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Results for “Remote Sensing”

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At least 973 records · Page 54Linked to original sources

USGS2000

Characteristic length scale of input data in distributed models: implications for modeling grain size

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m 2 modeling grid size. A modeling grid size of about 10×10 m 2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.

Idaho↗

USGS2016

Modeling martian thermal inertia in a distributed memory high performance computing environment

Modeling martian surface properties fusing high resolution, spatially enabled, remotely sensed data and derived thermophysical modeling is an essential tool for surface property characterization studies. In this work, we describe the development of a thermal inertia modeling tool that integrates the KRC thermal model and a nine-dimensional parameter interpolation with inputs draw from remotely sensed data. Our model is classifiable as operating in both the Big Data and Big Process domains. We utilize the KRC thermal model to generate a dense lookup table. We show that the overall size of the lookup table necessary to derive thermal inertia can be reduced, through sampling, by approximately 82% while maintaining a high level of accuracy at those regions of the parameter space where thermal inertia is most frequently derived. This level of data reduction supports the distributed, in-memory application of our model and we illustrate the computational performance through a classic scaling experiment. This work extends previous modeling efforts by allowing for pixel scale thermal inertia modeling at the highest spatial scales allowed, and enabling surface properties investigations at spatial scales relevant to addressing high-priority science and engineering questions.

Conference Paper↗

USGS2011

Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests

The timing of spring leaf development, trajectories of summer leaf area, and the timing of autumn senescence have profound impacts to the water, carbon, and energy balance of ecosystems, and are likely influenced by global climate change. Limited field-based and remote-sensing observations have suggested complex spatial patterns related to geographic features that influence climate. However, much of this variability occurs at spatial scales that inhibit a detailed understanding of even the dominant drivers. Recognizing these limitations, we used nonlinear inverse modeling of medium-resolution remote sensing data, organized by day of year, to explore the influence of climate-related landscape factors on the timing of spring and autumn leaf-area trajectories in mid-Atlantic, USA forests. We also examined the extent to which declining summer greenness (greendown) degrades the precision and accuracy of observations of autumn offset of greenness. Of the dominant drivers of landscape phenology, elevation was the strongest, explaining up to 70% of the spatial variation in the onset of greenness. Urban land cover was second in importance, influencing spring onset and autumn offset to a distance of 32 km from large cities. Distance to tidal water also influenced phenological timing, but only within ~5 km of shorelines. Additionally, we observed that (i) growing season length unexpectedly increases with increasing elevation at elevations below 275 m; (ii) along gradients in urban land cover, timing of autumn offset has a stronger effect on growing season length than does timing of spring onset; and (iii) summer greendown introduces bias and uncertainty into observations of the autumn offset of greenness. These results demonstrate the power of medium grain analyses of landscape-scale phenology for understanding environmental controls on growing season length, and predicting how these might be affected by climate change.

Global Change Biology↗

USGS2018

Evaluating potential distribution of high-risk aquatic invasive species in the water garden and aquarium trade at a global scale based on current established populations

Aquatic non‐native invasive species are commonly traded in the worldwide water garden and aquarium markets, and some of these species pose major threats to the economy, the environment, and human health. Understanding the potential suitable habitat for these species at a global scale and at regional scales can inform risk assessments and predict future potential establishment. Typically, global habitat suitability models are fit for freshwater species with only climate variables, which provides little information about suitable terrestrial conditions for aquatic species. Remotely sensed data including topography and land cover data have the potential to improve our understanding of suitable habitat for aquatic species. In this study, we fit species distribution models using five different model algorithms for three non‐native aquatic invasive species with bioclimatic, topographic, and remotely sensed covariates to evaluate potential suitable habitat beyond simple climate matches. The species examined included a frog (Xenopus laevis), toad (Bombina orientalis), and snail (Pomacea spp.). Using a unique modeling approach for each species including background point selection based on known established populations resulted in robust ensemble habitat suitability models. All models for all species had test area under the receiver operating characteristic curve values greater than 0.70 and percent correctly classified values greater than 0.65. Importantly, we employed multivariate environmental similarity surface maps to evaluate potential extrapolation beyond observed conditions when applying models globally. These global models provide necessary forecasts of where these aquatic invasive species have the potential for establishment outside their native range, a key component in risk analyses.

