1,721,293 research outputs found
- Influence of irrigation regime on seed yield of Phaseolus vulgaris L. in southern Italy
No full textA hybrid approach of features extraction from multi-date ASTER imagery for land cover transformations
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Landsat 9 Satellite Images Potentiality in Extracting Land Cover Classes in GEE Environment Using an Index-Based Approach: The Case Study of Savona City
No full textLand use and land cover modeling is an essential tool because it enables scientists and policymakers to foresee prospective changes in landscape heritage and examine trends to minimize potential dangers. To attain this purpose, a continuous stream of data needs be collected and examined. Landsat missions present a viable alternative since they have been collecting continuous data for five decades, and a new platform was launched at the end of September 2021 to avoid disrupting such a series. Consequently, the purpose of this research is to assess the quality of Landsat 9 data in extracting land use information using the index-based approach. Following the conclusion of the collection and pre-processing operations, two of the most often used vegetation indices, NDVI and MSAVI2, were derived from a Landsat 9 data covering Savona city, which was chosen as the pilot site due to its unique geomorphological characteristics. Lastly, maps accuracy was assessed by computing the confusion matrices, k estimator and overall, producer and users accuracy. The entire method was implemented in the free cloud computing platform Google Earth Engine by writing custom Java code. The generated land use/cover maps were both satisfactory, albeit the MSAVI2 had a greater overall accuracy (90.24% vs 79.60%) and K parameter (84.45% vs 71.70%) due to its ability to minimize soil spectral effect. Those findings are consistent with those derived from Landsat 8 images. This means that Landsat 9 is an excellent successor to Landsat 8
Consumi idrici delle colture di cavolo broccolo, spinacio, fagiolino e cetriolino in successione
No full textAn object-oriented spatial information extraction from IKONOS multispectral data to detect agricultural transformations
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Integrated Use of Geomatic Methodologies for Monitoring an Instability Phenomenon
No full textThe growing exposure of the Italian territory to hydrogeological risk, also worsened by the influence of climate change, has made the occurrence of catastrophic phenomena, such as landslides and floods, always more impactful. In this frame, geomatic methodologies can provide a crucial support in properly characterizing a potentially critical instability phenomenon, both from the spatial and kinematic view. In this work, the integrate use of geomatic methodologies, i.e., Multi-temporal Interferometric Synthetic Aperture Radar (MTInSAR) technology and structural sensors, namely biaxial tiltmeters, were employed to kinematically investigate the behavior of an urban area affected by a landslide, located in the Apulian territory. The MTInSAR analysis carried out on Sentinel-1 SAR acquisitions showed a strong non-linear behavior in the displacement-time trends, also highlighting the presence of differential motions constituting a threat for buildings. As regards the main retaining structure, currently damaged by the landslide, automatic measurements provided by the tiltmeters confirmed the presence of more active areas, as detected by the SAR observations. The outcomes of this work provided key information to the structures responsible for the management of the risk connected with the instability and allowed to address the proper design of the mitigation works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG
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