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Papadavid G.,Cyprus University of Technology | Papadavid G.,Agricultural Research Institute of Cyprus | Hadjimitsis D.,Cyprus University of Technology | Toulios L.,Greek National Agricultural Research Foundation | Michaelides S.,Meteorological Service of Cyprus
Journal of Applied Remote Sensing | Year: 2011

This paper aims to model leaf area index (LAI) and crop height to spectral vegetation indices (VI), such as normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and weighted difference vegetation index (WDVI). The intended purpose is to create empirical statistical models to support evapotranspiration algorithms applied under the current conditions in the island of Cyprus. Indeed, a traditionally agricultural area was selected in the Mandria Village in the Paphos District area in Cyprus, where one of the island's main exported crops, potatoes, are cultivated. A GER-1500 field spectroradiometer was used in this study in order to retrieve the necessary spectrum data of the different crops for estimating the VI's. A field campaign was undertaken with spectral measurements of LAI and crop height using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric measurements between March and April of 2008 and 2009. Regarding the measurements, the phenological cycle of potatoes was followed. Several regression models have been applied to relate LAI/crop height and the three indices. It was found that the best fitted vegetation index to both LAI and crop height was WDVI. When LAI was regressed against WDVI for potatoes, the determination coefficient (R2) was 0.72, while for crop height R 2 reached 0.78. Two Landsat TM-5 images acquired simultaneously during the spectroradiometric and LAI and crop height measurements are used to validate the proposed regression model. From the whole analysis it was found that the modeled results are very close to real values. This fact enables the specific empirical models to be used in the future for hydrological purposes. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).


Jacovides C.P.,National and Kapodistrian University of Athens | Tymvios F.S.,Meteorological Service of Cyprus | Boland J.,University of South Australia | Tsitouri M.,Meteorological Service of Cyprus
Atmospheric Research | Year: 2015

In this paper, simple Artificial Neural Network (ANN) models for estimating daily solar global broadband as well as solar spectral global UV and PAR radiant fluxes have been established. The data used in this analysis are global ultraviolet UV (GUV), global photosynthetic photon flux density (PPFD-QP), broadband global radiant flux (Gh), extraterrestrial radiant flux (G0), air temperature (T), relative humidity (rh), sunshine duration (n), theoretical sunshine duration (N), precipitable water (w) and ozone column density (O3). By using different combinations of the above variables as inputs, numerous ANN-models have been developed. For each model, the output is the daily global GUV, QP and Gh solar radiant fluxes. Firstly, a set of 2×365 point (2years) has been used for training each network-model, whereas a set of 365 point (1year) has been engaged for testing and validating the ANN-models. It has been found that the ANN-models' accuracy depends on the parameters employed as well as spectral range considered. Comparisons between proposed ANN-models and conventional regression models revealed that the results of both methods are statistically significant. On closer examination of many error measures, though, it is clear that the ANN-models perform better overall. From this point of view, it turned out that the neural network technique is better suited further suggesting that the ANN methodology is a promising and a more accurate tool for estimating both broadband and spectral radiant fluxes. © 2013 Elsevier B.V.


Alexakis D.D.,Cyprus University of Technology | Hadjimitsis D.G.,Cyprus University of Technology | Agapiou A.,Cyprus University of Technology | Themistocleous K.,Cyprus University of Technology | And 4 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

This study strives to highlight the potential of flood inundation monitoring and mapping in a catchment area in Cyprus (Yialias river) with the use of radar satellite images. Due to the lack of satellite data acquired during dates flood inundation events took place, the research team selected specific images acquired during dates that severe precipitation events were recorded from the rain gauge station network of Cyprus Meteorological Service in the specific study area. The relationship between soil moisture and precipitation was thoroughly studied and linear regression models were developed to predict future flood inundation events. Specifically, the application of fully polarimetric (ALOS PALSAR) and data acquired over different dates for soil moisture mapping is presented. The PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor carried by the ALOS (Advanced Land Observing Satellite) have quadruple polarizations (HH, VV, HV, VH). The amount of returned radiation (as backscatter echoes) that dictates the brightness of the image depends on factors such as the roughness, size of the target relative to the signal's wavelength, volumetric and diffused scattering. The variation in soil moisture pattern during different precipitation events is presented through soil moisture maps obtained from ALOS PALSAR and data acquired during different dates with different precipitation rates. Soil moisture variation is clearly seen through soil moisture maps and the developed regression models are used to simulate future inundation events. The results indicated the considerable potential of radar satellite images in soil moisture and flood mapping in catchments areas of Mediterranean region. © 2012 SPIE.


Alexakis D.D.,Cyprus University of Technology | Agapiou A.,Cyprus University of Technology | Tzouvaras M.,Cyprus University of Technology | Themistocleous K.,Cyprus University of Technology | And 3 more authors.
Natural Hazards | Year: 2014

This study considers the impact of landslides on transportation pavements in rural road network of Cyprus using remote sensing and geographical information system (GIS) techniques. Landslides are considered to be one of the most extreme natural hazards worldwide, causing both human losses and severe damages to the transportation network. Risk assessment for monitoring a road network is based on the combination of the probability of landslides occurrence and the extent and severity of the resultant consequences should the disasters (landslides) occur. Factors that can trigger landslide episodes include proximity to active faults, geological formations, fracture zones, degree and high curvature of slopes, water conditions, etc. In this study, the reliability and vulnerability of a rural network are examined. Initially, landslide locations were identified from the interpretation of satellite images. Different geomorphological factors such as aspect, slope, distance from the watershed, lithology, distance from lineaments, topographic curvature, land use and vegetation regime derived from satellite images were selected and incorporated in GIS environment in order to develop a decision support and continuous landslide monitoring system of the area. These parameters were then used in the final landslide hazard assessment model based on the analytic hierarchy process method. The results indicated good correlation between classified high-hazard areas and field-confirmed slope failures. The CA Markov model was also used to predict the landslide hazard zonation map for 2020 and the possible future hazards for transportation pavements. The proposed methodology can be used for areas with similar physiographic conditions all over the Eastern Mediterranean region. © 2013 Springer Science+Business Media Dordrecht.

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