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At least 433 records · Page 24Linked to original sources
USGS2015

U.S. Geological Survey Unmanned Aircraft Systems (UAS) Roadmap 2014

The U.S. Department of the Interior (DOI) is responsible for protecting the natural resources and heritage contained on almost 20 percent of the land in the United States. This responsibility requires acquisition of remotely sensed data throughout vast lands, including areas that are remote and potentially dangerous to access. One promising new technology for data collection is unmanned aircraft systems (UAS), which may be better suited (achieving superior science, safety, and savings) than traditional methods. UAS, regardless of their size, have the same operational components: aircraft, payloads, communications unit, and operator control unit. The aircraft is the platform that flies and carries any required payloads. For Department of the Interior missions these payloads will be either a sensor or set of sensors that can acquire the specific type of remotely sensed data that is needed. The aircraft will also carry the payload that is responsible for transmitting live airborne video images, compass headings, and location information to the operator control unit. The communications unit, which transfers information between the aircraft and the operator control unit, consists of the hardware and software required to establish both uplink and downlink communications. Finally, the operator control unit both controls and monitors the aircraft and can be operated either by a pilot on the ground or autonomously. This Roadmap provides operational procedures and lessons learned from completed proof-of-concept UAS missions in areas such as wildlife management, resource monitoring, and public land inspections. This information provides not only an implementation framework but can also help increase the awareness by resource managers, scientists, and others of the ability of UAS technology to advance data quality, improve personnel safety, and reduce data acquisition costs.

Open-File Report↗
USGS2022

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll- a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon , and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella , a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

New York, Oregon, Texas↗
USGS2022

The applicability of time-integrated unit stream power for estimating bridge pier scour using noncontact methods in a gravel-bed river

In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas.

Colorado↗
USGS2010

Advances in estimation methods of vegetation water content based on optical remote sensing techniques

Quantitative estimation of vegetation water content (VWC) using optical remote sensing techniques is helpful in forest fire assessment, agricultural drought monitoring and crop yield estimation. This paper reviews the research advances of VWC retrieval using spectral reflectance, spectral water index and radiative transfer model (RTM) methods. It also evaluates the reliability of VWC estimation using spectral water index from the observation data and the RTM. Focusing on two main definitions of VWC—the fuel moisture content (FMC) and the equivalent water thickness (EWT), the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed. Moreover, the measured information and the dataset are used to estimate VWC, the results show there are significant correlations among three kinds of vegetation water indices (i.e., WSI, NDII, NDWI 1640 , WI/NDVI) and canopy FMC of winter wheat ( n =45). Finally, the future development directions of VWC detection based on optical remote sensing techniques are also summarized.

Science China Technological Sciences↗
USGS2021

Grassland live fractional cover map creation and Geographic Information System (GIS) analysis for rangeland management supporting Kenya Northern Rangelands Trust Conservancies

The handbooks and synchronized MP4 recordings provide hands-on instruction for creating and analyzing vegetation live fractional cover (LFC) maps. The methods and protocols used in the instruction materials follow those developed and recorded in Rangoonwala and Ramsey (2019). The LFC mapping and geographic information system (GIS) analyses highlight the consortium of rangeland conservancies covering the semiarid central region of Kenya (approximately 44,000 square kilometers). The instruction materials are separated into two parts: processing and map-product creation based on remote-sensing images and GIS analyses of the created maps for rangeland management. The image processing is conducted using the advanced and professional software package SeNtinels Application Platform (SNAP) that is supported and maintained by the European Space Agency. SNAP is a free image analyses software package available for download. It is largely icon driven but offers simple to advanced program inserts and batch processing. The GIS analyses are conducted using the software package Quantum Geographic Information System (QGIS), another free and downloadable software. QGIS is compatible with numerous software, including the Esri suite of ArcGIS software and database structure. The image data includes both high-spatial-resolution Sentinel-2 optical data and Sentinel-1 synthetic aperture radar (SAR). Both datasets are freely available via a public portal that is maintained by the European Space Agency. The image-processing instruction handbook covers all aspects of acquiring and processing satellite-image data and importing vector-data sources into SNAP. The GIS analysis handbook covers final creation of map products from the maps created in SNAP and creation of GIS procedures in QGIS that are needed to manage the rangeland resources for wildlife and pastoral grazing. Although focused on the Kenyan conservancies and their semiarid environment, the processing methods and procedures are applicable for similar environments and management, and to a large part, even for integrated mapping and GIS functionality of any managed landscape resource. The instruction handbooks are synchronized to MP4 training videos created with U.S. Geological Survey-licensed Camtasia 9 software. The workbook and MP4 video combinations are suitable for a single user or a workshop setting.

