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At least 55 records · Page 3Linked to original sources
USGS2025

Automated methods for processing camera trap video data for distance sampling

Context Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’) offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process. Aims To develop an automated pipeline for processing videos of ungulates (Philippine deer, Rusa marianna ; and pigs, Sus scrofa ) on Andersen Air Force Base in Guam. Methods We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved. Key results The object detection and classification model achieved an overall accuracy >80% and F1 score ≥0.9 and saved 36.9 h of processing time. The automated distance estimation was fairly accurate, with a 1.1 m (±1.4 m) difference from manual distance estimates, and saved 16.8 h of processing time. Density estimates did not differ substantially between manual and automated distance estimation. Conclusions Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data. Implications Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.

Guam↗
USGS1989

Diapiric transfer of melt in Kilauea Iki lava lake, Hawaii: A quick, efficient process of igneous differentiation

Kilauea Iki lava lake, formed in 1959, is a large pond of picritic basalt (average MgO content = 15.34% by weight), which has cooled and crystallized as a small, self-roofed magma chamber. Repeated drilling of the upper crust of the lake, down to its molten core, and more recent (1981) drilling, through the thermal maximum and part way through the lower crust, have made it possible to monitor the differentiation processes in the lake in detail. Differentiation processes recognized as active in the lake include rather inefficient settling of the larger (2-10 mm) olivine phenocrysts, formation of segregation veins, and formation of diapir-like vertical olivine-rich bodies, all processes which occur in one or more of the other Kilauean lava lakes as well. In addition, most of the central part of Kilauea Iki has been affected by diapiric melt transfer. In this process, relatively low-density liquid, present at 1145-1160 °C, rose from within the loose crystal mush at the base of the lens of melt and intruded the equivalent thermal horizon at the top of the lens of melt, passing through the hotter but denser melt in the core of the lake without mixing. The source volume from which the low-density liquid was extracted is depleted in TiO 2 and other incompatible elements and enriched in FeO and CaO. The upper part of the lake shows the opposite effects. The crystalline assemblage present was olivine + augite + minor plagioclase. The crystallinity of both the source and receiving layers was low enough that no obvious textural imprint was left by the transfer process; the principal evidence for its occurrence is the chemical zonation of the lake seen in core from depths of 13 to 80 m. Diapiric melt transfer was active from 1960 to 1971 and has affected most of the central part of the lake from 13 m to at least 80 m. The process ran simultaneously with the other three main differentia tion processes but started and stopped independently of the others. Calculations suggest that between 21 and 42 wt % liquid has been extracted from the depleted zone at 56-78 m in the center of the lake, making this a very efficient process of chemical differentiation.

Hawaii↗
USGS2006

Hydratools manual version 1.0, documentation for a MATLAB ® -based post-processing package for the Sontek Hydra

The Sediment Transport Instrumentation Group (STG) at the U.S. Geological Survey (USGS) Woods Hole Science Center has a long-standing comitment to providing scientists with high quality oceanographic data. To meet this commitment, STG personnel are vigilant in checking data as well as hardware for signs of instrument malfunction. STG data sets are accompanied by processing histories to detail data processing procedures that may have modified the natural data signal while removing noise from the data. The history also allows the data to be reprocessed in the ligth of new insight into instrument function and moored conditions. This toolbox was compiled to meet these data quality commitments for data generated by Sontek Hydra systems using both ADV and PCADP probes. In the mid 1900's, the USGS Coastal and Marine Program began frequent deployments of Sontek Hydra systems in support of projects in estuaries, coastal, and continental shelf regions nationwide. Hydra data sets are large and complex in structure, and existing processing and editing tools consisted of fragments of MATLAB code written by USGS scientists to satisfy personal research needs. This code did not meet STG quality control criteria. This toolbox permits engineers and scientists to monitor data quality by: 1. processing data with interactive critical review; 2. preserving data quality indicators; 3. preserving minimally processed and partially processed versions of data sets. STG usually deploys ADV and PCADP probes configured as downward looking, mounted on bottom tripods, with the objective of measuring high-resolution near-bed currents. The velocity profiles are recorded with minimal internal data processing. Also recorded are parameters such as temperature, conductivity, optical backscatter, light transmission, and high frequency pressure. Sampling consists of high-frequency bursts(1–10 Hz) bursts of long duration (5–30 minutes) at regular and recurring intervals for a duration of 1 to 6 months. The result is very large data files, often 500 MB per Hydra, per deployment, in Sontek's compressed binary format. This section introduces the Hydratools toolbox and provides information about the history of the system's development. The USGS philosophy regarding data quality is discussed to provide an understating of the motivation for creating the system. General information about the following topics will also be discussed: hardware and software required for the system, basic processing steps, limitations of program usage, and features that are unique to the programs.

