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 aim of the study is to give assessment of and monitor the vegetation’s condition of the forest areas in which territories the predominantly forest species of the plantations is Eastern Mysian beech (Fagus orientalis), by combinative approach of Remote Sensing’s methods and generation of different vegetation indices (NDVI, NDGI). SAR and optical data of the Sentinel when the phenophase of the forest vegetation is the most active, from April to July, respectively, for the years of the selected period were chosen. Tasseled Cap Orthogonal Transformation is applied to the selected images, resulting in three components - TCT component of the "brightness", TCT component of the "wetness" and the TCT component of the "greenness". In the present research, the TCT component of the "greenness" was used, which is giving more accurate and precise data on the current state of the forest vegetation. A comparative analysis of the processed data obtained from the applied different methods and vegetation indices has been made, in order to select the higher quality and more precise results with purpose the analysis and assessment of the state of forest vegetation on the territory of the Natural Park.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.