BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//8LYGBX BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-8CHP3M@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T110000 DTEND;TZID=GMT:20230612T130000 DESCRIPTION:Enrico Borgogno Mondino\, Presidente AIT \nMonica Sebillo\, Pre sidente ASITA\nFrancesco Cupertino\, Rettore del Politecnico di Bari\nLeon ardo Damiani\, Direttore del DICATECh \nEugenio\, Di Sciascio\, Vicesinda co del Comune di Bari\nUmberto Fratino\, Presidente di Ordine Ingegneri di Bari \nAntonio Acquaviva\, Rappresentante del Consiglio nazionale dei Geo metri e Geometri laureati\nGiovanni Bruno\, Vicepresidente Ordine Geologi Puglia\n\nIl Telerilevamento nella Pubblica Amministrazione\, Tziana Bisa ntino (Dirigente del Centro Funzionale Decentrato della Protezione Civile – Regione Puglia) –\nNew Space Economy: Scenario and Perspectives for Earth Observation\, Antonio Messeni Petruzzelli (Delegato alla Ricerca del Politecnico di Bari)\n\n\nAIT2023 è l'11° Congresso della Associazione Italiana di Telerilevamento (AIT). L'AIT\, fin dalla sua fondazione nel 19 85\, è stata il soggetto di riferimento fondamentale per sostenere la com unicazione e il coordinamento delle attività scientifiche nel campo dell' Osservazione della Terra in Italia.\n\nL'AIT si propone di sostenere lo sv iluppo e la diffusione della cultura del Telerilevamento (TLR) in Italia\, favorendo le sue applicazioni ambientali e puntando ad avvicinare tra lor o i principali attori scientifici\, industriali e istituzionali. L'AIT sos tiene le iniziative nazionali di TLR in Italia favorendone l’internazion alizzazione. AIT organizza eventi e corsi e pubblica lo European Journal o f Remote Sensing in collaborazione con Taylors & Francis.\n\nAIT2023 è il luogo dove accademia\, industria\, professionisti e istituzioni\, in qual che modo coinvolti nel Telerilevamento e nell'Osservazione della Terra (EO )\, possono incontrarsi e discutere. Per i ricercatori AIT2023 è un'impor tante opportunità per presentare i loro recenti progressi a un pubblico v asto e transdisciplinare. Per l'industria è l'occasione per mostrare i re centi prodotti e servizi utili per la comunità del TLR. Infine\, ma non m eno importante\, per i partner professionali e per i decisori del territor io/acqua/urbano\, della conservazione\, della gestione delle risorse natur ali e della pianificazione territoriale\, AIT2023 è l'evento chiave per p resentare le proprie esperienze e aggiornare le proprie conoscenze nel cam po del TLR e dell’Osservazione della Terra. Per quanto riguarda il conve gno AIT\, saranno presi in considerazione tutti gli argomenti che riguarda no il telerilevamento remoto e prossimale\, l'analisi spaziale e la modell istica ambientale. DTSTAMP:20260522T041742Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:Opening Session AIT Congress - Enrico Borgogno-Mondino URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/8CHP3M/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-CVTH9W@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T151500 DTEND;TZID=GMT:20230612T153000 DESCRIPTION:Precision viticulture aims to enhance quality standards of wine production by improving vineyard management. In this framework\, satellit e optical remote sensing has already proved to be effective for mapping ve getation behavior in space and time. These maps\, properly processed\, are useful to optimize agronomic practices improving wine production/quality and mitigating environmental impacts. Nevertheless\, vineyards represent a challenge in this context because grapevine canopies are discontinuous\, and the observed reflectance signal is affected by background. In fact\, s atellite imagery ordinarily provides spectral measures with medium-low geo metric resolution (≥ 100 m2). Therefore\, spectral mixture between grape vine canopies\, grass and soils is expected within a satellite-derived ref lectance pixel and not considering this problem can deeply affect deductio ns based on this data. In this work\, Sentinel-2 (S2) NDVI maps (10 m reso lution) were computed and compared to the ones obtained from DJI P4 multis pectral UAV over a vineyard sizing 1.5 ha and located in Piemonte region ( NW Italy). The proportion of row and inter-row (α(x\,y) and 1-α(x\,y)) w ithin S2 pixel was computed and