Maria Teresa Melis

The increasing availability of spaceborne imaging spectroscopy data requires validation approaches capable of linking measurements acquired at different spatial scales. This study examines the temporary Sal ’e Porcus saline pond in western Sardinia, Italy, as a candidate reference surface for cross-scale spectral comparison. Surface reflectance was characterised using ASD FieldSpec ground spectroscopy, UAV-borne hyperspectral imagery acquired with a Specim AISA Kestrel 10 sensor, and temporally proximate PRISMA and EnMAP observations. UAV surface reflectance was derived by empirical-line calibration using field-measured radiometric targets. The four spectra were linearly interpolated onto a common 1 nm wavelength grid over their overlapping interval within 400–1000 nm and compared using correlation, linear regression and residual statistics. At the analysed sampling location, all dataset pairs showed strong spectral co-variation (Pearson’s r = 0.961–0.995). The strongest linear correspondence was obtained between UAV and ASD spectra (r = 0.995, slope = 1.001, R² = 0.990 and residual RMSE = 0.007). Comparisons involving satellite observations retained the broad spectral pattern but showed pair-dependent slopes and larger residual dispersion. Because the acquisitions were non-simultaneous and differed in spatial support and spectral response, these discrepancies cannot be attributed solely to spatial scale or sensor performance. The findings support the potential of UAV imaging spectroscopy as an intermediate observational scale and justify further investigation of Sal ’e Porcus as a candidate CAL/VAL surface.

High-resolution and regularly updatable land cover maps are essential for local-scale environmental monitoring, water resource management, and territorial governance, yet existing global and regional products fail to provide the spatial detail and thematic richness required for operational applications in complex Mediterranean landscapes. This study presents an operational workflow for high-resolution LULC mapping and its application to Sardinia for the reference year 2020, developed within the Sardinia Land Cover Mapping Project in collaboration with the Agenzia del Distretto Idrografico della Sardegna (ADIS). The workflow integrates multi-temporal SAR and multispectral satellite imagery with high-resolution ancillary geospatial vector datasets through a semi-automatic pipeline combining hierarchical cascade pixel-based classification, multi-resolution image segmentation, geometric overlay of infrastructure vector layers, and an iterative accuracy-driven reclassification cycle. The classification combines automated rule-based procedures, semi-automatic threshold-based methods, and expert photo-interpretation to address the high thematic and spatial complexity of the Sardinian landscape. The resulting map comprises 35 land cover classes at the third and selected fourth CORINE levels, with a minimum mapping unit of 400 m² and an overall weighted accuracy of 82.4%. Designed as a dynamic product updatable on an annual basis, it represents an operational tool for local environmental governance, spatial planning, and resource management.

1. Impact craters represent the most prominent features of the lunar surface, exhibiting a diverse range of morphological features, reflecting variations in impactor properties (including size, vel...