Risk Analysis↗

USGS2007

Land Cover Applications, Landscape Dynamics, and Global Change

The Land Cover Applications, Landscape Dynamics, and Global Change project at U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) seeks to integrate remote sensing and simulation models to better understand and seek solutions to national and global issues. Modeling processes related to population impacts, natural resource management, climate change, invasive species, land use changes, energy development, and climate mitigation all pose significant scientific opportunities. The project activities use remotely sensed data to support spatial monitoring, provide sensitivity analyses across landscapes and large regions, and make the data and results available on the Internet with data access and distribution, decision support systems, and on-line modeling. Applications support sustainable natural resource use, carbon cycle science, biodiversity conservation, climate change mitigation, and robust simulation modeling approaches that evaluate ecosystem and landscape dynamics.

Fact Sheet↗

USGS2012

United States Geological Survey (USGS) Natural Hazards Response

The primary goal of U.S. Geological Survey (USGS) Natural Hazards Response is to ensure that the disaster response community has access to timely, accurate, and relevant geospatial products, imagery, and services during and after an emergency event. To accomplish this goal, products and services provided by the National Geospatial Program (NGP) and Land Remote Sensing (LRS) Program serve as a geospatial framework for mapping activities of the emergency response community. Post-event imagery and analysis can provide important and timely information about the extent and severity of an event. USGS Natural Hazards Response will also support the coordination of remotely sensed data acquisitions, image distribution, and authoritative geospatial information production as required for use in disaster preparedness, response, and recovery operations.

Fact Sheet↗

USGS2022

Keeping an eye on water quality from the sky

You can learn a lot about rivers, lakes, estuaries, and oceans by looking down at them from the sky. Scientists use a technique called remote sensing to measure the amount of light or heat energy reflected and emitted from the Earth. Sensors can be on satellites or mounted on airplanes, helicopters, or drones. Scientists use this information to map the quality of water in the San Francisco Bay-Delta estuary. Remote sensing helps scientists see where and when there might be problems for human health or for the plants and animals living in the estuary.

California↗

USGS2018

Thresholds and relations for soil‐hydraulic and soil‐physical properties as a function of burn severity 4 years after the 2011 Las Conchas Fire, New Mexico, USA

Wildfire effects on soil‐physical and ‐hydraulic properties as a function of burn severity are poorly characterized, especially several years after wildfire. A stratified random sampling approach was used in 2015 to sample seven sites representing a spectrum of remotely sensed burn severity in the area impacted by the 2011 Las Conchas Fire in New Mexico, USA. Replicate samples from each site were analysed in the laboratory. Linear and linear indicator regression were used to assess thresholds in soil‐physical and ‐hydraulic properties and functional relations with remotely sensed burn severity. Significant thresholds were present for initial soil‐water content (θ i ) at 0–6 cm depth between the change in the Normalized Burn Ratio ( dNBR ) equal to 618–802, for bulk density (ρ b ) at 3–6 cm between dNBR equal to 416–533, for gravel fraction at 0–1 cm between dNBR equal to 416–533, for fines (the silt + clay fraction) at 0–1 cm for dNBR equal to 416–533, and for fines at 3–6 cm for dNBR equal to 293–416. Significant linear relations with dNB R were present between ρ b at 0–1 cm, loss on ignition (LOI) at 0–1 cm, gravel fraction at 0–1 cm, and the large organic fraction at 1–3 cm. No thresholds or effects on soil‐hydraulic properties of field‐saturated hydraulic conductivity or sorptivity were observed. These results suggest that ρ b and LOI at 0–1 cm have residual direct impacts from the wildfire heat impulse. The θ i threshold is most likely from delayed groundcover/vegetation recovery that increases evaporation at the highest burn severity sites. Gravel and silt + clay thresholds at 0–1 cm at the transition to high burn severity suggest surface gravel lag development from hydraulic erosion. Thresholds in ρ b from 3 to 6 cm and in silt + clay fraction from 3 to 6 cm appear to be the result of soil variability between sites rather than wildfire impacts. This work suggests that gravel‐rich soils may have increased resilience to sustained surface runoff generation and erosion following wildfire, with implications for assessments of postwildfire hydrologic and erosion recovery potential.

New Mexico↗

USGS2005

Determining forage availability and use patterns for bison in the Hayden Valley of Yellowstone National Park

