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National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to &gt, 258 million ha of contiguous Unites States forested ecosystems (1986&ndash, 2010). Nationally, 75.95 million forest ha (759,531 km2) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user&rsquo, s and 72.2% (0.02 S.E.) producer&rsquo, s accuracy. A survey of available national attribution datasets, from the data user&rsquo, s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain.

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290 .

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NA...

After more than 15 years of research and writing, the Landsat Legacy Project Team, in collaboration with the American Society for Photogrammetry and Remote Sensing (ASPRS), published an account of monitoring the Earth’s lands for a half-century with Landsat. Born of technologies that evolved from the Second World War, Landsat not only pioneered global land monitoring but in the process, drove innovation in digital imaging technologies and encouraged the development of global imagery archives. Access to this imagery led to early breakthroughs in natural resources assessments, particularly for agriculture, forestry, and geology. The technical Landsat remote sensing revolution was not simple or straightforward. Early conflicts between civilian and defense satellite remote sensing users gave way to disagreements over whether the Landsat system should be a public service or a private enterprise. The failed attempts to privatize Landsat nearly led to its demise. Only the combined engagement of civilian and defense organizations ultimately saved the Landsat program from the brink of collapse. With the emergence of 21st century Earth system science research, the full value of the Landsat concept and its continuous 50-year global archive has been recognized and embraced. Discussion of Landsat’s future continues but its heritage will not be forgotten. This innovative satellite system’s vital history is captured in this notable volume on Landsat’s Enduring Legacy.

article i nfo Forest carbon stocks and fluxes are highly dynamic following stand-clearing disturbances from severe fire and harvest and this presents a significant challenge for continental carbon budget assessments. In this work we use forest inventory data to parameterize a carbon cycle model to represent post-disturbance carbon trajectories of carbon pools and fluxes for specific forest types growing in high and low site productivity class settings. We then apply these trajectories to landscapes and regions based on forest age distributions derived from either the FIA data or from Landsat time series stacks (1985-2006) for 54 representative scenes throughout most of theconterminousUnitedStates.Weestimatethenetcarbonuptakeinforestscausedbypost-disturbancegrowth and decomposition ("regrowth sink") for forested regions across the country. At the landscape scale, the prevail- ing condition of positive net ecosystem productivity (NEP) is in stark contrast to local patches with large sources, particularly in the west where fires and clear cuts create contiguous disturbed patches. At the continental scale, regional differences indisturbance ratesreflect management patterns of high disturbanceratesin the Southeast- ernandSouthCentralstates,andlowerdisturbanceratesinthe NortheastandNorthernLakesStates.Despitelow contemporary disturbance rates in the Northeast and Northern Lakes States (0.61 and 0.74% y

[1] The history of forest change processes is written into forest age and distribution and affects earth systems at many scales. No one data set has been able to capture the full forest disturbance and land use record through time, so in this study, we combined multiple lines of evidence to examine trends, for six US regions, in forest area affected by harvest, fire, wind, insects, and forest conversion to urban/surburban use. We built an integrated geodatabase for the contiguous U.S. (CONUS) with data spanning the nation and decades, from remote sensing observations of forest canopy dynamics, geospatial data sets on disturbance and conversion, and statistical inventories, to evaluate relationships between canopy change observations and casual processes at multiple scales. Results show the variability of major change processes through regions across decades. Harvest affected more forest area than any other major change processes in the North East, North Central, Southeast, and South central regions. In the Pacific Coast and Intermountain West, more forest area was affected by harvest than forest fires. Canopy change rates at regional scales confounded the trends of individual forest change processes, showing the importance of landscape scale data. Local spikes in observed canopy change rates were attributed to wind and fire events, as well as volatile harvest regimes. This study improves the geographic model of forest change processes by updating regional trends for major disturbance and conversion processes and combining data on the dynamics of fire, wind, insects, harvest, and conversion into one integrated geodatabase for the CONUS.

The crimes committed against humanity during World War II shocked the global community into action. As the world continued to uncover the full extent of the Holocaust, international leaders in the newly formed United Nations (UN) drafted the Universal Declaration of Human Rights and established a mandate to protect human rights and prevent atrocities. However, certain situations increase the likelihood of human-rights violations and therefore the need for human-rights monitoring of conflicts (Edwards and Koettl 2011). Because of ongoing abuses, the un, along with other governmental and nongovernmental organizations (ngos), conducts widespread and intensive human-rights monitoring campaigns. Broadly described by the Office of the High Commission for Human Rights as the "active collection, verification and immediate use of information to address human rights problems" (OHCHR 2001, 3), human-rights monitoring documents any violations that include governmental and nonstate transgressions of human rights and the failures of the state to protect those rights. Remote sensing is a tool that improves detection of and potentially provides a deterrent to human-rights violations. Remote-sensing platforms include helicopters and fixed-wing or unmanned aerial vehicles. However, these platforms are limited in several aspects, including limited range and the need for basing facilities near conflict areas, which are often in very remote locations. These platforms also require overflight permission from the host government, which is often the alleged transgressor. For these reasons, organizations that monitor human rights have increasingly turned to space-based imagery to identify and document conflict. Language: en

