Farmer Perceptions of Land Cover Classification of UAS Imagery of Coffee Agroecosystems in Puerto Rico

Gwendolyn Klenke (), Shannon Brines (), Nayethzi Hernandez, Kevin Li, Riley Glancy, Jose Cabrera, Blake H. Neal, Kevin A. Adkins, Ronny Schroeder and Ivette Perfecto ()
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Gwendolyn Klenke: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Shannon Brines: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Nayethzi Hernandez: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Kevin Li: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Riley Glancy: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Jose Cabrera: Department of Aeronautical Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Blake H. Neal: Department of Aeronautical Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Kevin A. Adkins: Department of Aeronautical Science, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Ronny Schroeder: Department of Applied Aviation Sciences, Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA
Ivette Perfecto: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA

Geographies, 2024, vol. 4, issue 2, 1-22

Abstract: Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers.

Keywords: agroecosystem; drones; farm management (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2024
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