Long‐term trajectories of fractional component change in the Northern Great Basin, USA

Abstract

The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. The trade-off of lowering temporal density against removing cloud-contaminated years is justified as temporal density appears to have only a modest impact on trends and climate relationships until n ≤ 6, but multi-year gaps are proportionally more influential. Gradual change analysis is likely to be less sensitive to n than abrupt change. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.

Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: evidence from field and remote sensing data

“…Component data had a similar pattern whereby shrub and sagebrush cover declined in burned plots; however, cover of herbaceous components did not change (Table 1). validation data sets (Rigge et al 2019), it is unsurprising that we found some discrepancy between our field data and the component data. Given the moderate correspondence observed between the component data and AIM and independent (open black circles, Hist.-Burned), or before 2011 (open gray circles, Hist.-Unburned), and contemporary composition was estimated from data collected from those same plots in 2011-2016 (solid black circles, Cont.-Burned; solid gray circles, Contemp.-Unburned).…”

“…Validation of component cover data at pseudo-invariant pixels, where surface conditions and spectral characteristics are stable over time due to 100% bare ground cover, revealed stable bare ground cover that averaged~94% throughout the time period (Shi et al 2018). Additionally, Rigge et al (2019) reported a moderate relationship between AIM data and component data (average of r 2 of 0.29 across components used in this study; N = 1794), and between component data and independent validation data (average of r 2 of 0.37 across components; N = 156). Additionally, Rigge et al (2019) reported a moderate relationship between AIM data and component data (average of r 2 of 0.29 across components used in this study; N = 1794), and between component data and independent validation data (average of r 2 of 0.37 across components; N = 156).…”

“…We extracted component cover data collected at a 30 m resolution on an annual or biennial basis (with a few exceptions with 3-yr gaps) from the mid-1980s to 2016 , Shi et al 2018, Rigge et al 2019 for each contemporary plot (Appendix S1: Table S2). Plots in Idaho occurred in one of three Landsat scenes (path 40 and row 31, path 41 and row 30, or path 41 and row 31; Fig.…”

“…Briefly, the component cover time series (Shi et al 2018, Rigge et al 2019 were produced by evaluating spectral change between Landsat images in~2015 and other selected years through this time period. From the spectrally unchanged areas in a given date, component cover data in the~2015 base predictions were used to train Cubist regression tree predictions of component cover in the changed portion of that year, while the base year prediction was maintained in unchanged areas.…”

“…Each data set provides complementary information: Remotely sensed data typically span larger spatiotemporal scales than field data, whereas field data often contain more detailed information on species and functional groups present at a plot. Remotely sensed Landsat images of the Great Basin spanning more than 30 yr between 1984 and 2016 have recently been analyzed to estimate temporal changes in various fractional ground cover types in sagebrush steppe in relation to disturbances and climate change , Xian et al 2012, Shi et al 2018, Rigge et al 2019. These maps of fractional ground cover types, referred to as "components," were produced using regression tree models trained with field-sampled data linked with high-resolution imagery, and subsequently with Landsat and biophysical data as model inputs , Shi et al 2018.…”