Considerable interest has been given to forming an international collaboration to develop a virtual moderate spatial resolution land observation constellation through aggregation of data sets from comparable national observatories such as the US Landsat, the Indian ResourceSat and related systems. This study explores the complementarity of India's ResourceSat-1 Advanced Wide Field Sensor (AWiFS) with the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The analysis focuses on the comparative radiometry, geometry, and spectral properties of the two sensors. Two applied assessments of these data are also explored to examine the strengths and limitations of these alternate sources of moderate resolution land imagery with specific application domains. There are significant technical differences in these imaging systems including spectral band response, pixel dimensions, swath width, and radiometric resolution which produce differences in observation data sets. None of these differences was found to strongly limit comparable analyses in agricultural and forestry applications. Overall, we found that the AWiFS and Landsat TM/ETM+ imagery are comparable and in some ways complementary, particularly with respect to temporal repeat frequency. We have found that there are limits to our understanding of the AWiFS performance, for example, multi-camera design and stability of radiometric calibration over time, that leave some uncertainty that has been better addressed for Landsat through the Image Assessment System and related cross-sensor calibration studies. Such work still needs to be undertaken for AWiFS and similar observatories that may play roles in the Global Earth Observation System of Systems Land Surface Imaging Constellation.

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, "young" forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of "young" forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R(2) and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed. (C) 2011 Elsevier Inc. All rights reserved.

The North American Forest Dynamics (NAFD) study is a core project of the North American Carbon Program (NACP). The NAFD project is evaluating forest disturbance patterns and rates of disturbance by integrating U.S. Department of Agriculture (USDA) Forest Service Inventory and Analysis (FIA) field observations with temporally dense time series Landsat imagery. In Phase I of NAFD forest disturbance history was derived for 23 U.S. sample locations over the time period 1984 to 2005 from biennial Landsat time series stacks (LTSS). This study evaluates the accuracy of these Phase I NAFD disturbance history maps for 6 selected sample locations. We evaluate the disturbance maps using 2 reference datasets: 1) a design-based approach incorporating visual analysis of the LTSS in tandem with high resolution imagery and 2) the USDA FIA field observations. Overall accuracy for the NAFD disturbance product assessed at the individual time step level range from 77% to 86%. We examine the success rates of the mapping approach for capturing different types of disturbance and find that 82% of stand clearing events were detected. When we aggregate the data into change and no change categories the accuracy of stand clearing disturbance samples improved to over 92%. The majority of error in the disturbance maps was due to misclassification of partial disturbance as unchanged forest. We analyze the resulting errors of commission and omission as related to both reference datasets for each LTSS and present examples to illustrate the strengths and weaknesses of Phase I NAFD approach. In addition, we discuss the map biases observed in this work and what this may imply for estimating national forest disturbance rates with this approach.

Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.

A highly automated algorithm called vegetation change tracker (VCT) has been developed for reconstructing recent forest disturbance history using Landsat time series stacks (LTSS). This algorithm is based on the spectral–temporal properties of land cover and forest change processes, and requires little or no fine tuning for most forests with closed or near close canopy cover. It was found very efficient, taking 2–3 h on average to analyze an LTSS consisting of 12 or more Landsat images using an average desktop PC. This LTSS-VCT approach has been used to examine disturbance patterns with a biennial temporal interval from 1984 to 2006 for many locations across the conterminous U.S. Accuracy assessment over 6 validation sites revealed that overall accuracies of around 80% were achieved for disturbances mapped at individual year level. Average user's and producer's accuracies of the disturbance classes were around 70% and 60% in 5 of the 6 sites, respectively, suggesting that although forest disturbances were typically rare as compared with no-change classes, on average the VCT detected more than half of those disturbances with relatively low levels of false alarms. Field assessment revealed that VCT was able to detect most stand clearing disturbance events, including harvest, fire, and urban development, while some non-stand clearing events such as thinning and selective logging were also mapped in western U.S. The applicability of the LTSS-VCT approach depends on the availability of a temporally adequate supply of Landsat imagery. To ensure that forest disturbance records can be developed continuously in the future, it is necessary to plan and develop observational capabilities today that will allow continuous acquisition of frequent Landsat or Landsat-like observations.

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 cal...

