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At least 145 records · Page 8Linked to original sources
USGS2010

Thematic accuracy of the NLCD 2001 land cover for the conterminous United States

The land-cover thematic accuracy of NLCD 2001 was assessed from a probability-sample of 15,000 pixels. Nationwide, NLCD 2001 overall Anderson Level II and Level I accuracies were 78.7% and 85.3%, respectively. By comparison, overall accuracies at Level II and Level I for the NLCD 1992 were 58% and 80%. Forest and cropland were two classes showing substantial improvements in accuracy in NLCD 2001 relative to NLCD 1992. NLCD 2001 forest and cropland user's accuracies were 87% and 82%, respectively, compared to 80% and 43% for NLCD 1992. Accuracy results are reported for 10 geographic regions of the United States, with regional overall accuracies ranging from 68% to 86% for Level II and from 79% to 91% at Level I. Geographic variation in class-specific accuracy was strongly associated with the phenomenon that regionally more abundant land-cover classes had higher accuracy. Accuracy estimates based on several definitions of agreement are reported to provide an indication of the potential impact of reference data error on accuracy. Drawing on our experience from two NLCD national accuracy assessments, we discuss the use of designs incorporating auxiliary data to more seamlessly quantify reference data quality as a means to further advance thematic map accuracy assessment.

Remote Sensing of Environment↗
USGS2010

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Remote Sensing of Environment↗
USGS2010

Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R2 of the organic layer depth reduction models was 0.60 and 0.55 (pb0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R2 to 0.80 and 0.79 (pb0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (N200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.

Remote Sensing of Environment↗
USGS2010

Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat imagery change detection methods

A prototype method was developed to update the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 to a nominal date of 2006. NLCD 2001 is widely used as a baseline for national land cover and impervious cover conditions. To enable the updating of this database in an optimal manner, methods are designed to be accomplished by individual Landsat scene. Using conservative change thresholds based on land cover classes, areas of change and no-change were segregated from change vectors calculated from normalized Landsat scenes from 2001 and 2006. By sampling from NLCD 2001 impervious surface in unchanged areas, impervious surface predictions were estimated for changed areas within an urban extent defined by a companion land cover classification. Methods were developed and tested for national application across six study sites containing a variety of urban impervious surface. Results show the vast majority of impervious surface change associated with urban development was captured, with overall RMSE from 6.86 to 13.12% for these areas. Changes of urban development density were also evaluated by characterizing the categories of change by percentile for impervious surface. This prototype method provides a relatively low cost, flexible approach to generate updated impervious surface using NLCD 2001 as the baseline.

Remote Sensing of Environment↗
USGS2010

Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals

ASTER reflectance spectra from Cuprite, Nevada, and Mountain Pass, California, were compared to spectra of field samples and to ASTER-resampled AVIRIS reflectance data to determine spectral accuracy and spectroscopic mapping potential of two new ASTER SWIR reflectance datasets: RefL1b and AST_07XT. RefL1b is a new reflectance dataset produced for this study using ASTER Level 1B data, crosstalk correction, radiance correction factors, and concurrently acquired level 2 MODIS water vapor data. The AST_07XT data product, available from EDC and ERSDAC, incorporates crosstalk correction and non-concurrently acquired MODIS water vapor data for atmospheric correction. Spectral accuracy was determined using difference values which were compiled from ASTER band 5/6 and 9/8 ratios of AST_07XT or RefL1b data subtracted from similar ratios calculated for field sample and AVIRIS reflectance data. In addition, Spectral Analyst, a statistical program that utilizes a Spectral Feature Fitting algorithm, was used to quantitatively assess spectral accuracy of AST_07XT and RefL1b data.Spectral Analyst matched more minerals correctly and had higher scores for the RefL1b data than for AST_07XT data. The radiance correction factors used in the RefL1b data corrected a low band 5 reflectance anomaly observed in the AST_07XT and AST_07 data but also produced anomalously high band 5 reflectance in RefL1b spectra with strong band 5 absorption for minerals, such as alunite. Thus, the band 5 anomaly seen in the RefL1b data cannot be corrected using additional gain adjustments. In addition, the use of concurrent MODIS water vapor data in the atmospheric correction of the RefL1b data produced datasets that had lower band 9 reflectance anomalies than the AST_07XT data. Although assessment of spectral data suggests that RefL1b data are more consistent and spectrally more correct than AST_07XT data, the Spectral Analyst results indicate that spectral discrimination between some minerals, such as alunite and kaolinite, are still not possible unless additional spectral calibration using site specific spectral data are performed. ?? 2010.

