Earth Resources Observation And Science Research Articles

The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

Comparison of Historical Water Temperature Measurements with Landsat Analysis Ready Data Provisional Surface Temperature Estimates for the Yukon River in Alaska

Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.

Methods for Earth-Observing Satellite Surface Reflectance Validation

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets.

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α=0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α=0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

The response of runoff and sediment loading in the Apalachicola River, Florida to climate and land use land cover change

AbstractThe response of runoff and sediment loading in the Apalachicola River under projected climate change scenarios and land use land cover (LULC) change is evaluated. A hydrologic model using the Soil and Water Assessment Tool was developed for the Apalachicola region to simulate daily runoff and sediment load under present (circa 2000) and future conditions (2100) to understand how parameters respond over a seasonal time frame to changes in climate, LULC, and coupled climate/LULC. The Long Ashton Research Station‐Weather Generator was used to downscale temperature and precipitation from three general circulation models, each under Intergovernmental Panel on Climate Change (IPCC) carbon emission scenarios A2, A1B, and B1. Projected 2100 LULC data provided by the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center was incorporated for each corresponding IPCC scenario. Results indicate that climate change may induce seasonal shifts to both runoff and sediment loading. Changes in LULC showed that more sediment load was associated with increased agriculture and urban areas and decreased forested regions. A nonlinear response for both runoff and sediment loading was observed by coupling climate and LULC change, suggesting that both should be incorporated into hydrologic models when studying the future conditions. The outcomes from this research can be used to better guide management practices and mitigation strategies.

The global Landsat archive: Status, consolidation, and direction

ABSTRACT New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had two additional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program.

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 30m 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 53,084km2 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 5974km2 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/).

FastStor: improving the performance of a large scale hybrid storage system via caching and prefetching

Storing enormous amount of data on hybrid storage systems has become a widely accepted solution for today's production level applications in order to trade off the performance and cost. However, how to improve the performance of large scale storage systems with hybrid components (e.g. solid state disks, hard drives and tapes) and complicated user behaviors is not fully explored. In this paper, we conduct an in-depth case study (we call it FastStor) on designing a high performance hybrid storage system to support one of the world's largest satellite images distribution systems operated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center. We demonstrate how to combine conventional caching policies with innovative current popularity oriented and user-specific prefetching algorithms to improve the performance of the EROS system. We evaluate the effectiveness of our proposed solution using over 5 million real world user download requests provided by EROS. Our experimental results show that using the Least Recently Used (LRU) caching policy alone, we are able to achieve an overall 64 % or 70 % hit ratio on a 100 TB or 200 TB FTP server farm composed of Solid State Disks (SSDs) respectively. The hit ratio can be further improved to 70 % (for 100 TB SSDs) and 76 % (for 200 TB SSDs) if intelligent prefetching algorithms are used together with LRU.

Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data

This study provides a baseline quality check on provisional Landsat Surface Reflectance (SR) products as generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software. Characterization of the Landsat SR products leveraged comparisons between aerosol optical thickness derived from LEDAPS and measured by Aerosol Robotic Network (AERONET), as well as reflectance correlations with field spectrometer and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results consistently indicated similarity between LEDAPS and alternative data products in longer wavelengths over vegetated areas with no adjacent water, while less reliable performance was observed in shorter wavelengths and sparsely vegetated areas. This study demonstrates the strengths and weaknesses of the atmospheric correction methodology used in LEDAPS, confirming its successful implementation to generate Landsat SR products.

The global availability of Landsat 5 TM and Landsat 7 ETM + land surface observations and implications for global 30 m Landsat data product generation

With the advent of the free U.S. Landsat data policy it is now feasible to consider the generation of global coverage 30m Landsat data sets with temporal reporting frequency similar to that provided by the monthly Web Enabled Landsat (WELD) products. A statistical Landsat metadata analysis is reported considering more than 800,000 Landsat 5 TM and Landsat 7 ETM+ acquisitions obtained from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center archive. The global monthly probabilities of acquiring a cloud-free land surface observation for December 1998 to November 2001 (2000 epoch) and from December 2008 to November 2011 (2010 epoch) are reported to assess the availability of the Landsat data in the USGS Landsat archive for global multi-temporal land remote sensing applications. The global probabilities of acquiring a cloud-free land surface observation in each of three different seasons with the highest seasonal probabilities of cloud-free land surface observation are reported, considering one, two and three years of Landsat data, to assess the availability of Landsat data for global land cover mapping. The probabilities are derived considering Landsat 5 TM only, Landsat 7 ETM+ only, and both sensors combined, to examine the relative benefits of using one or both Landsat sensors. The results demonstrate the utility of combing both Landsat 5 TM and Landsat 7 ETM+ data streams to take advantage of their different acquisition patterns and to mitigate the deleterious impact of the Landsat 7 ETM+ 2003 scan line failure. Sensor combination provided a greater global acquisition coverage with a 1.7% to 14.4% higher percentage of land locations acquired monthly compared to considering Landsat 7 ETM+ data alone. The mean global monthly probability of a cloud-free land surface observation for the combined sensors was up to nearly 1.4 and 6.7 times greater than for ETM+ and TM alone respectively. The probability of acquiring a cloud-free Landsat land surface observation in different seasons was greater when more years of data were considered and when both Landsat sensor data were combined. Considering combined sensors and 36months of data, 86.4% and 84.2% of the global land locations had probabilities ≥0.95 for the 2000 and 2010 epochs respectively, with a global mean probability of 0.92 (σ 0.24) for the 2000 epoch and 0.90 (σ 0.28) for the 2010 epoch. These results indicate that 36months of combined Landsat sensor data will provide sufficient land surface observations for 30m global land cover mapping using a multi-temporal supervised classification scheme.