The national forests (NFs) in the United States are protected areas managed for multiple purposes, and therefore are subject to both natural and anthropogenic disturbances. Monitoring forest changes arising from such disturbances and the post-disturbance recovery processes is essential for assessing the conditions of the NFs and the effectiveness of management approaches. In this study, we used time series stacks of Landsat images (LTSS) to evaluate the dynamics of seven NFs in eastern United States, including the De Soto NF, the Talladega NF, the Francis Marion NF, and the Uwharrie NF in southeastern U.S., and the Chequamegon NF, the Hiawatha NF, and the Superior NF in northern U.S. Each LTSS consisted of 12–14 Landsat images acquired for the same location, spanning from 1984 to 2006 with a nominal interval of one image every 2 years. Each LTSS was analyzed using a vegetation change tracker (VCT) algorithm to map forest disturbance. Accuracy assessments of the derived disturbance maps revealed that they had overall accuracy values of about 80%, with most of the disturbance classes having user's accuracies ranging from 70% to 95%. The producer's accuracies were generally lower, with the majority being in the range between 50% and 70%. While this may suggest that the disturbance maps could slightly underestimate disturbances, a more detailed assessment of the omission errors revealed that the majority of the disagreements were due to minor disturbances like thinning or storm damages that were identified by the image analysts but were not captured by the VCT algorithm. The derived disturbance year maps revealed that while each of the seven NFs consisted of 90% or more forest land, significant portions of the forests were disturbed since 1984. Mapped disturbances accounted for about 30%–45% of total land area in the four NFs in southeastern U.S. and about 10%–20% in the three NFs in northern U.S. The disturbance rates were generally higher in the buffer zones surrounding each NF, and varied considerably over time. The time series approach employed in this study represents a new approach for monitoring forest resources using the Landsat or similar satellite data records. The disturbance products derived using this approach were spatially explicit and contained much more temporal details than conventional bi-temporal change products, and likely will be found more useful by many users including ecologists and resources managers. The high disturbance rates found in the southeastern U.S. suggest that this region may have a more significant role in modulating the atmospheric carbon budget than currently recognized.

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.

The long-term, 34� year record of global Landsat remote sensing data is a critical resource to study the Earth system and human impacts on this system. The National Satellite Land Remote Sensing Data Archive (NSLRSDA) is charged by public law to: “maintain a permanent, comprehensive Government archive of global Landsat and other land remote sensing data for long-term monitoring and study of the changing global environment” (U.S. Congress, 1992). The advisory committee for NSLRSDA requested a detailed analysis of observation coverage within the U.S. Landsat holdings, as well as that acquired and held by International Cooperator (IC) stations. Our analyses, to date, have found gaps of varying magnitude in U.S. holdings of Landsat global coverage data, which appear to reflect technical or administrative variations in mission operations. In many cases it may be possible to partially fill these gaps in U.S. holdings through observations that were acquired and are now being held at International Cooperator stations.

The Landsat-7 Enhanced Thematic Mapper Plus instrument has two selectable gains for each spectral band. In the acquisition plan, the gains were initially set to maximize the entropy in each scene. One unintended consequence of this strategy was that, at times, dense vegetation saturated band 4 and deserts saturated all bands. A revised strategy, based on a land-cover classification and sun angle thresholds, reduced saturation, but resulted in gain changes occurring within the same scene on multiple overpasses. As the gain changes cause some loss of data and difficulties for some ground processing systems, a procedure was devised to shift the gain changes to the nearest predicted cloudy scenes. The results are still not totally satisfactory as gain changes still impact some scenes and saturation still occurs, particularly in ephemerally snow-covered regions. A primary conclusion of our experience with variable gain on Landsat-7 is that such an approach should not be employed on future global monitoring missions.

The long-term acquisition plan (LTAP) was developed to fulfill the Landsat-7 (L7) mission of building and seasonally refreshing an archive of global, essentially cloud-free, sunlit, land scenes. The LTAP is considered one of the primary successes of the mission. By incorporating seasonality and cloud avoidance into the decision making used to schedule image acquisitions, the L7 data in the U.S. Landsat archive is more complete and of higher quality than has ever been previously achieved in the Landsat program. Development of the LTAP system evolved over more than a decade, starting in 1995. From 2002 to 2004 most attention has been given to validation of LTAP elements. We find that the original expectations and goals for the LTAP were surpassed for Landsat 7. When the L7 scan line corrector mirror failed, we adjusted the LTAP operations, effectively demonstrating the flexibility of the LTAP concept to address unanticipated needs. During validation, we also identified some seasonal and geographic acquisition shortcomings of the implementation: including how the spectral vegetation index measurements were used and regional/seasonal cloud climatology concerns. Some of these issues have already been at least partially addressed in the L7 LTAP, while others will wait further attention in the development of the LTAP for the Landsat Data Continuity Mission (LDCM). The lessons learned from a decade of work on the L7 LTAP provide a solid foundation upon which to build future mission LTAPs including the LDCM.

