Vineyards are a high-income crop and can also have an important landscape value, especially in certain areas of Italy. Vineyards show large soil erosion rates depending on climate variability, local topography and vine management. Farming machineries can also determine compaction and affect soil hydraulic properties, runoff and erosion at different levels depending on inter-rows soil management. This work, with focus on a pilot area located in the municipality of Carpeneto, Alessandria province (Piedmont, NW Italy), concerns the adoption of satellite optical data from the Copernicus Sentinel 2 (S2) mission to describe vineyard properties. A first analysis was addressed at testing if and how S2 data can map differences in vineyards management and behaviour. It was achieved at field level with reference to some experimental plots presenting different inter-row soil management (tillage -CT-, tillage with downstream 10-m-strip grass cover -ST- and total permanent grass cover -GC-). Reference vineyards were placed on sloping areas and set with up-and-down tillage ('rittochino' in Italian language). A NDVI image time series (TS) was obtained from the level 2 S2 data for the growing season 2017-2018. TS was processed at plot level and proved to be effective in showing vegetation response to mechanical interventions during the growing season; in particular ripping in CT and shredding in GC in late spring and, during summer, mowing and topping were highlighted; minor variations in NDVI were observed after harvest in all plots. Secondly, a wider analysis was achieved including all the vineyards located in the municipality of Carpeneto. These were mapped by photo-interpretation and grouped in clusters with reference to the local NDVI TS (averaged at plot level). Clustering was obtained by K-means unsupervised classification. Results suggest that vineyards can be classified according to the intensity of inter-row's soil management. A further analysis was aimed at exploring the role of the native S2 bands in describing vineyard differences and inter-row coverage. Preliminary results suggest that single-bands data could be jointly used with vegetation indices to better describe vine growth dynamics in row-crops. Further studies on remotely sensed data could provide spatially variable inputs for applications in erosion risk management and land analysis.
Use of remotely sensed data for the evaluation of inter-row cover intensity in vineyards
Francesco Palazzi;Marcella Biddoccu;Eugenio Cavallo
2020
Abstract
Vineyards are a high-income crop and can also have an important landscape value, especially in certain areas of Italy. Vineyards show large soil erosion rates depending on climate variability, local topography and vine management. Farming machineries can also determine compaction and affect soil hydraulic properties, runoff and erosion at different levels depending on inter-rows soil management. This work, with focus on a pilot area located in the municipality of Carpeneto, Alessandria province (Piedmont, NW Italy), concerns the adoption of satellite optical data from the Copernicus Sentinel 2 (S2) mission to describe vineyard properties. A first analysis was addressed at testing if and how S2 data can map differences in vineyards management and behaviour. It was achieved at field level with reference to some experimental plots presenting different inter-row soil management (tillage -CT-, tillage with downstream 10-m-strip grass cover -ST- and total permanent grass cover -GC-). Reference vineyards were placed on sloping areas and set with up-and-down tillage ('rittochino' in Italian language). A NDVI image time series (TS) was obtained from the level 2 S2 data for the growing season 2017-2018. TS was processed at plot level and proved to be effective in showing vegetation response to mechanical interventions during the growing season; in particular ripping in CT and shredding in GC in late spring and, during summer, mowing and topping were highlighted; minor variations in NDVI were observed after harvest in all plots. Secondly, a wider analysis was achieved including all the vineyards located in the municipality of Carpeneto. These were mapped by photo-interpretation and grouped in clusters with reference to the local NDVI TS (averaged at plot level). Clustering was obtained by K-means unsupervised classification. Results suggest that vineyards can be classified according to the intensity of inter-row's soil management. A further analysis was aimed at exploring the role of the native S2 bands in describing vineyard differences and inter-row coverage. Preliminary results suggest that single-bands data could be jointly used with vegetation indices to better describe vine growth dynamics in row-crops. Further studies on remotely sensed data could provide spatially variable inputs for applications in erosion risk management and land analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
