Francesca Franci

Remote sensing analysis for flood risk management in urban sprawl contexts

Remote sensing can play a key role in risk assessment and management, especially when several concurrent factors coexist, such as a predisposition to natural disasters and the urban sprawl, spreading over highly vulnerable areas. In this context, multitemporal analysis can provide decision-makers with tools and information to reduce the impacts of disasters (e.g. flooding) and to encourage a sustainable development. The present work focuses on the employment of multispectral satellite imagery to produce multitemporal land use/cover maps for the city of Dhaka, which is subject to frequent flooding events. In particular, the evaluation of the urban growth, the analysis of the annual dynamics of flooding and the study of the 2004 catastrophic event were performed. For the change-detection procedure, Landsat images were used. These images allow the quantification of the very rapid growth of the metropolis, with an increase in built-up areas from 75 to 111 km2. The image of 2009 showed that an ordinary flood affects about 115 km2 (on a studied area of 591 km2). On the other hand, the analysis of the 2004 extreme flooding event, performed on a wider area, showed that the affected lands added up to 750 km2 (on about 3845 km2).

Aerial Thermography for Energetic Modelling of Cities

The rising attention to energy consumption problems is renewing interest in the applications of thermal remote sensing in urban areas. The research presented here aims to test a methodology to retrieve information about roof surface temperature by means of a high resolution orthomosaic of airborne thermal infrared images, based on a case study acquired over Bologna (Italy). The ultimate aim of such work is obtaining datasets useful to support, in a GIS environment, the decision makers in developing adequate strategies to reduce energy consumption and CO2 emission. In the processing proposed, the computing of radiometric quantities related to the atmosphere was performed by the Modtran 5 radiative transfer code, while an object-oriented supervised classification was applied on a WorldView-2 multispectral image, together with a high-resolution digital surface model (DSM), to distinguish among the major roofing material types and to model the effects of the emissivity. The emissivity values were derived from literature data, except for some roofing materials, which were measured during ad hoc surveys, by means of a thermal camera and a contact probe. These preliminary results demonstrate the high sensitivity of the model to the variability of the surface emissivity and of the atmospheric parameters, especially transmittance and upwelling radiance.

Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping

This work focuses on the exploitation of very high-resolution (VHR) satellite imagery coupled with multi-criteria analysis (MCA) to produce flood hazard maps. The methodology was tested over a portion of the Yialias river watershed basin (Nicosia, Cyprus). The MCA methodology was performed selecting five flood-conditioning factors: slope, distance to channels, drainage texture, geology and land cover. Among MCA methods, the analytic hierarchy process technique was chosen to derive the weight of each criterion in the computation of the flood hazard index (FHI). The required information layers were obtained by processing a VHR GeoEye-1 image and a digital elevation model. The satellite image was classified using an object-based technique to extract land use/cover data, while GIS geoprocessing of the DEM provided slope, stream network and drainage texture data. Using the FHI, the study area was finally classified into seven hazard categories ranging from very low to very high in order to generate an easily readable map. The hazard seems to be severe, in particular, in some urban areas, where extensive anthropogenic interventions can be observed. This work confirms the benefits of using remote sensing data coupled with MCA approach to provide fast and cost-effective information concerning the hazard assessment, especially when reliable data are not available.

Comparison between empirical and physically based models of atmospheric correction

A number of methods have been proposed for the atmospheric correction of the multispectral satellite images, based on either atmosphere modelling or images themselves. Full radiative transfer models require a lot of ancillary information about the atmospheric conditions at the acquisition time. Whereas, image based methods cannot account for all the involved phenomena. Therefore, the aim of this paper is the comparison of different atmospheric correction methods for multispectral satellite images. The experimentation was carried out on a study area located in the catchment area of Yialias river, 20 km South of Nicosia, the Cyprus capital. The following models were tested, both empirical and physically based: Dark object subtraction, QUAC, Empirical line, 6SV, and FLAASH. They were applied on a Landsat 8 multispectral image. The spectral signatures of ten different land cover types were measured during a field campaign in 2013 and 15 samples were collected for laboratory measurements in a second campaign in 2014. GER 1500 spectroradiometer was used; this instrument can record electromagnetic radiation from 350 up to 1050 nm, includes 512 different channels and each channel covers about 1.5 nm. The spectral signatures measured were used to simulate the reflectance values for the multispectral sensor bands by applying relative spectral response filters. These data were considered as ground truth to assess the accuracy of the different image correction models. Results do not allow to establish which method is the most accurate. The physics-based methods describe better the shape of the signatures, whereas the image-based models perform better regarding the overall albedo.

