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At least 1,315 records · Page 73Linked to original sources
USGS2021

Monitoring multi-decadal variations of urban heat island intensity

Urban development and associated land cover transitions alter the thermal and physical properties of the land surface, resulting the temperature in urban area higher than in rural area or urban heat island (UHI). Remote sensing and land cover data is usually used to assess UHI intensity and temporal change trends. In this study, we implemented a prototype approach to characterize the UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature products and annual land change information. We analyzed land surface temperature change in urban and surrounding non-urban lands to quantify the UHI intensity and change in the Atlanta and Sioux Falls metropolitan areas of the United States. Our results suggested that the land cover type in rural areas and urban imperviousness cover determine UHI intensity and the urban land cover dynamics plays a major role in controlling temporal trend of UHI.

Conference Paper↗
USGS2024

SSEBop evapotranspiration estimates using synthetically derived Landsat data from the continuous change detection and classification algorithm

The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.

Remote Sensing↗
USGS2014

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

Ecological Informatics↗
USGS2014

Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Open-File Report↗
USGS2022

Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA

We analyzed impacts of interannual disturbance on the water balance of watersheds in different forested ecosystem case studies across the United States from 1985 to 2016 using a remotely sensed long-term land cover monitoring record (U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science products), gridded precipitation and evaporation data, and streamgaging data using paired watersheds (high and low disturbance). LCMAP products were used to quantify the timing and degree of interannual disturbance and to gain a better understanding of how land cover change affects the water balance of disturbed watersheds. In this paper, we present how LCMAP science products can be used to improve knowledge for hydrologic modeling, climate research, and forest management. Anthropogenic influences (e.g., dams and irrigation diversions) often minimize the impacts of land cover change on water balance dynamics when compared to interannual fluctuations of hydroclimatic events (e.g., drought and flooding). Our findings show that each watershed exhibits a complex suite of influences involving climate variables and other factors that affect each of their water balances differently when land cover change occurs. In this study, forests within arid to semi-arid climates experience greater water balance effects from land cover change than watersheds where water is less limited.

Land↗
USGS1998

Fuel models and fire potential from satellite and surface observations

A national 1-km resolution fire danger fuel model map was derived through use of previously mapped land cover classes and ecoregions, and extensive ground sample data, then refined through review by fire managers familiar with various portions of the U.S. The fuel model map will be used in the next generation fire danger rating system for the U.S., but it also made possible immediate development of a satellite and ground based fire potential index map. The inputs and algorithm of the fire potential index are presented, along with a case study of the correlation between the fire potential index and fire occurrence in California and Nevada. Application of the fire potential index in the Mediterranean ecosystems of Spain, Chile, and Mexico will be tested.

International Journal of Wildland Fire↗
USGS2016

Effects of microhabitat and large-scale land use on stream salamander occupancy in the coalfields of Central Appalachia

Large-scale coal mining practices, particularly surface coal extraction and associated valley fills as well as residential wastewater discharge, are of ecological concern for aquatic systems in central Appalachia. Identifying and quantifying alterations to ecosystems along a gradient of spatial scales is a necessary first-step to aid in mitigation of negative consequences to aquatic biota. In central Appalachian headwater streams, apart from fish, salamanders are the most abundant vertebrate predator that provide a significant intermediate trophic role linking aquatic and terrestrial food webs. Stream salamander species are considered to be sensitive to aquatic stressors and environmental alterations, as past research has shown linkages among microhabitat parameters, large-scale land use such as urbanization and logging, and salamander abundances. However, there is little information examining these relationships between environmental conditions and salamander occupancy in the coalfields of central Appalachia. In the summer of 2013, 70 sites (sampled two to three times each) in the southwest Virginia coalfields were visited to collect salamanders and quantify stream and riparian microhabitat parameters. Using an information-theoretic framework, effects of microhabitat and large-scale land use on stream salamander occupancy were compared. The findings indicate that Desmognathus spp. occupancy rates are more correlated to microhabitat parameters such as canopy cover than to large-scale land uses. However, Eurycea spp . occupancy rates had a strong association with large-scale land uses, particularly recent mining and forest cover within the watershed. These findings suggest that protection of riparian habitats is an important consideration for maintaining aquatic systems in central Appalachia. If this is not possible, restoration riparian areas should follow guidelines using quick-growing tree species that are native to Appalachian riparian areas. These types of trees would rapidly establish a canopy cover, stabilize the soil, and impede invasive plant species which would, in turn, provide high-quality refuges for stream salamanders.

