BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//X9DMJW 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-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:20260517T085040Z 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-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:20260517T085040Z 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:20260517T085040Z 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