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At least 55 records · Page 3Linked to original sources
USGS2021

Radiometric performance of Landsat 8 Collection 2 products

The U.S. Geological Survey (USGS) archive of Earth images acquired by Landsat 1-8 sensors is organized in collections of consistently calibrated, geolocated, and processed data products. Such an organization ensures consistent quality of the archived data within a collection over time and across all instruments within the Landsat mission. In December 2020, the USGS completed reprocessing of the archived Landsat data and released a new collection, Collection 2, which introduced surface reflectance and surface temperature Level-2 products, implemented improved ground control and elevation datasets, and brought several geometric and radiometric calibration enhancements. Radiometric enhancements include absolute and relative gain updates for both imaging sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), aboard Landsat 8 and a change in calculation of bias for OLI. In this paper we present the analysis of Collection 2 Landsat 8 products demonstrating stability over the mission lifetime, the improvement in OLI signal to noise ratio and along-track striping performance resulting from enhanced bias correction, as well as reduction of cross-track striping in TIRS data as a result of relative gain updates. In addition, we discuss effects of two safehold events that Landsat 8 experienced in November 2020 on the radiometric calibration of both sensors and product performance.

Conference Paper↗
USGS2001

Landsat-7 ETM+ radiometric calibration: Two years on-orbit

Landsat-7 has been in orbit for 2 years as of April 15, 2001 and operationally providing calibrated data products for 2 years as of June 28, 2001. A radiometric calibration team consisting of scientists and analysts from the Landsat Project Science Office, the Landsat-7 Image Assessment System and four universities evaluates the calibration based on on-board and ground-look (vicarious) calibration methodologies. The results are assembled and compared semi-annually and the calibration parameter files are adjusted as necessary. To date the combined results for the reflective bands have not shown any change from pre-launch values. The pre-launch values continue to be used for data processing, with the uncertainty estimated at less than 5%. In the thermal band, the vicarious calibration results indicated a 0.31 W/m/sup 2/ sr /spl mu/m bias in the calibration. This bias results in the ETM+ derived temperatures being about 3K high. The calibration parameter file was updated October 1, 2000 to remove this bias, however the U.S. Landsat Product Generation System (LPGS) software required modification that was not incorporated until December 20, 2000. All LPGS data products generated since this date have the correct thermal band calibration, regardless of image acquisition date, with uncertainties at approximately the 1% level.

Conference Paper↗
USGS2025

Thirty years of the U.S. National Land Cover Database: Impacts and future direction

The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based landcover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, long-term land-cover and land-change dataset for the United States, ensuring continued support for broad national applications while complementing higher-resolution and global-mapping efforts.

Photogrammetric Engineering & Remote Sensing↗
USGS2007

Radiometric calibration status of Landsat-7 and Landsat-5

Launched in April 1999, Landsat-7 ETM+ continues to acquire data globally. The Scan Line Corrector in failure in 2003 has affected ground coverage and the recent switch to Bumper Mode operations in April 2007 has degraded the internal geometric accuracy of the data, but the radiometry has been unaffected. The best of the three on-board calibrators for the reflective bands, the Full Aperture Solar Calibrator, has indicated slow changes in the ETM+, but this is believed to be due to contamination on the panel rather then instrument degradation. The Internal Calibrator lamp 2, though it has not been used regularly throughout the whole mission, indicates smaller changes than the FASC since 2003. The changes indicated by lamp 2 are only statistically significant in band 1, circa 0.3% per year, and may be lamp as opposed to instrument degradations. Regular observations of desert targets in the Saharan and Arabian deserts indicate the no change in the ETM+ reflective band response, though the uncertainty is larger and does not preclude the small changes indicated by lamp 2. The thermal band continues to be stable and well-calibrated since an offset error was corrected in late-2000. Launched in 1984, Landsat-5 TM also continues to acquire global data; though without the benefit of an on-board recorder, data can only be acquired where a ground station is within range. Historically, the calibration of the TM reflective bands has used an onboard calibration system with multiple lamps. The calibration procedure for the TM reflective bands was updated in 2003 based on the best estimate at the time, using only one of the three lamps and a cross-calibration with Landsat-7 ETM+. Since then, the Saharan desert sites have been used to validate this calibration model. Problems were found with the lamp based model of up to 13% in band 1. Using the Saharan data, a new model was developed and implemented in the US processing system in April 2007. The TM thermal band was found to have a calibration offset error of 0.092 W/m 2 sr µm (0.68K at 300K) based on vicarious calibration data between 1999 and 2006. The offset error was corrected in the US processing system on April 2007 for all data acquired since April 1999.

