In recent years, forest fires have increased in terms of frequency, extent, and intensity, especially in Mediterranean countries. Wildfires affect the ecological functioning of ecosystems as they partially or completely burn the vegetation layers and affect post-fire processes such as soil erosion and vegetation recovery. Moreover, wildfires involve a great threat to property and human life, especially in the continuously increased Wildland-Urban Interface (WUI) areas. The present study explores the consequences of the 2021 wildfire that occurred in the broader area of the Varibobi district, which is situated at the foot of the forested Parnitha mountain in the northwestern part of Athens. Initially, the study aims to introduce the broadly used Composite Burn Index (CBI) for the assessment of fire severity in Greece and examine the correlation of the satellite image-derived fire severity indices with CBI. Furthermore, the impact of fire severity and fire frequency, along with the previously existing land use/cover and its changes through time, landscape characteristics, and the consequential erosional soil loss (using the RUSLE method) on vegetation recovery, were examined and evaluated. The RBR burn severity index, showed the best correlation with the CBI method. The results display the impact of the natural and anthropogenic parameters on the potential vegetation recovery, considering the fire frequency and urbanization pressures that are also related to the areas’ specific pre-fire land use. Finally, the results revealed that vegetation recovery is more pronounced within the pre-fire pine forest areas. Additionally, the topographic and geological sub-strata features and soil loss due to post-fire erosion processes were also found significant in defining the causes, spatial distribution, and percentage of post-fire vegetation recovery.
Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The present study aims to estimate the actual water requirements of crop fields based on the Crop Water Stress Index, combining multiple and multiscale data, such as infrared canopy temperature, air temperature, air relative humidity, near-infrared and thermal infrared image data, taken above the crop field using an innovative aerial micrometeorological station (AMMS), and two more compatible and advanced cameras, a multispectral and a thermal mounted in an Unmanned Aerial Vehicle (UAV), along with satellite-derived thermal data. Moreover, ground micrometeorological stations (GMMS) were installed in each crop. The study area was situated in Trifilia (Peloponnese, Greece) and the experimentation was conducted on two different crops, potato, and watermelon, which are representative cultivations of the area. The analysis of the results showed, in the case of the potato field, that the amount of irrigation water supplied in the rhizosphere far exceeds the maximum crop needs reaching values of about 394% more water than the maximum required amount needed by the crop. Finally, the correlation of the different remote and proximal sensors proved to be sufficiently high while the correlation with the satellite data was moderate. The overall conclusion of this research is that proper irrigation water management is extremely necessary and the only solution for agricultural sustainability in the future. The increasing demand for freshwater, mainly for irrigation purposes, will inevitably lead to groundwater overexploitation and deterioration of the area's already affected and semi-brackish coastal aquifers.
The growth rate monitoring of crops throughout their biological cycle is very important as it contributes to the
achievement of a uniformly optimum production, a proper harvest planning, and reliable yield estimation. Fertilizer
application often dramatically increases crop yields, but it is necessary to find out which is the ideal amount that has to
be applied in the field. Remote sensing collects spatially dense information that may contribute to, or provide feedback
about, fertilization management decisions. There is a potential goal to accurately predict the amount of fertilizer needed
so as to attain an ideal crop yield without excessive use of fertilizers cause financial loss and negative environmental
impacts.
The comparison of the reflectance values at different wavelengths, utilizing suitable vegetation indices, is commonly
used to determine plant vigor and growth. Unmanned Aerial Vehicles (UAVs) have several advantages; because they can
be deployed quickly and repeatedly, they are flexible regarding flying height and timing of missions, and they can obtain
very high-resolution imagery. In an experimental crop field in Eleftherio Larissa, Greece, different dose of pre-plant and
in-season fertilization was applied in 27 plots. A total of 102 aerial photos in two flights were taken using an Unmanned
Aerial Vehicle based on the scheduled fertilization.
Α correlation of experimental fertilization with the change of vegetation indices values and with the increase of the
vegetation cover rate during those days was made. The results of the analysis provide useful information regarding the
vigor and crop growth rate performance of various doses of fertilization.
Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications
are designed to provide farmers with timely crop monitoring and production information. Such information can be used
to identify crop vigor problems.
Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state
and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among
VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical
but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the
atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and
sun elevation influence direct comparability of vegetation indicators among different sensors.
In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of
Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively.
Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters
pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a
statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other
innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant
growth areas.
