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At least 1,639 records · Page 91Linked to original sources

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗

USGS2016

Thermal mapping of a pahoehoe lava flow, Kilauea Volcano

Pāhoehoe lava flows are a major component of Hawaiian eruptive activity, and an important part of basaltic volcanism worldwide. In recent years, pāhoehoe lava has destroyed homes and threatened parts of Hawai‘i with inundation and disruption. In this study, we use oblique helicopter-borne thermal images to create high spatial resolution (~1 m) georeferenced thermal maps of the active pāhoehoe flow on Kīlauea Volcano’s East Rift Zone. Thermal maps were created on 27 days during 2014–2016 in the course of operational monitoring, encompassing a phase of activity that threatened the town of Pāhoa. Our results illustrate and reinforce how pāhoehoe flows are multicomponent systems consisting of the vent, master tube, distributary tubes and surface breakouts. The thermal maps accurately depict the distribution and character of pāhoehoe breakouts through time, and also delineate the subsurface lava tube. Surface breakouts were distributed widely across the pāhoehoe flow, with significant portions concurrently active well upslope of the flow front, often concentrated in clusters of activity that evolved through time. Gradual changes to surface breakout distribution and migration relate to intrinsic processes in the flow, including the slow evolution of the distributary tube system. Abrupt disruptions to this system, and the creation of new breakouts (and associated hazards), were triggered by extrinsic forcing—namely fluctuations in lava supply rate at the vent which disrupted the master lava tube. Although the total area of a pāhoehoe flow has been suggested to relate to effusion rate, our results show that changes in the proportion of expansion vs. overplating can complicate this relationship. By modifying existing techniques, we estimate time-averaged discharge rates for the flow during 2014–2016 generally in the range of 1–2 m3 s-1 (mean: 1.3±0.4 m3 s-1) – less than half of Kīlauea’s typical eruption rate on the East Rift Zone and suggestive of a weak eruptive regime during 2014–2016. We caution, however, that this discharge rate approach requires further independent corroboration. The thermal maps provide the first synoptic characterization of pāhoehoe flow activity at high spatial resolution, essential both for operational hazard assessment and fundamental understanding of pāhoehoe behavior.

Hawaii↗

USGS2017

The California Seafloor and Coastal Mapping Program – Providing science and geospatial data for California's State Waters

The California Seafloor and Coastal Mapping Program (CSCMP) is a collaborative effort to develop comprehensive bathymetric, geologic, and habitat maps and data for California's State Waters. CSCMP began in 2007 when the California Ocean Protection Council (OPC) and the National Oceanic and Atmospheric Administration (NOAA) allocated funding for high-resolution bathymetric mapping, largely to support the California Marine Life Protection Act and to update nautical charts. Collaboration and support from the U.S. Geological Survey and other partners has led to development and dissemination of one of the world's largest seafloor-mapping datasets. CSCMP provides essential science and data for ocean and coastal management, stimulates and enables research, and raises public education and awareness of coastal and ocean issues. Specific applications include: •Delineation and designation of marine protected areas •Characterization and modeling of benthic habitats and ecosystems •Updating nautical charts •Earthquake hazard assessments •Tsunami hazard assessments •Planning offshore infrastructure •Providing baselines for monitoring change •Input to models of sediment transport, coastal erosion, and coastal flooding •Regional sediment management •Understanding coastal aquifers •Providing geospatial data for emergency response

California↗

USGS2015

Updating the USGS seismic hazard maps for Alaska

The U.S. Geological Survey makes probabilistic seismic hazard maps and engineering design maps for building codes, emergency planning, risk management, and many other applications. The methodology considers all known earthquake sources with their associated magnitude and rate distributions. Specific faults can be modeled if slip-rate or recurrence information is available. Otherwise, areal sources are developed from earthquake catalogs or GPS data. Sources are combined with ground-motion estimates to compute the hazard. The current maps for Alaska were developed in 2007, and included modeled sources for the Alaska-Aleutian megathrust, a few crustal faults, and areal seismicity sources. The megathrust was modeled as a segmented dipping plane with segmentation largely derived from the slip patches of past earthquakes. Some megathrust deformation is aseismic, so recurrence was estimated from seismic history rather than plate rates. Crustal faults included the Fairweather-Queen Charlotte system, the Denali–Totschunda system, the Castle Mountain fault, two faults on Kodiak Island, and the Transition fault, with recurrence estimated from geologic data. Areal seismicity sources were developed for Benioff-zone earthquakes and for crustal earthquakes not associated with modeled faults. We review the current state of knowledge in Alaska from a seismic-hazard perspective, in anticipation of future updates of the maps. Updated source models will consider revised seismicity catalogs, new information on crustal faults, new GPS data, and new thinking on megathrust recurrence, segmentation, and geometry. Revised ground-motion models will provide up-to-date shaking estimates for crustal earthquakes and subduction earthquakes in Alaska.

