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At least 37 records · Page 2Linked to original sources
USGS2026

Across-track tilt concept for sun glint mitigation in future Earth-observing missions

Sun glint is a major limitation for spaceborne optical remote sensing of aquatic environments, particularly in tropical and subtropical regions. By dominating the at-sensor radiance, sun glint complicates atmospheric correction and reduces the quality of aquatic reflectance products derived across Earth observation missions. This study presents a machine learning framework to quantify sun glint probability and evaluates the across-track tilt observational concept as a potential sun glint mitigation approach for satellite remote sensing missions. More than 14,000 cloud-free Landsat 8 and 9 scenes were analyzed to quantify glint occurrence as a function of sun-sensor geometry, latitude, and season. Logistic regression and machine learning models identified solar zenith angle (SZA), wind speed, and viewing zenith angle (VZA) as the primary predictors of sun glint probability, achieving >98% classification accuracy. Sun glint contamination was found to be negligible for SZA > 40° but frequent at SZA <35°, particularly in forward-scattering geometries. Introducing a 3° across-track westward tilt reduces the proportion of glint-contaminated pixels by ~21% in tropical and subtropical regions, while larger tilts of 6°, 9°, and 12° yield reductions of 43%, 59%, and 74%, respectively. These findings underscore the value of incorporating glint mitigation strategies in future Earth observing mission designs. A modest across-track tilt, combined with post-acquisition glint detection and correction, offers a practical pathway to increasing the availability of high-quality aquatic observations for monitoring inland and coastal water quality and ecosystems.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2005

Multispectral imaging contributions to global land ice measurements from space

Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world's glaciers, analyse them for glacier extent and changes, and assess change data for causes and implications for people and the environment. Although GLIMS is making use of multiple remote-sensing systems, ASTER (Advanced Spaceborne Thermal Emission and reflection Radiometer) is optimized for many needed observations, including mapping of glacier boundaries and material facies, and tracking of surface dynamics, such as flow vector fields and supraglacial lake development. Software development by GLIMS is geared toward mapping clean-ice and debris-covered glaciers; terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; multitemporal change analysis; visualization of images and derived data; and interpretation and archiving of derived data. A global glacier database has been designed at the National Snow and Ice Data Center (NSIDC, Boulder, Colorado); parameters are compatible with and expanded from those of the World Glacier Inventory (WGI). These technology efforts are summarized here, but will be presented in detail elsewhere. Our presentation here pertains to one broad question: How can ASTER and other satellite multispectral data be used to map, monitor, and characterize the state and dynamics of glaciers and to understand their responses to 20th and 21st century climate change? Our sampled results are not yet glaciologically or climatically representative. Our early results, while indicating complexity, are generally consistent with the glaciology community's conclusion that climate change is spurring glacier responses around the world (mainly retreat). Whether individual glaciers are advancing or retreating, the aggregate average of glacier change must be climatic in origin, as nonclimatic variations average out. We have discerned regional spatial patterns in glaciological response behavior; these patterns are best attributed to climate-change variability and to regional differences in glacier size and response times. In many cases, glacier length changes under-represent the magnitude of glacier ablation, because thinning (sometimes without immediate length changes) is also important. An expanded systematic, uniform analysis of many more glaciers is needed to isolate the glacier response components due to climatic and nonclimatic perturbations, to produce quantitative measures of regional variation in glacier changes, and to predict future regional glacier trends relevant to water resources, glaciological hazards, and global sea level. This comprehensive assessment (to be completed in stages) is expected to lend a critically needed filter to identify successful climate models that explain recent glacier changes and change patterns (and hence, are apt to describe future changes) and to eliminate unsuccessful models. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2022

CoastalImageLib: An open-source Python package for creating common coastal image products

CoastalImageLib is a Python library that produces common coastal image products intended for quantitative analysis of coastal environments. This library contains functions to georectify and merge multiple oblique camera views, produce statistical image products for a given set of images, and create subsampled pixel instruments for use in bathymetric inversion, surface current estimation, run-up calculations, and other quantitative analyses. This package intends to be an open-source broadly generalizable front end to future coastal imaging applications, ultimately expanding user accessibility to optical remote sensing of coastal environments. This package was developed and tested on data collected from the Argus Tower, a 43 m tall observation structure in Duck, North Carolina at the US Army Engineer Research and Development Center’s Field Research Facility that holds six stationary cameras which collect twice-hourly coastal image products. Thus, CoastalImageLib also contains functions designed to interface with the file storage and collection system implemented at the Argus Tower.

