Dynamics of the area of tree cover in the Moscow region for the years 2000-2013 (in Russian)

A number of modern products of remote sensing demonstrate significant changes in the forest cover in the Moscow region since the beginning of the current century. We have set a goal to test how the system works in both urban and suburban ecosystems, and to identify the main reasons and effects of changes in the forest cover area within the boundaries of one region. The estimation included not only forest plantations but gardens, parks and other areas covered with woody vegetation with the crown density percentage of above 30 %. The instrument to do it was the internet portal Geo-Wiki, which provides highresolution photos from Google Earth and the means for them to visual interpretation. In addition to automatic description of the state of the sector in different years, the network for spot checks by a team of image interpreters has been set up. We have examined the changes of tree plantations in Moscow, in the Moscow region as a whole, and in the Moscow educational and experimental forestry. Our special attention has been paid to the comparison of the obtained data with the official statistics taken from the forest plan of the Moscow region. The total loss of tree cover was streamlined into such groups as: logging, lost plantations (due to forest fires and outbreaks of pests or diseases), transfer of land to the other types of use (e.g. infrastructure projects or arable and agricultural lands). The areas of newly emerged tree plantations have been divided into reforestation and afforestation. The conclusions concerning the loss of areas covered with tree plantations have been formulated, and the possible reasons, which caused it, have been identified

Improved Vote Aggregation Techniques for the Geo-Wiki Cropland Capture Crowdsourcing Game

Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth’s surface. More recently, the Picture Pile game, which is a more generalized version of Cropland Capture, aims to identify tree loss over time from pairs of very high resolution satellite images. Despite recent progress in image analysis, the solution to these problems is hard to automate since human experts still outperform the majority of machine learning algorithms and artificial systems in this field on certain image recognition tasks. The replacement of rare and expensive experts by a team of distributed volunteers seems to be promising, but this approach leads to challenging questions such as: how can individual opinions be aggregated optimally, how can confidence bounds be obtained, and how can the unreliability of volunteers be dealt with? In this paper, on the basis of several known machine learning techniques, we propose a technical approach to improve the overall performance of the majority voting decision rule used in the Cropland Capture Game. The proposed approach increases the estimated consistency with expert opinion from 77% to 86%

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

How to Increase the Accuracy of Crowdsourcing Campaigns?

Crowdsourcing is a new approach to performing tasks, with a group of volunteers rather than experts. For example, the Geo-Wiki project [1] aims to improve the global land-cover map by crowdsourcing for image recognition. Though crowdsourcing gives a simple way to perform tasks that are hard to automate, analysis of data received from non-experts is a challenging problem that requires a holistic approach. Here we study in detail the dataset of the Cropland Capture game (part of Geo-Wiki project) to increase the accuracy of campaign’s results. Using this analysis, we developed a methodology for a generic type of crowdsourcing campaign similar to the Cropland Capture game. The proposed methodology relies on computer vision and machine learning techniques. Using the Cropland Capture dataset we showed that our methodology increases agreement between aggregated volunteers’ votes and experts’ decisions from 77% to 86%. [1] Fritz, Steffen, et al. “Geo-Wiki. Org: The use of crowdsourcing to improve global land cover.” Remote Sensing 1.3 (2009): 345-354

Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products

We review up-to-date, open access remote sensing (RS) products related to forest. We created a hybrid forest/non-forest map using geographically weighted regression (GWR) based on a number of recent RS products and crowdsourcing. The hybrid map has spatial resolution of 230 m and shows the extent of forest in Russia in 2010. We estimate area of Russian forest as 711 million ha (in accordance with Russian national forest definition). Compared to official data of the State Forest Register (SFR), RS estimates the area of forest to be considerably larger in European part (+12.2 million ha or +8%) and smaller in Asian (-39.8 million ha or -7%) part of Russia. We report the changing forest area in 2001-2010 and discuss main drivers: wildfire and encroachment of abandoned arable land. The methodology used here can be applied for monitoring of forest cover and enhancing the forest accounting system in Russia

Aggregated estimation of the basic parameters of biological production and the carbon budget of Russian terrestrial ecosystems: 1. Stocks of plant organic mass

The data presented were obtained at the first stage (1993–1999) of studies on evaluating the basic parameters of biological production in Russian terrestrial ecosystems in order to provide information for assessing and modeling the carbon budget of the entire terrestrial biota of the country. Stocks of phytomass (by fractions), coarse woody debris, and dead roots (underground necromass) were calculated by two independent methods, which yielded close results. The total amount of phytomass in Russian terrestrial ecosystems was estimated at 81 800 Tg (=1012 g = million t) dry matter, or 39 989 Tg carbon. Forest ecosystems comprise a greater part (82.1%) of live plant organic matter (here and below, comparisons are made with respect to the carbon content); natural grasslands and brushwoods account for 8.8%; the phytomass of wetlands (bogs and swamps), for 6.6%; and the phytomass of farmlands, for only 2.5%. Aboveground wood contains approximately two-thirds of the plant carbon (63.8%), and green parts contain 9.9%. For all classes of ecosystems, the proportion of underground phytomass averages 26.7% of the total amount, varying from 22.0% in forests to 57.1% in grasslands and brushwoods. The average phytomass density on lands covered with vegetation (1629.9 million hectares in Russia) is 5.02 kg/m2 dry matter, or 2.45 kg C/m2. The total amount of carbon in coarse woody debris is 4955 Tg C, and 9180 Tg C are in the underground necromass. In total, the vegetation of Russian terrestrial ecosystems (without litter) contains 54 124 Tg carbon

The Return of Nature to the Chernobyl Exclusion Zone: Increases in Forest Cover of 1.5 Times since the 1986 Disaster

For 34 years since the 1986 nuclear disaster, the Chernobyl Exclusion Zone (ChEZ) landscapes have been protected with very limited human interventions. Natural afforestation has largely occurred throughout the abandoned farmlands, while natural disturbance regimes, which dominantly include wildfires, have become more frequent and severe in the last years. Here, we utilize the dense time series of Landsat satellite imagery (1986–2020) processed by using the temporal segmentation algorithm LandTrendr in order to derive a robust land cover and forest mask product for the ChEZ. Additionally, we carried out an analysis of land cover transitions on the former farmlands. The Random Forest classification model developed here has achieved overall accuracies of 80% (using training data for 2017) and 89% on a binary “forest/non-forest” validation (using data from 1988). The total forest cover area within the ChEZ has increased from 41% (in 1986) to 59% (in 2020). This forest gain can be explained by the afforestation that has occurred in abandoned farmlands, which compensates for forest cover losses due to large fire events in 1992, 2015–2016, and 2020. Most transitions from open landscapes to dense forest cover occurred after the year 2000 and are possibly linked to past forest management practices. We conclude that a consistent forest strategy, with the aid of remote monitoring, is required to efficiently manage new forests in the ChEZ in order to retain their ecosystem functions and to ensure sustainable habitats

Wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire

Key message We propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss. Context The Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ. Aims The aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war. Methods The aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors. Results Random forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha−1 or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine (Pinus sylvestris L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020. Conclusion The change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible