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At least 829 records · Page 46Linked to original sources
USGS2020

Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling

Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal extents, which are difficult to collect from field inventories. Remote sensing provides an alternative but is limited in revealing post-fire recovery trajectories and the underlying processes that drive the recovery. We developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. We derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. We simulated the megafire and the post-megafire forest recovery from 1985 to 2015 using the LANDIS model. We demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances.

Environmental Modelling and Software↗
USGS2020

Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

Utah↗
USGS1993

Identification of requirements and sources for global digital topographic data

Many of the physical processes being studied by global change researchers are affects by land surface topography and consequently topographic data are an important requirement for these investigations. Remotely sensed data, especially those that will be collected by the instruments of the Earth Observing System, require significant correction to remove topographic effects. Although some requirements are met by existing topographic data, there are serious data shortages that will affect global change science. The interdisciplinary and multi temporal natural of global change research requires that remotely sensed data be processed using a consistent, highly accurate global topographic database so that information extracted from these data for different areas and times can be compared quantitively. Cartographic and remote sensing sources for the generation of new topographic data exist or are planned and will be helpful for fulfilling these requirements. More consistent use of accuracy statement terminology by data users and producers is necessary to better compare the requirements with existing or future data sets.

Pecora 12 Symposium↗
USGS1998

Geospatial Technology Applications and Infrastructure in the Biological Resources Division

Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat

Information and Technology Report↗
USGS1996

The EDOP radar system on the high-altitude NASA ER-2 aircraft

The NASA ER-2 high-altitude (20 km) aircraft that emulates a satellite view of precipitation systems carries a variety of passive and active (lidar) remote sensing instruments. A new Doppler weather radar system at X band (9.6 GHz) called the ER-2 Doppler radar (EDOP) has been developed and flown on the ER-2 aircraft. EDOP is a fully coherent Doppler weather radar with fixed nadir and forward pointing (33?? off nadir) beams that map out Doppler winds and reflectivities in the vertical plane along the aircraft motion vector. Doppler winds from the two beams can be used to derive vertical and along-track air motions. In addition, the forward beam provides linear depolarization measurements that are useful in discriminating microphysical characteristics of the precipitation. This paper deals with a general description of the EDOP instrument including the measurement concept, the system configuration and hardware, and recently obtained data examples from the instrument. The combined remote sensing package on the ER-2, along with EDOP, provides a unique platform for simulating spaceborne remote sensing of precipitation.

Journal of Atmospheric and Oceanic Technology↗
USGS2018

Analysis of different sensor performances in impervious surface mapping

The U.S. Geological Survey (USGS) has developed the National Land Cover Database (NLCD) to provide consistent land cover and land cover change products for the nation since 2001. As one of products in the NLCD, the percent impervious surface area (ISA), which was estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30-m grid and has been used to quantify urban land cover types and extents for the United States. However, it is still a challenge to clearly determine urban land cover intensity and extents using remote sensing data with spatial and spectral resolutions similar to Landsat in part because of highly heterogeneous features of urban land cover. Most urban areas, especially in low intensity development areas, exhibit sub-pixel characteristics that mix impervious surface with other land covers (e.g., grass and trees) in the 30-m resolution satellite imagery. Furthermore, the influence of highly heterogeneous features in many urban areas and how they alter the spectral signature of urban landscapes has not yet been fully studied. Recent advances in remote sensing technology have provided multiple spectral and spatial resolution data from several satellites including WorldView (WV), Sentinel-2, and the Landsat Operational Land Imager (OLI). Remote sensing images having different spectral bands and high spatial resolution provide the potential to derive detailed information on the nature and properties of different surface materials on the urban ground. This study focuses on performance of mapping impervious surface using data collected from WorldView-3, Sentinel-2, and Landsat OLI. We compared ISA results estimated from these sensors and evaluated benefits and limitations of radiometric and spatial resolutions for mapping impervious surface in a study area on the Eastern corridor between Washington, D.C., and Baltimore, where developed impervious surface containing both residential housings, office buildings, and roads, in the United States. The impact of different band combinations in Sentinel-2 imagery on mapping urban impervious surface and urban land cover was also evaluated.