Quaternary Science Reviews↗

USGS2017

Mapping tree canopy cover in support of proactive prairie grouse conservation in western North America

Invasive woody plant expansion is a primary threat driving fragmentation and loss of sagebrush ( Artemisia spp.) and prairie habitats across the central and western United States. Expansion of native woody plants, including conifer (primarily Juniperus spp.) and mesquite ( Prosopis spp.), over the past century is primarily attributable to wildfire suppression, historic periods of intensive livestock grazing, and changes in climate. To guide successful conservation programs aimed at reducing top-down stressors, we mapped invasive woody plants at regional scales to evaluate landscape level impacts, target restoration actions, and monitor restoration outcomes. Our overarching goal was to produce seamless regional products across sociopolitical boundaries with resolution fine enough to depict the spatial extent and degree of woody plant invasion relevant to greater sage-grouse (Centrocercus urophasianus) and lesser prairie-chicken (Tympanuchus pallidicinctus) conservation efforts. We mapped tree canopy cover at 1-m spatial resolution across an 11-state region (508 265 km 2 ). Greater than 90% of occupied lesser prairie-chicken habitat was largely treeless for conifers (< 1% canopy cover), whereas > 67% was treeless for mesquite. Conifers in the higher canopy cover classes (16 − 50% and > 50% canopy cover) were scarce (< 2% and 1% canopy cover), as was mesquite (< 5% and 1% canopy cover). Occupied habitat by sage-grouse was more variable but also had a relatively large proportion of treeless areas ( x − "> x− = 71, SE = 5%). Low to moderate levels of conifer cover (1 − 20%) were fewer ( x − "> x− = 23, SE = 5%) as were areas in the highest cover class (> 50%; x − "> x− = 6, SE = 2%). Mapping indicated that a high proportion of invading woody plants are at a low to intermediate level. Canopy cover maps for conifer and mesquite resulting from this study provide the first and most geographically complete, high-resolution assessment of woody plant cover as a top-down threat to western sage-steppe and prairie ecosystems.

Rangeland Ecology and Management↗

USGS2005

Mineral mapping on the Chilean-Bolivian Altiplano using co-orbital ALI, ASTER and Hyperion imagery: Data dimensionality issues and solutions

Hyperspectral data coverage from the EO-1 Hyperion sensor was useful for calibrating Advanced Land Imager (ALI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images of a volcanic terrane area of the Chilean-Bolivian Altiplano. Following calibration, the ALI and ASTER datasets were co-registered and joined to produce a 13-channel reflectance cube spanning the Visible to Short Wave Infrared (0.4-2.4 ??m). Eigen analysis and comparison of the Hyperion data with the ALI + ASTER reflectance data, as well as mapping results using various ALI+ASTER data subsets, provided insights into the information dimensionality of all the data. In particular, high spectral resolution, low signal-to-noise Hyperion data were only marginally better for mineral mapping than the merged 13-channel, low spectral resolution, high signal-to-noise ALI + ASTER dataset. Neither the Hyperion nor the combined ALI + ASTER datasets had sufficient information dimensionality for mapping the diverse range of surface materials exposed on the Altiplano. However, it is possible to optimize the use of the multispectral data for mineral-mapping purposes by careful data subsetting, and by employing other appropriate image-processing strategies.

Remote Sensing of Environment↗

USGS2018

Mapping forest change using stacked generalization: An ensemble approach

The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.