Conference Paper↗
USGS2015

Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information

The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover change at the native 30-m spatial resolution of the Landsat Thematic Mapper (TM). The database is designed to provide five-year cyclical updating of United States land cover and associated changes. The recent release of NLCD 2011 products now represents a decade of consistently produced land cover and impervious surface for the Nation across three periods: 2001, 2006, and 2011 (Homer et al., 2007; Fry et al., 2011). Tree canopy cover has also been produced for 2011 (Coluston et al., 2012; Coluston et al., 2013). With the release of NLCD 2011, the database provides the ability to move beyond simple change detection to monitoring and trend assessments. NLCD 2011 represents the latest evolution of NLCD products, continuing its focus on consistency, production, efficiency, and product accuracy. NLCD products are designed for widespread application in biology, climate, education, land management, hydrology, environmental planning, risk and disease analysis, telecommunications and visualization, and are available for no cost at http://www.mrlc.gov. NLCD is produced by a Federal agency consortium called the Multi-Resolution Land Characteristics Consortium (MRLC) (Wickham et al., 2014). In the consortium arrangement, the U.S. Geological Survey (USGS) leads NLCD land cover and imperviousness production for the bulk of the Nation; the National Oceanic and Atmospheric Administration (NOAA) completes NLCD land cover for the conterminous U.S. (CONUS) coastal zones; and the U.S. Forest Service (USFS) designs and produces the NLCD tree canopy cover product. Other MRLC partners collaborate through resource or data contribution to ensure NLCD products meet their respective program needs (Wickham et al., 2014).

Conterminous United States↗
USGS2009

Development of time series stacks of Landsat images for reconstructing forest disturbance history

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.

International Journal of Digital Earth↗
USGS2004

Four years of Landsat-7 on-orbit geometric calibration and performance

Unlike its predecessors, Landsat-7 has undergone regular geometric and radiometric performance monitoring and calibration since launch in April 1999. This ongoing activity, which includes issuing quarterly updates to calibration parameters, has generated a wealth of geometric performance data over the four-year on-orbit period of operations. A suite of geometric characterization (measurement and evaluation procedures) and calibration (procedures to derive improved estimates of instrument parameters) methods are employed by the Landsat-7 Image Assessment System to maintain the geometric calibration and to track specific aspects of geometric performance. These include geodetic accuracy, band-to-band registration accuracy, and image-to-image registration accuracy. These characterization and calibration activities maintain image product geometric accuracy at a high level - by monitoring performance to determine when calibration is necessary, generating new calibration parameters, and verifying that new parameters achieve desired improvements in accuracy. Landsat-7 continues to meet and exceed all geometric accuracy requirements, although aging components have begun to affect performance.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2021

Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires

Background The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L . species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.

Oregon, Idaho, Nevada, Utah↗
USGS2021

Preliminary assessment of the geometric improvements to the Landsat Collection-2 archive

The U.S. Geological Survey (USGS) has completed processing of the historical Landsat archive to Collection-2 as of December of 2020 and has released it to the public. As part of Collection-2, several geometric changes have been implemented, including changes to the ground control points (GCPs) and elevation datasets. These datasets are used as a geometric reference for all missions. In addition, mission specific improvements were included in Collection-2, such as improvements to the precision correction algorithms and updates of the calibration parameters. This paper discusses a preliminary analysis of a comparison between the Collection-1 and Collection-2 products of the entire Landsat archive. Compared to the Level 1 products in Collection-1, the number of Tier-1 precision- and terrain-corrected (L1TP) Level 1 products in Collection-2 increased by 6.26% across all sensors. Landsat 8 products showed an increase of Tier-1 L1TP products by 5.33%; Landsat 7 products showed an increase of Tier-1 products by 6.94%; and Landsat 5 and Landsat 4 Thematic Mapper (TM) products showed an increase of Tier-1 products by 9.35%. The geometric accuracy of the Tier-1 terrain corrected products also improved by 2 meters or more.

