The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5km×5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1km×1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1km×1km and precipitation measurements from the official raingauge network of the Cyprus’ Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks’ architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models’ performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.
Satellite imagery has been considered as an add-on tool to air quality and pollution monitoring due to its extensive spatial and temporal coverage of the Earth’s surface and atmosphere. The most widely used satellite parameter is the Aerosol Optical Depth (AOD). AOD has been extensively used to evaluate and enhance the satellite-based estimates of ground-level particulate matter (PM) as well as to reduce uncertainties in the studies of global health applications. This study attempts to identify correlations between AOD values retrieved from the new MODIS/Aqua high resolution 3km aerosol product and ground-based PM10 measurements obtained within the period 2002-2012 in the area of Athens, Greece. In parallel, it attempts to assess the applicability of the so called mixed effects models which take into account both the spatial and temporal variability of the underlying uncertainties in the estimation of PM10 levels from MODIS AOD measurements. The ground PM10 recordings were acquired from the archive of the in-situ operational air quality monitoring network of Athens. Results indicated that the new AOD product of 3km estimated better PM10 values against the AOD 10km product. Thus, the new 3km product may be better at characterizing aerosol distributions on local scale although bias was observed.
Remote sensing technology has been widely used for monitoring water quality parameters such as suspended solids (turbidity), Secchi Disk, chlorophyll, and phosphorus. Suspended matter plays an important role in water quality management of several inland- (such as lakes and reservoirs) and coastal-water bodies and can be used to estimate the Trophic State Index of different water bodies. However synoptic information on water quality parameters at a systematic basis is difficult to be obtained from routine in situ monitoring programs since suspended matter, phosphorus, and chlorophyll are spatially inhomogeneous parameters. To meet this need, an integrated use of Landsat satellite images, in situ data and water quality models can be used. Several algorithms were developed at a previous stage using water quality data collected during the in situ sampling campaigns taken place in 2010 and 2011 over Asprokremmos Reservoir (Paphos District) for the assessment of turbidity, Secchi Disk, and Trophic State Index fluctuations using spectroradiometric data. Remotely sensed data were atmospherically corrected and water quality models for the estimation of both the turbidity- and Secchi Disk- concentrations were further calibrated using in situ data for the case of Asprokremmos Reservoir and several coastal over Cyprus coastline (Limassol and Paphos District Areas). This methodology can be used as a supporting monitoring tool for water management authorities “gaining” additional information regarding the spatial and temporal alterations of the turbidity- and Secchi Disk- concentrations and the Trophic State Index values over several Case II water bodies.
This study describes the spatial and temporal variations observed at the Limassol Harbor area covering a period of more than 25 years. All multi-date satellite imagery data used for this study acquired during the summer period, covering a period from May to August. The goal is to examine the temporal alterations been observed at the Limassol Harbor area during the years passed and the spatial variation according to the distance from the Harbor. Two areas were selected, one covering the area inside the Harbor and one other covering a larger area involving the area inside and outside the Limassol Harbor. Those two areas where cut off and water area was masked in order to indicate the differences observed. Temporal and spatial variations were apparent after applying unsupervised classification and stretching methods.
Floods are among the most frequent and costly natural disasters in terms of human and economic loss and are considered to be a weather-related natural disaster. This study strives to highlight the potential of active remote sensing imagery in flood inundation monitoring and mapping in a catchment area in Cyprus (Yialias river). GeoEye-1 and ASTER images were employed to create updated Land use /Land cover maps of the study area. Following, the application of fully polarimetric (ALOS PALSAR) and dual polarimetric (ERS - 2) Synthetic Aperture Radar (SAR) data for soil moisture and inundation mapping is presented. For this purpose 2 ALOS PALSAR images and 3 ERS-2 images were acquired. This study offers an integrated methodology by the use of multi-angle radar images to estimate roughness and soil moisture without the use of ancillary field data such as field measurements. The relationship between soil moisture and backscattering coefficient was thoroughly studied and linear regression models were developed to predict future flood inundation events. Multi-temporal FCC images, classification, image fusion, moisture indices, texture and PCA analysis were employed to assist soil moisture mapping. Certain land cover classes were characterized as flood prone areas according to statistics of their signal response. The results will be incorporated in an integrated flood risk assessment model of Yialias catchment area.