Remote Sensing of Environment↗

USGS2022

Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia

The Glacier Bay area in southeastern Alaska and British Columbia, encompassing Glacier Bay National Park and Preserve, has experienced rapid glacier retreat since the end of the Little Ice Age in the mid-1800s. The impact that rapid deglaciation has had on the slope stability of valley walls and on the sedimentation of fans and deltas adjacent to fjords and inlets is an ongoing research topic. Using 3-year (2018–2020) Sentinel-1 datasets, and an automated time-series persistent scatterer interferometric synthetic aperture radar (PSInSAR) processing method, we detected landslides or delta subsidence at 27 sites within a vast 180 × 180 km remote region encompassing Glacier Bay proper. Most of the sites that we identified had not been previously identified. We categorized the hazard source areas that we identified into three general types:1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini (e.g., Tidal Inlet landslide), 2) slow-moving landslides directly adjacent to (< 2 km away from), and associated with glacier thinning and retreat, and 3) subsidence of glacial outwash fan deltas. In categories 1 and 2, we observed 22 landslides moving at velocities ranging from 0.5 to 4 cm/yr. In category 3, we detected five fan deltas subsiding at velocities ranging from 0.5 to 6 cm/yr. Within our measurement error, these velocities were consistent during the monitoring period. Because acceleration was not observed, the issuance of warnings of imminent rapid failure is not warranted, however, continued remote monitoring is warranted. Our interferometric synthetic aperture radar (InSAR) results could be combined with other data sets including field observations, subaerial and submarine landslide inventories, bedrock fabric mapping from newly available light detection and ranging (lidar) data, and geologic maps to produce an inherent susceptibility map for landslides in bedrock and fan deltas. This map could be used to forecast susceptibility for both earthquake and climatically induced landslides.

Alaska, British Columbia↗

USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗

USGS2000

A method for producing digital probabilistic seismic landslide hazard maps

The 1994 Northridge, California, earthquake is the first earthquake for which we have all of the data sets needed to conduct a rigorous regional analysis of seismic slope instability. These data sets include: (1) a comprehensive inventory of triggered landslides, (2) about 200 strong-motion records of the mainshock, (3) 1:24 000-scale geologic mapping of the region, (4) extensive data on engineering properties of geologic units, and (5) high-resolution digital elevation models of the topography. All of these data sets have been digitized and rasterized at 10 m grid spacing using ARC/INFO GIS software on a UNIX computer. Combining these data sets in a dynamic model based on Newmark's permanent-deformation (sliding-block) analysis yields estimates of coseismic landslide displacement in each grid cell from the Northridge earthquake. The modeled displacements are then compared with the digital inventory of landslides triggered by the Northridge earthquake to construct a probability curve relating predicted displacement to probability of failure. This probability function can be applied to predict and map the spatial variability in failure probability in any ground-shaking conditions of interest. We anticipate that this mapping procedure will be used to construct seismic landslide hazard maps that will assist in emergency preparedness planning and in making rational decisions regarding development and construction in areas susceptible to seismic slope failure. ?? 2000 Elsevier Science B.V. All rights reserved.

Engineering Geology↗

USGS2003

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).

Wyoming↗

USGS2001

Infrasound and the avian navigational map

Birds can accurately navigate over hundreds to thousands of kilometres, and use celestial and magnetic compass senses to orient their flight. How birds determine their location in order to select the correct homeward bearing (map sense) remains controversial, and has been attributed to their olfactory or magnetic senses. Pigeons can hear infrasound down to 0??05 Hz, and an acoustic avian map is proposed consisting of infrasonic cues radiated from steep-sided topographic features. The source of these infrasonic signals is microseisms continuously generated by interfering oceanic waves. Atmospheric processes affecting the infrasonic map cues can explain perplexing experimental results from pigeon releases. Moreover, four recent disrupted pigeon races in Europe and the north-eastern USA intersected infrasonic shock waves from the Concorde supersonic transport. Having an acoustic map might also allow clock-shifted birds to test their homeward progress and select between their magnetic and solar compasses.

Journal of Navigation↗

USGS2009

Environmental mapping of the World Trade Center area with imaging spectroscopy after the September 11, 2001 attack

The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was flown over the World Trade Center area on September 16, 18, 22, and 23, 2001. The data were used to map the WTC debris plume and its contents, including the spectral signatures of asbestiform minerals. Samples were collected and used as ground truth for the AVARIS mapping. A number of thermal hot spots were observed with temperatures greater than 700 °C. The extent and temperatures of the fires were mapped as a function of time. By September 23, most of the fires observed by AVIRIS had been eliminated or reduced in intensity. The mineral absorption features mapped by AVARIS only indicated the presence of serpentine mineralogy and not if the serpentine has asbestiform.

Book chapter↗

USGS2003

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to realtime resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

Environmental Monitoring and Assessment↗

USGS2021

A preliminary regional geomorphologic map in Utopia Planitia of the Tianwen-1 Zhurong Landing Region

A geomorphologic map is an important step to understanding the geologic context and history of a site; here, we present an initial geomorphologic map for an area spanning 22°–26°N, 108°–112°E in the Utopia Planitia (UP) region on Mars. This site is of special interest because it contains the May 2021 landing site of the Zhurong rover from Tianwen-1. Utopia Planitia exhibits many lobate features that have been proposed to be lava or mud flows. Lander and rover data should help solve the scientific question concerning the origin of UP flows. We use our map to generate an initial stratigraphic framework of geomorphological features in order to help place future Zhurong data into the regional geologic context. Our mapping effort has detailed the distribution of three geomorphologic units and 11 types of surface features.