mapped classifying DJI photogrammetry poin t cloud. Involving α(x\,y) and S2 NDVI values\, reversing spectral unmixi ng system was defined solving for two average endmembers NDVI values (row and inter-row) using a moving window (21x21 pixels) least squares approach . Results were compared at S2 pixel-level to the average ones computed fro m DJI\, showing a MAE of 0.15 and 0.10 of row and inter-row NDVI respectiv ely. DTSTAMP:20260522T041742Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Pixel Mixture Issue in Mapping Vineyard Phenology. A Possible Solut ion Based on Sentinel-2 Imagery and Local Least Squares - Enrico Borgogno- Mondino\, Francesco Parizia\, Federica Ghilardi\, Alessandro Farbo\, Filip po Sarvia\, Samuele De Petris URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/CVTH9W/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-J9M9XE@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T171500 DTEND;TZID=GMT:20230612T173000 DESCRIPTION:Deforestation is one of the main drivers of environmental degra dation around the world. Slash-and-burn is a common practice\, performed i n tropical forests to create new agricultural lands for local communities. In Madagascar\, this practice affects many natural areas including lemurs ’ habitats. Reforestation within natural reserves is desirable combining native species with fast-growing ones\, aiming at habitats restoration. I n this context\, the extensive detection of forest disturbances can effect ively support restoration actions\, providing an overall framework to addr ess priorities and maximizing ecological benefits. In this work and with r espect to a study area located around the Maromizaha New Protected Area (M adagascar)\, an analysis was conducted based on a time series of NDVI maps from Landsat missions (GSD = 30 m). The period 1991-2022 was investigated to detect location and moment of forest disturbances with the additional aim of quantifying the level of damage and of the recovery process at ever y disturbed location. It is worth to remind that the Maromizaha New Protec ted Area presently hosts 12 species of lemurs. Detection was operated at p ixel level by analyzing the local temporal profile of NDVI (yearly step). Time of the eventual detected disturbance was found within the profile loo king for the first derivative minimum. Significance of NDVI change was eva luated testing the Cebyšëv condition and the following parameters mapped : (i) level of damage\; (ii) year of disturbance\; (iii) year of the event ual “total” recovery\; (iv) rate of recovery. Finally\, temporal trend s of both forest lost and recovery were analyzed to investigate potential impacts onto local lemurs population and\, more in general\, to the entire Reserve. DTSTAMP:20260522T041742Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:A Possible Role of NDVI Time Series from Landsat Mission to Charact erize Lemurs’ Habitats Degradation in Madagascar - Enrico Borgogno-Mondi no\, Federica Ghilardi\, Samuele De Petris\, Valeria Torti\, Cristina Giac oma URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/J9M9XE/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-FSMFXW@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T100000 DTEND;TZID=GMT:20230613T101500 DESCRIPTION:The advent of satellite technologies has made it possible to ma ke georeferenced observations of the entire globe at periodic intervals of a few days and with high spatial resolutions.\nESA's Copernicus mission m akes available open-source data from the Sentinel-2 constellation created to provide useful information for agricultural purposes thanks to appropri ately calibrated multispectral images [2].\nThe NDVI (Normalized Vegetatio n Index) [1] can be correlated with some biophysical or agronomic variable s of the vineyard [3].\nThe work presents the results of a two-year work c arried out in the province of Turin in the Piedmont region\, that involved six vineyards cultivated with different varieties (Nebbiolo\, Erbaluce) a nd two vine training system (pergola and espalier). The NDVI georeferenced data were provided by the EOS Crop Monitoring web platform.\nThe experime ntal design divided the vineyards in three classes of vigor areas\, define d through a pre-survey operated by comparing the series of georeferenced N DVI images collected the summer before.