Journal of Ecology and the Natural Environment↗
USGS2020

Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2021

Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States

Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States.

Delaware, Maryland, New Jersey, New York, Pennsylv↗
USGS2015

Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion hydrological modeling

The Lake Michigan Diversion Accounting (LMDA) system has been developed by the U.S. Army Corps of Engineers, Chicago District (USACE-Chicago) and the State of Illinois as a part of the interstate Great Lakes water regulatory program. The diverted Lake Michigan watershed is a 673-square-mile watershed that is comprised of the Chicago River and Calumet River watersheds. They originally drained into Lake Michigan, but now flow to the Mississippi River watershed via three canals constructed in the Chicago area in the early twentieth century. Approximately 393 square miles of the diverted watershed is ungaged, and the runoff from the ungaged portion of the diverted watershed has been estimated by the USACE-Chicago using the Hydrological Simulation Program-FORTRAN (HSPF) program. The accuracy of simulated runoff depends on the accuracy of the parameter set used in the HSPF program. Nine parameter sets comprised of the North Branch, Little Calumet, Des Plaines, Hickory Creek, CSSC, NIPC, 1999, CTE, and 2008 have been developed at different time periods and used by the USACE-Chicago. In this study, the U.S. Geological Survey and the USACE-Chicago collaboratively analyzed the parameter sets using nine gaged watersheds in or adjacent to the diverted watershed to assess the predictive accuracies of selected parameter sets. Six of the parameter sets, comprising North Branch, Hickory Creek, NIPC, 1999, CTE, and 2008, were applied to the nine gaged watersheds for evaluating their simulation accuracy from water years 1996 to 2011. The nine gaged watersheds were modeled by using the three LMDA land-cover types (grass, forest, and hydraulically connected imperviousness) based on the 2006 National Land Cover Database, and the latest meteorological and precipitation data consistent with the current (2014) LMDA modeling framework. Results indicate that the North Branch and Hickory Creek parameter sets, which belong to the original calibration group, attained an overall “satisfactory” rating on monthly runoff volumes based on the three performance statistics selected, but the annual and over-the-period runoff volumes were generally underestimated. Parameter sets CTE and 2008 attained a similar satisfactory rating on monthly runoff volumes but the annual and over-the-period runoff volumes were overestimated in general. Although the percent bias was improved, the CTE and 2008 parameter sets also had increased residuals in monthly runoff volumes and decreased quality of the model fit to the measured streamflows relative to the North Branch and Hickory Creek parameter sets. The NIPC and 1999 parameter sets, on the other hand, had larger percent bias and residuals in monthly runoff volumes, and underestimated the annual and over-the-period runoff volumes. Recalibration of the HSPF parameters to the updated inputs and land covers was completed on two representative watershed models selected from the nine by using a manual method (HSPEXP) and an automatic method (PEST). The objective of the recalibration was to develop a regional parameter set that improves the accuracy in runoff volume prediction for the nine study watersheds. Knowledge about flow and watershed characteristics plays a vital role for validating the calibration in both manual and automatic methods. The best performing parameter set was determined by the automatic calibration method on a two-watershed model. Applying this newly determined parameter set to the nine watersheds for runoff volume simulation resulted in “very good” ratings in five watersheds, an improvement as compared to “very good” ratings achieved for three watersheds by the North Branch parameter set.

