Entity

Time filter

Source Type


Agapiou A.,Cyprus University of Technology | Hadjimitsis D.,Cyprus University of Technology | Alexakis D.,Cyprus University of Technology | Papadavid G.,Cyprus University of Technology | Papadavid G.,Agricultural Research Institute of Cyprus
GIScience and Remote Sensing | Year: 2012

The detection of buried archaeological remains using satellite remote sensing is still an open question in archaeological research. This research investigates how the phenological stages of crops can be used support the detection of buried archaeological remains. Ground remote sensing data using the GER-1500 spectroradiometer were obtained from two sites. One site was the Neolithic settlements in central Greece and the other was in Alampra village in Cyprus. For the latter, an archaeological environment was simulated and ground spectroradiometric measurements were systematically acquired over the different phases of the phenological cycle of barley crops. The acquired in situ reflectance measurements have been converted to "in-band" reflectance values of the Landsat TM/ETM+ using the satellite relative spectral responses filters (RSR). Based on the proposed methodology, 97 Landsat MSS, TM, and ETM+ satellite images were acquired (covering a period from 1983 to 2011), for the Thessalian (Greek) site. It has been found that phenological-cycle observations can provide valuable information for identifying buried archaeological remains. Such observations may be used in cases where the spatial resolution of satellite imagery is not high and therefore cannot help support the detection of archaeological remains using standard interpretation techniques.


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).


Papadavid G.,Cyprus University of Technology | Hadjimitsis D.,Agricultural Research Institute of Cyprus | Fedra K.,Environmental Software and Services GmbH | Michaelides S.,Meteorological Service
Advances in Geosciences | Year: 2011

This paper presents a research project which integrates technological tools for developing a complete system for monitoring and determining irrigation demand on a systematic basis in Cyprus. Such tools are multi-spectral remotely sensed data dynamic water budget simulation and optimization, crop evapotranspiration (ETc) models and micro-sensor technology. The main aim is to estimate ETc in Cyprus and, furthermore, to undertake the required measures for an effective irrigation water management in the future. Evapotranspiration is difficult to determine since it combines various meteorological and field parameters while in literature quite many different models for estimating ETc are put forward. The proposed wireless sensor network acts as a monitoring tool for providing measurements of the necessary parameters: meteorological, climatic data and other auxiliary parameters required by the irrigation model in order to determine the irrigation demand. Reflectance is determined directly from satellite images. Finally, using the WaterWare irrigation software, irrigation scheduling is planned for the area of interest in Paphos, Cyprus. This area is located at almost sea level and is characterized by mild micro-climate. The results of the paper refer to year 2009 and show the daily water requirements of the specific crop in study. © Author(s) 2011.


Papadavid G.,Agricultural Research Institute of Cyprus | Hadjimitsis D.,Cyprus University of Technology
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2014

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future. © 2014 SPIE.


Hadjimitsis D.G.,Cyprus University of Technology | Papadavid G.,Cyprus University of Technology | Papadavid G.,Agricultural Research Institute of Cyprus | Agapiou A.,Cyprus University of Technology | And 7 more authors.
Natural Hazards and Earth System Science | Year: 2010

Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydro-meteorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task.

Discover hidden collaborations