Grasslands in mountainous and marginal areas provide a wide variety of ecosystem services, in addition to agricultural goods and disaster protection. In the Apennines, extensive grassland systems face a gradual reduction in exploitation or abandonment. To successfully manage these ecosystems, it is important to define the proper stocking rate of livestock by estimating nutritional quality and pasture production. Optical satellite sensor imagery, which is increasingly available and free of charge also thanks to the Copernicus program, provides an important contribution to this estimation. As satellite imagery has become more accessible, platforms such as Google Earth Engine (GEE) have been developed for fast, simple, and powerful data processing. GEE enables global data analysis by exploiting a large catalog, as well as disseminating results to a wide audience through a simple web link. Our study is developed as part of the VISTOCK project (https://vistock.toscanallevatori.it/), supported by GAL START TOSCANA, which involves the integrated use of grazing herd management innovative technologies, known as Virtual Fencing collars and precision livestock tools. Our contribution involves the development of a code on the platform GEE, which allows, starting from the coordinates of a single point, the computation of several vegetation indices (VI) for different buffer areas, the automatic time series visualization through interactive plots, and the downloading of the data in .csv format. Thus, the VI are correlated to observed data (e.g. LAI, fPar, biomass and forage quality) taken along two grazing seasons (2020-2021). With our study, a code for pasture quality monitoring activities will be presented and disseminated freely. Future perspective will be the possibility of integrating the information on pasture quality and production directly from the field via tablet or smartphones to support farmers in setting a rational grazing in semi-extensive pastures according to the forage supply during the season

Exploitation of the GEE platform to Apennine grasslands monitoring / Laura Stendardi, Chiara Aquilani, Giovanni Argenti, Edoardo Bellini, Andrea Confessore, Marco Moriondo, Matilde Pisi, Carolina Pugliese, Nicolina Staglianò, Camilla Dibari. - ELETTRONICO. - (2022), pp. 164-164. ((Intervento presentato al convegno III CONVEGNO AISSA#UNDER40 - LA RICERCA SCIENTIFICA NEL PROCESSO DI TRANSIZIONE ECOLOGICA IN AGRICOLTURA.

Exploitation of the GEE platform to Apennine grasslands monitoring

Laura Stendardi;Chiara Aquilani;Giovanni Argenti;Edoardo Bellini;Andrea Confessore;Marco Moriondo;Carolina Pugliese;Nicolina Staglianò;Camilla Dibari
2022

Abstract

Grasslands in mountainous and marginal areas provide a wide variety of ecosystem services, in addition to agricultural goods and disaster protection. In the Apennines, extensive grassland systems face a gradual reduction in exploitation or abandonment. To successfully manage these ecosystems, it is important to define the proper stocking rate of livestock by estimating nutritional quality and pasture production. Optical satellite sensor imagery, which is increasingly available and free of charge also thanks to the Copernicus program, provides an important contribution to this estimation. As satellite imagery has become more accessible, platforms such as Google Earth Engine (GEE) have been developed for fast, simple, and powerful data processing. GEE enables global data analysis by exploiting a large catalog, as well as disseminating results to a wide audience through a simple web link. Our study is developed as part of the VISTOCK project (https://vistock.toscanallevatori.it/), supported by GAL START TOSCANA, which involves the integrated use of grazing herd management innovative technologies, known as Virtual Fencing collars and precision livestock tools. Our contribution involves the development of a code on the platform GEE, which allows, starting from the coordinates of a single point, the computation of several vegetation indices (VI) for different buffer areas, the automatic time series visualization through interactive plots, and the downloading of the data in .csv format. Thus, the VI are correlated to observed data (e.g. LAI, fPar, biomass and forage quality) taken along two grazing seasons (2020-2021). With our study, a code for pasture quality monitoring activities will be presented and disseminated freely. Future perspective will be the possibility of integrating the information on pasture quality and production directly from the field via tablet or smartphones to support farmers in setting a rational grazing in semi-extensive pastures according to the forage supply during the season
Proceedings of III CONVEGNO AISSA#UNDER40
III CONVEGNO AISSA#UNDER40 - LA RICERCA SCIENTIFICA NEL PROCESSO DI TRANSIZIONE ECOLOGICA IN AGRICOLTURA
Laura Stendardi, Chiara Aquilani, Giovanni Argenti, Edoardo Bellini, Andrea Confessore, Marco Moriondo, Matilde Pisi, Carolina Pugliese, Nicolina Staglianò, Camilla Dibari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1285978
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