Forests ecosystems are extremely vulnerable to the changes of climatic parameters such as quantity and seasonal distribution of precipitations, temperature variation, heat transfer, soil moisture content and others. The consequences of forest vegetation alterations include growth and productivity failures, insect outbreaks, biodiversity changes, and increase in the incidence of forest fires and floods. Remote sensing has been identified as an effective tool for better understanding how forest ecosystems respond to dynamics of climatic parameters and their impact to forest vegetation state in terms of the occurrence of hazard events. In the present study spectral indices like Normalized difference greenness indices (NDGI), Normalized Difference Vegetation Index (NDVI), Improved Modified Chlorophyll Adsorption Ratio Index (MCARI2), Moisture Stress Index (MSI), and Normalized Difference Water Index (NDWI), derived trough remote sensing methods have been applied for monitoring of the forest state before and after fire event occurred on 29 July 2016. Using a model based on the three major Tasseled Cap components, a Disturbance Index (DI) for the affected forest ecosystem was quantified. The study area is situated in southeastern Bulgaria – a region, highly vulnerable to forest disturbances due to climate changes. The results obtained after the application of the suggested indices show that changes observed in the forest ecosystem state could be assessed with a high accuracy. These results were confirmed with statistical analyses with high correlation coefficients for greenness component and Normalized Difference Vegetation Index (NDVI).
The aim of the study is to obtain a quantitative assessment of soil type impact on the soil-vegetation system through the respective models. To achieve this goal, the vegetation cover of a habitat under Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora (Habitats Directive) for the 2016-2019 time period and the influence of climatic factors (precipitation and temperature) on the vegetation cover by applying Interim Ecological Monitoring (IEM) based on remotely-sensed data. Multispectral satellite data (MSI) from Sentinel-2 have been used for quantification of the soil-vegetation system. Various types of Vegetation Indices like Normalized Differential Vegetation Index (NDVI), Soiled-adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI2), Normalized Difference Infrared Index (NDII), have been estimated to determine the actual state of the vegetation cover. For tracking the “soil-vegetation” relation Normalized Differential Greenness Index (NDGI) has been applied based on orthogonalization of satellite data from Sentinel-2. The results obtained are validated by interim ecological monitoring methodology (MIEM) for habitats and are expected to review habitat(s) trends for the indicated period and such for a near - future period. The study will demonstrate the benefits of IEM application for the purposes of reporting under art. 17 from Habitats Directive.
The pine biome is of great importance for the Mediterranean flora, because it forms natural forest stands from low altitudes up to the timberline in the Mediterranean Basin, which are capable of exhibiting large ecological adaptation toward climatic amplitudes. For Bulgaria, the significance of the pine forests is related to their role in reduction of soil erosion, through slowing the water runoff and thus helping to provide a year-round supply of water in the ecosystems. Such plantations become vulnerable to damages from natural and anthropogenic origins, such as wind-throws, snow breakages, wet snowfalls, fires. We have tested different sensors and platforms for relevant data collection in order to allow for an improved identification and understanding of the changes occurring in forest cover, forest structure, and disturbance regimes in mountain forests affected by snowfalls. For the purpose of the study, radar and optical satellite images from the sensors Sentinel 1, Sentinel 2 (ESA), Landsat 8 and MODIS (NASA) were used. Composite raster images from the Sentinel 2, Landsat 8 and MODIS sensors were obtained. Two types of polarization were used in order to recognize areas covered by wet snow. Thematic maps for qualitative and quantitative assessment of the consequences of snowfalls and snow breakages have been generated. Based on the satellite, GPS and terrain data, the areas of severe damages in forests have been successfully identified and assessed.
Wildfires are recurring in many terrestrial ecosystems all over the world. Accurate assessment of the forest ecosystem, affected by fire is of great importance for the fires spread predicting and modelling of the post-fire activities for recovery of the affected territories. High spatial and spectral resolution satellite data were used to evaluate the vegetation variation on a fire-affected territory, located on the northwest slopes of the Rila mountain, considering its spatial heterogeneity. The forest fire was spread on the area of deciduous forests Turkey oak (Quercus cerris L.), and coniferous: Scots pine (Pinus sylvestris L.) and European larch (Larix desidua, Mill.). Different spectral indices like Disturbance index (DI), Normalized difference greenness indices (NDGI) and Normalized Difference Vegetation Index (NDVI) and derived from remote sensing methods (satellite data from different sensors Landsat and Sentinel) as well as the Geographical Information System (GIS) were applied for the forest disturbance assessment in two periods after forest fire occurrence. The results of the applied integrated model provide a quantitative information about the fire effects for distinct forest types. The documented spatial distribution of the territory based on the obtained DI values shows clear differences between the fire-affected forest types, thus demonstrating the usefulness and accuracy of the approach followed.