Conference Paper↗
USGS1979

The brightness of lights on Earth at night, digitally recorded by DMSP satellite

The U.S. Air Force has operated its Defense Meteorological Satellite Program (DMSP) for nearly a decade, and film images from the system have been openly available since 1973. Films are well suited for the study of weather, and users of such films have derived much useful data. For many potential remote sensing applications, however, a quantitative measurement of the brightness of the imaged light patterns is needed, and it cannot be extracted with adequte accuracy from the films. Such information is contained in the telemetry from the spacecraft and is retained on digital tapes, which store the images for a few days while they await filming. For practical reasons, it has not heretofore been feasible for the Air Force to provide a remote-sensing user with these digital data, and the quantitative brightness information has been lost with the erasure of tapes for re-use. For the purpose of evaluation of tapes as a means for remote sensing, the Air Force recently did provide to the author six examples containing records of nighttime DMSP imagery similar to that which has previously 1 been evaluated by SRI International in a film format. The digital data create many new applications for these images, owing to a combination of several factors, the most important of which are the preservation of photometric information and of full spatial resolution. In this evaluation, stress has been placed upon determination of the broad potential value of the data rather than the full exploitation of any one aspect of it. The effort was guided by an objective to develop handling methods for the vast body of numbers--methods which will be practical for use in a research or engineering environment where budgets are limited, and specialized capabilities and image reproduction equipment has not already been developed. We report the degree of success obtained in this effort, pointing out the relative strengths and the relative limitations, as compared to the sophisticated, weather-oriented data processing which is well suited for the Air Force requirements. Both geometric and photometric calibration methods are evaluated. An image can be considered as a 3-dimensional array, X, Y, Z, in which X and Y are the coordinates of a picture element (pixel) and Z is the brightness at that location. A method of approach to handling these parameters, particularly Y and Z, is developed in a form quite different from that which serves the operational applications. The user of digital data will need the film images which are generated by the Air Force from the same data as is provided on digital tape. In the first stages of analysis, the films provide both a convenient index and a guide to identification of large patterns in the data. Additionally, the infrared (8 to 13 0 film provides a valuable indicator of cloud cover. Two general conclusions are drawn from this study. Firstly, the digital DMSP data have great potential value but their cost, in terms of the interruption of the present operational routine, is quite high. Therefore, if a program is undertaken to provide for the open availability of an archive of digital records, great care must be exercised in selecting only those records which have unusually high value in order that the effort will be cost-effective. Secondly, it is concluded that several aspects of the program, well designed for Air Force operational purposes, are not adapted to earth-sensing needs. This is probably inevitable, since the two applications are largely different and in some ways incompatible. For example, the nighttime visual sensor saturates in the center of major cities and in moderately large fires (such as gas flares). This saturation prevents the analyst from integrating photometric parameters. For weather observation, this inability is unimportant, and acceptance of such saturation makes feasible a decrease in the data rate. Such limitations in the data will probably be overcome only through modifying the existing system or the implementation of a similar system designed specifically to serve earth-sensing needs.

Open-File Report↗
USGS2025

Post-wildfire sediment fluxes and turbidity plumes in a coastal-draining watershed