Open-File Report↗
USGS2010

Accuracy of gap analysis habitat models in predicting physical features for wildlife-habitat associations in the southwest U.S.

Despite widespread and long-standing efforts to model wildlife-habitat associations using remotely sensed and other spatially explicit data, there are relatively few evaluations of the performance of variables included in predictive models relative to actual features on the landscape. As part of the National Gap Analysis Program, we specifically examined physical site features at randomly selected sample locations in the Southwestern U.S. to assess degree of concordance with predicted features used in modeling vertebrate habitat distribution. Our analysis considered hypotheses about relative accuracy with respect to 30 vertebrate species selected to represent the spectrum of habitat generalist to specialist and categorization of site by relative degree of conservation emphasis accorded to the site. Overall comparison of 19 variables observed at 382 sample sites indicated ???60% concordance for 12 variables. Directly measured or observed variables (slope, soil composition, rock outcrop) generally displayed high concordance, while variables that required judgments regarding descriptive categories (aspect, ecological system, landform) were less concordant. There were no differences detected in concordance among taxa groups, degree of specialization or generalization of selected taxa, or land conservation categorization of sample sites with respect to all sites. We found no support for the hypothesis that accuracy of habitat models is inversely related to degree of taxa specialization when model features for a habitat specialist could be more difficult to represent spatially. Likewise, we did not find support for the hypothesis that physical features will be predicted with higher accuracy on lands with greater dedication to biodiversity conservation than on other lands because of relative differences regarding available information. Accuracy generally was similar (>60%) to that observed for land cover mapping at the ecological system level. These patterns demonstrate resilience of gap analysis deductive model processes to the type of remotely sensed or interpreted data used in habitat feature predictions. ?? 2010 Elsevier B.V.

Ecological Modelling↗
USGS2003

Use of multispectral Ikonos imagery for discriminating between conventional and conservation agricultural tillage practices

There is a global concern about the increase in atmospheric concentrations of greenhouse gases. One method being discussed to encourage greenhouse gas mitigation efforts is based on a trading system whereby carbon emitters can buy effective mitigation efforts from farmers implementing conservation tillage practices. These practices sequester carbon from the atmosphere, and such a trading system would require a low-cost and accurate method of verification. Remote sensing technology can offer such a verification technique. This paper is focused on the use of standard image processing procedures applied to a multispectral Ikonos image, to determine whether it is possible to validate that farmers have complied with agreements to implement conservation tillage practices. A principal component analysis (PCA) was performed in order to isolate image variance in cropped fields. Analyses of variance (ANOVA) statistical procedures were used to evaluate the capability of each Ikonos band and each principal component to discriminate between conventional and conservation tillage practices. A logistic regression model was implemented on the principal component most effective in discriminating between conventional and conservation tillage, in order to produce a map of the probability of conventional tillage. The Ikonos imagery, in combination with ground-reference information, proved to be a useful tool for verification of conservation tillage practices.

Photogrammetric Engineering and Remote Sensing↗
USGS2008

Landsat continuity: Issues and opportunities for land cover monitoring

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.

Remote Sensing of Environment↗
USGS1983

U. S. GEOLOGICAL SURVEY LAND REMOTE SENSING ACTIVITIES.

USGS uses all types of remotely sensed data, in combination with other sources of data, to support geologic analyses, hydrologic assessments, land cover mapping, image mapping, and applications research. Survey scientists use all types of remotely sensed data with ground verifications and digital topographic and cartographic data. A considerable amount of research is being done by Survey scientists on developing automated geographic information systems that can handle a wide variety of digital data. The Survey is also investigating the use of microprocessor computer systems for accessing, displaying, and analyzing digital data.

