A dataset of forest biomass structure for Eurasia

The most comprehensive dataset of in situ destructive sampling measurements of forest biomass in Eurasia have been compiled from a combination of experiments undertaken by the authors and from scientific publications. Biomass is reported as four components: live trees (stem, bark, branches, foliage, roots); understory (above- and below ground); green forest floor (above- and below ground); and coarse woody debris (snags, logs, dead branches of living trees and dead roots), consisting of 10,351 unique records of sample plots and 9,613 sample trees from ca 1,200 experiments for the period 1930–2014 where there is overlap between these two datasets. The dataset also contains other forest stand parameters such as tree species composition, average age, tree height, growing stock volume, etc., when available. Such a dataset can be used for the development of models of biomass structure, biomass extension factors, change detection in biomass structure, investigations into biodiversity and species distribution and the biodiversity-productivity relationship, as well as the assessment of the carbon pool and its dynamics, among many others

ENDOGENOUS INTOXICATION IN ANIMALS OF DIFFERENT AGE GROUPS IN CASE OF POLYTRAUMA

Background.  Associated injury is a worldwide social and economic problem. Age related aspects of endogenous intoxication are not studied comprehensively. Annually, from 44 000 to 65 000 citizens die because of traumatic injuries. As a result, this number increased by 32.6% for the last 10 years.     The detoxification system, as a component of the functional systems of the organism, experiences significant changes in case of polytrauma.Objective. The study was aimed to discover pathogenetic peculiarities of the multiple trauma in age aspect in different disease periods and to explore the level of endogenous intoxication in this condition.Methods. The experiments were performed on 72 white male rats aged 3, 6 and 12 months, which underwent simulation of severe skeletal trauma and examination of the contents of middle mass molecules and endogenous intoxication index (markers of endogenous intoxication) in 1, 4 and 24 hours after the associated injury.Results. The most significant increase of the middle mass molecules was fixed in 24 hours after modeling of severe skeletal injuries in all groups of animals, especially it was the most pronounced in 12-month-old animals. The erythrocyte intoxication index reached the highest level in 4 hours after the injury, its increase was most significant in sexually mature adult animals.Conclusion. A significant increasing of endogenous intoxication markers in 12-month-old rats, if compared to 3- and 6-month-old animals, can be caused by the decrease in compensatory protection mechanisms

Impact of Disturbances on the Carbon Cycle of Forest Ecosystems in Ukrainian Polissya

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services

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

Methodology for generating a global forest management layer

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects

A crowdsourced global data set for validating built-up surface layers

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

Global forest management data for 2015 at a 100 m resolution

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

Energy intensity of wood biomass of Zhytomyr Polissia forests

The development of renewable energy is one of the principal areas of decarbonization of the energy sector of Ukraine, including in forestry. Improving the efficiency of using wood biomass for energy purposes, along with providing an appropriate level of technological processes and technical equipment, requires reliable information tools for managerial decision-making. An essential component of this toolkit is regional assessments of the energy function of forest phytocenoses. The information base of this study included information from the database of Industrial Association “Ukrderzhlisproekt”, which contains a specific inventory characteristic of tree stands of the region under study, as well as a system of mathematical models for quantitative assessment of phytomass and mortmass of forests. As a result, quantitative values of the total energy intensity of phytomass and mortmass of the forests of Zhytomyr Polissia were established. The total amount of energy accumulated in the plant biomass of the region’s forests is 3,035.7 PJ, which is equivalent to 100.2 million tonnes of conventional fuel. At the same time, the share of the total energy intensity of mortmass is 11.1%. The structure of the energy intensity of plant biomass in the region is dominated by pine stands, which accumulate over 60% of the energy of the forests of Zhytomyr Oblast, namely 70.8% – in the phytomass of tree trunks. Over 40% of the energy is accumulated in the plant biomass of stands of the site index class I, which mainly grow in relatively poor forest conditions (subors). In the overall structure of the energy intensity of the mortmass (336.2 PJ), more than 60% belongs to the forest floor (212.8 PJ), but it is not considered as a source of renewable energy, dry matter – 12.3% (41.3 PJ), dry branches – 17 .8% (almost 60 PJ). The results obtained in this study will serve as an information basis to devise a strategy for the development of forest bioenergy in the Zhytomyr regio