Mediterranean landscape has undergone many significant changes during last decades. Especially river catchments are among the threatened landscapes in the world, mainly due to human activities and land cover changes. This description demonstrates the case of Acheron River catchment, which is a typical of many Mediterranean catchments cases. Human activities, through its impact on land cover and use, affect this ecological succession at various degrees in the whole catchment area. The proposed analysis focuses on the use of the normalized difference vegetation index (NDVI), which can provide important information in terms of vegetation productivity and status, which represents one of the most sensitive landscape components to environmental degradation. Emphasis is given to the spatiotemporal dynamic patterns of land cover/use changes for the period 1984 – 2011 with Landsat-TM imagery. Land use disturbances in the catchment’s area decreased habitat integrity, with maximum habitat integrity recorded in the upper river reaches, known as the Straits of Acheron, through a narrow and magnificent gorge created by mountains. Human interventions have changed the river beds, increased landscape fragmentation, and led to the degradation and loss of wetland habitats. Additionally the current research could be a valuable tool for the river managers to develop area-specific policies that minimize human influences.
The purpose of this paper is to present a proposal for an e-learning course in the field of remote sensing and environmental applications. E-learning is an educational process supported by specially developed educational material. The students can study and learn without, necessary, the physical presence of a teacher. The educational profile of students is determined. These are High School students in the Secondary Education Grade. The educational material is described in such a way that it would be suitable for distance learning and to cover the educational needs and requirements of students that could be used as an additional material in the Physical Sciences. The educational material includes electronic components designed with Courselab and presented in the Moodle platform. The objective is the transfer of knowledge regarding remote sensing environmental applications in a "friendly" way to the students. Thus, students attending the course will have the ability to fulfill their knowledge and adapt new skills.
This study highlights the need for digital mapping of urban sprawl phenomenon in catchment areas with the use of both satellite and ground remote-sensing techniques. The Yialias River, located in the central part of Cyprus, was selected as a case study area. In this catchment, devastating flash flood events took place in both 2003 and 2009, with catastrophic results. Initially, ground spectroradiometric measurements were applied to investigate the spectral similarity of different classes such as those of "urban fabric" and "marl/chalk origin formations" within the watershed. Temporal land cover changes were analyzed by using multitemporal satellite imagery (ASTER) and by incorporating both pixel- and object-oriented classification techniques. To create effective land use and land cover maps, a classification model was proposed based on spectral, texture, and shape characteristics. The pixel-based classification results were compared and evaluated with the object-based classification products. The optimum classification products were imported to geographical information systems and FRAGSTATS software and were used to visually and statistically detect landscape identifying trends based on spatial landscape metrics. The final results indicated considerable urban expansion within the study area during the last 10 years.
Research indicates that aerosol optical thickness (AOT) values and particulate matter (PM10) measurements can be used as indicators of atmospheric pollution. The problem of relating AOT with suspended particulate matter near the ground is still an open question. While satellite images can provide reliable and synoptic measurements from space, comparisons with monitoring surface level air pollution continues to be a challenge since satellite measurements are column integrated quantities. In this study, in-situ spectroradiometric measurements were taken during satellite overpass using field spectrometers to obtain the reflectance values of the calibration targets used. Sun photometer measurements were taken with the Microtops hand-held sun photometer to measure AOT. Meteorological data was collected from nearby meteorological stations and PM10 measurements were collected from local mobile air pollution stations. Following, the darkest pixel method of atmospheric correction was applied to a series of Landsat satellite images. The reflectance values of the atmospherically-corrected image were used in the radiative transfer equation to solve for AOT. Thematic maps were generated in order to develop air quality indices. The image-derived AOT values were examined for a positive correlation with PM10 measurements. It appears there exists a significant correlation between AOT and PM10 measurements.
Pseudo-invariant targets are often used for atmospheric correction, as their reflectance values are stable across time. Sand is often used as a pseudo-invariant target, although there is conflicting research about its effectiveness as a pseudo invariant target. This study will examine the effectiveness of volcanic sand as a pseudo-invariant target. The study area is a 250x250 meter area of volcanic beach sand near Limassol, Cyprus. In-situ spectroradiometric measurements were taken using field spectrometers to obtain the reflectance values of volcanic sand over wet and dry conditions. The varying saturation levels of the sand due to rainfall, humidity and high temperatures was considered. A series of Landsat-5 TM and Landsat-7 ETM+ satellite imagery were atmospherically corrected using the darkest pixel method in order to assess the effectiveness of the volcanic sand as a pseudo-invariant target. The mean in-situ in-band reflectance values as found from the ground measurements were compared with the at-satellite reflectance values following atmospheric correction. It was found that precipitation conditions such as rainfall affected the reflectance values of sand. The study found that wet sand had a significantly lower reflectance value compared to dry sand. Further, salinization also affected the reflectance value of volcanic sand. Therefore, precipitation conditions need to be considered when using sand as a non-variant target for atmospheric correction.