Geophysical Research Letters↗

USGS2022

Mapping a magnetic superstorm: March 1989 geoelectric hazards and impacts on United States power systems

A study is made of the relationships between geomagnetic and geoelectric field variation, Earth-surface impedance, and operational interference (anomalies) experienced on electric-power systems across the contiguous United States during the March 13-14, 1989 magnetic storm. For this, a 1-minute-resolution sequence of geomagnetic field maps is constructed from magnetometer time series acquired at ground-based observatories. Induced geoelectric field maps are calculated by convolving the geomagnetic maps with magnetotelluric impedance tensors. During the storm, anomalies were concentrated where the lithosphere is electrically resistive, and when and where geoelectric field amplitudes were high. This was particularly true in the Mid-Atlantic, Northeast, and the upper Midwest. Few anomalies were experienced in other parts of the Midwest and across much of the West, where the lithosphere is more conductive, and when and where geoelectric field amplitudes were low. Peak 1-minute-resolution geoelectric field amplitude ranged from 21.66 V/km in Maine and 19.02 V/km in Virginia to < 0.02 V/km in Idaho. Latitude-dependent organization of geoelectric hazards by auroral-zone electrojet currents is detectable, but it is much weaker than geographic organization due to surface impedance. Hazardous geoelectric fields were induced during different storm phases, at different local times, and, by inference, by a variety of ionospheric currents. Compared to geoelectric field amplitudes realized across the United States during March 1989, hazard maps used by utility companies to estimate systems exposure have much less geographic detail and a much smaller maximum-to-minimum range in geoelectric field amplitude. Future research will benet from denser geomagnetic monitoring, additional magnetotelluric surveying, and access to power-system impact data.

Space Weather↗

USGS2025

Multi-scale spectroscopy to map intertidal microbial biofilm community and trait diversity

Intertidal microbial biofilms, or microphytobenthos, support estuarine biogeochemical cycling, the physical stability of mudflats, and food webs, particularly those of migratory shorebirds. Photosynthetic biofilms dominated by diatoms, cyanobacteria, and chlorophytes represent a significant fraction of biofilm biomass and contain pigments that can be detected with remote sensing. These diverse biofilm community types vary in indicator pigments and functional traits related to biogeochemical cycling and nutritional quality. We modeled and mapped spatial variation in intertidal biofilm distribution, quantity, diversity, and functional traits using multi-scale spectroscopic data collected within southern San Francisco Bay, California, USA (South SFB). We developed a new biofilm index (B-index) from 5 mm HySpex spectra to detect biofilm presence. We developed single and multiple response partial least squares regression (PLS) models of chlorophyll -a (chl -a ; biomass indicator), indicator pigments: fucoxanthin and diadinoxanthin (diatoms), zeaxanthin (cyanobacteria), and chl -b (chlorophytes), and functional traits: carbohydrates, lipids, and total organic carbon from paired in situ biofilm data and field spectra. The B-index and PLS models were scaled to South SFB with a 3.7 m AVIRIS-NG hyperspectral image. The model %RMSE calculated from AVIRIS-NG test samples ranged from 12.7% for chl -a to 49% for chl -b ; for six of the eight models, %RMSE was 23% or below. Mapped community types differed in mapped traits, with average lipid concentrations three times higher in areas indicated as diatoms compared to other groups. Available maps depict for the first time the spatial variation of an important shorebird food resource and inform the contribution of intertidal biofilm in carbon and nutrient cycling.

California↗

USGS2023

Mapping methane reduction potential of tidal wetland restoration in the United States

Coastal wetlands can emit excess methane in cases where they are impounded and artificially freshened by structures that impede tidal exchange. We provide a new assessment of coastal methane reduction opportunities for the contiguous United States by combining multiple publicly available map layers, reassessing greenhouse gas emissions datasets, and applying scenarios informed by geospatial information system and by surveys of coastal managers. Independent accuracy assessment indicates that coastal impoundments are under-mapped at the national level by a factor of one-half. Restorations of freshwater-impounded wetlands to brackish or saline conditions have the greatest potential climate benefit of all mapped conversion opportunities, but were rarer than other potential conversion events. At the national scale we estimate potential emissions reduction for coastal wetlands to be 0.91 Teragrams of carbon dioxide equivalents year −1 , a more conservative assessment compared to previous estimates. We provide a map of 1,796 parcels with the potential for tidal re-connection.

Communications Earth & Environment↗