The study, published in the Journal of Remote Sensing on July 8, focuses on drylands, which include arid, semi-arid, and dry sub-humid areas that make up over 40% of the Earth's land surface. These regions are crucial for supporting diverse wildlife, agriculture, and carbon storage but are highly susceptible to rapid ecological changes due to climate fluctuations and human activities.
Monitoring drylands is challenging because of their sparse vegetation, high variability, and fluctuating growth patterns. Traditional monitoring methods often fail to accurately detect ecological changes. To overcome these challenges, USDA-ARS scientists combined space-based and ground-level data collection methods to improve the monitoring of these ecosystems.
"We wanted to understand whether we could use images collected remotely from near-surface cameras or satellites to measure differences in dryland vegetation greenness over time and in response to rainfall," said Emily R. Myers, a SCINet postdoctoral fellow with the USDA-ARS and lead author of this study.
The researchers aimed to use this data to differentiate between various ecological states in drylands. The study utilized daily data from near-surface cameras (PhenoCam) and satellite imagery (Harmonized Landsat 8 and Sentinel-2, or HLS) at a desert grassland site in southwest New Mexico. Data from 12 different locations were analyzed over several years, from 2014 to 2022.
"Measurements from near-surface cameras and satellites were able to distinguish between different dryland vegetation responses to rainfall," said Dawn M. Browning, a senior research ecologist at the USDA-ARS and co-lead author of the study.
The findings revealed that grass-dominated ecosystems responded quickly to rainfall, showing significant increases in greenness and productivity during wet growing seasons, while shrub-dominated areas were less responsive to rainfall during the same periods.
"These differences in greenness responses may help us map and monitor dryland states and productivity using remotely sensed imagery," Myers said.
The research team hopes that by refining these remote sensing techniques, they can more effectively identify and monitor areas with significant grass cover, guiding management strategies to maintain these vital ecosystems and prevent the spread of shrubs.
"We would like to explore more ways to characterize the growing season, with a particular focus on measurements that are sensitive to the rapid increases in greenness that are indicative of grassy productivity," said Browning.
Research Report:Novel Use of Image Time Series to Distinguish Dryland Vegetation Responses to Wet and Dry Years
Related Links
U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS)
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