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At least 883 records · Page 49Linked to original sources
USGS1997

Debris-flow hazard map units from gridded probabilities

The common statistical practice of dividing a range of probabilities into equal probability intervals may not result in useful landslide-hazard map units for areas populated by equal-area cells, each of which has a unique probability. Most hazard map areas contain very large numbers of cells having low probability of failure, and as probability increases, the number of cells decreases in a non-linear fashion. Exploration of this distribution suggests that the spatial frequency of expected failures may be used to identify probability intervals that define map units. From a spatial database of gridded probabilities, map units that address the different objectives of land-use planners and emergency response officials can be defined.

Conference Paper↗
USGS2021

Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba in Canada and the State of North Dakota in the United States. Greater than average snowpack during the winter of 2010–11, along with record-setting rains in May and June 2011, resulted in historically unprecedented flooding in the Souris River Basin. The severity of the 2011 flood led the United States and Canada to request a review of the operating plan for any improvements of reservoir operations and flood control measures in the basin, and the Souris River Basin Task Force was formed. The International Souris River Study Board was then formed in 2017 to carry out the recommendations of the Souris River Basin Task Force laid out in a plan of study. To support the International Souris River Study Board, the U.S. Geological Survey (USGS), in cooperation with the North Dakota State Water Commission and the International Joint Commission, used the previously developed unregulated and regulated streamflow models and data for stochastic streamflow in the Souris River Basin to characterize climate and streamflow and support selection of streamflow traces based on their characterization. Components of the original stochastic hydrology models and their outputs were used in this phase of the study to (1) characterize historical and stochastic climate and streamflow for the Souris River Basin, (2) disaggregate monthly stochastic streamflow spatially and temporally to meet the needs of the U.S. Army Corps of Engineers, Hydrologic Engineering Center, Reservoir System Simulation model for the Souris River Basin, and (3) discuss selection of disaggregated streamflow traces (simulations) using the characteristics of climate and streamflow. A trace is a time series of a stochastic variable such as streamflow, potential evapotranspiration, or precipitation. To characterize climate conditions, precipitation, potential evapotranspiration (PET), and moisture deficit for the Souris River Basin and individual points at Rafferty, Grant Devine, and Lake Darling Reservoirs were determined annually and seasonally. The annual basin (November 1–October 31) precipitation for the 50-percent nonexceedance probability is 452 millimeters (mm). Spring (March–May) is the wettest season, followed by summer (June–August), fall (September–November), and winter (December–February). Annual moisture deficit was largest at Lake Darling Reservoir, followed by Rafferty Reservoir, and then Grant Devine Reservoir. Annual maximum monthly mean streamflow was determined for the Souris River below Rafferty Reservoir, Saskatchewan (Canadian streamgage 05NB036); Long Creek near Noonan (above Boundary Reservoir), North Dakota (USGS streamgage 05113600); Moose Mountain Creek near Oxbow, Saskatchewan (Canadian streamgage 05ND004); the Souris River near Sherwood, N. Dak. (USGS streamgage 05114000); the Des Lacs River at Foxholm, N. Dak. (USGS streamgage 05116500); and the Souris River above Minot, N. Dak. (USGS streamgage 05117500). When the seasonal maximum monthly mean streamflows are evaluated in contrast to annual maximum monthly mean streamflows separated by their seasonal occurrence, summer months of annual maximum monthly mean streamflows have a higher 50-percent exceedance probability of streamflow compared to annual maximum monthly mean streamflows that occur in spring, seasonal maximum monthly mean streamflows that occur in spring, and seasonal maximum monthly mean streamflows that occur in summer. When annual maximum monthly mean streamflows in summer are compared to annual maximum monthly mean streamflows in spring, they are consistently higher in streamflow but occur in less than 4.2 percent of years. Evaluation of whether the annual maximum monthly mean streamflows that occur in summer can be described as a separate population from annual maximum monthly mean streamflows that occur in spring was outside the scope of this study, and the summer and spring annual maximum monthly mean streamflows were not tested for statistical differences in mean or variance. Further investigation of seasonal weather patterns that induce flooding could lead to a better understanding of the seasonal differences in flooding. Long-term hydrologic drought was characterized by evaluating multiyear mean streamflow. Shorter averaging periods have greater streamflow variability than longer periods and hence have a wider range of values. As the averaging period is extended to a longer period, the variability of mean streamflow decreases, and the more extreme streamflow volumes seen in shorter averaging periods cannot be sustained. Stochastic streamflow time series were disaggregated spatially and temporally for use in a HEC–ResSim model. The combination of monthly and daily stochastic streamflow data was used to select traces with qualities that could be used to test alternatives focused on water supply, summer flooding, and apportionment.