Coastal watersheds impacted by wildfires experience higher erosion resulting in increased sediment delivery to the ocean that alters limiting factors (i.e., light) for marine organisms. With increasing wildfire magnitude and severity, it is critical to explore changes in riverine discharges to the ocean to assess cascading hazards associated with wildfires. In situ data, remotely sensed turbidity data, and hydrological model (Soil and Water Assessment Tool “SWAT”) simulations have been adapted to capture and investigate fire-related land use change impacts on Malibu Creek, California, USA. Modifying SWAT land cover inputs using burn severity data had minimal impact on simulations, requiring additional parameterization for acceptable model performance. Remotely sensed turbidity, in situ discharge, rating curve sediment loads, and SWAT simulated discharge and sediment loads increased following the Woolsey Fire. When compared to in situ and rating curve data in similar non-fire water years, the 2019 Woolsey Fire water year in situ discharge was 1.8 times higher, SWAT simulated discharges were 1.4–1.7 times higher, and rating curve sediment load was 1.3 times higher. However, the SWAT simulated sediment loads were slightly lower (0.8–0.9 times) than rating curve sediment loads in similar non-fire water years. Mean coastal turbidity increased to 18.2 Formazin Nephelometric Unit (FNU) during the first storm post-fire (mean background value of 4.3 FNU). Synergies between methods demonstrated rapid coastal sediment exports (remote sensing) and ongoing erosion post-fire (SWAT). These data are essential to understanding fire-related marine ecological changes and implementing effective management and conservation initiatives.

California↗
USGS2025

Satellite assessment of winter cover crop and conservation tillage outcomes to support adaptive management in working landscapes

The use of winter cover crops and conservation tillage are agricultural practices promoted to reduce nutrient and sediment loss from cropland, improve soil health, increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long-Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no-cost Harmonized Landsat and Sentinel-2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super-spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7-year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.

Maryland↗
USGS2021

Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates

Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation ( P ), actual evapotranspiration (ET), runoff ( R ), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P , 14 % to 26 % for ET, 28 % to 82 % for R , 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R , 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

Hydrology and Earth System Sciences↗
USGS2016

Comparing three approaches of evapotranspiration estimation in mixed urban vegetation; field-based, remote sensing-based and observational-based methods

Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia’s urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, and particularly urban parklands, is of growing concern. The estimation of ET and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water and climate characteristics. Accurate estimation of evapotranspiration (ET), which is the main component of a plant’s water requirement, in urban parks is highly desirable because this water maintains the health of green infrastructure and this in turn provides essential ecosystem services. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia.

Remote Sensing↗
USGS2016

Normalized burn ratios link fire severity with patterns of avian occurrence

Context Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains. Objectives We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire. Methods We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales. Results Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire. Conclusions DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Landscape Ecology↗
USGS2010

U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center-Fiscal Year 2009 Annual Report

The Earth Resources Observation and Science (EROS) Center is a U.S. Geological Survey (USGS) facility focused on providing science and imagery to better understand our Earth. As part of the USGS Geography Discipline, EROS contributes to the Land Remote Sensing (LRS) Program, the Geographic Analysis and Monitoring (GAM) Program, and the National Geospatial Program (NGP), as well as our Federal partners and cooperators. The work of the Center is shaped by the Earth sciences, the missions of our stakeholders, and implemented through strong program and project management and application of state-of-the-art information technologies. Fundamentally, EROS contributes to the understanding of a changing Earth through 'research to operations' activities that include developing, implementing, and operating remote sensing based terrestrial monitoring capabilities needed to address interdisciplinary science and applications objectives at all levels-both nationally and internationally. The Center's programs and projects continually strive to meet and/or exceed the changing needs of the USGS, the Department of the Interior, our Nation, and international constituents. The Center's multidisciplinary staff uses their unique expertise in remote sensing science and technologies to conduct basic and applied research, data acquisition, systems engineering, information access and management, and archive preservation to address the Nation's most critical needs. Of particular note is the role of EROS as the primary provider of Landsat data, the longest comprehensive global land Earth observation record ever collected. This report is intended to provide an overview of the scientific and engineering achievements and illustrate the range and scope of the activities and accomplishments at EROS throughout fiscal year (FY) 2009. Additional information concerning the scientific, engineering, and operational achievements can be obtained from the scientific papers and other documents published by EROS staff. We welcome comments and follow-up questions on any aspect of this Annual Report and invite any of our customers or partners to contact us at their convenience. To communicate with us, or for more information about EROS, contact: Communications and Outreach, USGS EROS Center, 47914 252nd Street, Sioux Falls, South Dakota 57198, jsnelson@usgs.gov, http://eros.usgs.gov/.

