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USGS2009

LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel assessment

LANDFIRE is a 5-year, multipartner project producing consistent and comprehensive maps and data describing vegetation, wildland fuel, fire regimes and ecological departure from historical conditions across the United States. It is a shared project between the wildland fire management and research and development programs of the US Department of Agriculture Forest Service and US Department of the Interior. LANDFIRE meets agency and partner needs for comprehensive, integrated data to support landscape-level fire management planning and prioritization, community and firefighter protection, effective resource allocation, and collaboration between agencies and the public. The LANDFIRE data production framework is interdisciplinary, science-based and fully repeatable, and integrates many geospatial technologies including biophysical gradient analyses, remote sensing, vegetation modelling, ecological simulation, and landscape disturbance and successional modelling. LANDFIRE data products are created as 30-m raster grids and are available over the internet at www.landfire.gov , accessed 22 April 2009. The data products are produced at scales that may be useful for prioritizing and planning individual hazardous fuel reduction and ecosystem restoration projects; however, the applicability of data products varies by location and specific use, and products may need to be adjusted by local users.

International Journal of Wildland Fire↗

USGS2019

Characterizing the influence of fire on hydrology in southern California

The chaparral-dominated national forests of southern California were in part established to provide water provision services to the surrounding urban populations and irrigation for agriculture. However, water provision in the form of groundwater recharge and surface runoff depends on the climatological conditions of any given year and also landscape-scale disturbances such as fire. Fire is increasing in frequency in southern California and understanding its impacts both immediately postfire and as vegetation recovers, and the interactions between fire and hydrology, are key components to managing federal lands effectively. In this study we focus on nine fires in a study area that encompasses the four southern California national forests (Los Padres, Angeles, San Bernardino, and Cleveland) and use a water balance model to investigate the effects of water provision services post-fire at a regional scale. We found that runoff and recharge increased post-fire, with increases in recharge being greater with recovery times ranging from 2 to 4 y post-fire. Vegetation recovery occurred 2 y post-fire for all basins as indicated by remotely sensed imagery measuring vegetation greenness having returned to or exceeded pre-fire values for the basin. We found that runoff and recharge were more sensitive to the effects of climate than to length of time post-fire. Findings from these modeling tools allow users to anticipate the impact of fire on water provision services in the region and develop management strategies that help reduce the impacts of wildfire.

California↗

USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗

USGS2019

Evaluating the factors responsible for post-fire water quality response in forests of the western USA

Wildfires commonly increase nutrient, carbon, sediment, and metal inputs to streams yet the factors responsible for the type, magnitude and duration of water quality effects are poorly understood. Prior work by the current authors found increased nitrogen, phosphorus and cation exports were common the first five post-fire years from a synthesis of 159 wildfires across the western United States. In the current study, an analysis is undertaken to determine factors that best explain post-fire streamwater responses observed in those watersheds. Increased post-fire total nitrogen and phosphorus loading were proportional to the catchment extent of moderate and high burn severity. Post-fire dissolved metal concentrations increased in catchments with < 2% pre-fire soil organic matter. In contrast, in catchments with > 2% soil organic matter, post-fire dissolved metal concentrations decreased compared to pre-fire conditions. Where post-fire normalized difference vegetation index (NDVI), a remote sensing indicator of live green vegetation, was low, total metal concentrations increased by 25% on average and by > 100% in some cases. When pre-fire soil field capacity exceeded 17%, there was a 750% median increase in total metals export to streams. Overall, the current analysis identified burn severity, post-fire vegetation cover, and several soil properties as the key variables explaining extended post-fire water quality response across a broad range of conditions found in the western US.

Arizona, California, Colorado, Idaho, Montana, New↗

USGS2007

Predicting and validating the motion of an ash cloud during the 2006 eruption of Mount Augustine volcano