Manitoba, North Dakota, Saskatchewan↗
USGS2017

Evaluation and use of U.S. Environmental Protection Agency Clean Watersheds Needs Survey data to quantify nutrient loads to surface water, 1978–2012

Changes in municipal and industrial point-source discharges over time have been an important factor affecting nutrient trends in many of the Nation’s streams and rivers. This report documents how three U.S. Environmental Protection Agency (EPA) national datasets—the Permit Compliance System, the Integrated Compliance Information System, and the Clean Watersheds Needs Survey—were evaluated for use in the U.S. Geological Survey National Water-Quality Assessment project to assess the causes of nutrient trends. This report also describes how a database of total nitrogen load and total phosphorous load was generated for select wastewater treatment facilities in the United States based on information reported in the EPA Clean Watersheds Needs Survey. Nutrient loads were calculated for the years 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1996, 2000, 2004, 2008, and 2012 based on average nitrogen and phosphorous concentrations for reported treatment levels and on annual reported flow values. The EPA Permit Compliance System (PCS) and Integrated Compliance Information System (ICIS), which monitor point-source facility discharges, together are the Nation’s most spatially comprehensive dataset for nutrients released to surface waters. However, datasets for many individual facilities are incomplete, the PCS/ICIS historical data date back only to 1989, and historical data are available for only a limited number of facilities. Additionally, inconsistencies in facility reporting make it difficult to track or identify changes in nutrient discharges over time. Previous efforts made by the U.S. Geological Survey to “fill in” gaps in the PCS/ICIS data were based on statistical methods—missing data were filled in through the use of a statistical model based on the Standard Industrial Classification code, size, and flow class of the facility and on seasonal nutrient discharges of similar facilities. This approach was used to estimate point-source loads for a single point in time; it was not evaluated for use in generating a consistent data series over time. Another national EPA dataset that is available is the Clean Watersheds Needs Survey (CWNS), conducted every 4 years beginning 1973. The CWNS is an assessment of the capital needs of wastewater facilities to meet the water-quality goals set in the Clean Water Act. Data collected about these facilities include location and contact information for the facilities; population served; flow and treatment level of the facility; estimated capital needs to upgrade, repair, or improve facilities for water quality; and nonpoint-source best management practices. Total nitrogen and total phosphorous load calculations for each of the CWNS years were based on treatment level information and average annual outflow (in million gallons per day) from each of the facilities that had reported it. Treatment levels categories (such as Primary, Secondary, or Advanced) were substituted with average total nitrogen and total phosphorous concentrations for each treatment level based on those reported in literature. The CWNS dataset, like the PCS/ICIS dataset, has years where facilities did not report either a treatment level or an annual average outflow, or both. To fill in the data gaps, simple linear assumptions were made based on each facility’s responses to the survey in years bracketing the data gap or immediately before or after the data gap if open ended. Treatment level and flow data unique to each facility were used to complete the CWNS dataset for that facility.

Scientific Investigations Report↗
USGS1981

Broad area forest fuels and topography mapping using digital Landsat and terrain data

A spatially registered digital data base of fuels and terrain information was generated for a test site on the Lolo National Forest in Montana. The data base was developed specifically for providing spatially relevant data to a mathematical fire behavior model developed by the Forest Service which integrates this information along with current weather data to produce realistic estimates of probable fire behavior. Methodologies for the processing and analysis of Landsat MSS and digital terrain data for the mapping of U.S. Forest Service fuel types were developed and demonstrated. Key elements in the mapping process were the development of a fuels terrain distribution model which provided a statistical description of the topographic distribution patterns of fuels within spectral classes, and secondly, the application of a layered classifier which incorporated the spectral and terrain data in a two-stage maximum likelihood classification framework for the mapping of fuels.

