There are currently no spatially explicit, openly accessible data available on forest certification below national level, so understanding the drivers of certification in the past, examining the scope for further certification and using this information for development of future sustainable forest management strategies is challenging. Hence, this paper presents a methodology for the development of a global map of certified forest areas at 1 km resolution in order to satisfy this information need. Validation of the map with certified areas in Russia showed reasonable results, but the lack of openly accessible data requires broadening the strategy for improving the global certification map in the future. Thus, the second aim of the paper is to present an online tool for visualization and interactive improvement of the global forest certification product through collaborative mapping, aiming at a range of stakeholders including third-party certifiers, green NGOs, forestry organizations, decision-makers, scientists and local experts. Such an approach can help to make more accurate information on forest certification available, promote the sharing of data and encourage more transparent and sustainable forest management, i.e. both producers and users can benefit from this online tool.
Global measurements of forest height, biomass are urgently needed as essential climate and ecosystem variables, but can benefit from greater co-operation between remote sensing (RS) and forest ecological communities. The Forest Observation System - FOS (https://forest-observation-system.net/ [https://forest-observation- system.net/]) is an international cooperation to establish a global in-situ forest biomass database to support earth observation and to encourage investment in relevant field-based observations and science. FOS aims to link the RS community with ecologists who measure forest biomass and estimating biodiversity in the field. The FOS aims to overcome data sharing issues and introduce a standard biomass data flow from tree-level measurement to the plot-level aggregation served in the most suitable form for the RS. Ecologists benefit from the FOS with improved access to global biomass information, data standards, gap identification and potentially improved funding opportunities to address the known gaps and deficiencies in the data. FOS closely collaborate with the CTFS-ForestGEO, the ForestPlots.net (incl. RAfNFOR, AfriTRON and T-FORCES), AusCover, TmFO and the llASA network. FOS is an open initiative with other networks and teams most welcome to join. The online database provides open access for forest plot location, canopy height and above-ground biomass. Plot size is 0.25ha or larger. Comparison of plot biomass data with available global and regional maps (incl. Kindermann et al., 2013; Thurner et al., 2013; Saatchi et al., 2011; Baccini et al., 2012; Avitabile et al., 2016; Hu et al., 2016; Santoro et al., 2018) shows wide range of uncertainties associated with biomass estimation.
We provide a global extent of planted trees landscapes (aggregated land use classes of planted forest, woody plantations and agroforestry). The layer has binary value and represents the status for the year 2015 at a spatial resolution of 100 m. The layer has been derived from a global forest management map produced by the Nature Map project (https://naturemap.earth/). The description of the methodology could be found here: https://doi.org/10.5281/zenodo.3933966
No abstract is provided for this article.
No abstract is provided for this article.
Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.
The current state and trajectories of future development of natural landscapes in high latitudes of Northern Eurasia are defined inter alia by (1) current unsatisfactory social and economic situation in boreal Northern Eurasia; (2) the dramatic magnitude of on-going and expected climatic change (warming up to 10-12C under global warming at 4C); (3) increasing anthropogenic pressure, particularly in regions of intensive oil and gas exploration and extraction; (4) large areas of sparsely populated and practically unmanaged land; (5) vulnerability of northern ecosystems which historically developed under cold climates and buffering capacity of which is not well known; (6) risk of catastrophic natural disturbances (fire, insect outbreaks) whose frequency and severity have accelerated during recent decades; and (7) high probability of irreversible changes of vegetation cover. These specifics are overlapped with insufficient governance of natural renewable resources (e.g., forests) and destructed practice of industrial development of new territories (oil and gas extraction and exploration, metallurgy etc.).
Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs.
The method for building forest digital map for Ukraine is presented based on the information of forest land in the global digital maps. The latest global maps covering the territory of Ukraine: Glob Cover 2009, MODIS v.5 and GLC-2000 were analyzed. Based on the developed method the digital map of Ukrainian forests was created with the information on forest land with high probability (all global products agreed) and land with low and medium probability of growing forest. The achieved results were compared with the national data of forest land account. The forest land was better defined in the Forest natural area, the Carpathians, and the Crimea; small forest was really badly identified in the steppe.