SoftwareX↗
USGS2013

Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+

The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed ( Arundo donax ), Lehmann lovegrass ( Eragrostis lehmanniana ), horehound ( Marrubium vulgare ) and buffelgrass ( Pennisteum ciliare ) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from <1.0 to 18%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with <50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land managers.

Texas↗
USGS2005

Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan, Canada

A plant–soil nitrogen (N) cycling model was developed and incorporated into the Integrated BIosphere Simulator (IBIS) of Foley et al. [Foley, J.A., Prentice, I.C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., Haxeltine, A., 1996. An integrated biosphere model of land surface process, terrestrial carbon balance and vegetation dynamics. Global Biogeochem. Cycles 10, 603–628]. In the N-model, soil mineral N regulates ecosystem carbon (C) fluxes and ecosystem C:N ratios. Net primary productivity (NPP) is controlled by feedbacks from both leaf C:N and soil mineral N. Leaf C:N determines the foliar and canopy photosynthesis rates, while soil mineral N determines the N availability for plant growth and the efficiency of biomass construction. Nitrogen controls on the decomposition of soil organic matter (SOM) are implemented through N immobilization and mineralization separately. The model allows greater SOM mineralization at lower mineral N, and conversely, allows greater N immobilization at higher mineral N. The model's seasonal and inter-annual behaviours are demonstrated. A regional simulation for Saskatchewan, Canada, was performed for the period 1851–2000 at a 10 km × 10 km resolution. Simulated NPP was compared with high-resolution (1 km × 1 km) NPP estimated from remote sensing data using the boreal ecosystem productivity simulator (BEPS) [Liu, J., Chen, J.M., Cihlar, J., Park, W.M., 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sens. Environ. 44, 81–87]. The agreement between IBIS and BEPS, particularly in NPP spatial variation, was considerably improved when the N controls were introduced into IBIS.

Ecological Modelling↗
USGS1985

Landsat multispectral scanner and Landsat thematic mapper images and interpretations of Hannapah and the Royston Hills, Nevada

This Open-File Report consists of twenty-one 35-mm slides showing material that was presented at the Fourth Thematic Conference on Remote Sensing of the Environment held in San Francisco, California on April 1-4, 1985 (Purdy and others, 1985). The work was carried out in conjunction with the U.S. Geological Survey's Conterminous U.S. Mineral Appraisal Program for the Tonopah, Nevada 1° by 2° quadrangle (fig. 1). The slides show Landsat Multispectral Scanner (MSS) images, Landsat Thematic Mapper (TM) images, and geologic interpretations made from these data.

Nevada↗
USGS2010

EAARL coastal topography-western Florida, post-Hurricane Charley, 2004: seamless (bare earth and submerged.

Project Description These remotely sensed, geographically referenced elevation measurements of lidar-derived seamless (bare-earth and submerged) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Coastal and Marine Geology Program (CMGP), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the western Florida coastline beachface, acquired post-Hurricane Charley on August 17 and 18, 2004. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website. Selected References Brock, J.C., Wright, C.W., Sallenger, A.H., Krabill, W.B., and Swift, R.N., 2002, Basis and methods of NASA airborne topographic mapper Lidar surveys for coastal studies: Journal of Coastal Research, v. 18, no. 1, p. 1-13. Crane, Michael, Clayton, Tonya, Raabe, Ellen, Stoker, Jason, Handley, Larry, Bawden, Gerald, Morgan, Karen, and Queija, Vivian, 2004, Report of the U.S. Geological Survey Lidar workshop sponsored by the Land Remote Sensing Program and held in St. Petersburg, FL, November 2002: U.S. Geological Survey Open-File Report 2004-1456, 72 p. Nayegandhi, Amar, Brock, J.C., and Wright, C.W., 2009, Small-footprint, waveform-resolving Lidar estimation of submerged and sub-canopy topography in coastal environments: International Journal of Remote Sensing, v. 30, no. 4, p. 861-878. Sallenger, A.H., Wright, C.W., and Lillycrop, Jeff, 2005, Coastal impacts of the 2004 hurricanes measured with airborne Lidar; initial results: Shore and Beach, v. 73, nos. 2-3, p. 10-14. Resources Included Readme.txt File