This paper presents a comparison of the darkest pixel (DP) and empirical line (EL) atmospheric correction methods in
order to examine their effectiveness to retrieve aerosol optical thickness (AOT) using the radiative transfer (RT)
equations. Research has found that the DP and the EL methods are the two simplest and most effective methods of
atmospheric correction; however, which of the two atmospheric correction methods is more effective in deriving
accurate AOT values remains an open question. The accuracy of the DP and EL atmospheric correction methods were
examined using pseudo-invariant targets in the urban area of Limassol in Cyprus, by using reflectance values before and
after atmospheric correction. Eleven Landsat 5 and Landsat 7 satellite images were atmospherically corrected using both
the DP and EL methods. The reflectance values following the DP and EL method of atmospheric correction were used in
the radiative transfer equation to derive the AOT values. Following, an accuracy assessment was conducted comparing
the in-situ AOT values as measured from sun photometers with the AOT values derived from the RT equations in order
to determine the effectiveness of the DP and EL methods for retrieving AOT. The study found that the EL atmospheric
correction method provided more accurate AOT values than the DP method.
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.
The problem of atmospheric intervention has received considerable attention from researchers in remote sensing who have developed a range of methods, either simple or sophisticated. The sophisticated methods require auxiliary information about the state of the atmosphere which is obtained either from standard databases or from simultaneous in-situ field measurements or by iterative techniques. It has been found that the darkest pixel atmospheric correction (DP) is one of the most effective atmospheric correction methods especially for visible spectral bands. The DP is the simplest and fully image-based correction method. The integrated use of the DP basic theory and the radiative transfer equation is implemented in this study. Indeed, this leads to the development of the proposed 'image-based atmospheric correction algorithm.' The proposed algorithm retrieves the aerosol optical thickness (AOT) only for areas with urban and maritime aerosols. The effectiveness of this algorithm is assessed by comparing the AOT values retrieved from the proposed 'image-based atmospheric correction algorithm' after applied to Landsat TM/ETM+ images with those measured in-situ both from MICROTOPS II hand-held sun photometer and the CIMEL sun photometer (AERONET). It has been found that the AOT values retrieved from the proposed algorithm were very close with those measured from the CIMEL sun photometer for the Limassol area in Cyprus.
The increase of flood inundation occuring in different regions all over the world have enhanced the need for effective flood risk management. As floods frequency is increasing with a steady rate due to ever increasing human activities on physical floodplains there is a respectively increasing of financial destructive impact of floods. A flood can be determined as a mass of water that produces runoff on land that is not normally covered by water. However, earth observation techniques such as satellite remote sensing can contribute toward a more efficient flood risk mapping according to EU Directives of 2007/60.
This study strives to highlight the need of digital mapping of urban sprawl in a catchment area in Cyprus and the assessment of its contribution to flood risk. The Yialias river (Nicosia, Cyprus) was selected as case study where devastating flash floods events took place at 2003 and 2009. In order to search the diachronic land cover regime of the study area multi-temporal satellite imagery was processed and analyzed (e.g Landsat TMETM+, Aster). The land cover regime was examined in detail by using sophisticated post-processing classification algorithms such as Maximum Likelihood, Parallelepiped Algorithm, Minimum Distance, Spectral Angle and Isodata. Texture features were calculated using the Grey Level Co-Occurence Matrix. In addition three classification techniques were compared : multispectral classification, texture based classification and a combination of both. The classification products were compared and evaluated for their accuracy. Moreover, a knowledge-rule method is proposed based on spectral, texture and shape features in order to create efficient land use and land cover maps of the study area. Morphometric parameters such as stream frequency, drainage density and elongation ratio were calculated in order to extract the basic watershed characteristics. In terms of the impacts of land use/cover on flooding, GIS and Fragstats tool were used to detect identifying trends, both visually and statistically, resulting from land use changes in a flood prone area such as Yialias by the use of spatial metrics. The results indicated that there is a considerable increase of urban areas cover during the period of the last 30 years. All these denoted that one of the main driving force of the increasing flood risk in catchment areas in Cyprus is generally associated to human activities.
The use of satellite remote sensing for water quality monitoring in inland waters has substantial advantages over the insitu
sampling method since it provides the ability for overall area coverage and also for study and supervision of isolated
locations. The development of algorithms for water quality monitoring using satellite data and surface measurements can
be widely found in literature. Such algorithms require validation and one of the major problems faced during these
attempts was the need for continuous surface measurements requiring numerous in-situ samplings that imply also very
high costs due to the need of increased human labour. The development of an automatic and autonomous sensor system
able to be remotely controlled, will cover this gap and will allow the real time combined analysis of satellite and surface
data for the continuous monitoring of water quality in dams as well as the overall water resources management. Wireless
Sensor Networks (WSN) can provide continuous measurements of parameters taken from the field by deploying a lot of
wireless sensors to cover a specific geographical area. An innovative, energy-autonomous floating sensor platform
(buoy) transferring data via wireless network to a remote central database has been developed for this study which can be
applied on all dams in Cyprus. Indeed this project describes the results obtained by an existing running campaign in
which in-situ spectroradiometric (GER1500 field spectroradiometer) measurements, water sampling measurements
(turbidity), sensor measurements (turbidity) and Landsat TM/ETM+ data have been acquired at the Asprokremmos Dam
in Paphos (Cyprus). By applying several regression analyses between reflectance against turbidity for all the spectral
bands that correspond to Landsat TM/ETM+ 1-2-3-4, the highest correlation was found for TM band 3 (R2=0.83).
