Spatial and Angular Correlation Functions in Cosmology

The purpose of this thesis is to study the role of the spatial and angular correlation functions for galaxies and clusters of galaxies. We discuss the various approaches to the problem of describing the statistical distribution of galaxies and discuss the connection between Peebles' and Limber's approach. The former describes the distribution as a point process, the latter considers the density distribution as a random function or process. The density distribution that corresponds to a particular spatial correlation function can be generated by use of spectral analysis. We discuss some of the problems involved in generating the density distribution in this way, and use this description to derive the relationship between the angular and the spatial correlation function for particular forms of the latter. Selection effects are introduced first by simply truncating all galaxies beyond a certain distance, and secondly by introducing the Schechter luminosity function and excluding galaxies whose apparent magnitude is above some threshold value. We briefly review the fractal description of galaxy distributions, and relate this to the description in terms of spatial correlation functions

Assessment of cotton phenological stages using agroclimatic indices: An innovative approach

Reliable long time-series of phenological observations are not widely available, since recording requires special expertise. In Greece, phenological stations network exists only in fruits trees and for the past 17 years. Thus, crops' phenological data are rare, whereas the few existing data bases are discontinuous. However, cotton (Gossypium hirsutum) is the most dynamic extensive cultivation in Greece and a crop of great importance for the national economy. Hence, there is a necessity for assessing cotton phenological stages using alternate methods. In this paper, an innovative approach for assessing phenological stages in cotton cultivation using agroclimatic indices is presented

Sustainable production zoning for agroclimatic classification using GIS and remote sensing

Agriculture is a primary productivity sector which is highly dependent on environmental conditions. The agroclimatic potential of agricultural areas has to be assessed in order to achieve sustainable and efficient use of natural resources in combination with production maximization. Temperature and rainfall, in terms of quantity and spatiotemporal variability, are variables which determine the type of crops suitable to a given location. Rainfall variable can also be interpreted as availability of sufficient water required for production of given crops. These variables, in combination with soil type and geomorphology, also determine areas where high levels of production are appropriate, avoiding the threat of degrading the natural resources. In the current work, zones indicating water availability are combined with topographic features and soil types in order to identify areas for sustainable production. Firstly, aridity index (AI) and vegetation health index (VHI) are used in order to define zones adequate for sustainable farming according to water limitations. As crop growth is affected by water supply, these zones are named water limited growth environment (WLGE) zones. AI and VHI are computed on monthly time step for twenty hydrological years, from October 1981 to September 2001. VHI is derived from NOAA/AVHRR data, while in AI computations both satellite and conventional field data are used. Then, WLGE zones are combined with soil maps and a digital elevation model (DEM) of the area under investigation in order to define zones appropriate for sustainable production. The study area is the aquatic district of Thessaly, located in Central Greece. The current application has resulted in the definition of sustainable production zones by means of parallelepiped supervised classification using the two indices, soil maps and DEM. These zones can be further used for agroclimatic classification

GIS-based weather radar siting procedure in mountainous terrain

This paper describes a weather radar siting procedure for a mountainous region in Greece using GIS. The established radar siting objectives consist of the detection and tracking of storms (rainstorms, hailstorms, and snowstorms) requiring operations at a low elevation angle as well as the detection of the vertical structure and evolution of a storm. Moreover, the objective includes precipitation intensity measurements and areal precipitation analysis, which is related to the incidence of permanent echoes and obstructions as well as to the maximum range achievable. Two types of siting criteria are employed; mainly strategic or regional criteria, and local criteria. The strategic criteria mainly consist of set back and communication considerations and local radar siting criteria include the area, geometry or morphology as well as local logistics and practical considerations. In this study, digitized maps of 1:100,000 scale with 100 m contour interval are used. These digitized contour data are used to build the Digital Terrain Model (DTM, raster image) and produce the three-dimensional visualization of the area. The potential sites for the weather radar installation are identified on this three-dimensional DTM. The potential radar sites are located in complex mountainous areas, which results in additional selection difficulties. The selected site and its alternative fulfill the established purposes using the above-mentioned criteria. (C) 2010 Published by Elsevier Ltd

Early cotton production assessment in Greece based on a combination of the drought Vegetation Condition Index (VCI) and the Bhalme and Mooley Drought Index (BMDI)

A new methodological approach is presented for quantifying the meteorological effects on cotton production during the growing season in Greece. The proposed Bhalme and Mooley Vegetation Condition Index (BMVCI) is based on the Vegetation Condition Index (VCI) extracted by National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. In this approach the VCI data is processed with the Bhalme and Mooley methodology to assess the accumulated meteorological effects on cotton from April to August. The resulting index is at the same scale as the Z-Index, which is the classification of the Palmer Drought Severity Index (PDSI) extensively used for drought monitoring. For this study 16 years of data are examined to illustrate that the weather development as identified from satellite data with the use of BMVCI confirm unfavourable conditions for cotton production. For the validation of BMVCI an empirical relationship between the cotton production and the BMVCI values is derived. The resultant high correlation coefficient refers to very encouraging results and confirms the usefulness of the proposed integrated methodological approach as an effective tool for early assessment of the cotton production in Greece