Remote Sensing Techniques in a Multidisciplinary Approach for the Preservation of Cultural Heritage Sites from Natural Hazard: The Case of Valmarecchia Rock Slabs (RN, Italy)

The Valmarecchia area (RN, Italy), located between the Emilia-Romagna and Marche regions, displays peculiar geological features, being characterized by rocky slabs lying on gentle slopes. The main fortified villages of the area, remarkable for historical and artistic assets, were built in the medieval period on these slabs for defense purposes. The area is affected by widespread landslide phenomena, involving both the rocky slabs and the underlying clayey shales. The main phenomena acting on the slabs are lateral spreading, with associated rock falls and topples. In this area, a multidisciplinary project, involving different expertise, like geology, geodesy, geomorphology, hydrogeology, soil and rocks mechanics is ongoing. In this particular context, in order to achieve a clear recognition of the instability phenomena, it is necessary to understand the movement patterns and the eventual differential displacement occurring in the slabs. Monitoring activities, joined with geological and geomorphological interpretation, are one of the fundamental step for a deep understanding of the movements and for the risk management purposes. In many cases, the monitoring system is missing or only poor data are available, therefore an approach for the Permanent Scatterers (PS) data analysis has been used, combining analysis on the PS velocity, on the direction of the movement and statistical consideration on the time series trend. Some preliminary results regarding the rock slab on which the town of Verucchio (RN, Italy) is located are here presented.

Integration of different geospatial data in urban areas: a case of study

Efficient management of the territory requires today the availability of comprehensive geographical data, accurate and up to date, supported by powerful databases. In this context, remote sensing data are used for a variety of applications related to urban areas; some examples are land use/cover mapping, urban growth and soil sealing evaluation, detection of green areas, updating of existing maps, energy applications and detection and characterization of buildings. This work aims to highlight how different geomatic techniques and data acquired from heterogeneous surveys can be today used together for producing or updating a digital cartography inside a GIS. The study has been conducted in the urban area of Bologna, Emilia-Romagna region, located in the North of Italy. A high resolution WorldView-2 satellite image and the DSM/DTM, obtained by airborne LiDAR, have been used to obtain a vector layer of the buildings. In particular, to distinguish the buildings among all the elements present in the study area, such as roads, trees, vegetated areas, etc., an object-oriented classification has been performed. This approach, working on groups of pixels (image objects), allows to expand the information content of the basic unit of classification. Therefore, features such as shape, texture and contextual information, coupled with spectral characteristics, potentially allow cartographers to generate products that are competitive, in terms of thematic contents, with those derived from the photo-interpretation. A first application described in this work is to perform a quick change analysis procedure based on the results of the classification compared to an existing numerical cartographic base or a previous classification.

VHR satellite imagery for humanitarian crisis management: a case study

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za’atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za’atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

Flood mapping using VHR satellite imagery: a comparison between different classification approaches