Forest fires are among the most dangerous natural threats that cause significant changes in forest ecosystems. For the better management of the wildfire-prone territories, the fire weather components like temperature, precipitation and evapotranspiration predictions and monitoring within the extreme fire seasons are of great importance. Remote sensing has been identified as an effective tool for better understanding how forest ecosystems respond to these components. Respective spectral indices, like Normalized difference greenness indices (NDGI), Normalized Difference Vegetation Index (NDVI), Improved Modified Chlorophyll Adsorption Ratio Index (MCARI2) and Moisture Stress Index (MSI), derived from remote sensing methods (satellite data from different sensors - Landsat and Sentinel) as well as the Geographical Information System (GIS) were applied for the monitoring of the climatic parameters in forest fire vulnerable regions in Bulgaria. The climatic parameters dataset from 2008 consisting of the ten-day period mean temperature and precipitation data were collected. The NDVI trends for the studied periods exhibited significant correlations with the mean precipitation and weak or no correlation with the temperature recorded. These results are largely linked to the relative air humidity. Different vegetation types were found to show distinct spatial responses to climatic changes.
Forest fires continue to burn large territories, both within and outside Europe. It is suitably to assess fire-induced changes in the vegetation, which in turn affects infiltration, runoff, and erosion potential. Therefore it is important to identify potential areas of concern and prioritize field reconnaissance. The development of a burn severity map will facilitate quantifying of the post-fire assessment phase. In this study the potential of Normalized Burn Index (NBR), Normalized Difference Vegetation Index (NDVI) and normalized difference greenness indices (NDGI) derived from remote sensing methods (satellite data from different sensors Sentinel and Landsat) and Geographical Information System (GIS) have been analyzed for forest fire severity assessment. For more accurate assessment of the fire severity, a hybrid model was developed, using satellite data from different sensors - Sentinel and Landsat. For this purpose, the area, affected by fires occurred in august 2017 on the northwest slopes of the Ajtovska Mountain (East part of the Stara planina mountain) in the Eastern part of Bulgaria was studied. The forest fire events were spread on the area of (508.5 ha) and the affected vegetation was composed by deciduous forests (309.4ha), coniferous (62.4ha), mixed forests (61.4 ha) and grass and shrubs (75.3ha). Through the model developed, results applicable to the actual forest ecosystem conditions for different time intervals have been obtained. These results provide quantitative information about fire severity for distinct forest types, thus allowing for designing relevant fire severity maps.
Assessment and mapping of the ecosystems state in the context of ecosystem services that they supply are important tasks to improve human well-being, especially in regions with considerable land degradation. Haskovo region is situated in the Southeastern part of Bulgaria and is considered as an extremely sensitive to land degradation in terms of climate change and human activities in result of unappropriated land management practices. In order to improve the conservation activities and ecosystem services of the region, rapid and available technics are needed in addition to the used analytical methods. The study presents the potential of remote sensing methods (satellite data from different sensors Sentinel and Landsat) and GIS for assessment of the current state of the landscapes to supply ecosystem services and allows a comprehensive evaluation of the main indicators for assessment of ecosystem services to be performed. The proposed methodology includes application of vegetation indices (NDVI, NDWI and MSAVI2) and SAR data. The results show that the referred technics can be used for a rapid and accurate assessment of the main indicators showing the state of the terrestrial ecosystems such as: soil degradation, land use and impact of human activities, responsible for the ecosystem services supply.
Soil is a dominant factor of the terrestrial geosystems in semi-arid and dry sub-humid zones, particularly through its effect on biomass production. Due to the climate changes and industrial development, soil resources in these zones are prone to degradation. On the other hand, degradation processes cause changes in land cover. Remote sensing optical data are widely used in the process of determining land cover change whereas SAR data is suitable for determination of soil moisture dynamics. In the present study, Tasseled Cap Transform (TCT) and modified Change Vector Analysis (mCVA) techniques are applied to Landsat and Sentinel 2 data in order to be determined magnitude and direction of land cover changes in the semi-natural areas of Haskovo Region, Southeast Bulgaria. The study of the vector direction presents some distinct changes in the soil characteristics for the whole territory and significant changes in vegetation characteristics and moisture content in part of the semi-mountainous territories of the examined region. It has been found that the magnitude of those changes increases up to 50% in some of the territories under investigation. SAR data has been used to evaluate the relative soil moisture content in various soil differences and to trace its dynamics during growing season. In order to achieve this aim, Relative Soil Moisture Index (RSMI) is used. The index estimates the relative variation of volumetric soil moisture content in a given time period and enables determination of its change in relative values. On the basis of integrated application of aforementioned techniques, a model providing key information about the impact of soil moisture change and dynamics upon processes related to land cover change. The suggested model is appropriate for estimation of ecosystem services and functions delivered by landscapes in semi-arid and dry sub-humid zones.
Soil is a dominant factor of the terrestrial geosystems in the dry sub-humid zones, particularly through its effect on
biomass production. Due to the climate changes and industrial development, soil resources in these zones are prone to
degradation. Mitigation of the negative effects of land degradation requires in-depth knowledge of the ongoing in the
geosystems processes and application of innovative end effective methods for their investigation. The recent study aims
to evaluate the relative soil moisture content in various soil differences and to trace its dynamics during growing season.
In order to achieve this aim, Relative Soil Moisture Index (RSMI) based on Synthetic Aperture Radar (SAR) data was
calculated. The index estimates the relative variation of volumetric soil moisture content in a given time period and
enables determination of its change in relative values. The generated results show very high level of correlation for the
investigated pilot areas which testifies that the RSMI is applicable in different territories.
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