Remote sensing from ground, UAV, aircraft and satellite platforms is increasingly central to monitoring Earth’s surface and supporting decision making. However, robust interpretation of multispectral/hyperspectral observations still depends on consistent links between satellite products and high-quality reference spectra measured in the field and laboratory, plus workflows that make these data interoperable across sensors, spatial scales and acquisition conditions. We present the INGV Spectral Library, a web-based, GIS-integrated platform designed to operationally connect in situ spectroscopy with airborne/satellite imaging spectroscopy, enabling reproducible pre-processing, cross-sensor harmonization, and geospatial querying of spectral datasets for environmental monitoring applications.The platform provides standardized spectral analytics commonly required in monitoring pipelines: continuum removal and absorption-feature characterization, derivative-based enhancement to emphasize diagnostic features, and sensor-aware resampling using Spectral Response Functions (SRFs) to harmonize high-resolution field spectra to specific sensors (e.g., Sentinel-2 and spaceborne hyperspectral missions). This “sensor-to-field” alignment enables direct comparability and supports spectral–spatial data fusion, where field-based endmembers and satellite reflectance/emissivity products can be jointly analysed. A key component is the GIS interface: spectra are linked to georeferenced samples and metadata and can be filtered by location, lithology/land cover context, acquisition conditions and spectral criteria, facilitating rapid exploration of spatial patterns and targeted selection of reference signatures for mapping and validation tasks.Two use cases illustrate the relevance to environmental monitoring and hazard-related contexts. (i) In Sardinia (Sale ’e Porcus), curated VNIR–SWIR FieldSpec measurements are ingested as a controlled reference set to support multi-sensor consistency checks and calibration/validation activities for satellite imaging spectroscopy. (ii) In Oman, PRISMA Level-2D surface reflectance is analysed through spectral indices and Spectral Angle Mapper (SAM), using PRISMA-resampled endmembers derived from reference spectra to delineate spatially coherent alteration patterns and potential copper-related signals; the resulting maps support field planning and prioritization of sampling targets, with new samples intended to validate and refine satellite-based interpretations.By combining standardized spectral pre-processing, SRF-based cross-sensor harmonization, and GIS-driven access to reference spectra, the INGV Spectral Library provides a practical platform for multi-scale environmental remote sensing, enabling more transparent, transferable and decision-oriented workflows for monitoring surface changes and hazard-relevant processes.This study is carried out within two projects: the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005 and PRIN2022_SH6_2022BTKA9Y-02 funded by Ministry of University and Research, MUR CUP D53D23000580006.

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions.

Climate change is deeply impacting mountain areas around the globe, especially in the Himalaya. However, the lack of long-term meteorological observations at high elevations poses significant challenges to understanding and predicting impacts at various scales. This also represents a serious limit for model-based projections of future behavior of crucial elements of the mountain cryosphere such as glaciers. Here, we present the Pyramid Meteorological Network, located in the Himalaya (Nepal) on the southern slopes of Mt. Everest. The network is composed of seven meteorological stations located between 2660 and 7986 ma.s.l., which have collected continuous climatic data during 30 recent years (1994–2023). In this paper, we provide details regarding instrument types and characteristics as well as data quality control and assessment. The obtained data series are available on a newly created geoportal. We leverage these unique records to present new knowledge on the Himalayan climate, also benefiting from the highest observational climatic series in the world (Pyramid station, located above 5000 ma.s.l., close to the Khumbu Glacier). These data will provide fundamental knowledge on climate dynamics in the Himalaya that will inform research at high elevations in the coming years. The dataset is freely accessible from https://geoportal.mountaingenius.org/portal/ (last access: 1 September 2025) (https://doi.org/10.5281/zenodo.15211352; Salerno et al., 2024).

Enceladus, Saturn’s sixth-largest moon (diameter: ~504 km) [1], is one of the geologically active bodies of the Solar System. Its icy shell its characterized by linear structures of tectonic origin that are the focus of this study [2]. With this work our intent is to provide an example of methodical mapping and classification of the geomorphological features characterizing the outer shell of an icy satellite.We mapped the single structures and families of structures – characterized by the same direction and possibly originated in the same tectonic event – at four locations – referred to as ‘areas of interest’ – sit in two out of the four geological provinces identified in [3]. Each area of interest had all the sides measuring 20° and contained one structure already identified. This choice was made to ensure the correct understanding of all the others structures since the study was conducted only using the available Cassini global mosaic (resolution 110 m/px) [4]. The classification method used in this work was the one proposed in [5] with Enceladus’s linear structures falling under five classes: scarp, trough, band, ridge, and chasma.We managed to identify a total of 69 between single structures and families of structures. They were classified into the aforementioned morphological categories based on their geometric characteristics after an analysis of their cast shadows. Due to the limited resolution of the available data and the absence of an available DTM at the time of the study, 7 of them remained undefined. Among the other 62, the vast majority (56) was identified as falling under the ‘trough’ class (11 of them were additionally sub-classified as ‘pit chains’), whilst 3 of them where chasmata, 1 was a band, and 1 was a scarp. We also managed to sketch a relative timeline for each area of interest by using the cross-cutting relationships existing between the structures. Main and secondary periods were defined to give a better understanding of the tectonic evolution of each area of interest.Our study, although moderate in its extent, represents a solid attempt at applying a methodical mapping process for an icy satellite. We provided a classification for a number of structures whose class had not yet been defined and theorized a possible tectonic evolution for each area based off the geomorphological cues at our disposal, setting up a possible workflow for future studies on other similar bodies in the outer Solar System.REFERENCES:[1] Thomas, P. C. (2010). Icarus, 208, 395–401.[2] Spencer, J. R. and Nimmo, F. (2013). The Annual Review of Earth and Planetary Science, 41, 693–717.[3] Schenk, P. M., Clark, R. N., Howett, C. J. A. Verbiscer, A. J., Hunter Waite, J. (2018). University of Arizona Press, ‎536 pages.[4] Bland, M. T., Becker, T. L., Edmundson, K. L., Roatsch, T., Archinal, B. A., Takir, D., et al. (2018). Earth and Space Science, 5, 604–621.[5] Nahm, A. L. and Kattenhorn, S. A. (2015). Icarus, 258, 67–81.