\nIn the different vineyards for ea ch of the chosen vigor areas\, five plants were identified and used as a g round reference to evaluate a series of vegetative-productive parameters. The total amount of plants monitored were 30 for Nebbiolo and 55 for Erbal uce.\nAll NDVI index showed significant predictability for the studied var iables.\nAs expected\, the trend of the quantitative variables was positiv ely related to the NDVI while the qualitative variables were negatively re lated. As far as the percentage mean error was concerned a high predictabi lity\, (error 1÷7% respectively for Erbaluce and Nebbiolo vineyards). Con sidering the canopy architecture\, the leaf layers were accurately predict ed from the NDVI (R2 0\,72 and 0\,55 respectively for Erbaluce and Nebbiol o) with an error around 10%. Regarding the fruit compartment a strong diff erence emerged between the systems. The shaded cluster percentage in the N ebbiolo vines was highly predictable with (R2 0\,57\, error 6%). In Erbalu ce the error was higher (36%) with a correlation index R2 of 0\,42. This f act derives from the higher variability of the plants in the compared plot s. The number of clusters were predicted with a minor error in Nebbiolo th an in Erbaluce (9% and 29%\, R2 0\,70 and 0\,16 respectively) and for the bud fertility (8% and 15%\, R2 0\,83 and 0\,36 respectively). In sum\, the true productive traits appeared as the less predictable in the Erbaluce v ineyards\, with 31% error in yield (R2 0\,26) compared to a less erroneous prediction (error 22% and R2 0\,63) in Nebbiolo vines. The pruning wood weight was similarly predicted from the NDVI with 21 and 23% error\, with a correlation index R2 of 0\,41 and 0\,28 for Erbaluce and Nebbiolo respec tively.\nThe PCA analysis\, allowed discriminating observations based on v igor attributes and consistently with the measured variables\, even when a ll the observations\, for the different varietal combinations\, are proces sed simultaneously with the same multivariate model. \nThe study confirmed the possibility to use Sentinel-2 NDVI output to map the vineyards variab ility also in small plots (< 1 ha)\, estimating the vineyard canopy densit y\, the productive and wine most important technological parameters. \n\n\ n\n[1] Giovos\, R.\, Tassopoulos\, D.\, Kalivas\, D.\, Lougkos\, N.\, & Pr iovolou\, A. (2021). Remote sensing vegetation indices in viticulture: A c ritical review. Agriculture\, 11(5)\, 457.\n[2] Sarvia\, F.\, De Petris\, S.\, Orusa\, T.\, & Borgogno-Mondino\, E. (2021). MAIA S2 versus sentinel 2: spectral issues and their effects in the precision farming context. In Computational Science and Its Applications–ICCSA 2021: 21st Internationa l Conference\, Cagliari\, Italy\, September 13–16\, 2021\, Proceedings\, Part VII 21 (pp. 63-77). \n[3] Vélez\, S.\, Rançon\, F.\, Barajas\, E.\ , Brunel\, G.\, Rubio\, J. A.\, & Tisseyre\, B. (2022). Potential of funct ional analysis applied to Sentinel-2 time-series to assess relevant agrono mic parameters at the within-field level in viticulture. Computers and Ele ctronics in Agriculture\, 194\, 106726. DTSTAMP:20260522T041742Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Monitoring Erbaluce and Nebbiolo vineyards by means of Sentinel-2 N DVI index maps - Enrico Borgogno-Mondino\, Alberto Cugnetto\, Giorgio Maso ero\, Peppino Sarasso URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/FSMFXW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-FCQMZP@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T170000 DTEND;TZID=GMT:20230613T171500 DESCRIPTION:Starting from 1962 the Common Agricultural Policy (CAP) has sup ported through contributions the agricultural sector aiming at preserving the environment and improving crops production. The local Paying Agencies (PA) verify the correctness\, completeness and compliance of farmers appli cations by administrative checks (ACs) and on-the-spot checks (OTSCs). ACs are performed on 100% of applications to automatically detect formal faul ts through informatics tools. OTSCs are performed on about the 5% of appli cations testing the compliance with envisaged commitments and obligations\ , verify eligibility criteria and checking the truthfulness of declared ar ea size. Recently\, the article 10 of the recent EU