Forest fires are regarded as one of the most threatening sources of disturbance for the property, infrastructure as well as ecosystems. The present study aimed at analyzing spectral information products derived from the Landsat–8 OLI sensor together with spectral indices to evaluate their ability to map burn scars and burn severity. In particular the study objectives were: (1) to identify the capability of OLI to burnt area mapping and burn severity, (2) to evaluate the contribution of several spectral indices to the overall accuracy (3) to assess post-fire effects such as flood risk and, (4) to investigate the vegetation re-growth in relation to the burn severity. As a case study, Chios Island was selected due to the recent fire event in the south-western part of the island (25/07/2016). Three multispectral Landsat-8 OLI images, acquired on 13/07/2016 (pre-fire), 15/09/2016 (post-fire) and 27/03/2017 (six months after the fire), were utilized. Several spectral indices were implemented to detect the burnt areas and assess the burn severity (Burn Area Index – BAI, Normalized Burn Ratio - NBR, Normalized Burn Ration + Thermal - NBRT), as well as to evaluate the vegetation conditions and re-growth six months after the fire event (Normalized Difference Vegetation Index - NDVI and the Normalized Difference Water Index - NDWI). Additionally, NBR index of pre- and post-fire images was calculated in a difference change detection procedure which estimates the Differenced Normalized Burn Ratio dNBR. Overall, a total burned area of 45,9 km2 was delineated, and both burned severity map and vegetation recovery map were created and evaluated.
Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop needs or health problems and provide solutions for a better crop management. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation.
In the present study, the experimental area is located near the village Eleftherion of Larissa Prefecture in the Thessaly Plain, and consisted of two adjacent agricultural fields of cotton and corn.
Imagery from WorldView-2 (WV2) satellite platform was obtained from European Space Imaging and Landsat-8 (L8) free of charge data were downloaded from the United States Geological Survey (USGS) archive. The images were selected for a four month span to evaluate continuity with respect to vegetation growth variation.
VIs for each satellite platform data such as the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Fraction Photosynthetically Radiation (FPAR) were calculated. The comparison of these VIs produced from the two satellite systems with different spatial and spectral resolution was made for each growth stage of the crops and their results were analyzed in order to examine their correlation.
Utilizing the WV2 new spectral data, several innovative chlorophyll and vegetation indices were created and evaluated so as to reveal their effectiveness in the detection of problematic plant growth areas. The Green Chlorophyll index appeared to be the most efficient index for the delineation of these areas.
The new European Observatory radar data of polar orbiting satellite system Sentinel-1 provide a continuous and systematic data acquisition, enabling flood events monitoring and mapping.
The study area is the basin of Sperchios River in Fthiotida Prefecture, Central Greece, having an increased ecological, environmental and socio-economic interest. The catchment area and especially the river delta, faces several problems and threats caused by anthropogenic activities and natural processes. The geomorphology of Sperchios catchment area and the drainage network formation provoke the creation of floods. A large flash flood event took place in late January early February 2015 following an intense and heavy rainfall that occurred in the area.
Two space born radar images, obtained from Sentinel-1 covering the same area, one before and another one during the flood event, were processed. Two different methods were utilized so as to produce flood hazard maps, which demonstrate the inundated areas. The results of the two methods were similar and the flooded area was detected and delineated ideally.
Policy and decision making in the context of sustainable development requires rapid, effective and efficient access to and integration of appropriate current information from a wide range of sources, including land cover changes information derived from remotely sensed data. Geomorphic factors, such as altitude, slope, aspect and lithology presented in the area comprise the main parameters, including the climate, influencing the distribution of land cover. The use of a Geographic Information System (GIS) allows further spatial analysis of the data derived from remotely sensed images and digital terrain spatial models, and analysis of the impact of land cover change on regional sustainable development. The remotely sensing data used in this study was Landsat 5 TM and Landsat 7 ETM+ images. Normalized Difference Vegetation Index (NDVI) and Selective Principal Component Analysis (SPCA) techniques were applied to detect land cover change and especially vegetation changes from multitemporal satellite data. The area under study is the basin of River Sperchios, which covers an area of some 1.780 km2, is approximately 60-80 km long, 20-30 km wide with its southern and western flanks characterized by high elevations and steep slopes, whilst its northern flank presents lower elevations and more gently slopes. The conclusions obtained show that extensive land cover changes has occurred in the last decades as a result of both natural forces and human activities, which has in turn impacted on the regional sustainable development. The results thus provide very useful information to local government for decision making and policy planning.
The Bam earthquake of 26/12/2003 (Mw=6.5) demolished the city of Bam and provoked serious damages in Baravat city, which are located in a tectonic intersection zone in the SE of Iran. The present study focus on Bam earthquake seismotectonic investigations and damages assessment based on Envisat interferometric coherence images. Field observations, SAR magnitude and multitemporal SAR images were also used to support and verify the coherence image interpretation. Concerning the damages assessment the results were very poor in terms of recognition and operational capabilities. On the contrary the used of interferometric coherence image came to be very useful for seismic fault and rupture zones detection. Through this method a hidden fault, a parallel segment of the already known Bam fault, was identified.
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