Geodaysit 2023

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.

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.