LAND COVER CHANGES IN THE DARIÉN AND EMBERÁ PROVINCES, PANAMA, DURING AND AFTER THE COVID-19 PANDEMIC
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Keywords: remote sensing, Sentinel-1, COVID-19, pandemic, land cover change, Panama
Abstract Type: Paper Abstract
Authors:
Otis Koranteng Akrasi, New Mexico State University
Michaela Buenemann, New Mexico State University
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Abstract
The COVID-19 pandemic has raised important questions regarding the role of land cover change in promoting the emergence of novel pathogens and hence the risk of pandemics and, conversely, the role of pandemics in driving land cover change. However, the dynamic linkages between pandemics and land cover change are poorly understood. As a step toward addressing this knowledge gap, we assessed land cover changes in the Darién Gap region of Panama before and during the pandemic. The region is characterized by high biodiversity and endemism and serves as an important corridor or barrier between North and South America in terms of both the movement of goods and people and the spread of diseases. We hypothesized that the pandemic has encouraged diverse land cover changes in the region due to relaxed environmental regulations, decreased law enforcement, and other factors. To assess recent changes in landscape composition and configuration in the region, we i) generated multi-temporal land cover maps by applying Random Forest to cloud-free Sentinel 1 image composites produced in Google Earth Engine for the years 2016, 2018, 2020, and 2022; ii) created change maps and statistics for the 2016-2018, 2018-2020, and 2020-2022 time periods using Post-Classification Comparison and Intensity Analysis; and iii) quantified changes in landscape configuration during the same time periods using FRAGSTATS. In this paper, we will present the results of this work, which in general suggest that there were more land cover changes during than before the pandemic, including increased forest habitat fragmentation.
LAND COVER CHANGES IN THE DARIÉN AND EMBERÁ PROVINCES, PANAMA, DURING AND AFTER THE COVID-19 PANDEMIC
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Paper Abstract