Novel use of image time-series to distinguish responses in dryland vegetation responses to wet and dry years

Authors: Myers, Emily; Browning, Dawn; Burkett, Laura; James, Darren; Bestelmeyer, Brandon

Submitted to: Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/21/2024
Publication Date: 7/8/2024
Citation: Myers, E.B., Browning, D.M., Burkett, L.M., James, D.K., Bestelmeyer, B.T. 2024. Novel use of image time-series to distinguish responses in dryland vegetation responses to wet and dry years. Journal of Remote Sensing. Article 0190. https://doi.org/10.34133/remotesensing.019.
DOI: https://doi.org/10.34133/remotesensing.019

Interpretive Summary: Producers, land managers, and indigenous communities are confronted with drought, shifts in the timing of rainfall and high temperatures that challenge our ability to produce food and fiber and threaten ecosystem services and livelihoods nationwide. Mitigating the harmful effects of climate change requires land surface monitoring, a prospect that is especially difficult in arid rangelands where landscapes are extensive, remote, and in some cases with modest vegetation cover. We evaluated the potential for time series images from near-surface cameras (PhenoCams) to complement data from the Harmonized Landsat-Sentinel image time series using Ecological Site concepts widely-used by USDA NRCS and Bureau of Land Management (BLM). We found that both HLS and PhenoCam greenness responses (or phenology) were useful for capturing the response of herbaceous vegetation cover in wet years. We found that greenness responses were more difficult to capture during dry years, but that contrasts between PhenoCam and HLS-measured phenology could still be useful in distinguishing between different grass- and shrub-dominated  states. PhenoCams could be an inexpensive tool to expand coverage for landscapes of concern and signal response from drought in ways not easily discerned via satellite imagery. Understanding the strengths and limitations of complementary instrument designs can help land managers, producers who practice precision agriculture, as well as scientists who formulate and test models to improve forecasts based on climate change scenarios.

Technical Abstract: Remote sensing methods are commonly used to assess and monitor ecosystem conditions in drylands, but accurate classification and detection of ecological state change is challenging due to sparse vegetation cover, high spatial heterogeneity, and high inter-annual variability in production. We evaluated whether phenological metrics are effective for distinguishing ecological states using imagery from near-surface camera (PhenoCam) and satellite (Harmonized Landsat 8 and Sentinel-2, hereafter HLS), and how effectiveness varied across wet and dry rainfall years. We analyzed time series over 92 site-years at a desert grassland site in southwest New Mexico. Rainfall was a driver of phenological response across all ecological states, with wet years correlating with later start of season, later peak, higher peak greenness, and shorter growing season. This rainfall response was strongest in sandy shrub-invaded grasslands. PhenoCam estimated significantly earlier start of season than HLS for gravelly shrubland states, and earlier end of season than HLS for sandy shrub-invaded grassland states. We propose integrating seasonal metrics from highfrequency PhenoCam time series with satellite assessments to exploit phenological differences across variable rainfall years, improve monitoring efforts in drylands, and capture the timing and strength of peak greenness for grass-dominated ecological states as an indicator of ecological state change.