Illinois, Indiana↗
USGS2007

Human influence on California fire regimes

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation. ?? 2007 by the Ecological Society of America.

Ecological Applications↗
USGS2019

Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

Remote Sensing↗
USGS2009

Climatic effects of 30 years of landscape change over the Greater Phoenix, Arizona, region: 1. Surface energy budget changes

This paper is part 1 of a two-part study that evaluates the climatic effects of recent landscape change for one of the nation's most rapidly expanding metropolitan complexes, the Greater Phoenix, Arizona, region. The region's landscape evolution over an approximate 30-year period since the early 1970s is documented on the basis of analyses of Landsat images and land use/land cover (LULC) data sets derived from aerial photography (1973) and Landsat (1992 and 2001). High-resolution, Regional Atmospheric Modeling System (RAMS), simulations (2-km grid spacing) are used in conjunction with consistently defined land cover data sets and associated biophysical parameters for the circa 1973, circa 1992, and circa 2001 time periods to quantify the impacts of intensive land use changes on the July surface temperatures and the surface radiation and energy budgets for the Greater Phoenix region. The main findings are as follows: since the early 1970s the region's landscape has been altered by a significant increase in urban/suburban land area, primarily at the expense of decreasing plots of irrigated agriculture and secondarily by the conversion of seminatural shrubland. Mean regional temperatures for the circa 2001 landscape were 0.12??C warmer than the circa 1973 landscape, with maximum temperature differences, located over regions of greatest urbanization, in excess of 1??C. The significant reduction in irrigated agriculture, for the circa 2001 relative to the circa 1973 landscape, resulted in dew point temperature decreases in excess of 1??C. The effect of distinct land use conversion themes (e.g., conversion from irrigated agriculture to urban land) was also examined to evaluate how the most important conversion themes have each contributed to the region's changing climate. The two urbanization themes studied (from an initial landscape of irrigated agriculture and seminatural shrubland) have the greatest positive effect on near-surface temperature, increasing maximum daily temperatures by 1??C. Overall, sensible heat flux differences between the circa 2001 and circa 1973 landscapes result in a 1 W m -2 increase in domain-wide sensible heating, and a similar order of magnitude decrease in latent heating, highlighting the importance of surface repartitioning in establishing near-surface temperature trends. In part 2 of this study, we address the role of the surface budget changes on the mesoscale dynamics/thermodynamics, in context of the large-scale environment. Copyright 2009 by the American Geophysical Union.

Journal of Geophysical Research D: Atmospheres↗
USGS2018

Ecological neighborhoods as a framework for umbrella species selection

Umbrella species are typically chosen because they are expected to confer protection for other species assumed to have similar ecological requirements. Despite its popularity and substantial history, the value of the umbrella species concept has come into question because umbrella species chosen using heuristic methods, such as body or home range size, are not acting as adequate proxies for the metrics of interest: species richness or population abundance in a multi-species community for which protection is sought. How species associate with habitat across ecological scales has important implications for understanding population size and species richness, and therefore may be a better proxy for choosing an umbrella species. We determined the spatial scales of ecological neighborhoods important for predicting abundance of 8 potential umbrella species breeding in Nebraska using Bayesian latent indicator scale selection in N-mixture models accounting for imperfect detection. We compare the conservation value measured as collective avian abundance under different umbrella species selected following commonly used criteria and selected based on identifying spatial land cover characteristics within ecological neighborhoods that maximize collective abundance. Using traditional criteria to select an umbrella species resulted in sub-maximal expected collective abundance in 86% of cases compared to selecting an umbrella species based on land cover characteristics that maximized collective abundance directly. We conclude that directly assessing the expected quantitative outcomes, rather than ecological proxies, is likely the most efficient method to maximize the potential for conservation success under the umbrella species concept.

Biological Conservation↗