Tue, Aug 03, 2021:On Demand
Background/Question/Methods
Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. Nevertheless, local monitoring is critical for natural resource managers to establish baseline conditions by which future changes, including change resulting from management actions and natural ecological processes, can be assessed. In this study, we developed a remote sensing framework to aid land managers in monitoring upland and riparian deciduous systems for the role of climate as a top-down control on vegetation condition. First, we created species distribution model to develop a fine scale, baseline map of deciduous vegetation using phenologic predictors. We segregated upland and riparian vegetation using a topographic wetness index. Then, we evaluated a 35-year Landsat time series to assess the rate and direction of change in the mapped deciduous vegetation with respect to climate (drought and non-drought periods). Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition.
Results/Conclusions The overall out-of-bag error estimate of the deciduous model was low (10.1%) and captured the juxtaposition of deciduous vegetation, including small, linear patches associated with woody, riparian vegetation. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12 years followed by a 9-year browning trend, before switching back to a greening trend for the last 13 years of the study. Our results indicate greening and browning trends are driven by the prevailing climate of the measurement period at the landscape scale, even in riparian environments. At the plot level we found little variability between the mean annual growing season Normalized Difference Vegetation Index and browse pressure assessed in the field. However, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. By first identifying top-down controls on vegetation condition, managers can then incorporate trends as indicators to assess the influence of bottom-up processes. Collectively, this information can be utilized at local scales to select habitat treatment, restoration, or field monitoring locations, and in turn, move from monitoring at the scale of individual projects toward an understanding of long-term vegetation trends at the landscape scale.
Results/Conclusions The overall out-of-bag error estimate of the deciduous model was low (10.1%) and captured the juxtaposition of deciduous vegetation, including small, linear patches associated with woody, riparian vegetation. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12 years followed by a 9-year browning trend, before switching back to a greening trend for the last 13 years of the study. Our results indicate greening and browning trends are driven by the prevailing climate of the measurement period at the landscape scale, even in riparian environments. At the plot level we found little variability between the mean annual growing season Normalized Difference Vegetation Index and browse pressure assessed in the field. However, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. By first identifying top-down controls on vegetation condition, managers can then incorporate trends as indicators to assess the influence of bottom-up processes. Collectively, this information can be utilized at local scales to select habitat treatment, restoration, or field monitoring locations, and in turn, move from monitoring at the scale of individual projects toward an understanding of long-term vegetation trends at the landscape scale.