D. Schepaschenko

Boreal forest health and global change.

The boreal forest, one of the largest biomes on Earth, provides ecosystem services that benefit society at levels ranging from local to global. Currently, about two-thirds of the area covered by this biome is under some form of management, mostly for wood production. Services such as climate regulation are also provided by both the unmanaged and managed boreal forests. Although most of the boreal forests have retained the resilience to cope with current disturbances, projected environmental changes of unprecedented speed and amplitude pose a substantial threat to their health. Management options to reduce these threats are available and could be implemented, but economic incentives and a greater focus on the boreal biome in international fora are needed to support further adaptation and mitigation actions.

Mapping global cropland and field size

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.

A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information

Despite being recognized as a key baseline dataset for many applications, especially those relating to biogeochemical cycles, land cover products in their current form are limiting. Typically they lack the thematic detail necessary for driving the models that depend upon them. This study has demonstrated the ability to produce a highly detailed (both spatially and thematically) land cover/land use dataset over Russia – by combining existing datasets into a hybrid information system. The resulting dataset contains detailed subclasses of land cover and attributes necessary for biogeochemical modeling. In lieu of suitable validation data, a confidence map was produced creating six classes of confidence in the agreement between the various remote sensing and statistical datasets. In specific regions, a significant difference between the remote sensing products and the official statistics was observed. For example, in the northwest of Russia the statistics appear to be underreporting the amount of forest land which has likely been increasing in recent decades because of encroachment of forests on abandoned marginal agricultural land.

Climate change and wildfires in Russia

The effect of climate change on the distribution, intensity, and transforming role of wild fires is considered. A general overview of the current wild fire regimes (WRs) and impacts on forest ecosystems and environment is provided. One distinctive feature of WRs is the increasing frequency of disastrous wild fires. The application of various remote sensing instruments has shown that the average vegetation wild fire area in Russia for 1998–2010 accounted for 8.2 ± 0.8 × 106 ha, with about two-thirds of wildfires occurring on forest lands and half on the forested lands. The average annual fire carbon balance during the above period was 121 ± 28 Tg C yr−1, including 92 ± 18 Tg C yr−1 emitted from the forested land. The forecasts based on the General Circulation Models suggest the dramatic acceleration of fire regimes by the end of the 21st century. Taking into account the increase in the dryness of the climate and the thawing of permafrost, this will likely lead to a dramatic loss of forested area and the impoverishment of the forest cover over a major part of the forest zone. A transition to adaptive forestry would allow a substantial decrease of the expected losses. This paper takes a brief look at the general principals of adapting forest fire protection system to climate change, which is considered an integral part of the transition to sustainable forest management in Russia.

The pool of organic carbon in the soils of Russia

An automated information system making it possible to estimate spatial distribution of soil organic carbon pool with a high spatial resolution (1 km2) has been developed. According to the obtained estimates, the total pool of organic carbon in the 1-m-deep soil layer on the territory of Russia reaches 317.1 Pg; the average organic carbon density in this layer for the entire Russia constitutes 19.2 kg C/m2. Of this amount, 14.4 Pg (or 0.90 kg C/m2) is stored in the litter horizon. The developed algorithm allows us to refine the results with the acquisition of new data on soils, vegetation, and the degree of their disturbance, which is particularly important in the changing world.

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

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.

Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities in Ukraine

Local climate zones (LCZs) divide the urban landscape into homogeneous types based on urban structure (i.e., morphology of streets and buildings), urban cover (i.e., permeability of surfaces), construction materials, and human activities (i.e., anthropogenic heat). This classification scheme represents a standardized way of capturing the basic urban form of cities and is currently being applied globally as part of the world urban database and portal tools (WUDAPT) initiative. This paper assesses the transferability of the LCZ concept to two Ukrainian cities, i.e., Kyiv and Lviv, which differ in urban form and topography, and considers three ways to validate and verify this classification scheme. An accuracy of 64% was achieved for Kyiv using an independent validation dataset while a comparison of the LCZ maps with the GlobeLand30 land cover map resulted in a match that was greater than 75% for both cities. There was also good correspondence between the urban classes in the LCZ maps and the urban points of interest in OpenStreetMap (OSM). However, further research is still required to produce a standardized validation protocol that could be used on a regular basis by contributors to WUDAPT to help produce more accurate LCZ maps in the future.

Terrestrial ecosystems and their change

This chapter considers the current state of Siberian terrestrial ecosystems, their spatial distribution, and major biometric characteristics. Ongoing climate change and the dramatic increase of accompanying anthropogenic pressure provide different but mostly negative impacts on Siberian ecosystems. Future climates of the region may lead to substantial drying on large territories, acceleration of disturbance regimes, deterioration of ecosystems, and positive feedback to global warming. The region requires urgent development and implementation of strategies of adaptation to, and mitigation of, negative consequences of climate change.

A global dataset of crowdsourced land cover and land use reference data

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.