Climate variability is one of the greatest risks for farmers. The ongoing increase of natural calamities suggests that insurance strategies have to be more dynamic than previously. In this work a remote sensing-based service prototype is presented aimed at supporting insurance companies by defining an operative tool to objectively calibrate insurance annual fares, tending to cost reduction able to attract more potential customers. Methodology was applied to an agriculture devoted area located in the Vercelli province (Piemonte - NW Italy). COPERNICUS Sentinel-2 Level 2A image time series were used for this purpose jointly with MODIS data. High resolution Sentinel-2 data (GSD = 10 m) were used to map local spatial differences of crop performance, aimed at locally tuning insurance risk and fares around the average one estimated with reference to MODIS data on a longer period. The agricultural seasons 2018 were considered for this purpose. Although the work with MODIS data was carried out by authors in previous works, their integration with S2 data proved to locally tune at single field and crop type level the agronomic performances of insured areas.
In green extensive context, RPAS (Remotely Piloted Aerial Systems) can provide information with a high geometric resolution. The photogrammetric survey shows the possibility of measuring morphometric parameters of forest stand or individual trees. The free accessibility to Copernicus Sentinel-2 (S2) data addresses to hypothesize scenarios where satellite spectral information and high geometric resolution of RPAS photogrammetric survey, jointly used, determine a deeper knowledge of tree characteristics. Study area is located within the “La Mandria” park (NW Italy). Survey was operated by a DJI-Phantom4 RPAS (GSD images = 5 cm). Image photogrammetric processing was achieved by AGISOFT Photoscan v1.2.4. The resulting point cloud was filtered and a raster DSM (Digital Surface Model) was generated with a GSD = 10 cm. The correspondent CHM (Canopy Height Model) was computed by difference using a DTM (Digital Terrain Model) available from the regional cartographic archive. An object-based approach (watershed segmentation) aimed at bordering tree crowns as vector polygons was run. Some tree stability parameters were obtained from CHM by zonal statistics for each crown that was also spectrally characterized (to explore its vigor) using a S2 image time series. The proposed method finds applications in the arboricultural field (ornamental context) for the survey of tree inventory data; the detected parameters can be used as input data for tree risk assessment/management models, especially in extensive contexts representing a new approach to single tree risk management based on innovative technologies and algorithms that can reduce costs of ground control/survey campaigns.
Land surface temperature (LST) is an important factor in global climate change, vegetation growth, and urban heat island (UHI). LST is one of the most important environmental variables measured by satellite remote sensing. Public domain data are available from the operational Landsat-8 Thermal Infrared Sensor (TIRS). The present study focuses on determining and mapping UHI for the metropolitan city of Turin in Piedmont Italy using Landsat 8 multitemporal collection dataset from 2013 to 2018. The main purpose of this research is to give an instrument for the present urban management and future urban planning in order to increase city resistance and resilience against climate change through mitigation and adaptation. Improving green areas using urban forestry can be a way to mitigate Summer heat waves and trying to regulate the high demand of energy for cooling buildings. LST has been estimated using the Radiative Transfer Equation (RTE) while the LSE (Land Surface Emissivity) according to the NDVI Thresholds Method. In the multitemporal collection the UHI has been detected after calculating zonal statistics. Surfaces with similar thermal behave have been mapped using an Unsupervised classification (K-means). Through the considered years, the analysis has revealed how UHI are very common and persistent in the metropolitan Turin area, where vegetation and water content are lower and where there are a high number of buildings in concrete and asphalt is widespread.
Very High Resolution Satellite (VHRS) images have already demonstrated their great potentialities both for the
generation of satellite orthoimages and for map production and updating at the middle scale (1:10000 - 1:5000).
