Drivers of tropical forest loss between 2008 and 2019

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform (www.geo-wiki.org) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public

Modelling the qualitative structure of Scots pine trunks using a random process

The study highlights a method for predicting the volume of qualitative categories of trunk wood for timber trunks of Scots pine, using a model of distribution of various qualitative zones along the trunk. The study is based on empirical material collected in the main-use cutting area, where the length of various qualitative wood zones from the butt to the top was determined for 245 model trees. The paper uses a semi-Markov probabilistic model to generalise the order of appearance and length of zones of timber wood, firewood, and waste, which was determined by three parameters: 1) input probabilities of the appearance of the corresponding qualitative zone in the butt part of the trunk (initial state); 2) matrix of distribution of zone lengths; 3) matrix of probabilities of changes in qualitative zones at different heights of the trunk. According to research data, it is generally accepted that pine trunks begin with timber wood. The beta distribution function is used to model the length of qualitative trunk zones, the parameters of which are selected depending on the relative height of the beginning of the corresponding trunk zone. The probabilities of changes in qualitative zones are calculated based on empirical data. The study identified that the distribution of timber and firewood lengths depends on the absolute height of the trunk location and the height of the trunk. For a mathematical generalisation of this process, the paper defines four zones within which the distribution of the length of the timber part of the trunk can be described by a single function. The probabilities of changes in qualitativ e zones are modelled depending on the relative height of trunks. On this basis, the method of simulation modelling of initial data sets has been developed, which can be used to develop tables of dimensional and qualitative wood structure for trunks of different diameters, heights, and categories of technical suitability. The study applies only to timber pine trunks, so other patterns are probable for trunks of other tree species, semi-timber, and firewood trunks. The developed methodology is appropriate to use when updating the tables of trunk volume distribution by variable-quality categories, which now need to be updated by introducing new requirements for the classification of timber wood in Ukrain

Crowdsourcing deforestation in the tropics during the last decade: Data sets from the “Driver of Tropical Forest Loss” Geo-Wiki campaign

The data set is the result of the Drivers of Tropical Forest Loss crowdsourcing campaign. The campaign took place in December 2020. A total of 58 participants contributed validations of almost 120k locations worldwide. The locations were selected randomly from the Global Forest Watch tree loss layer (Hansen et al 2013), version 1.7. At each location the participants were asked to look at satellite imagery time series using a customized Geo-Wiki user interface and identify drivers of tropical forest loss during the years 2008 to 2019 following 3 steps: Step 1) Select the predominant driver of forest loss visible on a 1 km square (delimited by a blue bounding box); Step 2) Select any additional driver(s) of forest loss and; Step 3) Select if any roads, trails or buildings were visible in the 1 km bounding box. The Geo-Wiki campaign aims, rules and prizes offered to the participants in return for their work can be seen here: https://application.geo-wiki.org/Application/modules/drivers_forest_change/drivers_forest_change.html . The record contains 3 files: One “.csv” file with all the data collected by the participants during the crowdsourcing campaign (1158021 records); a second “.csv” file with the controls prepared by the experts at IIASA, used for scoring the participants (2001 unique locations, 6157 records) and a ”.docx” file describing all variables included in the two other files. A data descriptor paper explaining the mechanics of the campaign and describing in detail how the data was generated will be made available soon