Conference Paper↗
USGS2024

A multi-sensor approach to characterize winter water-level drawdown patterns in lakes

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

Massachusetts↗
USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through remote sensing

Across the Central Valley of California, millions of wintering waterfowl rely on moist soil seed (MSS) plants that grow in managed seasonal wetlands as a critical source of food. Estimates of MSS plant production are used to set waterfowl habitat targets yet this information is not well known. We created the first Central Valley-wide time series maps of MSS plant distributions and productivity. We found that MSS plant seed yield declined in critical drought years, which corresponded with reduced water delivery to managed wetlands. Our results provide improved food resource estimates and information to help managers prioritize actions as water supply becomes more uncertain with climate change.

California↗
USGS2015

Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Remote Sensing↗
USGS1980

Aerial photography summary record system - five years later.

Describes the APSRS, an automated information system for conventional aerial photography projects, established after the formation of the National Cartographic Information Center in the US Geological Survey in 1974. -after Author

Photogrammetric Engineering and Remote Sensing↗
USGS1998

Regional characterization of land cover using multiple sources of data

Many organizations require accurate intermediate-scale land-cover information for many applications, including modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land-use planning, and policy development. While many techniques have been successfully used to classify land cover in relatively small regions, there are substantial obstacles in applying these methods to large, multiscene regions. The purpose of this study was to generate and evaluate a large region land-cover classification product using a multiple-layer land-characteristics database approach. To derive land-cover information, mosaicked Landsat thematic mapper (TM) scenes were analyzed in conjunction with digital elevation data (and derived slope, aspect, and shaded relief), population census information, Defense Meteorological Satellite Program city lights data, prior land-use and land-cover data, digital line graph data, and National Wetlands Inventory data. Both leaf-on and leaf-off TM data sets were analyzed. The study area was U.S. Federal Region III, which includes the states of Pennsylvania, Virginia, Maryland, Delaware, and West Virginia. The general procedure involved (1) generating mosaics of multiple scenes of leaves-on TM data using histogram equalization methods; (2) clustering mosaics into 100 spectral classes using unsupervised classification; (3) interpreting and labeling spectral classes into approximately 15 land-cover categories (analogous to Anderson Level 1 and 2 classes) using aerial photographs; (4) developing decision-making rules and models using from one to several ancillary data layers to resolve confusion in spectral classes that represented two or more targeted land-cover categories; and (5) incorporating data from other sources (for example, leaf-off TM data and National Wetlands Inventory data) to yield a final land-cover product. Although standard accuracy assessments were not done, a series of consistency checks using available sources of land-cover information were conducted to evaluate the effectiveness of this approach for generating accurate land-cover information for large regions.

Photogrammetric Engineering and Remote Sensing↗
USGS2025

Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures

The retention of surface water in wetlands and lakes can modify the timing, duration, and magnitude of river discharge. However, efforts to characterize the influence of surface water on discharge regimes have been generally limited to small, wetland-dense watersheds. We developed random forest models to explain spatial variability in six hydrologic signatures, reflecting flashiness, high, and low flow conditions, at 72 gaged watersheds with variable water storage capacity across the conterminous United States. In addition to variables representing meteorology and landscape characteristics, we also tested the inclusion of surface water dynamics, derived from Sentinel-1 and Sentinel-2. Models for all six signatures improved with the addition of catchment characteristics, including surface water dynamics, relative to models with only climate variables. Percent improvement in model adjusted R 2 , mean square error, and Akaike information criterion ranged from 4.00 to 14.33%, 5.00 to 20.30%, and 2.75–8.14, respectively. Automated variable selection can be indicative of the relative importance of certain variables over others. Using a forward selection process, five of the six signature models selected remotely sensed inundation or wetland variables ( p < 0.05). For example, the variable semi-permanent and permanent (SP + P) floodplain inundation (i.e., lakes along rivers) was associated with lower annual flashiness. Further, SP + P non-floodplain waters and geographically isolated wetlands significantly contributed to explaining variability in the low flow signatures. Our findings underscore the capacity of wetlands to stabilize and maintain flows during dry periods. Improved understanding of how surface water dynamics influence hydrologic signatures can inform wetland restoration efforts and facilitate improved resilience to extreme flow conditions.

