M. Lesiv

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.

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.

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement.

Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15

Total uncertainty in greenhouse gas (GHG) emissions changes over time due to “learning” and structural changes in GHG emissions. Understanding the uncertainty in GHG emissions over time is very important to better communicate uncertainty and to improve the setting of emission targets in the future. This is a diagnostic study divided into two parts. The first part analyses the historical change in the total uncertainty of CO2 emissions from stationary sources that the member states estimate annually in their national inventory reports. The second part presents examples of changes in total uncertainty due to structural changes in GHG emissions considering the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) emissions scenarios that are consistent with the EU’s “20-20-20” targets. The estimates of total uncertainty for the year 2020 are made under assumptions that relative uncertainties of GHG emissions by sector do not change in time, and with possible future uncertainty reductions for non-CO2 emissions, which are characterized by high relative uncertainty. This diagnostic exercise shows that a driving factor of change in total uncertainty is increased knowledge of inventory processes in the past and prospective future. However, for individual countries and longer periods, structural changes in emissions could significantly influence the total uncertainty in relative terms.

Comment on “The extent of forest in dryland biomes”

Bastin et al. (Reports, 12 May 2017, p. 635) claim to have discovered 467 million hectares of new dryland forest. We would argue that these additional areas are not completely “new” and that some have been reported before. A second shortcoming is that not all sources of uncertainty are considered; the uncertainty could be much higher than the reported value of 3.5%.

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and otherxa0GHG compounds (e.g., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the CO2-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100xa0m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.

A spatial assessment of the forest carbon budget for Ukraine

The spatial representation of forest cover and forest parameters is a prerequisite for undertaking a systems approach to the full and verified carbon accounting of forest ecosystems over large areas. This study focuses on Ukraine, which contains a diversity of bioclimatic conditions and natural landscapes found across Europe. Ukraine has a high potential to sequester carbon dioxide through afforestation and proper forest management. This paper presents a new 2010 forest map for Ukraine at a 60xa0m resolution with an accuracy of 91.6u2009±u20090.8% (CI 0.95), which is then applied to the calculation of the carbon budget. The forest cover map and spatially distributed forest parameters were developed through the integration of remote sensing data, forest statistics, and data collected using the Geo-Wiki application, which involves visual interpretation of very high-resolution satellite imagery. The use of this map in combination with the mapping of other forest parameters had led to a decrease in the uncertainty of the forest carbon budget for Ukraine. The application of both stock-based and flux-based methods shows that Ukrainian forests have served as a net carbon sink, absorbing 11.4u2009±u20091.7xa0Tgxa0Cxa0year−1 in 2010, which is around 25% less than the official values reported to the United Nations Framework Convention on Climate Change.

Spatial distribution of arable and abandoned land across former Soviet Union countries

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.