Optimizing statistical classification accuracy of satellite remotely sensed imagery for supporting fast flood hydrological analysis

The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus. For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods. Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity. Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data. The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images. These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded.     

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

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.     

Risk provision using field spectroscopy to identify spectral regions for the detection of defects in flexible pavements

Natural and physical hazards accelerate the deterioration of asphalted surfaces. Climatic factors are unavoidable and can affect the properties of asphalt mixtures, making them weaker and less durable. Thus, continuous monitoring of bituminous surfaces is something that can reduce the risks of public health. Remote sensing techniques have become an effective, noninvasive method for early detection of damaged asphalt pavements. This paper outlines a range of different remote sensing methodologies that can be used to monitor asphalt road pavements. This is complemented by the use of field spectroscopy for the examination of asphalt pavements of varying age and conditions. The results of the study found spectral differences regarding asphalt defects, such as physical cracking, patched cracking and polishing. These spectral changes were examined through “in-band” simulation analysis of the Landsat 7 ETM+ sensor, using appropriate relative spectral response filters, concluding that the ratio band 5/band 1 can be used to distinguish asphalt pavements of different date of construction and condition.