Wetland Ecology & Management↗
USGS2016

The international scale of the groundwater issue

Throughout history, and throughout the world, groundwater has been a major source of water for sustaining human life. Use of this resource has increased dramatically over the last century. In many areas of the world, the balance between human and ecosystem needs is difficult to maintain. Understanding the international scale of the groundwater issue requires metrics and analysis at a commensurate scale. Advances in remote sensing supplement older traditional direct measurement methods for understanding the magnitude of depletion, and all measurements motivate the need for common data standards to collect and share information. In addition to metrics of groundwater availability, four key international groundwater issues are depletion of water, degradation of water quality, the water-energy nexus, and transboundary water conflicts. This chapter is devoted to introducing these issues, which are also discussed in more detail in later chapters.

Book chapter↗
USGS2021

Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range

Invasive species are a global threat to ecosystem biodiversity and function; non-native grass invasion has been particularly problematic in sparsely vegetated ecosystems such as open dunes. Native plant population responses to invasion, however, are infrequently translated to landscape scales, limiting the effectiveness of these data for addressing conservation issues. We quantified population density, total population size, and age class distribution of the federally-endangered plant species Antioch Dunes evening primrose ( Oenothera deltoides subsp. howellii ), at sites along a non-native grass invasion gradient in California, USA. We then scaled relationships between invasion and plant density across the species’ range using spatial models and remote sensing data. Adult and juvenile O. deltoides subsp. howellii densities were more than 10 times higher in non-invaded areas (grids with 10% total plant cover) when compared to highly-invaded areas (grids with 80% total plant cover). The ratio of O. deltoides subsp. howellii juveniles to adults decreased to less than 1 at 54% total cover, highlighting sensitivity of the regeneration niche to invasion. Spatial models mapped hotspots of O. deltoides subsp. howellii abundance and population structure across the landscape at sub-meter scales. Scaling the impacts of increasing invasion on plant species of conservation concern holds promise when coupled with remote sensing approaches, especially in naturally low-cover ecosystems where readily available metrics (e.g., Normalized Difference Vegetation Index) can be used to quantify invasion. These spatial models inform how future invasive species management may influence population size and spatial distribution of species of conservation concern.

California↗
USGS2021

Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California

Migratory waterfowl are the primary reservoir of avian influenza viruses (AIV) which can be spread to commercial poultry. Surveillance efforts that track the location and abundance of wild waterfowl and link those data to inform assessments of risk and sampling for AIV currently do not exist. To assist surveillance and minimize poultry exposure to AIV, here we explored the utility of remotely sensed MODerate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with land-based climate measurements (e.g., temperature and precipitation) to predict waterfowl location and abundance in near real-time in the California Central Valley (CCV), where both wild waterfowl and domestic poultry are densely located. Specifically, remotely collected MODIS and climate data were integrated into a previously developed Boosted Regression Tree (BRT) model to predict and visualize waterfowl distributions across the CCV. Daily model-based predictions are publicly available during the winter as part of the dynamic California Waterfowl Tracker (CWT) web-app hosted on the University of California’s Cooperative Extension webpage. In this study, we analyzed 52 days of model predictions and produced daily spatio-temporal maps of waterfowl concentrations near the 605 commercial poultry farms in the CCV during January and February of 2019. Exposure of each poultry farm to waterfowl during each day was classified as “high”, “medium”, “low”, or “none” depending on the density of waterfowl within 4 km of a farm. Results indicated that farms were at substantially greater risk of “exposure” in January, when CCV waterfowl populations peak, than in February. For example, during January, 33% (199/605) of the farms were exposed ≥ 1 day to “high” waterfowl density versus 19% (115/605) of the farms in February. In addition to demonstrating the overall variability of waterfowl location and density, these data demonstrate how remote sensing can be used to better triage AIV surveillance and biosecurity efforts via the utilization of a functional web-app based tool. The ability to leverage remote sensing is an integral advancement toward improving AIV surveillance in waterfowl in close proximity to commercial poultry. Expansion of these types of remote sensing methods linked to a user-friendly web-tool could be further developed across the continental U.S. The BRT model incorporated into the CWT reflects a first attempt to give an accurate representation of waterfowl distribution and density relative to commercial poultry.

California↗