Riparian ecosystems are biodiversity hotspots that are often subject to intensive human activities, such as agriculture, urban growth, and water regulation. High rates of groundwater pumping and surface water diversions deplete aquifers and alter natural streamflow regimes, increasing water stress for riparian and riverine ecosystems. Natural resource management at a catchment or ecosystem scale is challenged in part due to its inherent interdisciplinary nature and knowledge gaps between biologists, physical scientists and water resource practitioners. In addition, biological data is often collected at different spatial and time scales compared to relevant hydrologic and geospatial data, making integration difficult. While remote sensing provides a scalable approach for monitoring landscape changes in vegetation, the resolution of many long-term satellites (e.g., MODIS, Landsat) have often been too coarse to detect changes in riparian ecosystems, which tend to be fragmented and narrow. However, the recently available (since June 2015) Sentinel-2 satellite imagery at 10-m resolution is much better suited for detecting landscape-level change in riparian ecosystems. Exploiting this new opportunity, we developed a user-friendly approach to leverage large, publicly-available datasets using Google Earth Engine to analyze drivers of riparian vegetation change in response to groundwater availability in California at a state-wide scale.
Results/Conclusions
In our study we analyzed the relationship between normalized difference vegetation index (NDVI) and depth to groundwater for four plant assemblages dominated by groundwater-dependent woody species - cottonwood, willow, valley oak, and coastal live oak - that have been mapped by the California Department of Water Resources, California Department of a Fish & Wildlife, and The Nature Conservancy. Regardless of the season or hydrological region, NDVI for all four of the vegetation communities declined with increasing depth to groundwater (DTW; p<0.05). ANCOVA results showed that cottonwood-dominated stands had a greater reliance on groundwater (i.e., a steeper slope of NDVI change with DTW) during the dry summer months in comparison to the spring, when surface flow and precipitation is available (p<0.05). In addition to temporal differences in the NDVI and DTW relationship, our results showed that for a given vegetation community, the sensitivity to groundwater depth varied across hydrological regions. This study illustrates a new opportunity to monitor riparian ecosystems by using the high temporal and spatial resolution of Sentinel-2 imagery, combined with state-wide hydrology datasets and the computing power of Google Earth Engine.