging in Amazonia. Here, we discuss different forms of disturbance in Amazonian forests and question how much of the apparently intact forest in this region remains relatively undisturbed.
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              Combining spectral and spatial information to map canopy damage from selective logging and forest fires


                Author and article information

                +45 39 96 59 00 , kdo@kandidatforum.dk , klaus.dons@gmail.com
                Carbon Balance Manag
                Carbon Balance Manag
                Carbon Balance and Management
                Springer International Publishing (Cham )
                29 June 2016
                29 June 2016
                December 2016
                : 11
                : 1
                [ ]Informi GIS, Stationsparken 37, 2600 Glostrup, Denmark
                [ ]Faculty of Science, University of Copenhagen, Rolighedsvej 23, DK-1958 Frb C, Copenhagen, Denmark
                [ ]United Nations Development Programme, UN House, Pulchowk, GPO Box 107, Lalitpur, Kathmandu, Nepal
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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