Various regions in Europe have suffered from severe flooding over the last decades. Flood disasters often have a broad extent and a high frequency. They are considered the most devastating natural hazards because of the tremendous fatalities, injuries, property damages, economic and social disruption that they cause. In this context, Earth Observation techniques have become a key tool for flood risk and damage assessment. In particular, remote sensing facilitates flood surveying, providing valuable information, e.g. flood occurrence, intensity and progress of flood inundation, spurs and embankments affected/threatened. The present work aims to investigate the use of Very High Resolution satellite imagery for mapping flood-affected areas. The case study is the November 2013 flood event which occurred in Sardinia region (Italy), affecting a total of 2,700 people and killing 18 persons. The investigated zone extends for 28 km2 along the Posada river, from the Maccheronis dam to the mouth in the Tyrrhenian sea. A post-event SPOT6 image was processed by means of different classification methods, in order to produce the flood map of the analysed area. The unsupervised classification algorithm ISODATA was tested. A pixel-based supervised technique was applied using the Maximum Likelihood algorithm; moreover, the SPOT 6 image was processed by means of object-oriented approaches. The produced flood maps were compared among each other and with an independent data source, in order to evaluate the performance of each method, also in terms of time demand.

Multitemporal analysis of Landsat images to detect land use land cover changes for monitoring soil sealing in the Nola area (Naples, Italy)

Land Use Land Cover Changes (LULCC) data provide objective information to support environmental policy, urban planning purposes and sustainable land development. Understanding of past land use/cover practices and current landscape patterns is critical to assess the effects of LULCC on the Earth system. Within the framework of soil sealing in Italy, the present study aims to assess the LULCC of the Nola area (Naples metropolitan area, Italy), relating to a thirty year period from 1984 to 2015. The urban sprawl affects this area causing the impervious surface increase, the loss in rural areas and landscape fragmentation. Located near Vesuvio volcano and crossed by artificial filled rivers, the study area is subject to landslide, hydraulic and volcanic risks. Landsat time series has been processed by means of the supervised per-pixel classification in order to produce multitemporal Land Use Land Cover maps. Then, post-classification comparison approach has been applied to quantify the changes occurring between 1984 and 2015, also analyzing the intermediate variations in 1999, namely every fifteen years. The results confirm the urban sprawl. The increase of the built-up areas mainly causes the habitat fragmentation and the agricultural land conversion of the Nola area that is already damaged by unauthorized disposal of urban waste. Moreover, considering the local risk maps, it was verified that some of the new urban areas were built over known hazardous sites. In order to limit the soil sealing, urgent measures and sustainable urban planning are required.

Use of Landsat imagery to detect land cover changes for monitoring soil sealing; case study: Bologna province (Italy)

Landsat archives (made accessible by USGS at no charge since 2011) have made available to the scientific community a large amount of satellite multispectral images, providing new opportunities for environmental information, such as the analysis of land use/cover changes, which represent important tools for planning and sustainable land management. Processing a time series images, the creation of land cover maps has been improved in order to analyze phenomena such as the soil sealing. The main topic of this work is in fact the detection of roads and buildings construction or everything that involve soil removing. This subject is highly relevant, given the impact of the phenomenon on land use planning, environmental sustainability, agricultural policies and urban runoff. The analysis, still in progress, has been applied to Bologna Province (Emilia-Romagna Region, Italy) that covers 3703 Km2. This area is strongly urbanized: 8,9% of the total surface is sealed against a national value of 6,7%, with the soil sealing rate which has been defined from recent studies as the fourth Italian value in the 2001/2011 period. Other information available for this territory derive from CORINE Land Cover and Copernicus Projects. In the first one, the minimum mapping unit is 25 ha and the one for change is 5 ha; these values are too large for an accurate detection of the soil sealing dynamics. On the other hand, the Copernicus Project provides an imperviousness layer with a better resolution (20x20 m2), but its maps start from 2006. Therefore, the potential of multispectral remote sensing analysis over large areas and the multitemporal Landsat availability have been combined for a better knowledge about land cover changes. For this work, Landsat 5 and Landsat 8 images have been acquired between 1987 and 2013, according to basic requirements as low cloud cover and a common acquisition season (summer). A supervised pixel-based classification has been performed, with maximum likelihood algorithm. Due to landscape heterogeneity, classification has been improved with auxiliary data, such as NDVI. Therefore, the obtained maps have been compared with a post-classification change detection procedure in order to quantify land use changes, with particular attention to the soil sealing increase.