Emmanouil Psomiadis

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes.

The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices

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.

Investigation on Bam (Iran) earthquake 26/12/03 based on interferometric coherence

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.

Remotely sensing data and thematic mapping for sustainable developing in Sperchios river basin (Central Greece)

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 Significance of Land Cover Delineation on Soil Erosion Assessment

The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965–92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment’s climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

Unmanned Aerial Vehicle (UAV) data analysis for fertilization dose assessment

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.

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

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.

High-resolution Earth observation data and spatial analysis for burn severity evaluation and post-fire effects assessment in the Island of Chios, Greece

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.

Flash flood area mapping utilising SENTINEL-1 radar data

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.