Open-File Report↗
USGS2005

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

Open-File Report↗
USGS2017

Systematic comparisons between PRISM version 1.0.0, BAP, and CSMIP ground-motion processing

A series of benchmark tests was run by comparing results of the Processing and Review Interface for Strong Motion data (PRISM) software version 1.0.0 to Basic Strong-Motion Accelerogram Processing Software (BAP; Converse and Brady, 1992), and to California Strong Motion Instrumentation Program (CSMIP) processing (Shakal and others, 2003, 2004). These tests were performed by using the MatLAB implementation of PRISM, which is equivalent to its public release version in Java language. Systematic comparisons were made in time and frequency domains of records processed in PRISM and BAP, and in CSMIP, by using a set of representative input motions with varying resolutions, frequency content, and amplitudes. Although the details of strong-motion records vary among the processing procedures, there are only minor differences among the waveforms for each component and within the frequency passband common to these procedures. A comprehensive statistical evaluation considering more than 1,800 ground-motion components demonstrates that differences in peak amplitudes of acceleration, velocity, and displacement time series obtained from PRISM and CSMIP processing are equal to or less than 4 percent for 99 percent of the data, and equal to or less than 2 percent for 96 percent of the data. Other statistical measures, including the Euclidian distance (L 2 norm) and the windowed root mean square level of processed time series, also indicate that both processing schemes produce statistically similar products.

Open-File Report↗
USGS2002

Standards for the Analysis and Processing of Surface-Water Data and Information Using Electronic Methods

Surface-water computation methods and procedures are described in this report to provide standards from which a completely automated electronic processing system can be developed. To the greatest extent possible, the traditional U. S. Geological Survey (USGS) methodology and standards for streamflow data collection and analysis have been incorporated into these standards. Although USGS methodology and standards are the basis for this report, the report is applicable to other organizations doing similar work. The proposed electronic processing system allows field measurement data, including data stored on automatic field recording devices and data recorded by the field hydrographer (a person who collects streamflow and other surface-water data) in electronic field notebooks, to be input easily and automatically. A user of the electronic processing system easily can monitor the incoming data and verify and edit the data, if necessary. Input of the computational procedures, rating curves, shift requirements, and other special methods are interactive processes between the user and the electronic processing system, with much of this processing being automatic. Special computation procedures are provided for complex stations such as velocity-index, slope, control structures, and unsteady-flow models, such as the Branch-Network Dynamic Flow Model (BRANCH). Navigation paths are designed to lead the user through the computational steps for each type of gaging station (stage-only, stagedischarge, velocity-index, slope, rate-of-change in stage, reservoir, tide, structure, and hydraulic model stations). The proposed electronic processing system emphasizes the use of interactive graphics to provide good visual tools for unit values editing, rating curve and shift analysis, hydrograph comparisons, data-estimation procedures, data review, and other needs. Documentation, review, finalization, and publication of records are provided for with the electronic processing system, as well as archiving, quality assurance, and quality control.