Montana↗
USGS2017

Challenges for creating a site-specific groundwater-use record for the Ozark Plateaus aquifer system (central USA) from 1900 to 2010

Hydrologic budgets to determine groundwater availability are important tools for water-resource managers. One challenging component for developing hydrologic budgets is quantifying water use through time because historical and site-specific water-use data can be sparse or poorly documented. This research developed a groundwater-use record for the Ozark Plateaus aquifer system (central USA) from 1900 to 2010 that related county-level aggregated water-use data to site-specific well locations and aquifer units. A simple population-based linear model, constrained to 0 million liters per day in 1900, provided the best means to extrapolate groundwater-withdrawal rates pre-1950s when there was a paucity of water-use data. To disaggregate county-level data to individual wells across a regional aquifer system, a programmatic hierarchical process was developed, based on the level of confidence that a well pumped groundwater for a specific use during a specific year. Statistical models tested on a subset of the best-available site-specific water-use data provided a mechanism to bracket historic groundwater use, such that groundwater-withdrawal rates ranged, on average, plus or minus 38% from modeled values. Groundwater withdrawn for public supply and domestic use accounted for between 48 and 74% of total groundwater use since 1901, highlighting that groundwater provides an important drinking-water resource. The compilation, analysis, and spatial and temporal extrapolation of water-use data remain a challenging task for water scientists, but is of paramount importance to better quantify groundwater use and availability.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2023

Strategic monitoring to minimize misclassification errors from conservation status assessments

Classifying species into risk categories is a ubiquitous process in conservation decision-making affecting regulatory procedures, conservation actions, and guiding resource allocation at global, national, and regional scales. However, monitoring programs often do not provide data required for accurate species classification decisions. Misclassification can lead to otherwise preventable species extinctions, undue regulatory burden, poor allocation of limited conservation resources, and can undermine species conservation legislation. We developed a framework that evaluates monitoring designs based on the ability to correctly inform a species classification decision, where minimizing the risk of misclassification is the central objective. We further evaluated monitoring designs by calculating the expected value of information and explored the relationship between statistical power to detect trends and misclassification. Our measure of misclassification risk, which can be tailored to the decision context, clarified the costs of over- and under-protection. High power to detect trends often corresponded to accurate species classification decisions. However, in several scenarios power to detect trends was low but the ability to correctly inform the classification decision was high. The value of information generally increased with monitoring intensity and quantified the tradeoffs between spatial and temporal replication. Our framework allows managers to assess monitoring program performance with direct implications for conservation decision-making. Our framework affords practitioners an opportunity to evaluate the effectiveness of monitoring programs a priori focusing on improving conservation decisions. We demonstrate that prioritizing monitoring to minimize misclassification errors can improve monitoring efficiency and conservation decision-making with considerable practical applications and benefits for species conservation.

Biological Conservation↗
USGS2011

Observations of debris flows at Chalk Cliffs, Colorado, USA: Part 2, changes in surface morphometry from terrestrial laser scanning in the summer of 2009

High resolution topographic data that quantify changes in channel form caused by sequential debris flows in natural channels are rare at the reach scale. Terrestrial laser scanning (TLS) techniques are utilized to capture morphological changes brought about by a high-frequency of debris-flow events at Chalk Cliffs, Colorado. The purpose of this paper is to compare and contrast the topographic response of a natural channel to the documented debris-flow events. TLS survey data allowed for the generation of high-resolution (2-cm) digital terrain models (DTM) of the channel. A robust network of twelve permanent control points permitted repeat scanning sessions that provided multiple DTM to evaluate fine-scale topographic change associated with three debris-flow events. Difference surfaces from the DTM permit the interpretations of spatial variations in channel morphometry and net volume of material deposited and eroded within and between a series of channel reaches. Each channel reach experienced erosion, deposition, and both net volumetric gains and losses were measured. Analysis of potential relationships between erosion and deposition magnitudes yielded no strong correlations with measures of channel-reach morphometry, suggesting that channel reach-specific predictions of potential erosion or deposition locations or rates cannot be adequately derived from statistical analyses of pre-event channel-reach morphometry.