A scene-average automated cloud-cover assessment (ACCA) algorithm has been used for the Landsat-7 Enhanced Thematic Mapper Plus (ETM� ) mission since its launch by NASA in 1999. ACCA assists in scheduling and confirming the acquisition of global “cloud-free” imagery for the U.S. archive. This paper documents the operational ACCA algorithm and validates its performance to a standard error of � 5 percent. Visual assessment of clouds in three-band browse imagery were used for comparison to the five-band ACCA scores from a stratified sample of 212 ETM� 2001 scenes. This comparison of independent cloud-cover estimators produced a 1:1 correlation with no offset. The largest commission errors were at high altitudes or at low solar illumination where snow was misclassified as clouds. The largest omission errors were associated with undetected optically thin cirrus clouds over water. There were no statistically significant systematic errors in ACCA scores analyzed by latitude, seasonality, or solar elevation angle. Enhancements for additional spectral bands, per-pixel masks, land/water boundaries, topography, shadows, multidate and multi-sensor imagery were identified for possible use in future ACCA algorithms.

In an effort to more full explore the potential of commercial remotely sensed land data sources, the NASA Earth Science Enterprise (ESE) implemented an experimental Scientific Data Purchase (SDP) that solicited bids from the private sector to meet ESE-user data needs. The images from the Space Imaging IKONOS system provided a particularly good match to the current ESE missions such as Terra and Landsat 7 and therefore serve as a focal point in this analysis.

The Space Imaging IKONOS observatory may provide an important benefit in terrestrial scientific research. The five-band, 1 m panchromatic and 4 m multispectral measurements have the potential to provide a source of measurements to evaluate subpixel land cover variability in measurements from observatories such as Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Terra MODIS sensor. The IKONOS observations are at a spatial scale equivalent to field measurements typically carried out in ecological and land cover research. As such, the IKONOS observations may serve as a source of “virtual” ground measurements, for the lower spatial resolution, global observatories. In this study we examine how well IKONOS sensor observations replicate Landsat 7 ETM+ visible/near infrared observations for selected Earth Observation System (EOS) validation sites in the United States. The sites examined—Beltsville, MD, Konza Prairie, KS, and Sevilleta, NM—sample the east–west moisture gradient across the United States. Observations for each site were acquired, as nearly time-coincident as possible, from ETM+ and the IKONOS sensor, several times over the growing season. This was done to insure that we compared these two sensors over the widest range of observing conditions possible. We also examined IKONOS imagery from Phoenix, AZ, where Space Imaging had and had not applied a modulation transfer function compensation (MTFC) process. The MTFC is their standard product. We found that this product, at the original 4 m spatial resolution, appears to have minor radiometric artifacts as a result of the process. When the IKONOS observations were aggregated to 30 m, this problem was essentially absent, allowing us to proceed with the remainder of our study. We processed the IKONOS sensor and ETM+ measurements to produce close approximates of each other. Our processing steps included ortho-rectification, calibration to planetary reflectance, pixel alignment and pixel aggregation. We initially found radiometric differences between the two sensors that increased with increasing wavelength. Space Imaging updated their calibration information, based on analyses from NASA Stennis Space Center staff, which removed much of this discrepancy. We now find that the IKONOS red and near infrared measurements differ between the two sensors, with IKONOS generally producing higher reflectance in the red band and lower reflectance in the near infrared band than the Landsat 7 ETM+ sensor. This results in the IKONOS sensor producing lower spectral vegetation index measurements, for the same target, than ETM+, a measurement variation that has been observed between other sensors. We also encountered far more cirrus cloud (and shadow) contamination in these paired observations that we had expected. After careful initial selection, we lost over half of our image pairs from the analysis because of cirrus cloud contamination. We do not know whether this is simply because of the paired, comparative design of this study or whether it relates to the increased spatial and radiometric resolution of the IKONOS sensor. The results of this study not only provide a baseline assessment of IKONOS versus Landsat 7 ETM+ visible and near infrared measurements but also suggest some of the issues that need more attention when comparing other sensor systems as well as developing the design of future land observatories.

