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Trent Biggs

Publications and source records attributed to Trent Biggs.

5 recordsLinked to original sources
USGS2026

Waves, watersheds, and sediment in a coral reef embayment: Towards parsimonious models of accumulation and composition

High sedimentation rates can damage coral reef ecosystems. Sedimentation rates are controlled by both sediment loads from watersheds and resuspension by waves and associated circulation patterns, but the outcomes are system specific and difficult to predict. The percent terrigenous (non-organic and non-carbonaceous) material in sediment is also often used as an indicator of watershed influence, but its dynamics are poorly understood. Sediment accumulation rates, particle size, and percent terrigenous were monitored quasi-monthly for one year (March 2014-April 2015) at nine sites in a coral reef-fringed embayment in American Samoa, where an aggregate quarry had increased sediment loads to the coast but mitigation reduced loads during the monitored period. Gross and net sediment accumulation rates were measured using sediment traps and SedPods (pods), respectively. Gross accumulation rates exceeded thresholds for impacts on coral health during at least one collection period at most sites, with more exceedances on the northern reef where water residence times and sediment availability are higher and corals show signs of sediment stress. Percent terrigenous of coarse sediment was higher in the traps and pods compared with the surrounding benthic sediment, indicating that some of the terrigenous sediment was advected through the bay without accumulating on the reef. The 95th percentile of hourly wave energy density ( E 95 ) taken from a global wave model (WaveWatch 3) was the best predictor of gross accumulation rates of both total and carbonate sediment in a log-log regression at most ( n = 6) sites ( R 2 range 0.72-0.92), indicating a strong role of resuspension of benthic sediment. Gross accumulation rates of terrigenous sediment were not correlated with E 95 and only correlated with SSY at the site nearest the stream mouth, indicating that most terrigenous sediment was not from resuspended benthic material but rather from a consistent watershed source. Percent terrigenous decreased with increasing wave energy due to high accumulation rates of carbonates during periods of high wave energy. Detection of the impact of sediment mitigation at the quarry on sediment accumulation was complicated by low wave energy in the period following mitigation. The use of gross accumulation rates and percent terrigenous as indicators of the magnitude and sources of sediment accumulation over time needs to account for wave-induced resuspension, which can be modelled with a simple power function using inputs from a global wave model.

Estuarine, Coastal and Shelf Science↗
USGS2025

Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud

Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.

conterminous United States↗
USGS2018

Crop water productivity estimation with hyperspectral remote sensing

Crop water productivity (CWP) is the ratio of accumulated crop biomass or yield (Y) to the water utilized to produce it, which is typically estimated using transpiration (ET C ). CWP is an important metric to test and monitor water-saving strategies in agroecosystems across the globe. Red and near-infrared broadbands have been used to estimate CWP, because they capture biophysical constraints based on crop-light interaction principles at pixel level (e.g., 30-meter resolution) over large areas through time. Hyperspectral remote sensing, which allows for the more precise measurement of crop-light interactions at higher spectral resolution, should in theory provide higher accuracy in CWP estimation but has been underutilized by the remote sensing community due to computational challenges and lack of availability. In this study, a simple methodology is presented to demonstrate how CWP could be estimated using hyperspectral remote sensing. Due to a lack of hyperspectral data, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data were used for the demonstration. Landsat is a broadband sensor that provides considerable spectral information for CWP estimation. New bands were identified in the workflow outside the typical Landsat bands used to estimate CWP and its components (Y and ET C ). Landsat bands 1 and 3 were the most effective at estimating CWP and Y with an R 2 of 0.72 (RMSE = 0.50 kg m −3 ) and 0.64 (RMSE = 0.31 kg m −2 ), respectively. All of the bands were poor at estimating ET C , with Landsat bands 1 and 7 being the most highly correlated (R 2 = 0.13, RMSE = 0.08 m). Future work should train models with multiple estimates of CWP and Y over the growing season, while ET C may be better estimated with thermal infrared bands not considered in this study. Finally, studies should also consider estimating CWP categorically, instead of continuously, if the same objectives of testing and monitoring are met.

Book chapter↗
USGS2016

Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)

Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimate crop ET. Ratio-based vegetation indices retrieved from optical remote sensing, like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index are critical components of these models, particularly for the partitioning of ET into transpiration and soil evaporation. These indices have their limitations, however, and can induce large model bias and error. In this study, micrometeorological and spectroradiometric data collected over two growing seasons in cotton, maize, and rice fields in the Central Valley of California were used to identify spectral wavelengths from 428 to 2295 nm that produced the highest correlation to and lowest error with ET, transpiration, and soil evaporation. The analysis was performed with hyperspectral narrowbands (HNBs) at 10 nm intervals and multispectral broadbands (MSBBs) commonly retrieved by Earth observation platforms. The study revealed that (1) HNB indices consistently explained more variability in ET (Δ R 2 = 0.12), transpiration (Δ R 2 = 0.17), and soil evaporation (Δ R 2 = 0.14) than MSBB indices; (2) the relationship between transpiration using the ratio-based index most commonly used for ET modeling, NDVI, was strong ( R 2 = 0.51), but the hyperspectral equivalent was superior ( R 2 = 0.68); and (3) soil evaporation was not estimated well using ratio-based indices from the literature (highest R 2 = 0.37), but could be after further evaluation, using ratio-based indices centered on 743 and 953 nm ( R 2 = 0.72) or 428 and 1518 nm ( R 2 = 0.69).

California↗