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At least 1,441 records · Page 80Linked to original sources
USGS2006

Three decades of urbanization: Estimating the impact of land-cover change on stream salamander populations

Urbanization has become the dominant form of landscape disturbance in parts of the United States. Small streams in the Piedmont region of the eastern United States support high densities of salamanders and are often the first habitats to be affected by landscape-altering factors such as urbanization. We used US Geological Survey land cover data from 1972 to 2000 and a relation between stream salamanders and land cover, established from recent research, to estimate the impact of contemporary land-cover change on the abundance of stream salamanders near Davidson, North Carolina, a Piedmont locale that has experienced rapid urbanization during this time. Our analysis indicates that southern two-lined salamander ( Eurycea cirrigera ) populations have decreased from 32% to 44% while northern dusky salamanders ( Desmognathus fuscus ) have decreased from 21% to 30% over the last three decades. Our results suggest that the widespread conversion of forest to urban land in small catchments has likely resulted in a substantial decline of populations of stream salamanders and could have serious effects on stream ecosystems.

Biological Conservation↗
USGS2026

Northern Yellowstone mule deer seasonal movement, habitat selection, and survival patterns

We determined the movement patterns and survival rates of 85 radio-marked adult female Rocky Mountain mule deer ( Odocoileus hemionus hemionus ) wintering on the Northern Yellowstone Winter Range (NYWR). We assessed seasonal movement and distribution patterns and individual fidelity to those patterns, along with seasonal habitat selection and survival rates. Twenty-three percent of the deer were nonmigratory, remaining on the winter range year-round, and 77% were migratory, moving 10–104 km from winter to summer ranges. No deer switched between migratory and nonmigratory status. All migratory deer that were monitored for ≥ 2 years exhibited traditional movement patterns and utilized the same winter and summer ranges in all years. Mean winter and summer home range sizes for migratory deer were 1076 ha (SE 129) and 1527 ha (SE 249), respectively, and mean home range size for resident deer was 3100 ha (SE 902). There was a dichotomy in winter habitat use, with one group of deer selecting cultivated and grassland vegetation land cover classes and a second group of deer selecting shrubland and grassland vegetation land cover classes. A dichotomy in summer habitat use was evident between those deer selecting for unburned forest and avoiding all burned land cover classes and those deer selecting for moderate and high burn intensity land cover classes and avoiding unburned forest. We estimated annual mean survival at 0.813 (SE 0.028) and found no differences in survival rates among migratory deer, nonmigratory deer, or deer exhibiting differences in winter or summer habitat selection patterns.

Idaho, Montana, Wyoming↗
USGS2004

The effects of habitat resolution on models of avian diversity and distributions: A comparison of two land-cover classifications

Quantifying patterns is a key element of landscape analysis. One aspect of this quantification of particular importance to landscape ecologists is the classification of continuous variables to produce categorical variables such as land-cover type or elevation stratum. Although landscape ecologists are fully aware of the importance of spatial resolution in ecological investigations, the potential importance of the resolution of classifications has received little attention. Here we demonstrate the effects of using two different land-cover classifications to predict avian species richness and the occurrences of six individual species across the conterminous United States. We compared models built with a data set based on 14 coarsely resolved land-cover variables to models built with a data set based on 160 finely resolved land-cover variables. In general, comparable models built with the two data sets fit the data to similar degrees, but often produced strikingly different predictions in various parts of the country. By comparing the predictions made by pairs of models, we determined in which regions of the US predictions were most sensitive to differences in land-cover classification. In general, these sensitive areas were different for four of the individual species and for predictions of species richness, indicating that alternate classifications will have different effects in the analyses of different ecological phenomena and that these effects will likely vary geographically. Our results lead us to emphasize the importance of the resolution to which continuous variables are classified in the design of ecological studies.

Landscape Ecology↗
USGS2026

Reassessing water availability in the Nile River Basin using satellite-based irrigation water use accounting

Existing estimates of irrigation areas and water use in the Nile River Basin vary widely and are based on inconsistent data and methodologies. This study leverages advances in remote sensing to provide an up-to-date and consistent basin-wide reassessment of water use in the Nile River Basin. Land cover and actual evapotranspiration data from 2013 to 2022 were used to quantify irrigated areas, irrigation water use, and the average naturalized water yield. Across the basin, 7.1 million hectares of irrigated land consume an average of 72.5 ± 2.6 billion cubic meters (BCM) of blue water annually. Egypt and Sudan together account for 92% of the irrigated area and 97% of the irrigation water use. The basin’s average naturalized yield, evaluated at Dongola station, is determined to be between 108 and 114.4 ± 3.2 BCM. Although conservative, this updated analysis indicates the basin’s average naturalized yield is 14%–20% higher than the previous naturalized yield estimate (95 BCM) and 29%–36% higher than the commonly cited observed flow of 84 BCM, which is frequently treated as the total basin-wide available water. Importantly, the study underscores that the 84 BCM represents flow at the Aswan gauging station after upstream consumptive uses, not the total water available in the basin under naturalized conditions. This distinction is critical for accurate water accounting, planning, and governance in this water-scarce basin. This study demonstrates the value of open-source remote sensing resources in data-scarce regions while emphasizing the need for region specific validation and bias correction for improved accuracy.