Wildfires are a known treat causing relevant impact on the ecosystem, population and economic infrastructures. They are becoming more and more frequent and severe due to climate changes, and future scenarios are now considering their occurrence into currently fire-resistant areas at higher latitudes. Because of this, the assessment of hazard associated to wildfires require considering also medium to long term effects on the environment. Wildfires induce physical and chemical changes on soil with consequent soil structure losses and formation of water repellent layers. These changes, coupled with canopy cover removal increases runoff and postfire erosion. Enhanced sediment transport is associated with vegetation removal and increased runoff and can remobilize previously deposited material stored in slopes and channels. Moreover, thermal spalling of rocks exposed to wildfire can produce new debris.Wildfire dramatically changes the degree of Sediment Connectivity: the degree of connection peaks during and just after the wildfire, due to canopy cover removal. Vegetation recovery intermittently changes the degree of sediment connection, affecting the susceptibility to erosion and debris flow likelihood.As a type case study, we choose the 2021 Montiferru-Planargia (Sardinia) wildfire. We conducted a three-year monitoring of the burnt scar. Immediately after the fire slopes and channels were covered by sparse debris produced by rockfall before the fire and by thermal spalling during the wildfire. Those debris were removed by postfire runoff and involved in postfire debris flows over 33 catchments. Postfire sediment connection changed as vegetation recovered: some catchments were stabilized after one year whereas others experienced debris flow even in the second year. We calculated NDVI over three years at one-month interval and successfully found a NDVI threshold which efficiently represents sediment disconnection induced by vegetation recovery. Our findings are expected to improve erosion susceptibility assessment after wildfire.

Climate change is deeply impacting mountain areas around the globe, especially in Himalaya. However, the lack of long-term meteorological observations at high elevations poses significant challenges to understand and predict impacts at various scales. This also represents a serious limit for model-based projections of future behavior of crucial elements of the mountain cryosphere such as glaciers. Here, we present the Pyramid Meteorological Network, located in Himalaya (Nepal), on the southern slopes of Mt. Everest. The network is composed of 7 meteorological stations located between 2660 and 7986 m a.s.l., which have collected continuous climatic data during the last 30 years (1994–2023). In this paper, details are provided regarding instrument types and characteristics as well as data quality control and assessment. The obtained data series are available on a newly created geoportal. We leverage these unique records to present new knowledge on the Himalayan climate, benefiting also from the highest observational climatic series in the world (Pyramid station, located at above 5000 m a.s.l., close to Khumbu Glacier). These data will provide fundamental knowledge on climate dynamics in Himalaya that will inform research at high elevations in the coming years. The dataset is available freely accessible from https://geoportal.mountaingenius.org/portal/ (https://zenodo.org/records/14450214) (Salerno et al., 2024).