Satellite and sUAS Multispectral Remote Sensing Analysis of Vegetation Response to Beaver Mimicry Restoration on Blacktail Creek, Southwest Montana

Abstract

Beaver dam analogs (BDAs) are being installed on streams where restoration goals include reconnecting the stream to its floodplain, increasing water storage in the stream corridor, and improving the extent and vigor of riparian vegetation. This study evaluated the effects on vegetation vigor of a BDA treatment on Blacktail Creek in southwest Montana, USA, using data from Sentinel-2 satellites and a small unmanned aerial system (sUAS; a.k.a. drone). The goal of this research was to determine if BDA installation increased the health of riparian vegetation. Sentinel-2 remote sensing data from 2016 to 2021 were used to compare the pre- and post-treatment periods, and to evaluate effects in the treated area relative to control areas. Enhanced Vegetation Index (EVI) values were calculated to quantify vegetation response from the addition of BDAs. These data suggest that installing BDAs at this site has not led to an apparent increase in late-summer vegetation vigor relative to the controls. One potential explanation for these results is that the vegetation was not water limited prior to treatment in this study reach. This is an important consideration for water resource managers prior to installation of BDAs if the main restoration goal is the improvement of riparian vegetation health. Two high spatial resolution sUAS multispectral datasets were collected to evaluate the bias introduced by using the relatively course resolution (10 m) satellite imagery to assess these changes. High-resolution sUAS data allow fine-scale differences in vegetation and inundated area to be distinguished; however, historical sUAS datasets are rarely available. Satellite-based remote sensing has much lower resolution; however, Sentinel-2 satellite data have been available for the entire earth since 2016. This study demonstrates that the combination of sUAS and satellite based remote sensing data provides a method to compare high-resolution datasets for spatial analysis while gaining insight into relatively low-resolution historical data for temporal analysis.