Conference Paper↗
USGS2011

Landsat 8 on-orbit characterization and calibration system

The Landsat Data Continuity Mission (LDCM) is planning to launch the Landsat 8 satellite in December 2012, which continues an uninterrupted record of consistently calibrated globally acquired multispectral images of the Earth started in 1972. The satellite will carry two imaging sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI will provide visible, near-infrared and short-wave infrared data in nine spectral bands while the TIRS will acquire thermal infrared data in two bands. Both sensors have a pushbroom design and consequently, each has a large number of detectors to be characterized. Image and calibration data downlinked from the satellite will be processed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using the Landsat 8 Image Assessment System (IAS), a component of the Ground System. In addition to extracting statistics from all Earth images acquired, the IAS will process and trend results from analysis of special calibration acquisitions, such as solar diffuser, lunar, shutter, night, lamp and blackbody data, and preselected calibration sites. The trended data will be systematically processed and analyzed, and calibration and characterization parameters will be updated using both automatic and customized manual tools. This paper describes the analysis tools and the system developed to monitor and characterize on-orbit performance and calibrate the Landsat 8 sensors and image data products.

Conference Paper↗
USGS2010

Conifer health classification for Colorado, 2008

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Scientific Investigations Map↗
USGS2007

Consistency of L4 TM absolute calibration with respect to the L5 TM sensor based on near-simultaneous image acquisition

The Landsat archive provides more than 35 years of uninterrupted multispectral remotely sensed data of Earth observations. Since 1972, Landsat missions have carried different types of sensors, from the Return Beam Vidicon (RBV) camera to the Enhanced Thematic Mapper Plus (ETM+). However, the Thematic Mapper (TM) sensors on Landsat 4 (L4) and Landsat 5 (L5), launched in 1982 and 1984 respectively, are the backbone of an extensive archive. Effective April 2, 2007, the radiometric calibration of L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) was updated to use an improved lifetime gain model, based on the instrument's detector response to pseudo-invariant desert site data and cross-calibration with the L7 ETM+. However, no modifications were ever made to the radiometric calibration procedure of the Landsat 4 (L4) TM data. The L4 TM radiometric calibration procedure has continued to use the Internal Calibrator (IC) based calibration algorithms and the post calibration dynamic ranges, as previously defined. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The number of coincident image pairs in the USGS EROS archive is limited, so the scene selection for the cross-calibration studies proved to be a challenge. Additionally, because of the lack of near-simultaneous images available over well-characterized and traditionally used calibration sites, alternate sites that have high reflectance, large dynamic range, high spatial uniformity, high sun elevation, and minimal cloud cover were investigated. The alternate sites were identified in Yuma, Iraq, Egypt, Libya, and Algeria. The cross-calibration approach involved comparing image statistics derived from large common areas observed eight days apart by the two sensors. This paper summarizes the average percent differences in reflectance estimates obtained between the two sensors. The work presented in this paper is a first step in understanding the current performance of L4 TM absolute calibration and potentially serves as a platform to revise and improve the radiometric calibration procedures implemented for the processing of L4 TM data.

Conference Paper↗
USGS2015

Landsat-8 Operational Land Imager (OLI) radiometric performance on-orbit

Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed in this paper include: signal-to-noise ratios that are an order of magnitude higher than previous Landsat missions; radiometric uniformity that shows little residual banding and striping, and continues to improve; a dynamic range that limits saturation to extremely high radiance levels; extremely stable detectors; slight nonlinearity that is corrected in ground processing; detectors that are stable and 100% operable; and few image artifacts.

Remote Sensing↗
USGS2016

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2002

Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.

Water-Resources Investigations Report↗
USGS2002

Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

Open-File Report↗
USGS2015

An automated approach for mapping persistent ice and snow cover over high latitude regions

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.

Remote Sensing↗
USGS1998

Combining satellite data with ancillary data to produce a refined land-use/land-cover map

As part of the U.S. Geological Survey's National Water-Quality Assessment Program in the Western Lake Michigan Drainages Study Unit, a current map of land use and land cover is needed to gain a better understanding of how land use and land cover may influence water quality. Satellite data from the Landsat Thematic Mapper provides a means to map and measure the type and amount of various land-cover types across the Study Unit and can be easily updated as changes occur in the landscape or in water quality. Translating these land cover categories to land use, however, requires the use of other thematic maps or ancillary data layers, such as wetland inventories, population data, or road networks. This report describes a process of (1) using satellite imagery to produce a land-cover map for the Fox/Wolf River basin, a portion of the Western Lake Michigan Drainages NAWQA Study Unit and (2) improving the satellite-derived land-cover map by using other thematic maps. The multiple data layers are processed in a geographic information system (GIS), and the combination provides more information than individual sources alone.

Michigan, Wisconsin↗