The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods

The increasing number of extreme natural phenomena, which are related to the climate variability and are mainly caused by anthropogenic factors, escalate the frequency and severity of natural disasters. Operational monitoring of natural hazards and assessment of the affected area impose quick and efficient methods based on large-scale data, readily available to the agencies. The growing number of satellite systems and their capabilities give rise to remote sensing applications to all types of natural disasters, including forest fires and floods. Remote sensing techniques can be used in all three aspects of disaster management viz: forecasting, monitoring and damage assessment. The purpose of this paper is to highlight the importance of satellite remote sensing for monitoring and near-real time assessment of the affected by forest fires and floods areas. As a tool, two satellite indices are presented, namely the Normalized Difference Vegetation Index (NDVI) and the Surface Temperature (ST), extracted by the meteorological satellite NOAA/AVHRR. In the first part of the paper, a review of utilized techniques using NDVI and ST is given. In the second part, the application of various methodologies to three case studies are presented: the forest fire of 21-24 July 1995 in Penteli Mountain near Athens and 16 September 1994 in Pelion Mountain in Thessaly region, central Greece, and finally the flood of 17-23 October 1994 in Thessaly region, central Greece. For all studies the NDVI has been utilized for hazard assessment. The method of ST has been applied to the flood event in Thessaly, for the estimation of the areal extent of the floods. As emerged from the studies, remote sensing data can be decisive for monitoring and damage assessment, caused by forest fires and floods

Study of the Spatiotemporal Variability of Oceanographic Parameters and Their Relationship to Holothuria Species Abundance in a Marine Protected Area of the Mediterranean Using Satellite Imagery

Marine protected areas (MPAs) are designated to protect marine ecosystems and, among other things, to monitor climate variability, which in turn affects aquatic species. The aim of this study is to examine the contribution of remotely sensed data as an indication of Holothuria abundance, by investigating the spatiotemporal variability of physicochemical parameters. The study area is in the National Marine Park of Alonissos Northern Sporades, which is included in the NATURA 2000 network. Firstly, the abundance of Holothuria species was measured by scuba diving. At the same time, depth profiles of five physicochemical parameters (temperature, salinity, pH, dissolved oxygen and Chl-a) were recorded by CTD (conductivity, temperature, depth), a primary instrument used to determine the essential physical and chemicals properties of seawater column profiles in the coastal zone. The physicochemical variables examined are the most common environmental parameters with the highest impact on growth, reproduction, productivity and survival rate of sea cucumber species, affecting the availability of food sources. Analysis of this data allows us to identify parameters which are essential for their existence. The analysis showed that only temperature and Chlorophyll-a (Chl-a) could be useful for identifying the abundance. These two parameters are readily available from satellite data. Additionally, particulate organic carbon (POC) is essential for Holothuria’s existence. Consequently, a time series of satellite data products from Terra/MODIS sensor were utilized from 2000 to 2020 for sea surface temperature (SST), Chl-a and POC. The monthly temporal trend shows that the abundance could be justified in areas where the Holothuria presence has been established. Monthly spatiotemporal analysis shows that SST, Chl-a and POC availability, could be an indication of the differences in abundance recorded. © 2022 by the authors

Agreement assessment of NOAA/AVHRR NDVI with landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas

Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece

Satellite data can significantly contribute to agricultural monitoring. The reflected radiation, as recorded by satellite sensors, provides an indication of the type, density and condition of canopy. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI) derived from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data provided in high temporal resolution. An extension of the NDVI is the Vegetation Condition Index (VCI). VCI is a tool for monitoring agrometeorological conditions, providing a quantitative estimation of weather impact to vegetation. The primary objective of this paper is the quantitative assessment of the cotton yield before the end of the growing season by examining the weather effects as they are depicted by the VCI. The study area comprises several cotton producing areas in Greece. Ten-day NDVI maximum value composites (MVC) are initially utilized for the period 1982-1999. The correlation between VCI images as extracted from NDVI and the 10-day intervals during the growing season is examined to identify the critical periods associated mostly with the yield. Empirical relationships between VCI and yield are developed. The models are tested on an independent dataset. The results show that an early estimation of the cotton yield trend is feasible by the use of the VCI

Study of the Spatiotemporal Variability of Oceanographic Parameters and Their Relationship to <i>Holothuria</i> Species Abundance in a Marine Protected Area of the Mediterranean Using Satellite Imagery

Marine protected areas (MPAs) are designated to protect marine ecosystems and, among other things, to monitor climate variability, which in turn affects aquatic species. The aim of this study is to examine the contribution of remotely sensed data as an indication of Holothuria abundance, by investigating the spatiotemporal variability of physicochemical parameters. The study area is in the National Marine Park of Alonissos Northern Sporades, which is included in the NATURA 2000 network. Firstly, the abundance of Holothuria species was measured by scuba diving. At the same time, depth profiles of five physicochemical parameters (temperature, salinity, pH, dissolved oxygen and Chl-a) were recorded by CTD (conductivity, temperature, depth), a primary instrument used to determine the essential physical and chemicals properties of seawater column profiles in the coastal zone. The physicochemical variables examined are the most common environmental parameters with the highest impact on growth, reproduction, productivity and survival rate of sea cucumber species, affecting the availability of food sources. Analysis of this data allows us to identify parameters which are essential for their existence. The analysis showed that only temperature and Chlorophyll-a (Chl-a) could be useful for identifying the abundance. These two parameters are readily available from satellite data. Additionally, particulate organic carbon (POC) is essential for Holothuria’s existence. Consequently, a time series of satellite data products from Terra/MODIS sensor were utilized from 2000 to 2020 for sea surface temperature (SST), Chl-a and POC. The monthly temporal trend shows that the abundance could be justified in areas where the Holothuria presence has been established. Monthly spatiotemporal analysis shows that SST, Chl-a and POC availability, could be an indication of the differences in abundance recorded