Case study of visualizing global user download patterns using Google Earth and NASA World Wind

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASA World Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the "hot spot" areas of research. Most importantly, our methods demonstrate an easy way to geo-visualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

Geospatial visualization of global satellite images with Vis-EROS

Geospatial data visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data, which carry extremely valuable information, are generated on daily basis. On the other hand, these data are not well utilized due to the lack of free and easily used data visualization tools. This paper describes a way of visualizing massive spatial data at no cost by utilizing publically available visualization tools like Google Earth. We illustrate our methods by visualizing a million global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS).

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000–2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.

On the Use of High-Resolution Topographic Data as a Proxy for Seismic Site Conditions (VS30)

An alternative method has recently been proposed for evaluating global seismic site conditions, or the average shear velocity to 30 m depth ( V S 30), from the Shuttle Radar Topography Mission (SRTM) 30 arcsec digital elevation models (DEMs). The basic premise of the method is that the topographic slope can be used as a reliable proxy for V S 30 in the absence of geologically and geotechnically based site-condition maps through correlations between V S 30 measurements and topographic gradient. Here we evaluate the use of higher-resolution (3 and 9 arcsec) DEMs to examine whether we are able to resolve V S 30 in more detail than can be achieved using the lower-resolution SRTM data. High-quality DEMs at resolutions greater than 30 arcsec are not uniformly available at the global scale. However, in many regions where such data exist, they may be employed to resolve finer-scale variations in topographic gradient, and consequently, V S 30. We use the U.S. Geological Survey Earth Resources Observation and Science (EROS) Data Center’s National Elevation Dataset (NED) to investigate the use of high-resolution DEMs for estimating V S 30 in several regions across the United States, including the San Francisco Bay area in California, Los Angeles, California, and St. Louis, Missouri. We compare these results with an example from Taipei, Taiwan, that uses 9 arcsec SRTM data, which are globally available. The use of higher-resolution NED data recovers finer-scale variations in topographic gradient, which better correlate to geological and geomorphic features, in particular, at the transition between hills and basins, warranting their use over 30 arcsec SRTM data where available. However, statistical analyses indicate little to no improvement over lower-resolution topography when compared to V S 30 measurements, suggesting that some topographic smoothing may provide more stable V S 30 estimates. Furthermore, we find that elevation variability in canopy-based SRTM measurements at resolutions greater than 30 arcsec are too large to resolve reliable slopes, particularly in low-gradient sedimentary basins.

Updated Radiometric Calibration for the Landsat-5 Thematic Mapper Reflective Bands

The Landsat-5 Thematic Mapper (TM) has been the workhorse of the Landsat system. Launched in 1984, it continues collecting data through the time frame of this paper. Thus, it provides an invaluable link to the past history of the land features of the Earth's surface, and it becomes imperative to provide an accurate radiometric calibration of the reflective bands to the user community. Previous calibration has been based on information obtained from prelaunch, the onboard calibrator, vicarious calibration attempts, and cross-calibration with Landsat-7. Currently, additional data sources are available to improve this calibration. Specifically, improvements in vicarious calibration methods and development of the use of pseudoinvariant sites for trending provide two additional independent calibration sources. The use of these additional estimates has resulted in a consistent calibration approach that ties together all of the available calibration data sources. Results from this analysis indicate a simple exponential, or a constant model may be used for all bands throughout the lifetime of Landsat-5 TM. Where previously time constants for the exponential models were approximately one year, the updated model has significantly longer time constants in bands 1-3. In contrast, bands 4, 5, and 7 are shown to be best modeled by a constant. The models proposed in this paper indicate calibration knowledge of 5% or better early in life, decreasing to nearly 2% later in life. These models have been implemented at the U.S. Geological survey earth resources observation and science (EROS) and are the default calibration used for all Landsat TM data now distributed through EROS.