Nevertheless a big research effort has still to be done in order to investigate how different data with similar features can
be integrated to improve the final result and especially to overcome the objective difficulty, for a common customer, of
getting stereopairs from a single sensor. In this work a Geo GeoEye image and an Orthoready QuickBird one covering
about 120 Km2 in the region of Tera (Niger), are considered to determine how successfully they can be integrated to
exploit the maximum of resident information required to describe as better as possible the test area. A comparative
process was employed to determine the planimetric positional difference affecting the original acquired images, the
orthoimages obtained through a Rational Function Model (RFM) approach based on the released RPC (Rational
Polynomial Coefficients) and a "rigorous" multi-sensor bundle adjustment performing the simultaneous orientation of
both the images in a single block.
Natural hazards monitoring and analysis have come to be very important sectors in environmental management. In the last decades, natural slope dynamics of high mountain relief in Aosta Valley has been analyzed by means of detailed geological and geomorphological field surveys. For a long time, remote sensing techniques have been an effective tool within inaccessible locations thanks to their wide analysis. This article intends to present some results coming from investigations on high elevation alpine environments, based on the high resolution hyperspectral airborne sensor MIVIS images. Considering the great problem due to the high geometric distortion of such data, related both to the sudden relief displacement and to the intrinsic whiskbroom recording system, we are willing to show an experimental neural network approach for geometric correction which is a very important item for measurement instances.
To enhance geomorphological characterization and hazards studies on large slope instabilities we have successively applied a neural network LVQ 2 (Modified Learning Vector Quantization) self-developed classifier exploiting spectral information coming from images. The application of the method to the Val Veny- Val Ferret area (Mont Blanc zone) allowed a better definition of the active deformational features connected to flexural toppling, deep creep and superficial fracturing along double ridges and steep slopes, eventually preparing for collapse along high rock walls. Hazard scenarios for deep seated gravitational slope deformations of the Upper Aosta Valleys can be more precisely outlined exploiting MIVIS images hyperspectral contents, but first the geometric correction problem has to be preventively solved.
Digital Terrain Models (DTM) represent an effective tool for many applications and in particular for terrain morphology investigation and orthoimages generation. The availability of satellite stereo images allows to generate updated DTMs through digital photogrammetric algorithms especially in those areas where old and poor maps exist. In this work we show some quality tests results about DEMs obtained from ASTER data (15m geometric resolution), elaborated through commercial software. We consider this information very important to understand which kind of applications can reasonably use these data. Shown results refer to a mountain area located in the NW Italian Alps, characterized by different height type regions (flat, hilly, mountain) whose evaluation can drive to consistent results for a better understanding of limits and forces of these data. Such tests, making use of the commercial software AsterDTM, take into consideration both qualitative and quantitative aspects. Height profiles comparisons, statistical analysis on differences and data mining have been carried out in order to evaluate accuracies and to define the nature of possible systematic errors. Reference data is the available Regional DTM (50m x 50m grid, accuracy of 2.5m) and the single height points extracted from technical maps at 1:10000 scale for more precise local investigation.
According to the recent incoming of high resolution images acquired by the new earth observation satellites (IKONOS, EROS, QUICKBIRD) we are suggested to considered their possible photogrammetric exploitation. High geometric (up to 0.6 m GSD) and radiometric (11-12 bit) resolution of such images drive us to consider them as possible substitutes of classic aerial images used for cartographic purposes at the 1:5000/1:2000 scale. In such context we can't omit the heavy incidence of terrain altimetry onto the images georeferencing operations; orthocorrection is necessary to be carried out. This paper, far away from solving the real orthoprojection instances related to the definition of the camera position and attitude, demonstrates as well complex urban DDEM (Dense Digital Elevation Model) completed with volume information of buildings, can improve the planimetric accuracies of the orthocorrected images. A proprietary software, developed by the authors, can automatically extract buildings DEM from a 3D cartography and integrate it with a simple terrain DEM. Results are referred to an orthocorrection carried out by a commercial software. They are certainly conditioned by the out-of-our-control geometric model used by the software itself. The purpose is simply to demonstrate the real improvement of the planimetric positioning obtained using DDEM.
An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.
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