Rangelands are critical working landscapes and are the focus of considerable conservation planning efforts globally. A key conservation challenge in these landscapes is that high interannual variability in both climatic conditions and land use greatly limits the utility of outdated or static vegetation maps for management decision-making. One potential solution to this problem lies in remote sensing-derived information; however, prospective users must have continuous and timely access to vegetation products tailored to their needs. Google Earth Engine (GEE) can overcome the many storage, processing, and visualization barriers associated with creating ready-to-use remote sensing products for the public. While GEE provides a platform for building tools to analyze data and share results with users in near real-time for adaptive management, monitoring products need to (1) provide accurate and stable estimates over time and (2) align with management goals and the ecology of the rangeland system in question. Here, we assess estimates of vegetation cover and above-ground biomass at two dominant phenological time periods (summer/green and fall/brown), as modeled from the Landsat 7 and Landsat 8 Climatic Data Record (CDR) product. Using a best-subset regression modeling approach, we modeled vegetation cover and biomass, finding that the best predictors vary by season, corresponding to vegetation phenology. We also found that sensor-specific models decreased the relative differences between mapped cover and biomass estimates when comparing Landsat 7 and Landsat 8 scenes one day apart in the summer and fall. Ultimately, we developed an automated model selection process driven by sensor and vegetation greenness that can run in GEE to monitor and analyze vegetation amounts across the grazing season for adaptive management.

Grassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.

Climate change is well documented at the global scale, but local and regional changes are not as well understood. Finer, local- to regional-scale information is needed for creating specific, place-based planning and adaption efforts. Here the development of an indicator-focused climate change assessment in Idaho is described. This interdisciplinary framework couples end users’ data needs with observed, biophysical changes at local to regional scales. An online statewide survey of natural resource professionals was conducted to assess the perceived impacts from climate change and determine the biophysical data needed to measure those impacts. Changes to water resources and wildfire risk were the highest areas of concern among resource professionals. Guided by the survey results, 15 biophysical indicator datasets were summarized that included direct climate metrics (e.g., air temperature) and indicators only partially influenced by climate (e.g., wildfire). Quantitative changes in indicators were determined using time series analysis from 1975 to 2010. Indicators displayed trends of varying likelihood over the analysis period, including increasing growing-season length, increasing annual temperature, increasing forest area burned, changing mountain bluebird and lilac phenology, increasing precipitation intensity, earlier center of timing of streamflow, and decreased 1 April snowpack; changes in volumetric streamflow, salmon migration dates, and stream temperature displayed the least likelihood. A final conceptual framework derived from the social and biophysical data provides an interdisciplinary case example useful for consideration by others when choosing indicators at local to regional scales for climate change assessments.