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.
Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these ...data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.
Soil map is one of the basic tools in any agricultural development planning and generating a digital one is even more effective and more productive for natural resources evaluation. Moreover, remote ...sensing and GIS have added to soil classification different concept and enforcement. The study aim was to produce digital soil maps for the study area following different classification systems (ST and WRB) and to define the spatial distribution and characteristics all the soil classes in the study area, which will be indispensable for future development planning. This work has been done as a part of the 29th Course Professional Master in IAO institution, Florence, Italy. The study area was Kilte Awulaelo district in Tigray region, Ethiopia, Which is characterized by different topographies and geomorphologies with different agro ecological conditions. Eleven main soil groups and sixty soil types were identified in the study area. The main soil groups are: Leptosols, Vertisols, Fluvisols, Stagnosols, Kastanozems, Phaeozems, Calcisols, Luvisols, Arenosols, Cambisols and Regosols. Regosols and Cambisols are the dominant soils in the study area which is characteristic soils of rainfed agriculture and land affected by erosion. Using spatial distribution map of each soil group was very helpful to connect soil characteristics with soil forming factors. Lastly, GIS and remote sensing were very effective tools in this study and gave higher value for the final study results.
Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation ...activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.
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