The University of Arizona

College of Agriculture and Life Sciences

Describing trends in vegetation recovery after land treatments in the southwestern US with associations between environmental variables, satellite greenness patterns, and ground-based measurements

Wednesday, February 14, 2018

Speaker:  Stella Copeland, USGS.
 
Date: Wednesday, February 14th, 2018
 
Time: 3:00-4:00 pm
 
Location: ENR2, S107
 
ABSTRACT: Treatments such as prescribed fire, seeding, and thinning are commonly applied on public lands to meet various management goals, such as increasing vegetation productivity and restoring ecosystem function. Identifying characteristics associated with successful treatment outcomes is challenging due to a lack of monitoring information in many locations and time periods. The effectiveness of treatments also vary due to site conditions, such as the type of disturbance and treatment, soil type and climate. Satellite derived vegetation indices, which indicate productivity, can be used to determine vegetation recovery related to treatments. In this study, we derived soil-adjusted total vegetation indices (SATVI) from 1988-2016 Landsat satellite imagery (30 m resolution) to describe vegetation trajectories over time in land treatment areas in Bureau of Land Management lands in the southwestern United States. We tested whether site climate and soil characteristics and weather during the treatment period were associated with greenness trends and recovery rate relative to pre-treatment greenness. We also compared SATVI to on-the-ground plot measurements of vegetation cover for a separate set of locations on the Colorado Plateau. Our results suggest that site soil, climate, and weather following treatment can influence recovery patterns as indicated by greenness. However, we also observed that high cover of annual species, likely frequently non-native species, detected by seasonal greenness patterns, can significantly affect greenness trends and relationships with environmental variables. Our results suggest that this approach, a combination of satellite imagery and ground-based monitoring, can be useful in predicting and understanding past recovery rates, particularly if annual species are accounted for in the analysis.
 
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