Geodaysit 2023

Enrico Borgogno Mondino, Presidente AIT
Monica Sebillo, Presidente ASITA
Francesco Cupertino, Rettore del Politecnico di Bari
Leonardo Damiani, Direttore del DICATECh
Eugenio, Di Sciascio, Vicesindaco del Comune di Bari
Umberto Fratino, Presidente di Ordine Ingegneri di Bari
Antonio Acquaviva, Rappresentante del Consiglio nazionale dei Geometri e Geometri laureati
Giovanni Bruno, Vicepresidente Ordine Geologi Puglia

Il Telerilevamento nella Pubblica Amministrazione, Tziana Bisantino (Dirigente del Centro Funzionale Decentrato della Protezione Civile – Regione Puglia) –
New Space Economy: Scenario and Perspectives for Earth Observation, Antonio Messeni Petruzzelli (Delegato alla Ricerca del Politecnico di Bari)

AIT2023 è l'11° Congresso della Associazione Italiana di Telerilevamento (AIT). L'AIT, fin dalla sua fondazione nel 1985, è stata il soggetto di riferimento fondamentale per sostenere la comunicazione e il coordinamento delle attività scientifiche nel campo dell'Osservazione della Terra in Italia.

L'AIT si propone di sostenere lo sviluppo e la diffusione della cultura del Telerilevamento (TLR) in Italia, favorendo le sue applicazioni ambientali e puntando ad avvicinare tra loro i principali attori scientifici, industriali e istituzionali. L'AIT sostiene le iniziative nazionali di TLR in Italia favorendone l’internazionalizzazione. AIT organizza eventi e corsi e pubblica lo European Journal of Remote Sensing in collaborazione con Taylors & Francis.

AIT2023 è il luogo dove accademia, industria, professionisti e istituzioni, in qualche modo coinvolti nel Telerilevamento e nell'Osservazione della Terra (EO), possono incontrarsi e discutere. Per i ricercatori AIT2023 è un'importante opportunità per presentare i loro recenti progressi a un pubblico vasto e transdisciplinare. Per l'industria è l'occasione per mostrare i recenti prodotti e servizi utili per la comunità del TLR. Infine, ma non meno importante, per i partner professionali e per i decisori del territorio/acqua/urbano, della conservazione, della gestione delle risorse naturali e della pianificazione territoriale, AIT2023 è l'evento chiave per presentare le proprie esperienze e aggiornare le proprie conoscenze nel campo del TLR e dell’Osservazione della Terra. Per quanto riguarda il convegno AIT, saranno presi in considerazione tutti gli argomenti che riguardano il telerilevamento remoto e prossimale, l'analisi spaziale e la modellistica ambientale.

Precision viticulture aims to enhance quality standards of wine production by improving vineyard management. In this framework, satellite optical remote sensing has already proved to be effective for mapping vegetation 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, satellite imagery ordinarily provides spectral measures with medium-low geometric resolution (≥ 100 m2). Therefore, spectral mixture between grapevine canopies, grass and soils is expected within a satellite-derived reflectance pixel and not considering this problem can deeply affect deductions based on this data. In this work, Sentinel-2 (S2) NDVI maps (10 m resolution) were computed and compared to the ones obtained from DJI P4 multispectral 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)) within S2 pixel was computed and mapped classifying DJI photogrammetry point cloud. Involving α(x,y) and S2 NDVI values, reversing spectral unmixing 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 from DJI, showing a MAE of 0.15 and 0.10 of row and inter-row NDVI respectively.

Deforestation is one of the main drivers of environmental degradation around the world. Slash-and-burn is a common practice, performed in 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. In this context, the extensive detection of forest disturbances can effectively support restoration actions, providing an overall framework to address priorities and maximizing ecological benefits. In this work and with respect to a study area located around the Maromizaha New Protected Area (Madagascar), 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 every disturbed location. It is worth to remind that the Maromizaha New Protected Area presently hosts 12 species of lemurs. Detection was operated at pixel level by analyzing the local temporal profile of NDVI (yearly step). Time of the eventual detected disturbance was found within the profile looking for the first derivative minimum. Significance of NDVI change was evaluated testing the Cebyšëv condition and the following parameters mapped: (i) level of damage; (ii) year of disturbance; (iii) year of the eventual “total” recovery; (iv) rate of recovery. Finally, temporal trends of both forest lost and recovery were analyzed to investigate potential impacts onto local lemurs population and, more in general, to the entire Reserve.