Colorado↗
USGS2022

Interaction between transect design and animal distribution in distance sampling of deer

We conducted a simulation study to evaluate the consequences of violating statistical assumptions of distance sampling (DS) on the bias and precision of population estimates of white-tailed deer ( Odocoileus virginianus ). Distance sampling is a method for estimating the density of organisms using a distribution of observed distances to individuals. A key assumption of DS is that sampling transects are randomly located with respect to the population being sampled. Most DS transects used in National Parks follow roads and trails, which are not positioned randomly. We constructed spatially explicit simulations of surveys using 7 different types of deer spatial distributions and 3 survey designs. A significant interaction between survey type and deer distribution type explained most of the variation in population estimates across simulations and this interaction was also a significant predictor of the coefficient of variation (CV) of population estimates. Simulation results suggested that 1) non-road surveys were more robust to bias than were road surveys, 2) effectiveness of each of 3 survey types was dependent on the way deer were distributed across the landscape, and 3) non-road surveys produced unbiased estimates of populations affected by roads, whereas road surveys did not. The results of our study suggest that DS surveys using pre-existing roads and trails have potential biases resulting from study design, something that could be considered by land managers when constructing surveys.

Maryland↗
USGS2004

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895—1993 on a 0.5° latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers.

Climate Research↗
USGS2012

Developing ShakeCast statistical fragility analysis framework for rapid post-earthquake assessment

When an earthquake occurs, the U. S. Geological Survey (USGS) ShakeMap estimates the extent of potentially damaging shaking and provides overall information regarding the affected areas. The USGS ShakeCast system is a freely-available, post-earthquake situational awareness application that automatically retrieves earthquake shaking data from ShakeMap, compares intensity measures against users’ facilities, sends notifications of potential damage to responsible parties, and generates facility damage assessment maps and other web-based products for emergency managers and responders. We describe notable improvements of the ShakeMap and the ShakeCast applications. We present a design for comprehensive fragility implementation, integrating spatially-varying ground-motion uncertainties into fragility curves for ShakeCast operations. For each facility, an overall inspection priority (or damage assessment) is assigned on the basis of combined component-based fragility curves using pre-defined logic. While regular ShakeCast users receive overall inspection priority designations for each facility, engineers can access the full fragility analyses for further evaluation.

Conference Paper↗
USGS2025

Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers

The adaptation of suspended-sediment surrogate technologies continues to rapidly expand across geomorphology and fluvial sediment monitoring efforts. Over a decade of research and development shows increased reliability and accuracy of in-situ surrogates with reduced program cost as compared to traditional sample-based methods, but environmental fouling and probe damage can be problematic. The SedCam technique is a unique non-contact close-range remote sensing method to estimate suspended-sediment concentration from multispectral imagery of a river surface. In contrast to typical airborne- or satellite-based platforms, SedCam uses broadband sensors with lower spectral resolution (three bands covering wavelengths of 340 to 1100 nm) but greater spatial resolution (0.5 mm pixel size; equivalent to medium to coarse sand) and temporal resolution (15-min intervals during daylight hours). This paper summarizes lessons learned from two studies, utilizing three consumer-grade digital cameras (each with different spectral signatures) at two different rivers (each with different sediment characteristics). >90,000 images and 174 concurrent physical samples represent a collective period of 26 months. A subset of these data pairs supports the development of four regression models. Statistical diagnostics show model error can be <40 % when surface point samples are used, with coefficients of determination ≥0.90. This novel approach shows similar accuracy to other surrogate methods such as instream turbidity. Results of this study indicate that optimizing spectra based on expected suspended-sediment concentration increases model performance.