Atmospheric correction is still considered as the most important part of pre-processing of satellite remotely sensed
images. The accuracy assessment of the existing atmospheric correction must be monitored on a systematic basis since
the user must be aware about the effectiveness of each algorithm intended for specific application. Indeed this study
integrates the following measurements coincided with the satellite overpass (ASTER and Landsat TM/ETM+) in order to
assess the accuracy of the most widely used atmospheric correction algorithms (such as darkest pixel, atmospheric
modelling, ATCOR, 6S code etc.): spectroradiometric measurements of suitable calibration targets using GER1500 or
SVC HR-1024 field spectro-radiometers, MICROTOPS hand held sun-photometers, LIDAR backscattering system,
CIMEL sun photometer (Cyprus University of Technology recently joined with AERONET).
It has been shown by Hadjimitsis et al. (2009) that the use of suitable non-variant targets in conjunction with the
application of the empirical line method can remove atmospheric effects from satellite images effectively. The method is
based on the selection of a number of suitable generic non-variant targets, on the basis that they are large, distinctive in
shape, and occur in many geographical areas. The need to further test such method by suggesting more suitable nonvariant
targets is one of the main aims of this study. Indeed, six targets have been already identified in the Lemesos
District area in Cyprus, near the harbour and tested. In-situ spectro-radiometric measurements using the SVC HR-1024
field spectro-radiometer have been made on November 2009 and from February 2010 to April 2010. Some of the in-situ
measurements were coincided with the Landsat TM/ETM+ overpass and the removal of atmospheric effects was very
effective. The above targets have been scanned using a 3D terrestrial laser scanner (Leica ScanStation C10) so as to
investigate the non-variability and uniformity of the proposed targets (through the laser scanner intensity values).
Remote sensing technology provides a cost-effective tool for monitoring changes in land-cover. The effective use of satellite remote sensing data and a suitable blend with socio-economic data helps in achieving a local specific prescription to achieve sustainable development of a region. This paper presents the results obtained from using remote sensing and GIS techniques to map land-cover changes in Skiathos Island for a period of 13 years. A set of three multidate Landsat TM images were used for the detection and iventory of disturbance and other changes that occur in land use, cover type, and cover condition in areas of research interest. The burnt areas during the 13-years period were well defined showing the changes in the landscape. It is shown that the use of satellite remote sensing can be used not only to improve the understanding of the significant land-cover changes that have been occurred over the past 13 years but also to enable better management decisions to be made.
Atmospheric correction is an essential part of the pre-processing of satellite remote sensing data. Several atmospheric correction approaches can be found in the literature ranging from simple to sophisticated methods. The sophisticated methods require auxiliary data, however the simple methods are based only on the image itself and are served to be suitable for operational use. One of the most widely used and well-known simple atmospheric correction methods is the darkest pixel (DP). Despite of its simplicity, the user must be aware of several key points in order to avoid any erroneous results. Indeed, this paper addresses a new strategy for selecting the suitable dark object based on the proposed analysis of digital number histograms and image examination. Several case studies, in which satellite remotely sensed image data intended for environmental applications have been atmospherically corrected using the DP method, are presented in this article.
Atmospheric correction is a complex process, which requires substantial modelling and computation, and a major difficulty is to obtain appropriate input parameters for the models. Numerous investigators have dealt with the development of simple or sophisticated approaches for the atmospheric correction of satellite images. However there is uncertainty about the effectiveness of such techniques especially when dealing with historical datasets in which input parameters for atmospheric models prove difficult to be obtained. The use pseudo-invariant targets in conjunction with
radiative transfer calculations is an alternative atmospheric correction technique which offers a relatively simple mean of
removing atmospheric effects in multi-temporal series of image data; providing that suitable pseudo-invariant targets can be easily identified on the satellite images and records on the their spectral characteristics are available. The spectral data of the proposed pseudo-invariant targets can be easily found in the literature from other studies. Indeed, this paper explores the need for identifying suitable pseudo-invariant targets, which are large in size, distinctive in shape and common in many geographical areas. This paper presents an application of use pseudo-invariant targets for removing atmospheric effects from Landsat TM and ETM+ satellite imagery acquired over different geographical areas such as in
UK, Cyprus, Kazakhstan and Greece for environmental applications.
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