The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects

The Forest Service Remote Sensing Applications Center (RSAC) and the U.S. Geological Survey Earth Resources Observation and Science (EROS) Data Center produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Response (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of fire effects on soils, but green and nonphotosynthetic vegetation and other materials also affect the spectral properties of post-fire imagery. Our objective was to assess how well satellite image-derived burn severity indices relate to a suite of immediate post-fire effects measured on the ground. We measured or calculated fire effects variables at 418 plots, nested in 50 field sites, located across the full range of burn severities observed at the 2003 Black Mountain, Cooney Ridge, Robert, and Wedge Canyon wildfires in western Montana, the 2003 Old and Simi wildfires in southern California, and the 2004 Porcupine and Chicken wildfires in interior Alaska. We generated the Normalized Burn Ratio (NBR), differenced Normalized Burn Ratio (dNBR), Relative dNBR (RdNBR), Normalized Difference Vegetation Index (NDVI), and differenced NDVI (dNDVI) burn severity indices from Landsat 5 Thematic Mapper (TM) imagery across these eight wildfires. The NBR correlated best with the fire effects measures but insignificantly, meaning other indices could act as suitable substitutes. The overstory (trees in Montana and Alaska, shrubs in California) measures appear to correlate best to the image variables, followed by understory and surface cover measures. Exposed mineral soil and soil water repellency were poorly correlated with the image variables, while green vegetation was most highly correlated. The BARC maps are more indicative of post-fire vegetation condition than soil condition. We conclude that the NBR and dNBR, from which BARC maps of large wildfires in the United States are currently derived, are sound choices for rapid assessment of immediate post-fire burn severity across the three ecosystems sampled. Our future research will focus on spectral mixture analysis (SMA) because it acknowledges that pixel reflectance is fundamentally a mixture of charred, dead, green and nonphotosynthetic vegetation, soil, rock and ash materials that are highly variable at fine scales.

A Project for Monitoring Trends in Burn Severity

Elected officials and leaders of environmental agencies need information about the effects of large wildfires in order to set policy and make management decisions. Recently, the Wildland Fire Leadership Council (WFLC), which implements and coordinates the National Fire Plan (NFP) and Federal Wildland Fire Management Policies (National Fire Plan 2004), adopted a strategy to monitor the effectiveness of the National Fire Plan and the Healthy Forests Restoration Act (HFRA). One component of this strategy is to assess the environmental impacts of large wildland fires and identify the trends of burn severity on all lands across the United States. To that end, WFLC has sponsored a six-year project, Monitoring Trends in Burn Severity (MTBS), which requires the U.S. Department of Agriculture Forest Service (USDA-FS) and the U.S. Geological Survey (USGS) to map and assess the burn severity for all large current and historical fires. Using Landsat data and the differenced Normalized Burn Ratio (dNBR) algorithm, the USGS Center for Earth Resources Observation and Science (EROS) and USDA-FS Remote Sensing Applications Center will map burn severity of all fires since 1984 greater than 202 ha (500 ac) in the east, and 404 ha (1,000 ac) in the west. The number of historical fires from this period combined with current fires occurring during the course of the project will exceed 9,000. The MTBS project will generate burn severity data, maps, and reports, which will be available for use at local, state, and national levels to evaluate trends in burn severity and help develop and assess the effectiveness of land management decisions. Additionally, the information developed will provide a baseline from which to monitor the recovery and health of fire-affected landscapes over time. Spatial and tabular data quantifying burn severity will augment existing information used to estimate risk associated with a range of current and future resource threats. The annual report of 2004 fires has been completed. All data and results will be distributed to the public on a Web site.

Revised Landsat-5 Thematic Mapper Radiometric Calibration

Effective April 2, 2007, the radiometric calibration of Landsat-5 (L5) Thematic Mapper (TM) data that are processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) will be updated. The lifetime gain model that was implemented on May 5, 2003, for the reflective bands (1-5, 7) will be replaced by a new lifetime radiometric-calibration curve that is derived from the instrument's response to pseudoinvariant desert sites and from cross calibration with the Landsat-7 (L7) Enhanced TM Plus (ETM+). Although this calibration update applies to all archived and future L5 TM data, the principal improvements in the calibration are for the data acquired during the first eight years of the mission (1984-1991), where the changes in the instrument-gain values are as much as 15%. The radiometric scaling coefficients for bands 1 and 2 for approximately the first eight years of the mission have also been changed. Users will need to apply these new coefficients to convert the calibrated data product digital numbers to radiance. The scaling coefficients for the other bands have not changed.