Data Series↗
USGS2008

Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery

The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution satellite imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution satellite imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution satellite imagery offers significant potential for the mapping of invasive plant species in estuarine environments. ?? 2007 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2022

Assessing the accuracy and potential for improvement of the national land cover database’s tree canopy cover dataset in urban areas of the conterminous United States

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Remote Sensing↗
USGS2003

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5-11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15-20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4-5 classes, 71% for 7-8 classes, 65% in 9-11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5-10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2016

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R 2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Remote Sensing of Environment↗
USGS2001

Comments on "Failures in detecting volcanic ash from a satellite-based technique"

The recent paper by Simpson et al. [Remote Sens. Environ. 72 (2000) 191.] on failures to detect volcanic ash using the 'reverse' absorption technique provides a timely reminder of the danger that volcanic ash presents to aviation and the urgent need for some form of effective remote detection. The paper unfortunately suffers from a fundamental flaw in its methodology and numerous errors of fact and interpretation. For the moment, the 'reverse' absorption technique provides the best means for discriminating volcanic ash clouds from meteorological clouds. The purpose of our comment is not to defend any particular algorithm; rather, we point out some problems with Simpson et al.'s analysis and re-state the conditions under which the 'reverse' absorption algorithm is likely to succeed. ?? 2001 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2020

Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next- Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Remote Sensing of Environment↗
USGS2015

Prospective HyspIRI global observations of tidal wetlands

Tidal wetlands are highly productive and act as critical habitat for a wide variety of plants, fish, shellfish, and other wildlife. These ecotones between aquatic and terrestrial environments also provide protection from storm damage, run-off filtering, and recharge of aquifers. Many wetlands along coasts have been exposed to stress-inducing alterations globally, including dredge and fill operations, hydrologic modifications, pollutants, impoundments, fragmentation by roads/ditches, and sea level rise. For wetland protection and sensible coastal development, there is a need to monitor these ecosystems at global and regional scales. Recent advances in satellite sensor design and data analysis are providing practical methods for monitoring natural and man-made changes in wetlands. However, available satellite remote sensors have been limited to mapping primarily wetland location and extent. This paper describes how the HyspIRI hyperspectral and thermal infrared sensors can be used to study and map key ecological properties, such as species composition, biomass, hydrology, and evapotranspiration of tidal salt and brackish marshes and mangroves, and perhaps other major wetland types, including freshwater marshes and wooded/shrub wetlands.

Remote Sensing of Environment↗
USGS2008

Space-based detection of wetlands' surface water level changes from L-band SAR interferometry

Interferometric processing of JERS-1 L-band Synthetic Aperture Radar (SAR) data acquired over south Florida during 1993-1996 reveals detectable surface changes in the Everglades wetlands. Although our study is limited to south Florida it has implication for other large-scale wetlands, because south Florida wetlands have diverse vegetation types and both managed and natural flow environments. Our analysis reveals that interferometric coherence level is sensitive to wetland vegetation type and to the interferogram time span. Interferograms with time spans less than six months maintain phase observations for all wetland types, allowing characterization of water level changes in different wetland environments. The most noticeable changes occur between the managed and the natural flow wetlands. In the managed wetlands, fringes are organized, follow patterns related to some of the managed water control structures and have high fringe-rate. In the natural flow areas, fringes are irregular and have a low fringe-rate. The high fringe rate in managed areas reflects dynamic water topography caused by high flow rate due to gate operation. Although this organized fringe pattern is not characteristic of most large-scale wetlands, the high level of water level change enables accurate estimation of the wetland InSAR technique, which lies in the range of 5-10??cm. The irregular and low rate fringe pattern in the natural flow area reflects uninterrupted flow that diffuses water efficiently and evenly. Most of the interferograms in the natural flow area show an elongated fringe located along the transitional zone between salt- and fresh-water wetlands, reflecting water level changes due to ocean tides. ?? 2007 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet , recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Remote Sensing of Environment↗
USGS2020

Landsat 9: Empowering open science and applications through continuity

The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.

Remote Sensing of Environment↗
USGS2014

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Remote Sensing of Environment↗