UC Berkeley

Abstract This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring. Plain Language Summary In this study, we aim to understand how soil and topographic properties influence the spatial distribution of plant communities within a floodplain‐hillslope system, located in a mountainous East River watershed in Colorado. Watersheds are vulnerable to environmental change, including earlier snowmelt, changes in precipitation, and temperature trends, all of which can alter plant communities and associated water and nutrient cycles within the watershed. However, tractable yet accurate quantification of plant communities is challenging to do at a scale that also permits investigations of the key controls on the distribution. Here we developed a framework that uses a new approach to estimate plant distributions, one which exploits both remote sensing (satellite) images and surface geophysical data. Joint consideration of the aboveground‐and‐belowground data sets allows us to characterize both plant and soil properties at high spatial resolution and to identify the main environmental controls for plant distribution. In our analysis, we found that soil moisture and microtopography characteristics influence how plant communities are spatially distributed. Considering that each community responds to external perturbation in a different way, this method can be used within a multitemporal framework to characterize, temporally, the environmental heterogeneity at local scale and capture plant responses caused by climate‐related perturbations. Key Points Fusion of satellite and LiDAR data, along with contextual information, enabled the estimation of key meadow communities at high resolution Combining remote sensing and surface ERT data allowed us to explore the influence of topographic and soil properties on plant distribution The topographic slope acts as a significant control on soil moisture and associated plant community distribution at hillslope scale