The Water Framework Directive of the European Union aims to protect water bodies from feature degradation. Monitoring is essential for assessment and comprehensive overview of water status. Annex V of WFD define tree type of water quality parameters which need to be monitored (biological and two supported one – hydro morphological and physic chemical) in order to assess ecological status of water bodies. Remote sensing data can be used for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. However, this technique must to be integrated with traditions in situ sampling method and field surveying in order to provide precise results. Various empirical, semi-analytics and machine learning algorithms exist to derive relationship between multi spectral image surface reflectance and water quality indicators derived from in situ measurement. In this study we evaluate the capabilities of Landsat 8 satellite image for assessment of abundance of phytoplankton’s (biological parameters) and Turbidity, Dissolved oxygen, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia. The Neuron Networks are used to analyzing correlation between in situ measurements and 7 Landsat 8 atmospherically corrected satellite images acquired in 2013. In situ data are obtained from Agency for environment protection of Serbia. Our results shows that satellite-based monitoring, in combination with in situ data, provide an improved basis for more effective monitoring of large number of water bodies over large geographical area. Relationship between derived and WFD quality parameters is established in order to provide usage of remote sensing data for ecological status classification according to WFD.
Floods are one of the most serious and common natural disasters that cause fatalities and considerable economic losses worldwide every year. In order to reduce and manage risk that floods pose to human health, the environmental and economy flood risk map, as a crucial element of flood risk management, need to be generated. Most often flood risk is estimated based on Digital Elevation Model and projected water levels therefor DEM’s resolution and accuracy highly influence on the reliability of flood risk map especially in lowlands area, where the offset of few decimeters in the elevation data have a significant impact. Airborne light detection and ranging (LiDAR) remote sensing has been a widely used method that provides high-resolution topographical datasets. However LiDAR data are expensive and hard to acquire, usually limited by availability of technology and legal constrains. The main aim of this paper is to present usability of DEM, crated based on UAV RGB images, for flood risk assessment in Vojvodina Porvince, Republic of Serbia therefor flood risk assessment by using the UAV DEM was compared with a flood risk assessment based on LiDAR DEM. Additionally, UAV point cloud was compared with high resolution LiDAR point cloud.
In Spain there are more than 500,000 ha of Eucalyptus plantations. These represent 3,5% of the national forest and the 25% of the timber harvested. Galicia monocultures of Eucalyptus globulus Labill. plantations cover 177,679 ha, and mixed stands of eucalyptus cover 200,000 ha more. This high productivity has been powered by the absence of pests and pathogens. However, since 1991 the health and productivity of these stands has been threatened by the Eucalyptus snout beetle (Gonipterus scutellatus Gyll.), which causes a severe defoliation to eucalyptus stands in Galicia.
The aim of this paper is to establish a workflow to locate the areas affected by the defoliator, and determinate the basics patterns of spatial distribution, in order to predict future hot spots and develop more integrated pests management. This information will be part of a wider Information System, develop to improve the forest management and monitoring of these stands. The damaged area and the level of defoliation will be mapped using satellite imagery. The additional information of stand conditions, such as site index, climate and microclimatic conditions, digital terrain model, dendrometric and dasometric variables, will be integrated also in a Geographical Information System.