Mesic ecosystems are fundamental to conservation efforts in semi-arid systems, but are threatened by climate change and development. Newer earth observation datasets, including Sentinel-1 and −2, provide opportunities to monitor mesic ecosystems at meaningful spatial scales, but are insufficient for measuring decadal-scale changes. Conversely, the Landsat time series has decades of data, but images are spatially coarse relative to many of the mesic ecosystem areas that sustain dryland systems, resulting in classifications with mixed pixels inadequate for effective monitoring. We developed a workflow that uses 10-m classifications produced from fusion of the Sentinel-1 and −2 time series (2017–2020) to estimate sub-pixel proportions of Landsat time series observations (2004–2020). Using random forest regression models, we quantified water resource proportions (WRP) of surface water, mesic vegetation, and upland land covers within each 30-m Landsat pixel. We incorporated ancillary covariates to account for varying topographic conditions, land cover, and climate. Results indicate that our approach consistently estimates sub-pixel proportions of Landsat pixels more accurately compared to spectral mixture analysis (SMA). The WRP product for surface water had up to 8% less error than SMA as measured by Mean Absolute Error (MAE) and up to 17% less error as measured by Root Mean Squared Error (RMSE). For mesic vegetation, the WRP product outperformed SMA by up to 4% (MAE) and 7% (RMSE). Finally, we demonstrated the ability of our time series to characterize historical water resource availability at a case study site with a well documented restoration history by qualitatively examining the mesic vegetation dynamics time series to identify system responses to restoration efforts. Our approach allows us to hindcast observations of Sentinel products and measure water resource dynamics with greater precision over larger temporal scales. We envision these WRP data to be useful for measuring the impacts of conservation interventions, disturbance recovery, or land use changes that pre-date the Sentinel time series.
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Kolarik, N. E.; Shrestha, N.; Caughlin, T.; and Brandt, J. S. (2024). "Leveraging High Resolution Classifications and Random Forests for Hindcasting Decades of Mesic Ecosystem Dynamics in the Landsat Time Series". Ecological Indicators, 158, 111445. https://doi.org/10.1016/j.ecolind.2023.111445