Water-Resources Investigations Report↗
USGS2024

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗
USGS2016

Towards simplification of hydrologic modeling: Identification of dominant processes

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Hydrology and Earth System Sciences↗
USGS2026

Continental-scale prediction of hydrologic signatures and processes

Understanding how dominant hydrologic processes and their drivers vary across diverse continental-scale landscapes is critical for hydrologic modeling and water management applications. Our research addresses this question by synthesizing large-sample watershed datasets, Caravan and GAGES-II, and developing random forest models to identify patterns in hydrologic function. We assessed dominant processes by examining hydrologic signatures – summary indicators of watershed function derived from hydroclimatic time series and random forest models across 14 146 gauged United States watersheds. The results reveal clear continental-scale gradients in hydrologic processes, including baseflow, overland flow, storage, and water balance losses. Our map of dominant processes highlights, for example, the transition from baseflow to fast responses and back to baseflow along the elevation gradient from the Appalachian spine, through the Piedmont, to the Eastern Coastal Plain; a distinct outer ring around the Great Lakes region; and sharp contrasts between coastal and inland processes in the West. Variable importance analysis from random forest models show that processes in the western U.S. are primarily controlled by climate, whereas in the eastern U.S., soil, geology, and topography play larger roles, with distinct human influences apparent in urban areas. Our approach of estimating dominant processes and their drivers facilitates extending process knowledge from research watersheds to the continental scale, assessing current hydrological understanding, and evaluating hydrological model structures.

conterminous United States↗
USGS2011

Aeolian and fluvial processes in dryland regions: The need for integrated studies

Aeolian and fluvial processes play a fundamental role in dryland regions of the world and have important environmental and ecological consequences from local to global scales. Although both processes operate over similar spatial and temporal scales and are likely strongly coupled in many dryland systems, aeolian and fluvial processes have traditionally been studied separately, making it difficult to assess their relative importance in drylands, as well as their potential for synergistic interaction. Land degradation by accelerated wind and water erosion is a major problem throughout the world's drylands, and although recent studies suggest that these processes likely interact across broad spatial and temporal scales to amplify the transport of soil resources from and within drylands, many researchers and land managers continue to view them as separate and unrelated processes. Here, we illustrate how aeolian and fluvial sediment transport is coupled at multiple spatial and temporal scales and highlight the need for these interrelated processes to be studied from a more integrated perspective that crosses traditional disciplinary boundaries. Special attention is given to how the growing threat of climate change and land‐use disturbance will influence linkages between aeolian and fluvial processes in the future. We also present emerging directions for interdisciplinary needs within the aeolian and fluvial research communities that call for better integration across a broad range of traditional disciplines such as ecology, biogeochemistry, agronomy, and soil conservation.

California↗
USGS2009

Evolution and preservation potential of fluvial and transgressive deposits on the Louisiana inner shelf: Understanding depositional processes to support coastal management

The barrier-island systems of the Mississippi River Delta plain are currently undergoing some of the highest rates of shoreline retreat in North America (~20 m/year). Effective management of this coastal area requires an understanding of the processes involved in shoreline erosion and measures that can be enacted to reduce loss. The dominant stratigraphy of the delta plain is fluvial mud (silts and clays), delivered in suspension via a series of shallow-water delta lobes that prograded across the shelf throughout the Holocene. Abandonment of a delta lobe through avulsion leads to rapid land subsidence through compaction within the muddy framework. As the deltaic headland subsides below sea level, the marine environment transgresses the bays and wetlands, reworking the available sands into transgressive barrier shorelines. This natural process is further complicated by numerous factors: (1) global sea-level rise; (2) reduced sediment load within the Mississippi River; (3) diversion of the sediment load away from the barrier shorelines to the deep shelf; (4) storm-induced erosion; and (5) human alteration of the littoral process through the construction of hardened shorelines, canals, and other activities. This suite of factors has led to the deterioration of the barrier-island systems that protect interior wetlands and human infrastructure from normal wave activity and periodic storm impact. Interior wetland loss results in an increased tidal prism and inlet cross-sectional areas, and expanding ebb-tidal deltas, which removes sand from the littoral processes through diversion and sequestration. Shoreface erosion of the deltaic headlands does not provide sufficient sand to balance the loss, resulting in thinning and dislocation of the islands. Abatement measures include replenishing lost sediment with similar material, excavated from discrete sandy deposits within the muddy delta plain. These sand bodies were deposited by the same cyclical processes that formed the barrier islands, and understanding these processes is necessary to characterize their location, extent, and resource potential. In this paper we demonstrate the dominant fluvial and marine-transgressive depositional processes that occur on the inner shelf, and identify the preservation and resource potential of fluvio-deltaic deposits for coastal management in Louisiana.

