Papers by Nicolas Dalezios
Springer Atmospheric Sciences, 2012
Precision agriculture or operational agriculture is a dynamically developing field of agricultura... more Precision agriculture or operational agriculture is a dynamically developing field of agricultural science, which is based on geoinformatics. Agrometeorological remote sensing contributes through processing and analysis of high resolution satellite images to decision support at field level. In this paper, in HYDROSENSE project, the classification of organic matter zones is presented aiming at the assessment of water and fertilizer operational needs per zone for cotton crops in Thessaly during the phenological cycle. Two pan-sharpened satellite images WorldView-2 (0.5 m) of the region are used, one before planting and the other at the maximum plant cover, as well as one pan-sharpened reference satellite image (2007) IKONOS-2 (1 m). The methodological processing approach includes image filtering and preprocessing, as well as processing, which involves principal component analysis and clustering leading to the production of thematic maps. These maps are combined with mapping through geostatistical analysis of weekly periodic field measurements in three selected cotton fields referring to NDVI, Red/Red edge, NIR/Red and the development of a mini GIS. The results of this combined analysis lead to organic matter zones of the whole region for decision support in precision agriculture.
Proceedings of the …, 2006
... 101 Coordinating Role of the Food and Agriculture Organization in Developing Tools and ... it... more ... 101 Coordinating Role of the Food and Agriculture Organization in Developing Tools and ... it is the informational products that are the framework for any knowledge-based decision process. ... Information may be used in crop management systems that extension services provide to ...
International Journal of Global Environmental Issues, 2016
Advances in Plants and Agriculture Research, Jul 16, 2018
Farmers throughout the world are constantly searching for ways to maximize their returns. Remote ... more 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.
Proceedings of SPIE, Oct 25, 2016
Remote Sensing applications are designed to provide farmers with timely crop monitoring and produ... more 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.
Water intelligence online, 2017
Multiple criteria decision making, 2018
Agriculture is highly affected by environmental conditions and the assessment of the agroclimatic... more Agriculture is highly affected by environmental conditions and the assessment of the agroclimatic potential is necessary for sustainability and productivity. The climate is among the most important factors that determine the agricultural potentialities of a region and the suitability of a region for a specific crop, whereas the yield is determined by weather conditions. In this chapter the first objective is to identify sustainable production zones in Thessaly by conducting contemporary agroclimatic classification based on remote sensing and GIS. The agroclimatic conditions of agricultural areas have to be assessed in order to achieve sustainable and efficient use of natural resources in combination with production optimization. Thus, a quantitative understanding of the climate of a region is essential for developing improved farming systems. The second objective derives from the first; it develops a decision support system (DSS) by using multi-criteria analysis combining different criteria to a utility function under a set of constraints concerning different categories of agroclimatic, social, cultural and economic conditions and so we can achieve an optimum agricultural production plan. In order to support the realization of the proposed production zoning and DSS in real-time, a Sensor Web service platform is proposed to be implemented based on the Sensor Web technologies, which extracts Real-time environmental and agronomic data.
CRC Press eBooks, Jul 20, 2017
Natural Hazards and Earth System Sciences, Oct 18, 2013
International journal of multicriteria decision making, 2019
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Papers by Nicolas Dalezios