Evaluating Changes in Agricultural Productivity for Central Kentucky using Landsat and sUAS derived Vegetation Indices: 1988 to 2018

Authors: Jeremy Sandifer*, Kentucky State University, Buddhi R Gyawali, Kentucky State University, Kevin Gurtowski, Kentucky Cooperative Extension
Topics: Land Use and Land Cover Change, Agricultural Geography, Environment
Keywords: agriculture, decision support, NDVI, drone, sUAS
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download

In 2015, Kentucky agriculture contributed indirectly to $50 billion in economic stimulus and 250,000 jobs in the state. Yearly estimates vary, particularly for small operations (the bulk of farms in the state), but the 2017 Agricultural Census reported almost 1 million acres removed from agriculture activity during the previous 5 years, including 100,000 acres lost permanently to development.
Given the macro scale changes in the agricultural industry, how has the distribution and magnitude of agricultural productivity for the growing season (June to October) changed during the period 1988 to 2018?
Peak production of the agricultural season in Kentucky falls between the month of June (latest planting) and the month of October (end of harvest) and covers a wide array of crops and environmental conditions.
This project utilizes data from the Landsat Mission Archive (TM, OLI) to derive normalized difference vegetation indices (NDVI) for estimating the productivity of farmland and to calculate annualized productivity as the magnitude of change in NDVI between June to October of each year.
Preliminary results indicate that 1) the spatial distribution of productivity changed (decreased in area) and 2) the mean productivity, in contrast, has increased significantly, in some cases doubling overall productivity means.
These contrasting trends highlight expected changes, such as, 1) decrease in number of farms, 2) decrease in the overall land dedicated to farming, and 3) significant increases in overall productivity for remaining farms.
Lastly, preliminary results are presented that incorporate (sUAS) small unmanned aerial systems-based mulit-spectral imagery for validation of NDVI estimates.

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