Geomorphology↗
USGS2020

A classification of streamflow patterns across the coastal Gulf of Alaska

Streamflow controls many freshwater and marine processes, including salinity profiles, sediment composition, fluxes of nutrients, and the timing of animal migrations. Watersheds that border the Gulf of Alaska (GOA) comprise over 400,000 km 2 of largely pristine freshwater habitats and provide ecosystem services such as reliable fisheries for local and global food production. Yet no comprehensive watershed-scale description of current temporal and spatial patterns of streamflow exists within the coastal GOA. This is an immediate need because the spatial distribution of future streamflow patterns may shift dramatically due to warming air temperature, increased rainfall, diminishing snowpack, and rapid glacial recession. Our primary goal was to describe variation in streamflow patterns across the coastal GOA using an objective set of descriptors derived from flow predictions at the downstream-most point within each watershed. We leveraged an existing hydrologic runoff model and Bayesian mixture model to classify 4,140 watersheds into 13 classes based on seven streamflow statistics. Maximum discharge timing (annual phase shift) and magnitude relative to mean discharge (amplitude) were the most influential attributes. Seventy-six percent of watersheds by number showed patterns consistent with rain or snow as dominant runoff sources, while the remaining watersheds were driven by rain-snow, glacier, or low-elevation wetland runoff. Streamflow classes exhibited clear mechanistic links to elevation, ice coverage, and other landscape features. Our classification identifies watersheds that might shift streamflow patterns in the near future and, importantly, will help guide the design of studies that evaluate how hydrologic change will influence coastal GOA ecosystems.

Alaska↗
USGS2007

Mapping the invasive species, Chinese tallow, with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress-tupelo forest, senescing Chinese tallow with red leaves ('red tallow'), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress-tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non-active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs. ?? 2005 US Government.

International Journal of Remote Sensing↗
USGS2017

Assessment of an in-channel redistribution technique for large woody debris management in Locust Creek, Linn County, Missouri

The U.S. Geological Survey, in cooperation with the Missouri Department of Conservation and Missouri Department of Natural Resources, completed a study to assess a mechanical redistribution technique used for the management of large woody debris (LWD) jams in Locust Creek within Pershing State Park and Fountain Grove Conservation Area, Linn County, Missouri. Extensive LWD jams were treated from 1996 to 2009 using a low-impact technique in which LWD from the jams was redistributed to reopen the channel and to mimic the natural geomorphic process of channel migration and adjustment to an obstruction. The scope of the study included the comparison of selected channel geometry characteristics and bed material particle-size distribution in seven LWD treatment reaches with that of adjacent untreated reaches (unaffected by LWD accumulations) of Locust Creek. A comparison of 1996 and 2015 survey cross sections in treated and untreated reaches and photograph documentation were used to assess channel geomorphic change and the stability of redistributed LWD. The physical characteristics of LWD within jams present in the study reach during 2015–16 also were documented. Based on the general lack of differences in channel metrics between treated and untreated reaches, it can be concluded that the mechanical redistribution technique has been an effective treatment of extensive LWD jams in Locust Creek. Channel alterations, including aggradation, streamflow piracy, and diversions, have resulted in temporal and spatial changes in the Locust Creek channel that may affect future applications of the redistribution technique in Pershing State Park. The redistribution technique was used to effectively manage LWD in Locust Creek at a potentially lower financial cost and reduced environmental disturbance than the complete removal of LWD. A comparison of four channel metrics (bankfull cross-sectional area, channel width, streamflow capacity, and width-depth ratio) for individual treatment reaches with adjacent untreated reaches indicated no statistically significant difference in most comparisons. Where statistically significant differences in channel metrics were determined between individual reaches, the channel metrics in treatment reaches were significantly less than adjacent untreated reaches in some comparisons, and significantly greater than adjacent untreated reaches in others. Without immediate posttreatment cross sections in treated and untreated reaches for comparison, it is impossible to say with certainty that a lack of significant differences in channel metrics is a result of posttreatment channel adjustment or, conversely, that any significant differences that remain are a result of the treatment of LWD. Characteristics of LWD in accumulations sampled within the study area in 2015 indicate that most sampled pieces were in the 1–2 foot diameter size class, the 5–16 foot length class, and the advanced decay class. Most of documented LWD pieces were loose and not buried, about 20 percent on average had a root wad attached, and about 6.5 percent on average were sawn logs. Most of sampled material was less than one-half of the bankfull channel width, indicating it was easily transportable, and the advanced decay class of material entering the study area indicated that it was likely sourced from outside of Pershing State Park. Redistributed LWD associated with treatment seems to be intact in the 1996 treated reaches from direct observation and from inference because there was net channel aggradation between 1996 and 2015 in comparison surveys. The change in channel area resulting from aggradation in time (1996 to 2015) in treated and untreated reaches exceeded the differences in channel characteristics between the treated and untreated channels in 2015 surveys.