Geo-Marine Letters↗
USGS2022

gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring

gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.

Computers & Geosciences↗
USGS2005

Animating the biodynamics of soil thickness using process vector analysis: A dynamic denudation approach to soil formation

This paper expands the dynamic denudation framework of landscape evolution by providing new process insights and details on how soil and its signature morphological feature, the biomantle, form and function in the environment. We examine soils and their biomantles from disparate parts of the world, from the tropics through midlatitudes and hyperarid through perhumid, a range that exhibits varying environments for, and of, life. We then explicate the process pathways that cause soils to thicken and thin, and to even disappear, then reform. We do this by examining thickness relationships, where soil thickness stand biomantle thickness bt are functions of upbuilding u and deepening d minus removal r processes, hence st/bt=f(u+d-r). Upbuilding has two subsets, u1, which includes all exogenous (allochthonous-outside) mineral and/or organic inputs to the soil system, and u2, which includes all endogenous (autochthonous-in situ) processes and productions, including weathering. Exogenous u1 inputs include eolian and slopewash inputs (sedimentations) of mineral and organic materials, mass wasting accumulations and the like. Endogenous u2 processes and productions include the sum of in situ bioturbations, biosynthetic productions, organic accumulations, biovoid productions, weathering and volume increases caused by their sum. Endogenous upbuildings, which dominantly occur in the biomantle, are basically biodynamic bd processes and productions, hence u2=bd. Therefore, if exogenous upbuildings u1 are minimal or zero, then biomantle thickness bt is expressed by bt=f(u2-r) or bt=f(bd-r). Drawing on these relationships, we employ a graphic-conceptual device called process vector analysis in a digital animation (see supplementary materials or cf. https://netfiles.uiuc.edu/jdomier/www/temp/ biomantle.html) that illustrates the main pathways that form both Earth's soil and its unique epidermis, the biomantle. We then discuss the main elements of the animation using still frames that represent thickness turning points. We end by encouraging researchers, instructors and students to view Earth's landforms and soils as integrated dynamic entities that are constantly coevolving elements of a global subaerial-subaqueous continuum. ?? 2004 Elsevier B.V. All rights reserved.

Geomorphology↗
USGS2010

Interactions of solutes and streambed sediment: 2. A dynamic analysis of coupled hydrologic and chemical processes that determine solute transport

Solute transport in streams is determined by the interaction of physical and chemical processes. Data from an injection experiment for chloride and several cations indicate significant influence of solutestreambed processes on transport in a mountain stream. These data are interpreted in terms of transient storage processes for all tracers and sorption processes for the cations. Process parameter values are estimated with simulations based on coupled quasi-two-dimensional transport and first-order mass transfer sorption. Comparative simulations demonstrate the relative roles of the physical and chemical processes in determining solute transport. During the first 24 hours of the experiment, chloride concentrations were attenuated relative to expected plateau levels. Additional attenuation occurred for the sorbing cation strontium. The simulations account for these storage processes. Parameter values determined by calibration compare favorably with estimates from other studies in mountain streams. Without further calibration, the transport of potassium and lithium is adequately simulated using parameters determined in the chloride-strontium simulation and with measured cation distribution coefficients.

