Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Earthquake damage assessment in urban area from Very High Resolution satellite data

The use of remote sensing within the domain of natural hazards and disaster management has become increasingly popular, due in part to increased awareness of environmental issues, including climate change, but also to the improvement of geospatial technologies and the ability to provide high quality imagery to the public through the media and internet. As technology is enhanced, demand and expectations increase for near-real-time monitoring and images to be relayed to emergency services in the event of a natural disaster. During a seismic event, in particular, it is fundamental to obtain a fast and reliable map of the damage of urban areas to manage civil protection interventions. Moreover, the identification of the destruction caused by an earthquake provides seismology and earthquake engineers with informative and valuable data, experiences and lessons in the long term. An accurate survey of damage is also important to assess the economic losses, and to manage and share the resources to be allocated during the reconstruction phase. Satellite remote sensing can provide valuable pieces of information on this regard, thanks to the capability of an instantaneous synoptic view of the scene, especially if the seismic event is located in remote regions, or if the main communication systems are damaged. Many works exist in the literature on this topic, considering both optical data and radar data, which however put in evidence some limitations of the nadir looking view, of the achievable level of details and response time, and the criticality of image radiometric and geometric corrections. The visual interpretation of optical images collected before and after a seismic event is the approach followed in many cases, especially for an operational and rapid release of the damage extension map. Many papers, have evaluated change detection approaches to estimate damage within large areas (e.g., city blocks), trying to quantify not only the extension of the affected area but also the level of damage, for instance correlating the collapse ratio (percentage of collapsed buildings in an area) measured on ground with some change parameters derived from two images, taken before and after the earthquake. Nowadays, remotely sensed images at Very High Resolution (VHR) may in principle enable production of earthquake damage maps at single-building scale. The complexity of the image forming mechanisms within urban settlements, especially of radar images, makes the interpretation and analysis of VHR images still a challenging task. Discrimination of lower grade of damage is particularly difficult using nadir looking sensors. Automatic algorithms to detect the damage are being developed, although as matter of fact, these works focus very often on specific test cases and sort of canonical situations. In order to make the delivered product suitable for the user community, such for example Civil Protection Departments, it is important to assess its reliability on a large area and in different and challenging situations. Moreover, the assessment shall be directly compared to those data the final user adopts when carrying out its operational tasks. This kind of assessment can be hardly found in the literature, especially when the main focus is on the development of sophisticated and advanced algorithms. In this work, the feasibility of earthquake damage products at the scale of individual buildings, which relies on a damage scale recognized as a standard, is investigated. To this aim, damage maps derived from VHR satellite images collected by Synthetic Aperture Radar (SAR) and optical sensors, were systematically compared to ground surveys carried out by different teams and with different purposes and protocols. Moreover, the inclusion of a priori information, such as vulnerability models for buildings and soil geophysical properties, to improve the reliability of the resulting damage products, was considered in this study. The research activity presented in this thesis was carried out in the framework of the APhoRISM (Advanced PRocedures for volcanIc Seismic Monitoring) project, funded by the European Union under the EC-FP7 call. APhoRISM was aimed at demonstrating that an appropriate management and integration of satellite and ground data can provide new improved products useful for seismic and volcanic crisis management

earthquake damage mapping by using remotely sensed data the haiti case study

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale

A Stable Gaussian Fitting Procedure for the Parameterization of Remote Sensed Thermal Images

An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand its intensity trend and also to perform quantitative comparisons among different images in time and space. The proposed procedure is computationally rapid and stable, executing an initial guess parameter estimation by a multiple regression before the iterative nonlinear fitting. The Gaussian fit was applied to both low and high resolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization shown. As expected, a reduction of the correlation coefficient between the map values and the Gaussian surface was observed for the image with the higher spatial resolution due to the greater variability of the SUHI values. Since the fitting procedure provides a smoothed Gaussian surface, it has better performance when applied to low resolution images, even if the reliability of the SUHI pattern representation can be preserved also for high resolution images

Comparison of Different Neural Network Approaches for the Tropospheric Profiling over the Inter-tropical lands Using GPS Radio Occultation Data