On 11 January 2006, Mount Augustine volcano in southern Alaska began erupting after 20- year repose. The Anchorage Forecast Office of the National Weather Service (NWS) issued an advisory on 28 January for Kodiak City. On 31 January, Alaska Airlines cancelled all flights to and from Anchorage after multiple advisories from the NWS for Anchorage and the surrounding region. The Alaska Volcano Observatory (AVO) had reported the onset of the continuous eruption. AVO monitors the approximately 100 active volcanoes in the Northern Pacific. Ash clouds from these volcanoes can cause serious damage to an aircraft and pose a serious threat to the local communities, and to transcontinental air traffic throughout the Arctic and sub-Arctic region. Within AVO, a dispersion model has been developed to track the dispersion of volcanic ash clouds. The model, Puff, was used operational by AVO during the Augustine eruptive period. Here, we examine the dispersion of a volcanic ash (or aerosol) cloud from Mount Augustine across Alaska from 29 January through the 2 February 2006. We present the synoptic meteorology, the Puff predictions, and measurements from aerosol samplers, laser radar (or lidar) systems, and satellites. Aerosol samplers revealed the presence of volcanic aerosols at the surface at sites where Puff predicted the ash clouds movement. Remote sensing satellite data showed the development of the ash cloud in close proximity to the volcano consistent with the Puff predictions. Two lidars showed the presence of volcanic aerosol with consistent characteristics aloft over Alaska and were capable of detecting the aerosol, even in the presence of scattered clouds and where the ash cloud is too thin/disperse to be detected by remote sensing satellite data. The lidar measurements revealed the different trajectories of ash consistent with the Puff predictions. Dispersion models provide a forecast of volcanic ash cloud movement that might be undetectable by any other means but are still a significant hazard. Validation is the key to assessing the accuracy of any predictions. The study highlights the use of multiple and complementary observations used in detecting the trajectory ash cloud, both at the surface and aloft in the atmosphere.

Alaska↗

USGS1982

Evaluation of AMOEBA: a spectral-spatial classification method

Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely used spectral classifier. The test showed that irrigated crop types can be classified as accurately with the AMOEBA method as with the generally used spectral method ISOCLS; the AMOEBA method, however, requires less computer time.

Journal of Applied Photographic Engineering↗

USGS1973

Fluorescent spectroscopy, a technique for characterizing surface films

A relationship is established between fluorescent spectra obtained by using a light path through the liquid solution and the fluorescent spectra obtained by a direct reading of surface reflection (remote sensing). A brief review of quantum fundamentals provides the necessary information to conclude that "see-through" and reflectance spectra are identical in wavelength response. Many floating films contain fluorescent materials; thus fluorescent spectroscopic techniques were used to obtain the fluorescent spectra of lube oil, crude oil, and lignosulfonie acid in an effort to detect, identify, and quantify these representative fluorescent materials in water solutions. For each material tested the emission maximum was established. The emission maximum was then held constant while the absorption spectrum was recorded and the absorption maximum established. The complete spectral curves are presented.

Journal of Research of the U.S. Geological Survey↗

USGS2012

Enhanced visualization for the interpretation of Magellan radar data: Supplement to the Magellan special issue

The differences of radar data from more familiar photographic images, coupled with the unique geologic characteristics of Venus's surface, can make interpretation of the standard Magellan data products difficult for both planetary scientists and nonspecialists. We describe a set of digital processing techniques for transforming individual remote‐sensing datasets in order to make the information they contain more apparent, and for combining multiple datasets of diverse resolution and information content into synthetic images that allow the viewer to explore correlations among the data. The creation of derived data products by techniques like those we describe is becoming increasingly widespread and important, both for scientific research and for use in educational and media presentations. We apply our processing techniques to Magellan synthetic aperture radar images, altimetry, and microwave emissivity measurements of four areas that exemplify some of the most important styles of geologic activity on Venus.

Journal of Geophysical Research E: Planets↗

USGS2024

Software application for spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A tool for identifying cyanobacteria genera from remotely sensed data

Remote sensing is often used to detect algae, but standard techniques do not provide information on the types of algae present or their potential to form a harmful algal bloom (HAB). We developed a framework for identifying algal genera based on reflectance: SMASH, short for Spectral Mixture Analysis for Surveillance of HABs. The Software Application for SMASH (SAS) was developed in MATLAB and makes use of a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm implemented in Python but packaged as a standalone executable. SAS includes functions for importing hyperspectral images, resampling spectral libraries, evaluating endmember spectral separability, performing MESMA, and generating various output data products.

Journal of Open Research Software (JORS)↗

USGS2016

@KarlTheFog has been mapped!

Within the world of mapping, clouds are a pesky interference to be removed from satellite remote sensed imagery. However, to many of us, that is a waste of pixels. Cloud maps are becoming increasingly valuable in the quest to understand land cover change and surface processes. In coastal California, the dynamic summertime interactions between air masses, the ocean, and topography result in blankets of fog and low clouds flowing into low lying areas of the San Francisco Bay Area. The low clouds and fog advected from the Pacific bring moisture and shade to coastal ecosystems. This acts to reduce temperatures and evapotranspiration stress during the otherwise arid Mediterranean climate season, in turn impacting vegetation distribution, irrigation needs, and urban energy consumption.