Nile River Basin↗
USGS2021

Evaluating low flow patterns, drivers and trends in the Delaware River Basin

In the humid, temperate Delaware River Basin (DRB) where water availability is generally reliable, summer low flows can cause competition between various human and ecological water uses. As temperatures continue to rise, population increases and development expands, it is critical to understand historical low flow variability to anticipate and plan for future flows. Using a sample of 325 U.S. Geological Survey gages, we evaluated spatial patterns in several low flow metrics, the biophysical and climatic drivers of these metrics, and trends in low flows for two periods: 1950-2018 and 1980-2018. We calculated the annual 7-day low flow and date, low flow deficit as the departure below a long-term daily flow threshold and the number of discrete low flow periods below this threshold. We also aggregated several climate metrics to watershed scale and used existing watershed properties quantifying land cover, topography, soils, geology, and human activity. Random forest models were used to assess the hierarchy of variable importance in explaining mean-annual low flow variability for each low flow metric using all gages. We find muted regional patterns in mean-annual low flow and low flow variability, likely due to the myriad of anthropogenic, landscape, and flow modifications that obscure flow regimes from their natural characteristics. In contrast, individual years show markedly different spatial patterns in low flow magnitude and severity. Coincident with increases in precipitation, 7-day low flows have generally increased and low flow deficits decreased for both 1950-2018 and 1980-2018 periods. However, 7-day low flows have decreased in the Coastal Plain physiographic province where water use and impervious area have increased in recent decades, highlighting the effects of land and water management on low flows. With continued change expected in the DRB, additional research needs are highlighted to enable estimation of future low flows and to plan for periods of prolonged low flow.

Delaware, New Jersey, New York, Pennsylvania↗
USGS2015

Historic and forecasted population and land-cover change in eastern North Carolina, 1992-2030

The Southeast Regional Partnership for Planning and Sustainability (SERPPAS) was formed in 2005 as a partnership between the Department of Defense (DOD) and State and Federal agencies to promote better collaboration in making resource-use decisions. In support of this goal, the U.S. Geological Survey (USGS) conducted a study to evaluate historic population growth and land-cover change, and to model future change, for the 13-county SERPPAS study area in southeastern North Carolina (fig. 1). Improved understanding of trends in land-cover change and the ability to forecast land-cover change that is consistent with these trends will be a key component of efforts to accommodate local military-mission imperatives while also promoting sustainable economic growth throughout the 13-county study area. The study had three principal objectives: 1. Evaluate historic changes in population and land cover for the period 1992–2006 using both previously existing as well as newly generated land-cover data. 2. Develop models to forecast future change in land cover using the data gathered in objective 1 in conjunction with ancillary data on the suitability of the various sub-areas within the study area for low- and high-intensity urban development. 3. Deliver these results—including an executive-level briefing and a USGS technical report—to DOD, other project cooperators, and local counties in hard-copy and digital formats and via the Web through a map-based data viewer. This report provides a general overview of the study and is intended for general distribution to non-technical audiences.

North Carolina↗
USGS2007

Regional dynamics of grassland change in the western Great Plains

This paper examines the contemporary land-cover changes in two western Great Plains ecoregions between 1973 and 2000. Agriculture and other land uses can have a substantial effect on grassland cover that varies regionally depending on the primary driving forces of change. In order to better understand change, the rates, types, and causes of land conversion were examined for 1973, 1980, 1986, 1992, and 2000 using Landsat satellite data and a statistical sampling strategy. The overall estimated rate of land-cover change between 1973 and 2000 was 7.4% in the Northwestern Great Plains and 11.5% in the Western High Plains. Trends in both ecoregions have similarities, although the dynamics of change differ temporally depending on driving forces. Between 1973 and 1986, grassland cover declined when economic opportunity drove an expansion of agriculture. Between 1986 and 2000, grassland expanded as public policy and a combination of socioeconomic factors drove a conversion from agriculture to grassland. ?? 2007 Copyright by the Center for Great Plains Studies, University of Nebraska-Lincoln.

Conference Paper↗
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↗