Petar Dimitrov

Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria

The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km2 test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single- and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%–77% (PROBA-V 100 m data) and 66%–70% (PROBA-V 300 m data) with four classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted. Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a “gap-filler mission”.

Mapping Carbon Storage Using Land Cover/Land Use Data in the Area of Beklemeto, Central Balkan

This chapter presents the results of land cover and carbon storage mapping in a study area located in the Central Balkan Mountains. WorldView-2 satellite images and ortophoto maps were used to define the land cover in the area. CORINE land cover classification at the fourth level was applied for the mapping. The carbon stock was determined using InVEST model, and results were validated with in situ data from eight experimental sites in different land use classes.

Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

In this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets — one with applied topographic correction and one without applied topographic correction — consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were −0.83 and −0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was −0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32–33% using the plotwise data, and 43–45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.

The Natural Capital of Selected Mountain Areas in Bulgaria

The purpose of this study is to analyse and assess the potential of selected Bulgarian mountains on the basis of the concepts of natural capital and landscape multifunctionality. These concepts are essential in modern geo-spatial research in the context of the debate on sustainability and its role in avoiding problems of environmental degradation, land use conflicts and natural resources overuse. The Central Balkan and the Western Rhodopes are selected as case studies representative for mountains in Bulgaria. The authors interpret natural capital as a set of potentially possible landscape functions and, thus, study them within the geosystem boundaries of the landscape. The investigation is based on a systemic analysis of the landscape structure and its anthropogenic transformation by integrating the hemeroby index. On this basis, it performs qualitative valuation of ecosystem/landscape services in the mountain areas. Integration of the opinions and perspectives of the local population is an important aspect of the valuation.

Validation of MERIS LAI and FAPAR products for winter wheat-sown test fields in North-East Bulgaria

Progress in deriving land surface biophysical parameters in a spatially explicit manner using remotely sensed data has greatly enhanced our ability to model ecosystem processes and monitor crop development. A multitude of satellite sensors and algorithms have been used to generate ready-to-use maps of various biophysical parameters. Validation of these products for different vegetation types is needed to assess their reliability and consistency. While most of the current satellite biophysical products have spatial resolution of one kilometre, a recent effort utilizing data from the Medium Resolution Imaging Spectrometer (MERIS) provided leaf area index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and other canopy parameters in a resolution as fine as 300 m over the European continent. This resolution would be more appropriate for application at the regional scale, particularly for crop monitoring. This higher-resolution MERIS product has been evaluated in a limited number of studies to date. This article aims to validate LAI and FAPAR from the MERIS 10-day composite BioPar BP-10 product over winter wheat fields in northeast Bulgaria. The ground measurements of LAI and FAPAR were up-scaled and 30 m resolution reference raster layers were created using empirical relationships with Landsat TM (RMSE = 0.06 and RMSE = 0.22 for FAPAR and LAI, respectively). MERIS FAPAR and LAI were found to have significant correlation with FAPAR and LAI from the reference raster layers (R2 = 0.84 and R2 = 0.78, respectively). When MERIS Green LAI was calculated (incorporating the fraction of vegetation and brown vegetation cover from the BioPar BP-10 product), better correspondence with LAI values from the reference raster layer was achieved, with RMSE and bias reduced by 30–35%. The results from this study confirm the findings of previous validations showing that MERIS Green LAI tends to overestimate LAI values lower than 1. As a conclusion of the study, the BioPar BP-10 product was found to provide reliable estimates of FAPAR and acceptably accurate estimates of LAI for winter wheat crops in North-East Bulgaria.

PATHOLOGICAL FEATURES OF EXPERIMENTAL BOVINE LEUKAEMIA VIRAL (BLV) INFECTION IN RATS AND RABBITS

Abstract Rabbits and rats were inoculated with material derived from FLK cells producing permanently bovine leukaemia virus (BLV). The viral presence in the inoculum was proved by transmission electron microscopy, immunofluorescence, immunogold labelling demonstrating viral Tax protein, and PCR analysis. About 30 % of the infected animals sustained BLV seropositivity during the experiment, and demonstrated symptoms of lympholeukaemia - clinical manifestation of an immunosuppressive condition, increased number of lymphocytes and lymphoblasts, and preneoplastic lymphoid cell accumulations in the liver, lungs, kidneys, and lymph nodes. BLV DNA, detected by PCR in diseased animals, indicates the role of BLV as an aetiological factor of lympholeukaemia, developed in these animals after BLV infection. The alterations in rats were more pronounced than those in rabbits. The results prove that these two species of laboratory animals, especially rats, are suitable models for the in vivo studies of leukaemogenesis caused by BLV/HTLV infections.

Monitoring of winter crop status in Bulgaria using a series of NOAA AVHRR NDVI images

The study is an application of satellite remote sensing methods for monitoring winter crops in Bulgaria over the time period 1997–2008 based on 7-day NOAA AVHRR NDVI level 3 satellite imagery. A satellite image geodatabase was created in ArcGIS/ArcInfo 9.2 and was supplemented with ground-based phenological data. The monitoring is focused on vegetation status and stress situations, making use of vegetation condition index (VCI) images obtained from the NDVI time series. The VCI images covered the four main phenophases of winter crops for the agricultural years 2006–2007 and 2007–2008. By analyzing the average values of these VCI images, the status of each phenophase for the considered years was assessed. The results pointed to drought conditions for 2006–2007 and favourable to optimal conditions for 2007–2008.