The advent of satellite technologies has made it possible to make georeferenced observations of the entire globe at periodic intervals of a few days and with high spatial resolutions.
ESA's Copernicus mission makes available open-source data from the Sentinel-2 constellation created to provide useful information for agricultural purposes thanks to appropriately calibrated multispectral images [2].
The NDVI (Normalized Vegetation Index) [1] can be correlated with some biophysical or agronomic variables of the vineyard [3].
The work presents the results of a two-year work carried out in the province of Turin in the Piedmont region, that involved six vineyards cultivated with different varieties (Nebbiolo, Erbaluce) and two vine training system (pergola and espalier). The NDVI georeferenced data were provided by the EOS Crop Monitoring web platform.
The experimental design divided the vineyards in three classes of vigor areas, defined through a pre-survey operated by comparing the series of georeferenced NDVI images collected the summer before.
In the different vineyards for each of the chosen vigor areas, five plants were identified and used as a ground reference to evaluate a series of vegetative-productive parameters. The total amount of plants monitored were 30 for Nebbiolo and 55 for Erbaluce.
All NDVI index showed significant predictability for the studied variables.
As expected, the trend of the quantitative variables was positively related to the NDVI while the qualitative variables were negatively related. As far as the percentage mean error was concerned a high predictability, (error 1÷7% respectively for Erbaluce and Nebbiolo vineyards). Considering the canopy architecture, the leaf layers were accurately predicted from the NDVI (R2 0,72 and 0,55 respectively for Erbaluce and Nebbiolo) with an error around 10%. Regarding the fruit compartment a strong difference emerged between the systems. The shaded cluster percentage in the Nebbiolo vines was highly predictable with (R2 0,57, error 6%). In Erbaluce the error was higher (36%) with a correlation index R2 of 0,42. This fact derives from the higher variability of the plants in the compared plots. The number of clusters were predicted with a minor error in Nebbiolo than 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 vineyards, 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 respectively.
The PCA analysis, allowed discriminating observations based on vigor attributes and consistently with the measured variables, even when all the observations, for the different varietal combinations, are processed simultaneously with the same multivariate model.
The study confirmed the possibility to use Sentinel-2 NDVI output to map the vineyards variability also in small plots (< 1 ha), estimating the vineyard canopy density, the productive and wine most important technological parameters.

[1] Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., & Priovolou, A. (2021). Remote sensing vegetation indices in viticulture: A critical review. Agriculture, 11(5), 457.
[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 International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VII 21 (pp. 63-77).
[3] Vélez, S., Rançon, F., Barajas, E., Brunel, G., Rubio, J. A., & Tisseyre, B. (2022). Potential of functional analysis applied to Sentinel-2 time-series to assess relevant agronomic parameters at the within-field level in viticulture. Computers and Electronics in Agriculture, 194, 106726.

Starting from 1962 the Common Agricultural Policy (CAP) has supported 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 applications by administrative checks (ACs) and on-the-spot checks (OTSCs). ACs are performed on 100% of applications to automatically detect formal faults through informatics tools. OTSCs are performed on about the 5% of applications testing the compliance with envisaged commitments and obligations, verify eligibility criteria and checking the truthfulness of declared area size. Recently, the article 10 of the recent EU regulation (N. 1173/2022), defined new controls based on remote sensing, specifically by adopting Copernicus Sentinel-2 (S2) imagery, or “other data” at least equivalent value. The adoption of S2 imagery allows to monitor all areas declared by farmers’ applications longing for irregularities detection. Consequently, this type of control can be applied to all CAPs (no longer 5%) applications in each member state. In this framework, the new CAP 2023-2027, requires a gradual implementation of such remote-sensing based tools within member states control systems, becoming compulsory in 2024. Furthermore, the 2023-2027 CAP will introduce some new types of contributions called '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, possibly related to a specific CAP contribution requirement. Focuses will concern: (a) basic payment; (b) eco-schemes; (c) enhanced conditionality.

Sustainable agriculture is one of the main focus of the 2023 – 2027 Common Agricultural Policy (CAP). For this reason, the new CAP strategic 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. minimizing soil disturbance, organic and carbon farming, maintaining permanent 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 estimation of how much cultivated area is currently managed in this way should be estimated. Within the farmer CAP application, single (i.e. winter or summer) and a double crop could be included even if more crops can indeed be cultivated afterwards. Accordingly, the scope of this research is to design and validate an approach to classify and map the fields where a crop cover maintenance is promoted rather than the single crop based on Copernicus Sentinel-2 (S2) data. The study area is located in Austria, where a representative sample of the main crop types cultivated in the region was derived from the declarations to the Integrated Administration and Control System (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 as Overall Accuracy and Cohen's kappa coefficient were derived from the confusion matrices and the differences between field data and mapping results were analysed. A new map showing single vs double-crop systems was generated for further spatial analysis and interpretation.