Open-File Report↗
USGS2023

DisasterNet: Causal Bayesian networks with normalizing flows for cascading hazards

Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.

Conference Paper↗
USGS2008

Monitoring fine-grained sediment in the Colorado River ecosystem, Arizona — Control network and conventional survey techniques

In 2002, fine-grained sediment (sand, silt, and clay) monitoring in the Colorado River downstream from Glen Canyon Dam was initiated to survey channel topography at scales previously unobtainable in this canyon setting. This report presents the methods used to establish the high-resolution global positioning system (GPS) control network required for this effort as well as the conventional surveying techniques used in the study. Using simultaneous, dual-frequency GPS vector-based methods, the network points were determined to have positioning accuracies of less than 0.03 meters (m) and ellipsoidal height accuracies of between 0.01 and 0.10 m at a 95-percent degree of confidence. We also assessed network point quality with repeated, electronic (optical) total-station observations at 39 points for a total of 362 measurements; the mean range was 0.022 m in horizontal and 0.13 in vertical at a 95-percent confidence interval. These results indicate that the control network is of sufficient spatial and vertical accuracy for collection of airborne and subaerial remote-sensing technologies and integration of these data in a geographic information system on a repeatable basis without anomalies. The monitoring methods were employed in up to 11 discrete reaches over various time intervals. The reaches varied from 1.3 to 6.4 kilometers in length. Field results from surveys in 2000, 2002, and 2004 are described, during which conventional surveying was used to collect more than 3000 points per day. Ground points were used as checkpoints and to supplement areas just below or above the water surface, where remote-sensing data are not collected or are subject to greater error. An accuracy of +or- 0.05 m was identified as the minimum precision of individual ground points. These results are important for assessing digital elevation model (DEM) quality and identifying detection limits of significant change among surfaces generated from remote-sensing technologies.

Arizona↗
USGS2025

Correspondence between satellite-derived and long-term field observations of vegetation cover at Great Basin experimental treatments.

Vegetation treatments are frequently utilized in Western US rangelands to reduce woody plant cover in sagebrush stands threatened by increased wildfire risk and in pinyon-juniper woodlands expanding into formerly high-value sagebrush habitats. Despite widespread use of these treatments, monitoring data to evaluate long-term vegetation responses are often insufficient or absent. Long-term field experiments and remote-sensing based vegetation data may be complementary for assessing treatment effectiveness across temporal and spatial scales. The SageSTEP project experimentally implemented treatments at numerous sites across the Intermountain West and monitored the subsequent response of vegetation cover components with 15+ yr of field observations. However, while pretreatment data were collected in the year of implementation, long-term observations of pretreatment vegetation conditions are lacking in the SageSTEP database. Remote-sensing based time-series maps (1985–2023) of vegetation cover from Rangeland Condition Monitoring Assessment and Projection (RCMAP) could fill temporal gaps in monitoring data and scale findings across broader extents. We evaluate the relationship between pretreatment vegetation cover in the RCMAP data and the post-treatment response in both the RCMAP and field observations. Additionally, we explore the correspondence between SageSTEP field observations and RCMAP at various scales, and examine key factors related to the strength of relationships. Overall, SageSTEP and RCMAP data show a similar direction of treatment effect for each component, and to a lesser extent the magnitude of effect. SageSTEP and RCMAP data tended to agree most strongly where treatment effects were strong; when averaged across broader spatial scales; and for components such as tree and bare ground that are more easily distinguished spectrally. Remote sensing tools such as RCMAP, in combination with field-based climate and vegetation observations, can help assess postdisturbance recovery trajectories and facilitate regional decision-making around treatment alternatives, fire risk reduction, and protection of critical habitats.