Water Resources Research↗
USGS2007

An innovative carbonate coprecipitation process for the removal of zinc and manganese from mining impacted waters

Although mine drainage is usually thought of as acidic, there are many cases where the water is of neutral pH, but still contains metal species that can be harmful to human or aquatic animal health, such as manganese (Mn) and zinc (Zn). Typical treatment of mine drainage waters involves pH adjustment, but this often results in excessive sludge formation and removal of nontoxic species such as magnesium and calcium. Theoretical consideration of the stability of metal carbonate species suggests that the target metals could be removed from solution by coprecipitation with calcium carbonate. The U.S. Geological Survey has developed a limestone-based process for remediation of acid mine drainage that increases calcium carbonate saturation. This treatment could then be coupled with carbonate coprecipitation as an innovative method for removal of toxic metals from circumneutral mine drainage waters. The new process was termed the carbonate coprecipitation (CCP) process. The CCP process was tested at the laboratory scale using a synthetic mine water containing 50 mg/L each of Mn and Zn. Best results showed over 95% removal of both Mn and Zn in less than 2 h of contact in a limestone channel. The process was then tested on a sample of water from the Palmerton zinc superfund site, near Palmerton, Pennsylvania, containing over 300 mg/L Zn and 60 mg/L Mn. Treatment of this water resulted in removal of over 95% of the Zn and 40% of the Mn in the limestone channel configuration. Because of the potential economic advantages of the CCP process, further research is recommended for refinement of the process for the Palmerton water and for application to other mining impacted waters as well. ?? Mary Ann Liebert, Inc.

Environmental Engineering Science↗
USGS2021

Great expectations: Deconstructing the process pathways underlying beaver-related restoration

Beaver-related restoration is a process-based strategy that seeks to address wide-ranging ecological objectives by reestablishing dam building in degraded stream systems. Although the beaver-related restoration has broad appeal, especially in water-limited systems, its effectiveness is not yet well documented. In this article, we present a process-expectation framework that links beaver-related restoration tactics to commonly expected outcomes by identifying the set of process pathways that must occur to achieve those expected outcomes. We explore the contingency implicit within this framework using social and biophysical data from project and research sites. This analysis reveals that outcomes are often predicated on complex process pathways over which humans have limited control. Consequently, expectations often shift through the course of projects, suggesting that a more useful paradigm for evaluating process-based restoration would be to identify relevant processes and to rigorously document how projects do or do not proceed along expected process pathways using both quantitative and qualitative data.

BioScience↗
USGS2026

Bridging ecology and geosciences in riverscapes: Implications for process-based restoration

There has been a growing interest in integrating geological and ecological processes for sustainable river management and restoration. Lotic systems are shaped by diverse physical processes, including geology, geomorphology, hydrology, and interactions with terrestrial processes. However, restoration practices often prioritize specific habitats or river forms without fully considering the underlying physical processes that support biological communities and ecosystem functions. In this paper, we synthesize studies that integrally examine geological and ecological processes across different scales and components of riverscapes, including geohydrological processes, which have been less investigated in riverscape studies. We begin by examining processes at broader spatial scales, including river–watershed and river–riparian interactions, and gradually narrow our focus to the dynamics that occur among habitats within river channels, through which we highlight the significance of conceptualizing rivers as dynamic “networks” rather than linear features. Finally, we identify both scientific and practical challenges that can be addressed to bridge the gap between basic-science implications and their implementation in riverscape restorations.

Ecological Research↗
USGS2023

GeoAI for spatial image processing

The development of digital image processing, as a subset of digital signal processing, depended upon the maturity of photography and image science, introduction of computers, discovery and advancement of digital recording devices, and the capture of digital images. In addition, government and industry applications in the Earth and medical sciences were paramount to the growth of the technology. From the early days when photography was first introduced to science to today, artificial intelligence and deep learning technologies have been intensively used to analyze imagery. Spatial image processing has experienced breakthroughs and evolutions. This chapter presents an overview of the history of image processing, GeoAI-based image processing applications, and the role of GeoAI in advancing image processing methods and research. We also discussed the remaining challenges to using GeoAI for image processing regarding training data annotation, the issues of scale, resolution, and change in space over time.

Book chapter↗