Enabling efficient monitoring of mesic vegetation dynamics in semi-arid United States
Nawaraj Shrestha, Boise State University, nawarajshrestha@boisestate.edu (Presenter)
Nicholas Kolarik, Boise State University, nicholaskolarik@u.boisestate.edu
Nancy Glenn, Boise State University, nancyglenn@boisestate.edu
Jodi Brandt, Boise State University, jodibrandt@boisestate.edu
Climate change poses a severe threat to water availability and wetland habitats in semi-arid regions. A better understanding of how water availability is responding to climate change and human management could improve our ability to adapt to climate change. However, the dramatic temporal variability and small size of many critical water resources in semi-arid regions presents a challenge for monitoring. Here, we present a Mesic Vegetation Persistence (MVP) workflow that takes advantage of every usable image in the Sentinel (10-m) and Landsat (30-m) archives to generate a dense time-series of water availability that are continuously updated as new images become available in Google Earth Engine. Our method takes advantage of the fact that most water resources in semi-arid regions occur as mesic vegetation, and infra-red bands in Sentinel and Landsat can reliably detect green vegetation with band-based indices. Normalized Difference Vegetation Index (NDVI) has been the gold standard for using mesic vegetation as an indicator of water availability in semi-arid systems, but NDVI gets confounded by soil-background in low vegetation and becomes saturated at high ranges of vegetation reflectiveness, which limits its ability to reliably detect intra- and inter-annual changes in mesic vegetation. In place of NDVI, for our MVP workflow, we combine a moisture change index (MCI) with the modified chlorophyll absorption ratio index (MCARI2). MCI is the difference in soil moisture condition between an individual pixel’s state and the dry and wet reference reflectance in the image, derived using 5th and 95th percentiles of the visible and shortwave infrared drought index (VSDI). Our preliminary accuracy assessment indicates that our MVP product outperforms NDVI in mesic habitats with particularly high and low vegetation cover, and also in uplands and forested areas. We applied our MVP product at specific case study sites with known changes resulting from drought, wildfire, and restoration. We found that MVP more accurately characterizes differences in mesic persistence, late-season water availability, and restoration success compared to NDVI. MVP provides a greater understanding of how mesic habitats respond to climate change and management and the workflow is easily replicable for other semi-arid systems across the globe.
Poster: Poster_Shrestha_2-11_131_35.pdf
Associated Project(s):
Poster Location ID: 2-11
Presentation Type: Poster
Session: Poster Session 2
Session Date: Wed (May 10) 5:15-7:15 PM
CCE Program: BDEC