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Climate change is expected to impact the wine industry by shifting suitable growing regions away from established regions and increasing the demand for freshwater supplies to maintain vineyard health. This creates challenges for maintaining vineyards since the grapevines need to be appropriately stressed to produce quality grapes for quality wine. This requires precision management of freshwater application and canopy management to produce a grape that has the appropriate concentration of flavors and sugars. Vineyards in the U.S. are typically monitored by vineyard managers at the large scale, lacking the resolution needed to identify grapevine growth at the subfield level. Vineyard managers make decisions from planting and fertilizing to water schedules and harvesting. Unfortunately, since vineyards are large areas, it will require more vineyard managers to monitor and identify grapevine health which will cost more for resources and labor. Remote sensing provides coverage over large areas, a cost-effective tool to monitor large vineyard areas, giving growers informed decisions on irrigation timing and amounts related to vineyard grape development. By using remote sensing, it will allow vineyard managers to improve in decision-making to maintain grapevine health.

For our research, satellite remote sensing data was used to analyze two varietals - Chardonnay and Cabernet Sauvignon - at a vineyard in the North Fork of Long Island, NY during the 2017 and 2018 growing season. This research used Landsat-8 and Sentinel-2 satellite data to generate Normalized Difference Vegetation Index (NDVI), an indicator of vegetation “leafiness”, for the following research goals. First, to identify how well each satellite tracks grapevine growth and health between the two growing season (May to November 2017 and 2018) using differences maps and time-series analysis. Second, to use image classification to determine how well each satellite dataset identifies the vineyard by varietal type in terms of location and through the growing season. Ground data collected during each growing season will verify the accuracy of Landsat-8 and Sentinel-2 observations.


This poster won first place for Group (STEM and non-STEM) posters at the 30th Semi-Annual Honors and Undergraduate Research Scholars Poster Presentation at New York City College of Technology, May 1, 2019.

Mentors: Profs. R. Blake (Physics) and J. Liou-Mark (Mathematics)



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