Leaf area index (LAI) is a critical vegetation variable that regulates photosynthesis, respiration, and precipitation interception. This variable also shows strong variability in space and time. In addition, specific management like prunings or structural plant protection (e.g. anti-hail nets) could highly affect its time behavior or spatial distribution. For this reason, the accurate LAI characterization is a key factor for precise management of, e.g., irrigation, nutrition, harvest and soil management. In the last decades several methods have been developed to measure this index but with specific spatial and temporal resolutions. Ground measurements are in fact precise but localized in space. Remote and proximal sensing approaches can cover larger areas but the time resolution could be limited to detect fast changes in plant growth. In the present study we aim to compare different methods for the characterization of the LAI based on three techniques that span different temporal and spatial resolutions. The study is conducted at three experimental sites in Emilia Romagna (Italy), two vineyards and one orchard, within the SWAMP project H2020. Measurements are performed based on AccuPAR LP80 ceptometer on the ground and by drones equipped with a multi-spectral camera RedEdge-M which captures five narrow spectral bands and allows to generate plant health indices, as NDVI. Remote sensing products (Sentinel-2A) are finally compared. The analysis focuses on identifying their limitations in characterizing vegetation growth and in possible improvements and corrections. Specific field sampling designs for ground observations and operational drone flights are also discussed. Overall, the study identifies the need to adapt spatial and temporal resolutions of the specific measurements to local agro-environmental conditions for correctly characterize the plant growth. This variable strategy should be considered to achieve a right support for a more precise agricultural management.
Assessment of leaf area index in orchards and vineyards at different spatial and temporal scales
Vincenzo Alagna
;Gabriele Baroni;Tamara Ricchi;Attilio Toscano;Paolo Castaldi;Massimiliano Menghini;Tullio Salmon Cinotti
2019
Abstract
Leaf area index (LAI) is a critical vegetation variable that regulates photosynthesis, respiration, and precipitation interception. This variable also shows strong variability in space and time. In addition, specific management like prunings or structural plant protection (e.g. anti-hail nets) could highly affect its time behavior or spatial distribution. For this reason, the accurate LAI characterization is a key factor for precise management of, e.g., irrigation, nutrition, harvest and soil management. In the last decades several methods have been developed to measure this index but with specific spatial and temporal resolutions. Ground measurements are in fact precise but localized in space. Remote and proximal sensing approaches can cover larger areas but the time resolution could be limited to detect fast changes in plant growth. In the present study we aim to compare different methods for the characterization of the LAI based on three techniques that span different temporal and spatial resolutions. The study is conducted at three experimental sites in Emilia Romagna (Italy), two vineyards and one orchard, within the SWAMP project H2020. Measurements are performed based on AccuPAR LP80 ceptometer on the ground and by drones equipped with a multi-spectral camera RedEdge-M which captures five narrow spectral bands and allows to generate plant health indices, as NDVI. Remote sensing products (Sentinel-2A) are finally compared. The analysis focuses on identifying their limitations in characterizing vegetation growth and in possible improvements and corrections. Specific field sampling designs for ground observations and operational drone flights are also discussed. Overall, the study identifies the need to adapt spatial and temporal resolutions of the specific measurements to local agro-environmental conditions for correctly characterize the plant growth. This variable strategy should be considered to achieve a right support for a more precise agricultural management.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