regulation (N. 1173/20 22)\, defined new controls based on remote sensing\, specifically by adopt ing Copernicus Sentinel-2 (S2) imagery\, or “other data” at least equi valent value. The adoption of S2 imagery allows to monitor all areas decla red by farmers’ applications longing for irregularities detection. Conse quently\, this type of control can be applied to all CAPs (no longer 5%) a pplications in each member state. In this framework\, the new CAP 2023-202 7\, requires a gradual implementation of such remote-sensing based tools w ithin member states control systems\, becoming compulsory in 2024. Further more\, the 2023-2027 CAP will introduce some new types of contributions ca lled 'eco-schemes' related to the climate\, environment and animal welfare . Nevertheless\, a proper review of how remote sensing-based tools can be applied to these new contributions is missing. Therefore\, in this work we preliminary explore which marker can be detected by Copernicus S2 data in terms of field surface\, agronomic practices and monitor period\, possibl y related to a specific CAP contribution requirement. Focuses will concern : (a) basic payment\; (b) eco-schemes\; (c) enhanced conditionality. DTSTAMP:20260522T041742Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Remote sensing and Sentinel-2 data role within the Common Agricultu ral Policy 2023-2027 - Enrico Borgogno-Mondino\, Alessandro Farbo\, Filipp o Sarvia\, Samuele De Petris\, Elena Xausa\, Gianluca Cantamessa URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/FCQMZP/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-V9QAJW@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T171500 DTEND;TZID=GMT:20230613T173000 DESCRIPTION:Sustainable agriculture is one of the main focus of the 2023 – 2027 Common Agricultural Policy (CAP). For this reason\, the new CAP s trategic plan presents greater ambitions on climate and environment action in comparison of the previous programming period and stronger incentives that promote climate- and environment-friendly farming practices (i.e. min imizing soil disturbance\, organic and carbon farming\, maintaining perman ent ground cover and adopting combined rotations) are provided. Among the several options\, avoiding bare soil conditions and consequently promoting cover crops\, or even to cultivate two main crops in a year\, can provide excellent benefits. In particular\, soil erosion and nitrate percolation are limited and soil structure\, fertility\, organic carbon sequestration and adaptability to climate change are supported. Consequently\, an estima tion of how much cultivated area is currently managed in this way should b e estimated. Within the farmer CAP application\, single (i.e. winter or su mmer) and a double crop could be included even if more crops can indeed be cultivated afterwards. Accordingly\, the scope of this research is to des ign and validate an approach to classify and map the fields where a crop c over maintenance is promoted rather than the single crop based on Copernic us Sentinel-2 (S2) data. The study area is located in Austria\, where a re presentative sample of the main crop types cultivated in the region was de rived from the declarations to the Integrated Administration and Control S ystem (IACS) for the year 2021. The approach relies on the classification of reflectance data from S2 time series including nine vegetation indices that were used to identify single or double crop systems. For this purpose \, two supervised classifiers were applied namely One-Class Support Vector Machine (OneClassSVM) and Random Forest (RF). Statistical measures such a s Overall Accuracy and Cohen's kappa coefficient were derived from the con fusion matrices and the differences between field data and mapping results were analysed. A new map showing single vs double-crop systems was genera ted for further spatial analysis and interpretation. DTSTAMP:20260522T041742Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Double Crop Mapping using Sentinel-2 Data in Support to Implementat ion and Monitoring of the 2023-2027 Common Agricultural Policy within Rura l Development Interventions - Enrico Borgogno-Mondino\, Filippo Sarvia\, E mma Izquierdo\, Francesco Vuolo URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/V9QAJW/ END:VEVENT END:VCALENDAR