In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by the FORMOSAT-3/COSMIC satellites, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary European Centre for Medium-Range Weather Forecast data. Such a new retrieval algorithm was chosen to solve the atmospheric profiling problem without the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation

Downscaling of the land surface temperature over urban area using Landsat data

In this work, the land surface temperature (LST) retrieval using Landsat Thematic Mapper (TM) data over the city of Florence, Italy, characterized also by the presence of rural and vegetated zones, was compared with a high-resolution (1 m ground pixel size) thermal image provided by an airborne survey made on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM resolution of the thermal channel (120 m) on the LST estimation over an urban texture. The thermal data were downscaled at 30 m using a statistical approach employing spectral indices: such a method highlights the potentials and limits of the LST downscaling performed over an heterogeneous urban area

Mapping the Land Surface Temperature over Urban Areas from Space: a Downscaling Approach

Since decades, the land surface temperature (LST) is a parameter widely considered in the urban area mapping from space. LST has been often retrieved and mapped to evaluate the surface urban heat island (SUHI) using different spaceborne platforms, such as AATSR, ASTER, MODIS and Landsat. Several factors need to be assessed in the LST retrieval from satellite thermal infrared data: sensor radiometric calibration, atmospheric correction, surface emissivity estimation. Particularly, in urban area mapping issue, the satellite sensor spatial resolution may be a limiting factor in detailing the fine scale spatial variability, especially in the presence of impervious surfaces and sharp transitions (e.g., buildings, roads, parking lots, riverside, restricted vegetated zones), such as in a urban texture. The growing demand of remote sensing maps with finer and finer spatial resolution to successfully monitor the SUHI effects at district level and to avoid temperature underestimation stimulates the development of downscaling techniques when the actual sensor measurements do not meet the spatial detail requirements. In this work we perform the downscaling of coarse resolution LST maps from MODIS and Landsat to finer resolutions with the aim to increase the information content of the original maps, using summer satellite images over Milan, Rome and Florence. The downscaling is the enhancement of the spatial resolution of the original pixel data using ancillary information at higher spatial resolution. Different physical and statistical downscaling approaches have been proposed in literature: in this work, a statistical LST downscaling approach regression-based using different spectral indices over heterogeneous urban landscape is proposed, and the reliability assessed. This analysis allows to select the spectral indices and their combinations providing the best results in the LST image sharpening. First, the downscaling was performed using the Landsat TM data over Milan and Rome, assuming the 120 m spatial resolution image as reference. Then, the same downscaling regressive schemes were applied on the contemporary - 31 - coarse resolution LST MODIS image and verified with the reference LST Landsat map. A further downscaling assessment at finer resolution was carried out using the LST retrieved from Landsat TM over the city of Florence: in this case the sharpened image was compared with a high-resolution thermal image provided by an airborne survey carried out on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM thermal channel resolution (120 m) on the LST estimation over a heterogeneous urban texture. Again, thermal data were downscaled at 30 m with a statistical algorithm using a regression on different spectral indices. The proposed approaches and comparisons allows us to assess potentials and limits of the LST downscaling performed over an heterogeneous urban area

Comparison between surface and canopy layer urban heat island using MODIS data

Urban heat island (UHI) maps were produced over the city of Milan, Italy, using data provided by the Moderate-resolution Imaging Spectroradiometer (MODIS). Two types of UHI were analyzed simultaneously: the canopy layer heat island (CLHI) and the surface urban heat island (SUHI). The SUHI and CLHI maps allow to monitor the spatial and temporal evolution of surface and air heating and also to highlight the different features (e.g. magnitude, spatial extent, orientation and UHI centre location) using a Gaussian surface fitting. This results indicate that the SUHI effect is a noticeable phenomenon throughout the whole diurnal cycle: it has a stronger intensity in the daytime with peaks around 9-10 K while in the nighttime it decreases by a factor of 2. In contrast, the CLHI during the daytime is absent and after sunset shows features similar to the nighttime SUHI. Although the 1-km spatial resolution of MODIS may represent a limitation for a finer scale analysis, the four daily passes are essential to monitor the urban heat island at different times during the day