Band 6, Heft 1 - First International Conference on Remote Sensing and Geoinformation of Environment-RSCy2013

This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship. The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model. It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM2.5 ground concentrations. Finally, we studied the relationship between PM2.5 and AOD at the intra-urban scale (≤10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM2.5 relationship does not depend on relative humidity and air temperatures below ~7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM2.5 relationship provides a means for obtaining spatially-resolved PM2.5 concentrations.

The Data Interpolating Empirical Orthogonal Functions method is a special technique based on Empirical Orthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.

The use of GIS and Spatial Analysis for predictive models is an important topic in preventive archaeology. Both of these tools play an important role in the Support Decision System (SDS) for archaeological research and for providing information useful to reduce archaeological risk. Over the years, a number of predictive models in the GIS environment have been developed and proposed. The existing models substantially differ from each other in methodological approaches and parameters used for performing the analysis. Until now, only few works consider spatial autocorrelation, which can provide more effective results. This paper provides a brief review of the existing predictive models, and then proposes a new methodological approach, applied to the neolithic sites in the Apulian Tavoliere (Southern Italy), that combines traditional techniques with methods that allow us to include spatial autocorrelation analysis to take into account the spatial relationships among the diverse sites.

This paper explores the potential of using satellite radar inteferometry to monitor time-varying land movement prior to any visible tension crack signs. The idea was developed during dedicated geotechnical studies at a large open-pit lignite mine, where large slope movements (10–20 mm/day) were monitored and large fissures were observed in the immediate area outside the current pit limits. In this work, differential interferometry (DInSAR), using Synthetic Aperture Radar (SAR) ALOS images, was applied to monitor the progression of land movement that could potentially thwart mine operations. Early signs of land movements were captured by this technique well before their visual observation. Moreover, a qualitative comparison of DInSAR and ground geodetic measurements indicates that the technique can be used for the identification of high risk areas and, subsequently, for the optimization of the spatial distribution of the available ground monitoring equipment. Finally, quantitative land movement results from DInSAR are shown to be in accordance with simultaneous measurements obtained by ground means.

The main goal of this study is the derivation of Carlson’s Trophic State Index (TSI) through the remote sensing of four different Case-2 waters in the Mediterranean region such as Cyprus and Greece. TSISD is derived through extensive field ground campaign of Secchi Disk Depth measurements for the Asprokremmos Dam, located in Paphos District in Cyprus; Alyki Salt Lake, located in Larnaca District in Cyprus; and in Karla Lake, located in Volos District in Greece; and finally to three coastal water areas in the Limassol coastal area. Several regression models have been applied in order to develop the best regression model between the TSISD and in-band reflectance values for Landsat TM/ETM derived from spectroradiometric measurements using a GER-1500 field spectroradiometer over the main case study area in Asprokremmos Dam in Cyprus. Finally, we apply several regression models for Asprokremmos Dam for retrieving the suitable Landsat TM/ETM band or band combinations (obtained from field spectroradiometric measurements) in which TSISD can be determined. Indeed, the best regression model has been obtained by correlating ‘TSI Versus Band2/Band3’, with R2=0.89. All field TSISD and in-band reflectance values from the four different water bodies have been used to develop the best fitted model for the established TSI SD Versus Band2/Band3 model. We find that the exponential regression model provides the best fitted equation over the four different water bodies.

This study presents an integrated method for the estimation and analysis of potential wind-energy resources in Cyprus, which is applied at selected sites on the western side of the island. Firstly, a statistical analysis of wind speed and direction data was conducted at six meteorological stations in western Cyprus, establishing daily, monthly and annual variations of wind speed. Also examined were the Weibull distributions of the wind at each site. These wind statistics serve as the basis for estimating corrected statistical distributions over the extended study areas, which were calculated using the Wind Atlas Analysis and Application Program (WAsP) that modifies wind flow estimates based on local topographic effects. As a result, a geographic and wind-resource database was formulated around each station. Aggregation of this data using statistical weighting methods allows the extrapolation of observed results and the visualization for selected hours of the day over the western part of Cyprus. The results indicate the strong influence of the sea-breeze on the island’s wind potential, and identify a number of areas of higher wind-energy potential suitable for wind-resource exploitation. It is hoped that both the methodology applied and results obtained can be further used by potential investors and wind-energy developers.

To estimate the seismic response according to Eurocode (EC8) and almost all other national codes, site conditions have to be properly characterized so that soil amplification and the corresponding peak ground motion can be calculated. In this work, different geophysical and geotechnical methods are combined in order to define the detailed ground conditions in selected sites of the Hellenic Accelerometric Network (HAN) in Crete. For this purpose, the geological information of the sites and shear wave velocity, calculated from surface wave measurements, is used. Additionally, ground acceleration data recorded through HAN have been utilized from intermediate depth earthquakes in the broader area of South Aegean Sea. Using the recorded ground motion data and the procedure defined in EC8, the corresponding elastic response spectrum is calculated for the selected sites. The resulting information is compared to the values defined in the corresponding EC8 spectrum for the seismic zone that includes the island of Crete. The comparison shows that accurate definition of ground type through geological, geotechnical and geophysical investigations is important. However, our current comparison focuses on the distribution of values rather than the absolute values of EC8-prescribed spectra, and the results should be considered in this context.

Vegetation is a natural source of Volatile Organic Compounds (VOC) that plays an important role in atmospheric chemistry. The main objective of the current study is to implement a model to quantify process-based VOC emissions from plants that focuses on the relationship between the sensitivity of VOC emission estimates to spatial resolution data, based on scientific knowledge and vegetation dynamics derived from satellite observations. The Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were elected to examine this issue using different resolutions of satellite-derived products: 22m from the DEIMOS-1 satellite, and 250m and 1000m provided by MODIS. The study is focused on an area of 80×80km2 in Portugal for 2011. Detailed land cover and meteorological data are also included in the emission quantification algorithm. The primary outcomes were determined using a multi-scale analysis showing spatial and temporal variations in the vegetation parameters and modeling results. The results confirm that the emissions model is highly sensitive to the spatial resolution of the satellite-derived data, resulting in about a 30% difference in total isoprene emissions for the study area.

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Steady technological progress has led to a noticeable advancement in disciplines associated with Earth observation. This has enabled information transition regarding changing scenarios, both natural and urban, to occur in (almost) real time. In particular, the need for integration on a local scale with the wider territorial framework has occurred in analysis and monitoring of built environments over the last few decades. The progress of Geographic Information (GI) science has provided significant advancements when it comes to spatial analysis, while the almost free availability of the internet has ensured a fast and constant exchange of geo-information, even for everyday users’ requirements. Due to its descriptive and semantic nature, geo-spatial information is capable of providing a complete overview of a certain phenomenon and of predicting the implications within the natural, social and economic context. However, in order to integrate geospatial data into decision making processes, it is necessary to provide a specific context, which is well supported by verified data. This paper investigates the potentials of geo-portals as planning instruments developed to share multi-temporal/multi-scale spatial data, responding to specific end-users’ demands in the case of Energy efficiency in Buildings (EeB) across European countries. The case study regards the GeoCluster geo-portal and mapping tool (Project GE2O, FP7), built upon a GeoClustering methodology for mapping of indicators relevant for energy efficiency technologies in the construction sector.