A new processing chain was developed at the University of Maryland and NASA-GSFC for an improved AVHRR Land Pathfinder II (ALP II) data set. Most data sets derived from AVHRR sensor were created for specific applications and have used algorithms that have been improved or replaced in recent years. The understanding of many processes such as navigation, calibration and atmospheric water vapour absorption have increased, and can now be used to increase the quality of AVHRR data sets. The ALP II data set was compared with three data sets, the ALP I (8 km) and FASIR (1° 2 1°) data sets produced at NASA-GSFC, and LASUR (16 km) developed at CESBIO. This Letter describes evaluations of the initial 8 km AVHRR ALP II data set. This evaluation has shown a greater accuracy in atmospheric correction and temporal profiles for ALP II data set than the other AVHRR data sets.

In this study, four approaches to estimate atmospheric water vapor from Advanced Very High Resolution Radiometer (AVHRR) observations were tested with data from the Boreal Ecosystem–Atmosphere Study (BOREAS) and the Oklahoma Mesonetwork. The approaches studied were (i) the split-window difference of the thermal channels (Channel 4: 10.3–11.3 μm and Channel 5: 11.5–12.5 μm) by Dalu [Int. J. Remote Sens. 7 (1986) 1089.], (ii) the ratio of variances by Jedlovec [J. Appl. Meteorol. 29 (1990) 863.], (iii) the regression slope by Goward et al. [Ecol. Appl. 4 (1994) 322.], and (iv) a look-up table derived from radiative transfer model output. Although these techniques were primarily developed to estimate total column precipitable water, we used them to estimate near-surface water vapor, within a few meters of the surface. Near-surface water vapor is needed for hydrologic and biospheric modeling. Analysis showed the total column precipitable water to be highly correlated ( r 2 =.79) with near-surface absolute humidity for clear-sky conditions at the BOREAS and the Oklahoma study sites. Correlation of all the retrieval techniques with ground observations was very low. For the split-window approach, water vapor can only be estimated on a per pixel basis and is ambiguous for anything but a single site. The regression slope and variance ratio techniques showed very little correlation with ground observations with r 2 =.02 when compared with data from BOREAS, and .17 for the variance ratio and .24 for the regression slope when compared with Mesonet data. The spatial variability of water vapor across the landscape hampers the use of these contextual approaches. The highest correlation was for the look-up table approach, with r 2 =.36 when compared with data from the BOREAS site. The look-up table was applied using AVHRR Channels 4 and 5 brightness temperatures, surface temperature, and near-surface air temperature. Surface temperature and air temperature were both estimated from the satellite readings. Combining the satellite data with air temperature measured at meteorological ground stations improved the correlation to .50. The relatively low r 2 values were at least partly due to spatial and temporal mismatches between surface and satellite measurements. Simulation of Moderate Resolution Imaging Spectrometer (MODIS) thermal Channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) brightness temperatures showed that Channels 31 and 32 provide similar information as AVHRR Channels 4 and 5. The additional thermal information provided by Channel 29 shows promise for future water vapor detection efforts.

Land soil moisture conditions play a critical role in evaluating terrestrial environmental conditions related to ecological, hydrological, and atmospheric processes. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are still underway. Among the various methods, several investigators have explored a combination of surface temperatures and spectral vegetation index (SVI) measurements, the TVX method, as a means to account for the variable influence of vegetation cover in soil moisture assessment. Although considerable empirical evidence has been presented exploring the potential of TVX methods to assess regional moisture conditions, less attention has been given to assessing the underlying biophysics of the observed TVX patterns. In this study, the Simplified Simple Biosphere (SSiB) model is exploited to examine the factors that lead to the observed TVX relation. For a range of typical, midlatitude, growing season conditions, the SSiB model produces the expected TVX relationship, surface temperature decreases with increasing SVI values. The most critical factors that cause the TVX relation to vary include near-surface soil moisture (2 cm), incident radiation (IR), and, to a lesser degree, wind speed. Whereas many empirical studies have suggested that the slope of the TVX relation may provide an important diagnostic of soil moisture conditions, in this analysis, the impact of plant stomatal function is shown to confuse this interpretation of the TVX slope. However, other aspects of the TVX metrics, specifically bare soil temperature and canopy temperature, do provide diagnostic near-surface soil moisture information. Growing season variations in TVX metrics were examined for the conditions recorded at the Hydrological and Atmospheric Pilot Experiment—Modelisation du Bilan Hydrique (HAPEX-Mobilhy) study site. The results from this analysis indicate that soil and canopy temperatures vary as a function of soil moisture conditions and, to a lesser degree, as a result of varying solar insolation and wind speed. The results also show that the TVX metrics are able to provide daily soil moisture variation up to 2 cm of soil depth and seasonal trend up to 10 cm. Using the satellite-derived surface temperatures and a SSiB-derived retrieval equation, the retrieved soil moistures at the HAPEX-Mobilhy site generally closely approximate the conditions recorded on the ground.