Missouri↗
USGS2013

Seasonal climate variation and caribou availability: Modeling sequential movement using satellite-relocation data

Livelihood systems that depend on mobile resources must constantly adapt to change. For people living in permanent settlements, environmental changes that affect the distribution of a migratory species may reduce the availability of a primary food source, with the potential to destabilize the regional social-ecological system. Food security for Arctic indigenous peoples harvesting barren ground caribou ( Rangifer tarandus granti ) depends on movement patterns of migratory herds. Quantitative assessments of physical, ecological, and social effects on caribou distribution have proven difficult because of the significant interannual variability in seasonal caribou movement patterns. We developed and evaluated a modeling approach for simulating the distribution of a migratory herd throughout its annual cycle over a multiyear period. Beginning with spatial and temporal scales developed in previous studies of the Porcupine Caribou Herd of Canada and Alaska, we used satellite collar locations to compute and analyze season-by-season probabilities of movement of animals between habitat zones under two alternative weather conditions for each season. We then built a set of transition matrices from these movement probabilities, and simulated the sequence of movements across the landscape as a Markov process driven by externally imposed seasonal weather states. Statistical tests showed that the predicted distributions of caribou were consistent with observed distributions, and significantly correlated with subsistence harvest levels for three user communities. Our approach could be applied to other caribou herds and could be adapted for simulating the distribution of other ungulates and species with similarly large interannual variability in the use of their range.

Alaska, Yukon Territory↗
USGS2001

A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas

Forest cover is of great interest to a variety of scientific and land management applications, many of which require not only information on forest categories, but also tree canopy density. In previous studies, large area tree canopy density had been estimated at spatial resolutions of 1km or coarser using coarse resolution satellite images. In this study, a strategy is developed for estimating tree canopy density at a spatial resolution of 30 m. This strategy is based on empirical relationships between tree canopy density and Landsat data, established using linear regression and regression tree techniques. One-meter digital orthophoto quadrangles were used to derive reference tree canopy density data needed for calibrating the relationships between canopy density and Landsat spectral data. This strategy was tested over three areas of the United States. In general, models derived using both linear regression and regression tree techniques were statistically significant. The regression tree was found more robust than linear regression, primary due to its capability of approximating complex non-linear relationships using a set of linear equations. This strategy will be recommended for use in developing a nation wide tree canopy density data set at a 30 m resolution as part of the Multi-Resolution Land Characteristics 2000 project.

Conference Paper↗
USGS2008

Advances and limitations of individual-based models to analyze and predict dynamics of mangrove forests: A review

Mangrove ecosystems are considered vulnerable to climate change as coastal development limits the ecosystem services and adaptations important to their survival. Although they appear rather simple in terms of species diversity, their ecology is complex due to interacting geophysical forces of tides, surface runoff, river and groundwater discharge, waves, and constituents of sediment, nutrients and saltwater. These interactions limit developing a comprehensive framework for science-based sustainable management practices. A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes. The models have progressed from statistical tables representing growth and yield to more sophisticated models describing various system components and processes. Among these models are three individual-based models (IBMs) (FORMAN, KIWI, and MANGRO). A comparison of models’ designs reveal differences in the details of process description, particularly, regarding neighbor competition among trees. Each model has thus its specific range of applications. Whereas FORMAN and KIWI are most suitable to address mangrove forest dynamics of stands, MANGRO focuses on landscape dynamics on larger spatial scale. A comparison of the models and a comparison of the models with empirical knowledge further reveal the general needs for further field and validation studies to advance our ecological understanding and management of mangrove wetlands.

Aquatic Botany↗
USGS2014

Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation

There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus , at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R 2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R 2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species ( Typha spp.: %RMSE = 18.5%; S. acutus : %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R 2 = 0.56, %RMSE = 20.9%, compared to R 2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R 2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.

California↗