In our original Proposal, we identified 4 objectives: 1. Delineate seasonal forage use patterns by bison in the Hayden Valley; 2. Determine long and short term effects of ungulate foraging on vegetation in the Hayden Valley; 3. Determine efficacy of vegetation monitoring approaches; 4. Estimate annual production and standing crop available during non-growing seasons for herbaceous and shrub layers in major habitat types in the Hayden Valley. Our efforts to describe forage use by bison focused on assessing finer scale habitat use is a core summer range for bison in YNP. We also collected information on bison food habits and forage quality to begin to explain the “whys” of bison distribution. Short-term impacts of bison forage utilization were addressed by comparing standing biomass in plots protected from grazing with plots exposed to grazing. Historical data were not available to directly address long-term effects of ungulate foraging in the Hayden Valley, but we were able to indirectly assess some aspects of this question by determining the frequency of repeat grazing over a 3-year period and the rate at which trees along the margins of the Hayden Valley were being killed by bison rubbing The third objective, determining the relative efficacy of different vegetation monitoring approaches, was accomplished by comparing estimates of standing biomass and biomas: utilization obtained via conventional exclosure techniques with estimates based on remote sensing techniques (ground-based and satellite-borne multi-spectral radiometry|[MSR]). We addressed efficacy in terms of precision and accuracy of estimates, reliability, and logistical costs at different coverage scales. The fourth objective, estimation of forage available for ungulates in the Hayden Valley, was achieved using conventional exclosure methodology and remote sensing. We were able to estimate herbaceous biomass production during 3 different years. Exclosures allowed us to estimated changes instanding crop of herbaceous vegetation at the plant community (conventional cover types, moisture plant growth form groups, and communities defined by dominant graminoids) and catena (a repeating sequence of communities tied to landscape physiognomy) scales. We developed empirical approaches that allowed us to estimate standing biomass of herbaceous plants from reflectance data obtained from ground-based and satellite-borne multi-spectral radiometry (MSR) units. We demonstrated the potential to estimate biomass of shrubs using the same approaches. We did not have time and resources to complete vegetation maps that would optimize estimates from remote sources, but we have outlined procedures that can be followed in the future to obtain biomass estimates at the landscape scale.

Wyoming↗

USGS2015

Quantifying and predicting fuels and the effects of reduction treatments along successional and invasion gradients in sagebrush habitats

Sagebrush shrubland ecosystems in the Great Basin are prime examples of how altered successional trajectories can create dynamic fuel conditions and, thus, increase uncertainty about fire risk and behavior. Although fire is a natural disturbance in sagebrush, post-fire environments are highly susceptible to conversion to an invasive grass-fire regime (often referred to as a “grass-fire cycle”). After fire, native shrub-steppe plants are often slow to regenerate, whereas nonnative annuals, especially cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), can establish quickly and outcompete native species. Once fire-prone annuals become established, fire occurrences increase, further promoting dominance of nonnative species. The invasive grass-fire regime also alters nutrient and hydrologic cycles, pushing ecosystems beyond ecological thresholds toward steady-state, fire-prone, nonnative communities. These changes affect millions of hectares in the Great Basin and increase fire risk, decrease habitat quality and biodiversity, accelerate soil erosion, and degrade rangeland resources for livestock production. In many sagebrush landscapes, constantly changing plant communities and fuel conditions hinder attempts by land managers to predict and control fire behavior, restore native communities, and provide ecosystem services (e.g., forage production for livestock). We investigated successional and nonnative plant invasion states and associated fuel loads in degraded sagebrush habitat in a focal study area, the Morley Nelson Snake River Birds of Prey National Conservation Area (hereafter the NCA), in the Snake River Plain Ecoregion of southern Idaho. We expanded our inference by comparing our findings to similar data collected throughout seven major land resource areas (MLRAs) across the Great Basin (JFSP Project “Fire Rehabilitation Effectiveness: A Chronosequence Approach for the Great Basin” [09-S-02-1]). 4 We used a combination of field-sampling, experimental treatments, and remotely sensed data to address the following questions: (1) How do fuel loads change along gradients of succession and invasion in sagebrush ecological sites? (2) How do fuel reduction treatments influence fuels in invaded areas formerly dominated by sagebrush? (3) How do fuel loads vary across landscapes and which remote sensing techniques are effective for characterizing them?

Idaho↗

USGS2025

System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing. We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager. The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.

Open-File Report↗

USGS2004

An integrated geospatial approach to monitoring the Bering Glacier system, Alaska

The Bering Glacier is the largest and longest glacier in continental North America, with an area of approximately 5,175 km2, and a length of 190 km. It is also the largest surging glacier in America, having surged at least five times during the twentieth century. The last surge of the Bering Glacier occurred in 1993-1995, since then, the glacier has undergone constant and significant retreat thereby expanding the boundaries of Vitus Lake and creating a highly dynamic system, both ecologically and hydrologically. This study utilized GIS to integrate remote sensing observations, with detailed bathymetric, hydrographic and in situ water quality measurements of the rapidly expanding Vitus Lake. Vitus Lake has nearly doubled in surface area from 58.4 km2 to 108.8 km2, with a corresponding increase in water volume from 6.1 km3 to 10.5 km3 over the same period. The remote sensing observations were used to direct a systematic bathymetric, hydrographic and water quality measurement survey in Vitus Lake which revealed a complex three dimensional structure that is the result of sea water inflow, convection generated by ice melting and the injection of fresh water from beneath the glacier.