Landsat-7 science mission requires large-area analysis of land to cover to quantify anthropogenic and natural changes in Earth's terrestrial environment. Such a goal involves processing (and reprocessing) thousands of Enhanced Thematic Mapper Plus (ETM+) scenes, but current analysis methodologies that rely on “handcrafting” individual scenes cannot scale to this data flow. As an alternative, we have constructed a prototype computer system, REALM (Research Environment for Advanced Landsat Monitoring), to automate preprocessing and analysis of large volume of Landsat imagery. Users can submit arbitrary algorithms to the database using a query language to generate science results “on the fly.” The current prototype, running on a cluster of Linux-based PCs, has created a preliminary forest-cover map for the northeastern US from 9 GB of ETM+ data in just 25 min, for an aggregate throughput of 6 MB/s. The exercise demonstrates the processing Landsat data over very large areas is now feasible.

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation, and seasonally repetitive monitoring. Substantial improvements in calibration procedures, both prior to launch and during normal operations, have also been made to ensure long-term stability in the acquired spectral radiometry. Landsat 7 data acquisitions are being driven by a long-term data acquisition plan that was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the US for all land areas of the globe. NASA competitively selected a Landsat Science Team, consisting of representatives from US universities and government agencies, to exploit the Landsat 7 record for global change research. This team is addressing the technical and analytical means to process and analyze the core of this observation record, and for the first time in the history of the Landsat mission, the technical and operational aspects of the mission are being driven by the goals of the US science community. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational, and research purposes. Pathways to achieve effective Landsat continuity in the early decades of the 21st century are also being given careful attention, and there is no question that the lessons learned from the Landsat 7 mission will strongly influence these next-generation sensor systems.

The Landsat-7 Long-Term Acquisition Plan (LTAP) automates the selection of Landsat scenes to periodically refresh a global archive of sunlit, substantially cloud-free land images. Its automatic nature reduces the workload on both operations and science office management, and makes the best use of limited resources both on board the satellite and in the ground-processing systems. The core of the LTAP is the definition of seasonality for each scene of interest, where seasonality is defined as the occurrence of change over time. When significant change is occurring, acquisition frequencies are high. Conversely, if little change is occurring, acquisition frequencies are low. This definition of seasonality was augmented with suggested acquisition frequencies from the various science community niches that have historically relied on Landsat data to support their research goals. Cloud climatology for each scene was determined, as monthly historical averages, so it could be compared during scheduling with the daily cloud-cover predictions to discriminate between scenes cloudier than usual (less worthy of acquisition) and scenes clearer than normal (more worthy of acquisition). A scheduler system executes the LTAP and builds the daily schedule by evaluating many factors for each scene, including seasonality, acquisition history, cloud cover, and availability of resources. The result of this evaluation is a list of the “best” 250 scenes to be acquired that day. An important tool in the development of the scheduling system and in the evaluation of the LTAP performance is a modeling capability that allows “what if” analyses to be made and proposed changes to the LTAP or the scheduling software to be evaluated. This modeling capability has proven key to understanding the impacts of potential changes in strategy or software. By considering cloud climatology and current cloud-cover predictions, Landsat 7 has acquired imagery with less cloud contamination than previous Landsat data. After 16 months of operations, a relatively cloud-free global archive exists that captures land-cover change where and when it occurs. Additional user requests have been low, suggesting users are finding suitable data already in the archive. Short-term science campaigns and special requests can be accommodated in the LTAP without perturbing the seasonality of the archive or exceeding the available resources.

Climate-land surface interactions have been mainly investigated either in terms of short-term modification of fluxes of matter, momentum, and energy, which primarily affect the atmosphere, or in the long-term, biogeographical impacts of climatic conditions on the type of vegetation that occupies a site. Logically, there must be time and space scales at which these short-term and biogeographical climate-land surface interactions are both relevant. It is proposed that it is these intermediate scales, at which physiological and biogeographical processes cannot easily be separated, which are most relevant in the study of climate change, and that new analytical and modeling approaches are needed which include both. Moreover, periods of rapid climate change and periods of increasing anthropogenic impacts on the land surface can be expected to induce a wide range of transient vegetation dynamics. The timescale of interest here excludes equilibria and demands a consideration of the outcomes of processes that have a wide variety of temporal frequencies. A recent workshop brought together ecologists and climatologists with interests in observing, modeling, and predicting the dynamics of land-atmosphere processes at the inteannual to decadal timescale. The result was a proposal to exploit recently developed archives of remotely sensed data and others such as paleobotanical and paleoclimatological records in order to advance beyond the polarized concept of land-atmosphere processes which comes from a consideration only of short and long time periods, while ignoring their interactions. The new demands of this agenda for observational and analytical methods are considered.

