Geology topics

Jerry D. Davis

Publications and source records attributed to Jerry D. Davis.

3 recordsLinked to original sources

USGS2025

UAS-based geomorphic change detection of incised montane meadow stream channels with low-tech process-based restoration treatments

Context Montane meadows play an important hydrologic role in headwater catchments, but past land use has largely degraded their condition. Low-tech restoration methods, such as beaver dam analogs (BDAs), are increasingly used to support recovery of incised streams by promoting key geomorphic processes. However, there remains a need for studies that leverage UAS for monitoring low-tech restoration treatments in incised meadow systems. Objectives This study maps and characterizes geomorphic changes in two incised meadow stream channels in Red Clover Valley, CA with installed beaver dam analog structures. We used UAS-based photogrammetric surveys to track changes over a three-year period (2021–2023). Methods Geomorphic change was assessed using DEM differencing with error thresholding, repeat geomorphic unit (GU) classification, and Shannon Diversity Index (SHDI) to measure spatial shifts in geomorphic complexity. Results Geomorphic responses varied by site and survey period. The subchannel B (SCB) site exhibited net deposition, while the lower Dixie Creek (LDC) site showed net erosion. BDAs appeared to enhance geomorphic activity, particularly in LDC, where near BDA areas showed greater sediment deposition and localized erosion compared to reference sites. SHDI values were positively correlated with erosion at both sites, suggesting that erosional processes may have promoted geomorphic diversity by creating or reorganizing GU in the incised channels. Conclusions UAS-SfM surveys captured erosion and deposition patterns and revealed the influence of BDAs and local channel characteristics on geomorphic change and unit diversity. These findings highlight the utility of UAS methods for monitoring restoration impacts in incised montane meadow streams.

California↗

USGS2024

An open-source workflow for scaling burn severity metrics from drone to satellite to support post-fire watershed management

Wildfires are increasing in size and severity across much of the western United States, exposing vulnerable wildland-urban interfaces to post-fire hazards. The Mediterranean chaparral region of Northern California contains many high sloping watersheds prone to hazardous post-fire flood events and identifying watersheds at high risk of soil loss and debris flows is a priority for post-fire response and management. Uncrewed Aerial Systems (UAS; aka drones) offer post-fire management teams the ability to quickly mobilize and survey burned areas with very high-resolution imagery (∼1 cm), facilitating emergency management and post-fire hazard assessment. However, adoption of this technology by hazard response teams may be hindered by complicated workflows for UAS data acquisition, image processing and analysis. We present an open-source workflow using mature Geographic Information Systems (GIS) software and Python packages in a Jupyter Notebook environment that guides users through classification of true-color UAS imagery to generate high resolution burn severity maps which can then be scaled across larger watersheds using Sentinel-2 normalized burn ratio (NBR) images. Soil burn severity classifications using a weighted brightness (WB) image and Char Index (CI) generated from UAS imagery were validated with in-situ data and random stratified points, resulting in the CI having the highest overall accuracy of 87.5%. CI also displayed a marginally stronger relationship over the WB with the post-fire Sentinel-2 NBR, R 2 = 0.79 and R 2 = 0.78 respectively. Our methods offer the unique opportunity to standardize GIS workflows, promoting replication through transparency, while improving the user's understanding of scientific GIS functionality.

California↗

USGS2017

Comparing automated classification and digitization approaches to detect change in eelgrass bed extent during restoration of a large river delta

Native eelgrass ( Zostera marina ) is an important contributor to ecosystem services that supplies cover for juvenile fish, supports a variety of invertebrate prey resources for fish and waterbirds, provides substrate for herring roe consumed by numerous fish and birds, helps stabilize sediment, and sequesters organic carbon. Seagrasses are in decline globally, and monitoring changes in their growth and extent is increasingly valuable to determine impacts from large-scale estuarine restoration and inform blue carbon mapping initiatives. Thus, we examined the efficacy of two remote sensing mapping methods with high-resolution (0.5 m pixel size) color near infrared imagery with ground validation to assess change following major tidal marsh restoration. Automated classification of false color aerial imagery and digitized polygons documented a slight decline in eelgrass area directly after restoration followed by an increase two years later. Classification of sparse and low to medium density eelgrass was confounded in areas with algal cover, however large dense patches of eelgrass were well delineated. Automated classification of aerial imagery from unsupervised and supervised methods provided reasonable accuracies of 73% and hand-digitizing polygons from the same imagery yielded similar results. Visual clues for hand digitizing from the high-resolution imagery provided as reliable a map of dense eelgrass extent as automated image classification. We found that automated classification had no advantages over manual digitization particularly because of the limitations of detecting eelgrass with only three bands of imagery and near infrared.

Washington↗