Emanuele Mandanici

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

The availability of new generation multispectral sensors of the Landsat 8 and Sentinel-2 satellite platforms offers unprecedented opportunities for long-term high-frequency monitoring applications. The present letter aims at highlighting some potentials and challenges deriving from the spectral and spatial characteristics of the two instruments. Some comparisons between corresponding bands and band combinations were performed on the basis of different datasets: the first consists of a set of simulated images derived from a hyperspectral Hyperion image, the other five consist instead of pairs of real images (Landsat 8 and Sentinel-2A) acquired on the same date, over five areas. Results point out that in most cases the two sensors can be well combined; however, some issues arise regarding near-infrared bands when Sentinel-2 data are combined with both Landsat 8 and older Landsat images.

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.

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 °C and 1.0 °C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground.

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.

Atmospheric correction issues for water quality assessment from remote sensing: the case of Lake Qarun (Egypt)

Water quality assessment and monitoring from remote sensing data is strongly affected by the accuracy of the atmospheric effect correction. Two algorithms, based respectively on Modtran 4 and on 6SV radiative transfer codes, and an empirical image-based method have been compared, also examining the sensitivity to different parameterizations of water vapour content and aerosols. The experimentation has been carried out on a specific case study, lake Qarun, a conservation area located in the Fayyum Oasis (Egypt). Simple water quality indicators have been computed by multispectral and hyperspectral data and compared to literature data.

Aerial thermography for energy efficiency of buildings: the ChoT project

The ChoT project aims at analysing the potential of aerial thermal imagery to produce large scale datasets for energetic efficiency analyses and policies in urban environments. It is funded by the Italian Ministry of Education, University and Research (MIUR) in the framework of the SIR 2014 (Scientific Independence of young Researchers) programme. The city of Bologna (Italy) was chosen as the case study. The acquisition of thermal infrared images at different times by multiple aerial flights is one of the main tasks of the project. The present paper provides an overview of the ChoT project, but it delves into some specific aspects of the data processing chain: the computing of the radiometric quantities of the atmosphere, the estimation of surface emissivity (through an object-oriented classification applied on a very high resolution multispectral image, to distinguish among the major roofing materials) and sky-view factor (by means of a digital surface model). To collect ground truth data, the surface temperature of roofs and road pavings was measured at several locations at the same time as the aircraft acquired the thermal images. Furthermore, the emissivity of some roofing materials was estimated by means of a thermal camera and a contact probe. All the surveys were georeferenced by GPS. The results of the first surveying campaign demonstrate the high sensitivity of the model to the variability of the surface emissivity and the atmospheric parameters.

Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery

The derivation of surface temperature from thermal images requires a proper modelling of the spectral characteristics of the observed surfaces, in particular emissivity. Several possible approaches have been developed in literature. A first category of methods relies on the availability of multiple bands in the thermal region, while a second family of methods, which can be applied also with a single channel sensor, requires the derivation of emissivity values from ancillary data.

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.

Geomatics Applications in Cahuachi and Nasca Territory

Several geomatics techniques were applied and integrated for the ceremonial center of Cahuachi and the Nasca territory, including GNSS surveys, digital-elevation-model generation from satellite imagery, and close-range photogrammetry . Accurate 3D models of the landscape in which the archaeological site is set and, at different scale, of structures and individual archaeological finds constitute a powerful means of documentation and study.