Many recent studies have applied satellite remote sensing data to large-scale hydrologic and biospheric modeling. It is widely accepted that the thermal infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) have the potential to estimate land surface conditions, such as surface temperature, near surface air temperature, and near surface water vapor. In this study, algorithms to estimate all three variables are presented and applied to an area covering the state of Oklahoma for a six day period in August, 1994. The results were validated using ground observations from the 111 station Oklahoma Mesonet. Validation of the remote sensing algorithms with Mesonet observations produced comparable results to previous validation studies. In addition, the validation process revealed inadequacies in thermal modeling that had not been detected in previous validation studies leading to the development of a new approach to estimate atmospheric water vapor.

The presence of plant litter on the soil surface influences the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Quantifying plant litter cover is important for interpreting vegetated landscapes and for evaluating the effectiveness of conservation tillage practices. Current methods of measuring litter cover are subjective, requiring considerable visual judgment. Reliable and objective methods are needed. The spectral reflectance (0.4-2.5 μm) of wet and dry soils (six types) and plant litters (2 crops, 14 forest, and 2 grasses) of different ages were measured. Discrimination of plant litters from the soils was ambiguous in the visible and near-infrared (0.4-1.1μm) wavelength region. An absorption feature associated with cellulose and lignin was observed at 2.1 μm in the spectra of dry plant litter, which was not present in the spectra of soils. A new spectral variable, cellulose absorption index (CAI), was defined using the relative depth of the reflectance spectra at 2.1 μm. CAI of dry litter was significantly greater than CAI of soils. CAI generally decreased with age of the litter. Water absorption dominated the spectral properties of both soils and plant litter and significantly reduced the CAI of the plant litters. Nevertheless, the CAI of wet litter was significantly greater then CAI of wet soil. This study provides a new methodology to discriminate plant litter from soils by differences in spectral reflectance produced by their physical and chemical attributes. This remote sensing method should improve quantification of plant litter cover and thus improve estimates of phytomass production, surface energy balance, and the effectiveness of soil conservation practices. Plant litter reflectance is a verifiable component in vegetative landscapes and should be labeled and modeled separately from soils in landscape studies.

Results are provided of a project to derive improved products from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data record for land investigations. As part of this project, a prototype AVHRR processing system has been developed. This paper describes the different components of this system, which include radiometric in-flight vicarious calibration for the visible and near infrared channels, geometric correction and atmospheric correction as pre-processing steps. The processed data are then stored in a new intermediate data format, which enables flexible compositing approaches. The system generates surface reflectance and vegetation index products as well as new higher order products of reflectance at 3.75 mum and active fires. A comparison of a significant sample of data with widely used precursor AVHRR products is presented to evaluate the processing chain and the improvements it provides.

Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of ∼22km2 per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.

Maps of net and gross primary production, autotrophic respiration, biomass, and other biophysical variables were generated for 106 km2 of boreal forest in central Canada (the Boreal Ecosystem-Atmosphere (BOREAS) region) using a production efficiency model (PEM) driven with remotely sensed observations at 1 km2 spatial resolution. The PEM was based on carbon yields of absorbed photosynthetically active radiation for both gross and net primary production (GPP and NPP), accounting for environmental stress and autotrophic respiration (Ra). Physiological control was modeled using remotely sensed maps of air temperature, vapor pressure deficit, and soil moisture. The accuracy of the inferred variables was generally within 10–30% of point measurements at the surface and independent model results (both at the stand level). Biomass maps were derived from visible reflectance measurements and were also compared to independently derived maps. Area-averaged GPP was 604 g C m−2 yr−1 compared with average canopy respiration of 428 g C m−2 yr−1 and NPP of 235 g C m−2 yr−1. Net annual carbon uptake in net primary production for the region totaled 175 teragrams. Canopy carbon exchange (GPP and Ra) differed widely between land cover types even though the model does not use land cover information. Extensive areas of the least productive cover types (e.g., lowland needleleaf species) accounted for the greatest amount of NPP.

Application and validation of a modified production efficiency model (PEM) appropriate for the regional and global scales is presented. The model calculates not just the conversion efficiency of absorbed photosynthetically active radiation (APAR) but also the component carbon fluxes that ultimately determine net and gross primary production. This approach, driven with remotely sensed observations, moves beyond simple correlative or associative models to a more mechanistic basis and avoids the need for a full suite of ecophysiological process algorithms that require explicit (e.g. species-specific) parameterization. We show that surface variables recovered from the satellite observations, including net primary production, are in good agreement with field measurements and independent model simulations in a number of ecosystems. These results illustrate the utility of PEMs for terrestrial primary production modeling over large areas and suggest that some complex ecophysiological models may be functionally simpler than their structure suggests.