BayGEO Journal↗

USGS2011

Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico Oil spill

Information regarding the present condition, historical status, and dynamics of mangrove forests is needed to study the impacts of the Gulf of Mexico oil spill and other stressors affecting mangrove ecosystems. Such information is unavailable for Louisiana at sufficient spatial and thematic detail. We prepared mangrove forest distribution maps of Louisiana (prior to the oil spill) at 1 m and 30 m spatial resolution using aerial photographs and Landsat satellite data, respectively. Image classification was performed using a decision-tree classification approach. We also prepared land-cover change pairs for 1983, 1984, and every 2 y from 1984 to 2010 depicting “ecosystem shifts” (e.g., expansion, retraction, and disappearance). This new spatiotemporal information could be used to assess short-term and long-term impacts of the oil spill on mangroves. Finally, we propose an operational methodology based on remote sensing (Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER], hyperspectral, light detection and ranging [LIDAR], aerial photographs, and field inventory data) to monitor the existing and emerging mangrove areas and their disturbance and regrowth patterns. Several parameters such as spatial distribution, ecosystem shifts, species composition, and tree height/biomass could be measured to assess the impact of the oil spill and mangrove recovery and restoration. Future research priorities will be to quantify the impacts and recovery of mangroves considering multiple stressors and perturbations, including oil spill, winter freeze, sea-level rise, land subsidence, and land-use/land-cover change for the entire Gulf Coast.

Journal of Coastal Research↗

USGS2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. We used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. We compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

Oregon, Washington↗

USGS2007

Oahu: perspective from space

Satellite remote sensing provides us with a unique perspective from space. This perspective is synoptic in nature and provides regional views of most of the land areas of the earth. The orbital characteristics of the Landsat system are such that repetitive imagery of the same area may be obtained. Because of the permanent nature of the imagery, it may be retrieved for comparative analysis at any time. Comparisons of this image of Oahu with maps of the island (for example, the Oahu, Hawaii, 1:250,000-scale topographic map) will enable the reader to readily identify the place names discussed in this article and permit a more detailed interpretation of the image.

Hawaii↗

USGS2013

The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth

Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong ( R 2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.

Great Basin↗

USGS2011

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades-An overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction，have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

International Journal of Environmental Research an↗

USGS1985

PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

Journal of Imaging Technology↗

USGS2015

Quantitative imaging of volcanic plumes — Results, needs, and future trends

Recent technology allows two-dimensional “imaging” of trace gas distributions in plumes. In contrast to older, one-dimensional remote sensing techniques, that are only capable of measuring total column densities, the new imaging methods give insight into details of transport and mixing processes as well as chemical transformation within plumes. We give an overview of gas imaging techniques already being applied at volcanoes (SO 2 cameras, imaging DOAS, FT-IR imaging), present techniques where first field experiments were conducted (LED-LIDAR, tomographic mapping), and describe some techniques where only theoretical studies with application to volcanology exist (e.g. Fabry–Pérot Imaging, Gas Correlation Spectroscopy, bi-static LIDAR). Finally, we discuss current needs and future trends in imaging technology.

Journal of Volcanology and Geothermal Research↗

USGS2021

Ecological correlates of fecal corticosterone metabolites in female Greater Sage-Grouse (Centrococercus urophasianus)

Measurement of physiological responses can reveal effects of ecological conditions on an animal and correlate with demographic parameters. Ecological conditions for many animal species have deteriorated as a function of invasive plants and habitat fragmentation. Expansion of juniper (Juniperus spp.) trees and invasion of annual grasses into sagebrush (Artemisia spp.) ecosystems have contributed to habitat degradation for Greater Sage-Grouse (Centrococercus urophasianus (Bonaparte, 1827); hereafter, “Sage-Grouse”), a species of conservation concern throughout its range. We evaluated relationships between habitat use in a landscape modified by juniper expansion and annual grasses and corticosterone metabolite levels (stress responses) in feces (FCORTm) of female Sage-Grouse. We used remotely sensed data to estimate vegetation cover within hens’ home ranges and accounted for factors that influence FCORTm in other vertebrates, such as age and weather. We collected 36 fecal samples from 22 radio-collared hens during the brood-rearing season (24 May–26 July) in southwestern Idaho 2017–18. Concentrations of corticosterone increased with home range size but decreased with reproductive effort and temperature. The importance of home range size suggests that maintaining or improving habitats that promote smaller home ranges would likely facilitate a lower stress response by hens, which should benefit Sage-Grouse survival and reproduction.

Idaho↗