Great Basin↗
USGS2023

Guidelines for calibration of uncrewed aircraft systems imagery

Executive Summary This report outlines quality assurance (QA) processes, including radiometric and geometric calibration guidelines, and guidelines for data acquisition and quality control to be followed by U.S. Geological Survey (USGS) researchers for acquiring and processing uncrewed aircraft systems (UAS) data. These QA processes ensure that UAS data can be used for quantitative analysis and are comparable with other standard geospatial data. Remote sensing data play a critical role in monitoring Earth’s resources. Traditionally, the USGS and Department of the Interior have used well calibrated metric sensors mounted on satellite or aircraft platforms to collect these data. These sensors and platforms are stable, and data have been processed using standard pipelines. These processes ensured that the data are generally consistent with each other and benefitted a diverse group of users. These data are shared among multiple researchers around the world through the internet and other means, using standard formats and metadata. In the last few years, UAS platforms have democratized the remote sensing data collection further, bringing an unparalleled level of control of time, sensors, and processes to individual researchers. Together with the development of cheaper and lighter sensors and relaxation of prohibitions against UAS operation in the National Airspace System by the Federal Aviation Authority, researchers can collect remote sensing data using UAS platforms. Researchers often customize the sensors on these UAS systems based on their specific requirements and use ad hoc processing steps to generate data. A challenge is that these data are often produced by a wide array of sensors and processes that render them potentially inconsistent with each other. Therefore, unlike data collected from metric sensors, UAS-based data are designed to benefit only specific user groups. The data thus generated often lack traceability to known standards, making them difficult to use with other geospatial data. This report provides radiometric and geometric calibration guidelines, as well as guidelines for data acquisition and quality control, that can be followed by USGS researchers in acquiring and processing UAS data. Instead of calibrating sensors, researchers collecting UAS data can focus on calibrating the data. Various radiometric calibration processes are provided, and the two panel empirical line method is highlighted for radiometric calibration. For geometric calibration, USGS and Department of the Interior researchers are experienced in following standard calibration procedures provided by standard UAS data processing software. However, researchers may be aided in paying attention to the tie points and the accuracy of the ground control points used for geometric calibration and data production. The accuracy of ground control points are related to the requirements of the project. The ground control points and the quality of tie points directly contribute to the geometric accuracy of the data, regardless of the ground sample distance of the imagery. The guidelines outlined in this report are intended to ensure that the data are in common units and are quantifiable and comparable with other data. These QA and calibration processes can be critical in ensuring that these datasets are used to the maximum extent possible. Including the calibration parameters (and their uncertainties) as part of metadata can allow for easier data discovery and analytical filters.

Open-File Report↗
USGS2005

Establishing an analytical framework for calculating consistent and sensitive measures of the rate of biodiversity change

The Convention on Biological Diversity’s (CBD) VIth Conference of the Parties (COP) and the World Summit on Sustainable Development agreed to a target to achieve a significant reduction of the rate of biodiversity loss by 2010. The VIIth COP adopted a conceptual framework to facilitate the assessment of global progress toward 2010 and to encourage national and regional targets and indicators. However, most of the indicators identified are not and will not be adequately quantified at desired accuracy and with desired frequency by 2010. What is possible is the establishment of an analytic framework that can provide guidelines and baselines for biodiversity estimates that can steadily improve with time. A judicious combination of remotely sensed data, Geographic Information System data, ground samples, and expert knowledge of species trends and species’ use of habitats form a solid basis for assessing biodiversity. National development of sensitive, consistent, and affordable indicators that can contribute to the CBD global indicators requires access to a baseline set of global ecosystem, climate, topography, land cover, and human impact information. Fortunately, over the last ten years, a number of relevant information sources have become available. Such information, as well as appropriate methods for use, should be promoted and made highly accessible to countries and organizations that support the CBD. The NASA-Non-Governmental Organization (NGO) Conservation Working Group is collaborating to evaluate and demonstrate the meaningful role remote sensing can play in developing biodiversity indicators to meet the targets specified. The Working Group members are evaluating indicators across a range of biomes and scales. The results are being synthesized in a handbook describing the use of remote sensing for monitoring the 2010 target to be published in 2006.

Conference Paper↗