With the development of long-term remotely-sensed data sets through the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder programme, decadal length studies of the land surface are being conducted. However, data from five different Advanced Very High Resolution Radiometer (AVHRR) instruments and thus five different filter functions for each thermal channel must be used. If variations in sensor filter functions are not taken into account when the split window technique is applied, considerable error in the estimation of land surface temperature can be produced. For example, the Becker and Li (1990) equation was designed to be used with data from the AVHRR instrument on the NOAA-9 satellite. If it is applied to data from an AVHRR sensor other than NOAA-9's, the error in surface temperature can be as large as 2.3 degK.

Mapping of remotely-sensed data to a common projection can potentially degrade the quality of the data. The Canada Centre for Remote Sensing (CCRS) has developed the Geocoding and Compositing System (GEOCOMP) to map Advanced Very High Resolution Radiometer (AVHRR) data to a 1 km2 equal area projection. Using data from the Boreal Ecosystem-Atmosphere Study (BOREAS), we tested GEOCOMP's processing of individual pixels and its ability to preserve the spatial integrity of the original AVHRR observations. Our study shows that GEOCOMP processing does not significantly degrade the ability to derive environmental variables and indices using a contextual approach. Errors produced by the GEOCOMP system are largest for scenes with high radiometric contrast and the parts of scenes with large satellite view angles.

The relation between the normalized difference vegetation index (NDVI) and the fraction of absorved photosynthetically active radiation (f APAR ) was examined for ten different forest types by using the scattering-from-arbitrarily-inclined-leaves (SAIL) radiative transfer model. Leaf reflectance and transmittance, twigreflectance, and background reflectance data were collected as part of field experiments whose sites contain species whose ranges cover a significant part of western and northern North America. This provides a sense of various that occur at continental scales. Actual backgrounds of forests include litter and mosses; these materials did not fall along a soil line in red-near infrared reflectance space. the simulations indicated that, at low values of the leaf area index (LAI), the background reflectance had a significant effect on the canopy reflectance, although little effect on photosynthetically active radiation (PAR) absorption. At higher values of LAI, leaf optical properties were the factors that dominated canopy reflectance and NDVI. Variations in canopy reflectance due to leaf optical properties were large, but most species had similar reflectance patterns. Green leaf optical properties, among the species studied, had little effect on f APAR . The presence of twigs in the canopy had a noticeable effect on canopy reflectance and absorption of PAR, but these effects were secondary to the effects of background and leaf optical properties.

Air temperature is an important descriptor of terrestrial environmental conditions across the earth. Standard meteorological observations generally provide reasonable descriptions of temporal variations in air temperature for the site sampled but may not describe the spatial heterogeneity typically encountered in this variable over larger land areas. If a reasonable estimate of spatial patterns of air temperature can be derived from satellite remote sensing, this pattern, in combination with the temporal precision of ground measurements, should significantly improve our knowledge of terrestrial environmental conditions. In this study, we explore a methodology for estimating air temperature directly from remotely sensed observations using the (observed) correlation between a spectral vegetation index and surface temperature (temperature-vegetation index). Inference of air temperature is based on the hypothesis that the bulk temperature of an infinitely thick vegetation canopy is close to ambient air temperature. Advanced very high resolution radiometer observations for five sites in northeastern Kansas were used to estimate air temperatures on 31 days during the 1987 growing season. These air temperature estimates were compared with coincident ground-measured air temperatures recorded at standard meteorological stations. A strong correlation (r=0.93) was found between the satellite estimates and measured air temperatures with a mean error of 2.92 C°. However, there was a consistent positive bias in the satellite estimates. It is not clear at this time whether the bias is due to an actual difference between air temperature and the temperature of an infinitely thick canopy or whether it is an artifact of the measurements themselves. Within the errors of the methods used, estimation of standard meteorological shelter height air temperatures recorded at the time of satellite overpass appears possible. Further refinements of the remote sensing methods used here are possible and can be expected in the era of the National Aeronautics and Space Administration's Earth Observing System.

The new NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land Data Set has been used to drive the GLObal Production Efficiency (GLO-PEM) primary production model for vegetation at a spatial resolution of 8 x 8 km for the entire land cover of the Earth. The principal advantage of the Pathfinder data was the organization of a vast archive of AVHRR global area coverage (GAC) data into a consistent data set in which post-flight sensor calibrations and atmospheric corrections have been applied. The principal difficulties arose from the compositing of thermal channels into 10-day composites using a maximum Normalized Difference Vegetation Index (NDVI) criterion, the failure of the cloud screening procedure, the absence of coastal data, and some aspects of the data format. Overall the data set represents a significiant advance over the NOAA Global Vegetation Index (GVI), which has long been the only publicly available, complete global data set.