Geology topics

Barry J. Allred

Publications and source records attributed to Barry J. Allred.

4 recordsLinked to original sources

USGS2023

Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery

Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

Indiana, Michigan, Ohio, Wisconsin↗

USGS2020

Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes

Effective and efficient methods are needed to map agricultural subsurface drainage systems. Visible-color (VIS-C), multispectral (MS), and thermal infrared (TIR) imagery obtained by unmanned aerial vehicles (UAVs) may provide a means for determining drainage pipe locations. Aerial surveys using a UAV with VIS-C, MS, and TIR cameras were conducted at 29 agricultural field sites in the Midwest U.S.A. to evaluate the potential of this technology for mapping buried drainage pipes. Overall results show VIS-C imagery detected at least some drain lines at 48 % of the sites (14 out of 29), MS imagery detected drain lines at 59 % of the sites (17 out of 29), and TIR imagery detected drain lines at 69 % of the sites (20 out of 29). Three key findings, listed as follows and emphasized in this article by site examples, were extracted from the overall results. (1) Although TIR generally worked best, there were sites where either VIS-C or MS proved more effective than TIR for mapping subsurface drainage systems. Consequently, to ensure the greatest chance for successfully determining drainage pipe patterns in a field, UAV surveys need to be carried out with all three types of cameras, VIS-C, MS, and TIR. (2) Timing of UAV surveys relative to recent rainfall can sometimes have an important impact on drainage pipe detection results. (3) Linear features representing drain lines and farm field operations can be confused with one another and are often both depicted on site aerial imagery. Knowledge of subsurface drainage system installation and farm field operations can be employed to distinguish linear features representing drain lines from those representing farm field operations. The overall results and extracted key findings from this study clearly indicate that VIS-C, MS, and TIR imagery obtained with UAVs have significant potential for use in mapping agricultural drainage pipe systems.

Indiana, Iowa, Michigan, Ohio↗

USGS2018

Delineation of tile-drain networks using thermal and multispectral imagery—Implications for water quantity and quality differences from paired edge-of-field sites

As part of the Great Lakes Restoration Initiative, paired edge-of-field sites were established in high priority subwatersheds to assess the effectiveness of agricultural management practices. One pairing was in Black Creek, a tributary to the Maumee River and Lake Erie. These fields were paired because of similarity in soils, topography, and agricultural management. Following two years of baseline data collection from these fields, consistent differences in water quantity and quality were observed for tile networks draining the fields, despite these fields being adjacent and managed together. Consequently, it was hypothesized that differences in subsurface water movement, specifically tile-drain density and connectivity, were the source of the observed differences. Our objective was to map the tile-drain network using remote sensing methodology in order to improve the understanding of nutrient and water transport as well as management on these fields. A combination of multispectral and thermal imagery, collected in spring of 2017, was incorporated to delineate the tile-drain network within each field. This imagery led to locating a cracked tile, which provided a direct path for overland flow to enter the tile-drain system and suggested that a tile-drain segment under the road connected the two fields. A ground-penetrating radar survey verified multiple tile locations, including the tile segment under the road. The distribution of these tiles helps explain the difference in water quantity and quality in the two fields.

Journal of Soil and Water Conservation↗

USGS2010

Introduction to the JEEG Agricultural Geophysics Special Issue

Near-surface geophysical methods have become increasingly important tools in applied agricultural practices and studies. The great advantage of geophysical methods is their potential rapidity, low cost, and spatial continuity when compared to more traditional methods of assessing agricultural land, such as sample collection and laboratory analysis. Agricultural geophysics investigations commonly focus on obtaining information within the soil profile, which generally does not extend much beyond 2 meters beneath the ground surface. Although the depth of interest oftentimes is rather shallow, the area covered by an agricultural geophysics survey can vary widely in scale, from experimental plots (10 s to 100 s of square meters), to farm fields (10 s to 100 s of hectares), up to the size of watersheds (10 s to 100 s of square kilometers). To date, three predominant methods—resistivity, electromagnetic induction (EMI), and ground-penetrating radar (GPR)—have been used to obtain surface-based geophysical measurements within agricultural settings. However, a recent conference on agricultural geophysics (Bouyoucos Conference on Agricultural Geophysics, September 8–10, 2009, Albuquerque, New Mexico; www.ag-geophysics.org) illustrated that other geophysical methods are being applied or developed. These include airborne electromagnetic induction, magnetometry, seismic, and self-potential methods. Agricultural geophysical studies are also being linked to ground water studies that utilize deeper penetrating geophysical methods than normally used.

Journal of Environmental & Engineering Geophysics↗