Rumiana Vatseva

Bio: Rumiana Vatseva is an academic researcher from Bulgarian Academy of Sciences. The author has contributed to research in topics: Volunteered geographic information & Spatial data infrastructure. The author has an hindex of 6, co-authored 13 publications receiving 164 citations. Previous affiliations of Rumiana Vatseva include Óbuda University.

Assessing VGI Data Quality

Abstract: Uncertainty over the data quality of Volunteered Geographic Information (VGI) is the largest barrier to the use of this data source by National Mapping Agencies (NMAs) and other government bodies. A considerable body of literature exists that has examined the quality of VGI as well as proposed methods for quality assessment. The purpose of this chapter is to review current data quality indicators for geographic information as part of the ISO 19157 (2013) standard and how these have been used to evaluate the data quality of VGI in the past. These indicators include positional, thematic and temporal accuracy, completeness, logical consistency and usability. Additional indicators that have been proposed for VGI are then presented and discussed. In the final section of the chapter, the idea of integrated indicators and workflows of quality assurance that combine many assessment methods into a filtering system is highlighted as one way forward to improve confidence in VGI.

An effective Building Neighborhood Green Index model for measuring urban green space

Abstract: Urban green space forms an integral part of urban ecosystems. Quantifying urban green space is of substantial importance for urban planning and development. Considering the drawbacks of previous urban green space index models, which established either through a grid method or green distribution, and the difficulty of the validation process of earlier urban green space index models, this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index (BNGI) model. The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters – Green Index (GI), proximity to green, building sparsity, and building height. Comparing BNGI with GI in different types of characteristic building regions, it was found that BNGI evaluates urban green space more sensitively. It was also found that high-rise low-sparsity area has a lower mean value of BNGI (0.56) as compared...

Sources of VGI for Mapping

Abstract: The concept of Volunteered Geographic Information (VGI) is often exemplified by the mapping of features in OpenStreetMap (OSM), yet there are many other sources of VGI available. Some VGI is very focused on the creation of map-based products, while in other applications location is simply one attribute that is routinely collected, due to the proliferation of Global Positioning System (GPS) enabled devices, e.g. mobile phones and tablets. This chapter aims to provide an overview of the variety of sources of VGI currently available, categorised according to whether they can contribute to framework data (i.e. the type of data that are commonly part of the spatial data infrastructure of national mapping agencies and governments) or not and whether the data have been actively or passively collected. A range of examples are presented to illustrate the different types of VGI in each of these main categories. Finally, the chapter discusses some of the main issues surrounding the use of VGI and points to chapters in the book where these issues are described in more detail.

Landscape change analysis and assessment (case studies in Slovakia and Bulgaria)

Abstract: Landscape change assessment was conducted in selected areas of Slovakia and Bulgaria in 1990–2000 using CORINE land cover (CLC) data layer analysis. Assessment of causes that led to these changes was undertaken, with an emphasis on those that determined the extensification of agriculture. The LC data were obtained under the CLC90 and I&CLC2000 projects, jointly managed by the European Environment Agency in Copenhagen, Denmark and the Joint Research Centre of the European Commission in Ispra, Italy. The CLC1990-2000-changes data layer was generated by overlaying the CLC90 and CLC2000 data layers for change in areas of a minimum 5 ha. The analysed causes of changes (driving forces) were then classified. Land cover (LC) changes characterizing urbanization processes occurred only in the Trnava and Tatras areas. Intensification of agriculture was also higher in these two areas. LC changes characterizing the extensification of agriculture were dominant in Plovdiv and Trnava. Deforestation and forestation were identified in all areas (Trnava, Tatras, Plovdiv, and Burgas). The basic reasons of these changes were related to the transformation of national economies from being centrally planned to market controlled, following the fall of socialism and before the countries joined the European Union.

Implementation of the "kineros" model for estimation of the flood prone territories in the malki iskar river basin

Abstract: This article presents main results from the implementation of the KINEROS model to the Malki Iskar river basin above the town of Etropole. KINEROS is a mathematical model based on fundamental physical laws aimed at simulating the spatial distribution of water balance elements for a given meteoro- logical event. A detailed study of the set of physical geographical flood risk factors (relief, climate, waters, soils, LC/LU) has been performed. The analysis has been carried out on the basis of specific methods for estimation of the role of each factor. The results show the importance and practicality of implementation of the GIS, RS and flood risk modeling in generally unpredictable dangerous situations, which are typical for small mountain catchments. The application of the KINEROS model for simulation of events depending on the changes in the landscape conditions may in- crease significantly the effect of flood risk management in mountainous munici- palities.

Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass

Abstract: Abstract This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm leaf − 2 ), sun leaf mass per area (LMA, g dry matter m leaf − 2 ), canopy leaf area index (LAI, m 2 leaf m soil − 2 ) and leaf canopy biomass (B leaf , g dry matter m soil − 2 ). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougeres forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm − 2 for CHL, 9.1 g m − 2 for LMA, 1.7 m 2 m − 2 for LAI and 50.6 g m − 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND chl = ( ρ 925 − ρ 710 )/( ρ 925 + ρ 710 ), for leaf mass per area: ND LMA = ( ρ 2260 − ρ 1490 )/( ρ 2260 + ρ 1490 ), for leaf area index: D LAI = ρ 1725 − ρ 970 , and for canopy leaf biomass: ND Bleaf = ( ρ 2160 − ρ 1540 )/( ρ 2160 + ρ 1540 ).