Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.
In green extensive context, RPAS (Remotely Piloted Aerial Systems) can provide information with a high geometric resolution. The photogrammetric survey shows the possibility of measuring morphometric parameters of forest stand or individual trees. The free accessibility to Copernicus Sentinel-2 (S2) data addresses to hypothesize scenarios where satellite spectral information and high geometric resolution of RPAS photogrammetric survey, jointly used, determine a deeper knowledge of tree characteristics. Study area is located within the "La Mandria" park (NW Italy). Survey was operated by a DJI-Phantom4 RPAS (GSD images = 5 cm). Image photogrammetric processing was achieved by AGISOFT Photoscan v1.2.4. The resulting point cloud was filtered and a raster DSM (Digital Surface Model) was generated with a GSD = 10 cm. The correspondent CHM (Canopy Height Model) was computed by difference using a DTM (Digital Terrain Model) available from the regional cartographic archive. An object-based approach (watershed segmentation) aimed at bordering tree crowns as vector polygons was run. Some tree stability parameters were obtained from CHM by zonal statistics for each crown that was also spectrally characterized (to explore its vigor) using a S2 image time series. The proposed method finds applications in the arboricultural field (ornamental context) for the survey of tree inventory data; the detected parameters can be used as input data for tree risk assessment/management models, especially in extensive contexts representing a new approach to single tree risk management based on innovative technologies and algorithms that can reduce costs of ground control/survey campaigns.
Rising temperature, rainfall, and wind regime changes, increasing of frequency and intensity of extreme events are only some of the effects of climate change affecting the agro-forestry sector. Earth Observation data from satellite missions (often available for free) can certainly support analysis of climate change effects on vegetation, making possible to improve land management in space and time. Within this context, the present work aims at investigating natural and agricultural vegetation, as mapped by Corine Land Cover (CLC) dataset, focusing on phenological metrics trends that can be possibly conditioned by the ongoing climate-change. The study area consists of the entire Piemonte region (NW-Italy). MOD13Q1-v6 dataset from TERRA MODIS mission was used to describe pluri-annual (2001–2019) phenological behavior of vegetation focusing on the following CLC classes: Non-irrigated arable land, Vineyards, Pastures, and Forests. After computing and mapping some phenological metrics as derivable from the interpretation of at-pixel level NDVI (Normalized Difference Vegetation Index) temporal profile, we found that the most significant one was the maximum annual NDVI (MaxNDVI). Consequently, its trend was analyzed at CLC class level for the whole Piemonte region. Natural and semi-natural vegetation classes (Pastures and Forests) were furtherly investigated testing significance of the Percent Total Variation (TV %) of MaxNDVI in the period 2001–2019 for different altitude classes. Results proved that Non-irrigated arable land showed a not significant trend of MaxNDVI; differently, vineyards and forests showed a significant increasing one. Concerning TV %, it was found that it increases with altitude for the Forests CLC class, while it decreases with altitude for the pastures class.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.