Conference Paper↗

USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗

USGS2018

Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery

Sandbars of large sand-bedded rivers of the central United States serve important ecological functions to many species, including the endangered Interior Least Tern ( Sternula antillarum , ILT). The ILT is a colonial bird that feeds on fish and nests primarily on riverine sandbars during its annual breeding season of around May through July, depending on region. During this time, ILTs require bare sand of sufficient elevation so as not to be inundated between nest initiation and fledging of hatchlings. Partly because of decreases in available sandbar habitat from river channelization and impoundment, ILTs were listed as endangered in 1985. Sandbars used by ILTs in central United States rivers are highly dynamic and undergo substantive changes across a wide range of temporal and spatial scales. River hydrology is the primary driver of sandbar morphodynamics in these systems. Better characterization of sandbar area with time, accounting for varying flow regimes, allows for a better understanding of landscape-scale ecology for sandbar-dependent species such as the ILT. This work uses remote-sensing techniques to quantify sandbar area that may be used by ILTs at the land-scape scale and how it has changed with time. The assessment of landscape-scale trends in sandbar area with time requires datasets with high temporal resolution and long record periods covering large geographic areas. Evaluation of remotely sensed datasets requires consideration of river stage fluctuations. To make this assessment, we developed land-cover classification datasets within active channel masks using all available images from the Landsat Thematic Mapper series of satellites meeting cloud-free (40 percent or less) and ice-free criteria. Landsat imagery was selected because of its long record period, spatial coverage, and regular reimaging cycle, making it well suited to monitor ILT sandbar habitat with time. We also attributed each scene with discharge or stage using a new database integrating U.S. Geological Survey and U.S. Army Corps of Engineers river data with Landsat metadata. This report documents development of these riverine classification datasets with a focus on applicability to the ILT. This framework may be used to continue monitoring the ILT sandbar nesting habitat or to evaluate other aquatic and terrestrial species whose life cycles are related to sandbars and channel complexity.

Data Series↗

USGS2011

Hydrogeomorphic processes of thermokarst lakes with grounded-ice and floating-ice regimes on the Arctic coastal plain, Alaska

Thermokarst lakes cover > 20% of the landscape throughout much of the Alaskan Arctic Coastal Plain (ACP) with shallow lakes freezing solid (grounded ice) and deeper lakes maintaining perennial liquid water (floating ice). Thus, lake depth relative to maximum ice thickness (1·5–2·0 m) represents an important threshold that impacts permafrost, aquatic habitat, and potentially geomorphic and hydrologic behaviour. We studied coupled hydrogeomorphic processes of 13 lakes representing a depth gradient across this threshold of maximum ice thickness by analysing remotely sensed, water quality, and climatic data over a 35-year period. Shoreline erosion rates due to permafrost degradation ranged from < 0·2 m/year in very shallow lakes (0·4 m) up to 1·8 m/year in the deepest lakes (2·6 m). This pattern of thermokarst expansion masked detection of lake hydrologic change using remotely sensed imagery except for the shallowest lakes with stable shorelines. Changes in the surface area of these shallow lakes tracked interannual variation in precipitation minus evaporation (P − E L ) with periods of full and nearly dry basins. Shorter-term (2004–2008) specific conductance data indicated a drying pattern across lakes of all depths consistent with the long-term record for only shallow lakes. Our analysis suggests that grounded-ice lakes are ice-free on average 37 days longer than floating-ice lakes resulting in a longer period of evaporative loss and more frequent negative P − E L . These results suggest divergent hydrogeomorphic responses to a changing Arctic climate depending on the threshold created by water depth relative to maximum ice thickness in ACP lakes.

Alaska↗

USGS2014

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.

Amhara region↗

USGS2010

Mapping products of Titan's surface

Remote sensing instruments aboard the Cassini spacecraft have been observed the surface of Titan globally in the infrared and radar wavelength ranges as well as locally by the Huygens instruments revealing a wealth of new morphological features indicating a geologically active surface. We present a summary of mapping products of Titan's surface derived from data of the remote sensing instruments onboard the Cassini spacecraft (ISS, VIMS, RADAR) as well as the Huygens probe (DISR) that were achieved during the nominal Cassini mission including an overview of Titan's recent nomenclature.

Book chapter↗

USGS2009

Mapping products of Titan's surface

Book chapter↗