Remote Sensing of Environment↗
USGS2011

Continuity of Landsat observations: Short term considerations

As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor's design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community's concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record.

Remote Sensing of Environment↗
USGS2011

A simple and effective method for filling gaps in Landsat ETM+ SLC-off images

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original "gap free" data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data.

Remote Sensing of Environment↗
USGS2011

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft , as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.

Alaska↗
USGS2011

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

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.

Remote Sensing of Environment↗
USGS2012

Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems

The focus of the study was to assess gradual changes occurring throughout a range of natural ecosystems using decadal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) time series data. Time series data stacks were generated for four study areas: (1) a four scene area dominated by forest and rangeland ecosystems in the southwestern United States, (2) a sagebrush-dominated rangeland in Wyoming, (3) woodland adjacent to prairie in northwestern Nebraska, and (4) a forested area in the White Mountains of New Hampshire. Through analyses of time series data, we found evidence of gradual systematic change in many of the natural vegetation communities in all four areas. Many of the conifer forests in the southwestern US are showing declines related to insects and drought, but very few are showing evidence of improving conditions or increased greenness. Sagebrush communities are showing decreases in greenness related to fire, mining, and probably drought, but very few of these communities are showing evidence of increased greenness or improving conditions. In Nebraska, forest communities are showing local expansion and increased canopy densification in the prairie–woodland interface, and in the White Mountains high elevation understory conifers are showing range increases towards lower elevations. The trends detected are not obvious through casual inspection of the Landsat images. Analyses of time series data using many scenes and covering multiple years are required in order to develop better impressions and representations of the changing ecosystem patterns and trends that are occurring. The approach described in this paper demonstrates that Landsat time series data can be used operationally for assessing gradual ecosystem change across large areas. Local knowledge and available ancillary data are required in order to fully understand the nature of these trends.

Remote Sensing of Environment↗
USGS2011

Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km 2 Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R 2 of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R 2 between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km 2 within the study area where the modeled wetland water areas ranged from about 2 to 14 km 2 . These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.

North Dakota↗
USGS2012

The next Landsat satellite: The Landsat Data Continuity Mission

The National Aeronautics and Space Administration (NASA) and the Department of Interior United States Geological Survey (USGS) are developing the successor mission to Landsat 7 that is currently known as the Landsat Data Continuity Mission (LDCM). NASA is responsible for building and launching the LDCM satellite observatory. USGS is building the ground system and will assume responsibility for satellite operations and for collecting, archiving, and distributing data following launch. The observatory will consist of a spacecraft in low-Earth orbit with a two-sensor payload. One sensor, the Operational Land Imager (OLI), will collect image data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS), will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. Both sensors offer technical advancements over earlier Landsat instruments. OLI and TIRS will coincidently collect data and the observatory will transmit the data to the ground system where it will be archived, processed to Level 1 data products containing well calibrated and co-registered OLI and TIRS data, and made available for free distribution to the general public. The LDCM development is on schedule for a December 2012 launch. The USGS intends to rename the satellite "Landsat 8" following launch. By either name a successful mission will fulfill a mandate for Landsat data continuity. The mission will extend the almost 40-year Landsat data archive with images sufficiently consistent with data from the earlier missions to allow long-term studies of regional and global land cover change.

Remote Sensing of Environment↗
USGS2012

Landsat: building a strong future

Conceived in the 1960s, the Landsat program has experienced six successful missions that have contributed to an unprecedented 39-year record of Earth Observations that capture global land conditions and dynamics. Incremental improvements in imaging capabilities continue to improve the quality of Landsat science data, while ensuring continuity over the full instrument record. Landsats 5 and 7 are still collecting imagery. The planned launch of the Landsat Data Continuity Mission in December 2012 potentially extends the Landsat record to nearly 50 years. The U.S. Geological Survey (USGS) Landsat archive contains nearly three million Landsat images. All USGS Landsat data are available at no cost via the Internet. The USGS is committed to improving the content of the historical Landsat archive though the consolidation of Landsat data held in international archives. In addition, the USGS is working on a strategy to develop higher-level Landsat geo- and biophysical datasets. Finally, Federal efforts are underway to transition Landsat into a sustained operational program within the Department of the Interior and to authorize the development of the next two satellites — Landsats 9 and 10.

Remote Sensing of Environment↗
USGS2012

Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool to detect long-term seismotectonic motions by reducing the atmospheric artifacts, thereby providing more precise deformation signal. The commonly used approaches such as persistent scatterer InSAR (PSInSAR) and small baseline subset (SBAS) algorithms need to resolve the phase ambiguities in interferogram stacks either by searching a predefined solution space or by sparse phase unwrapping methods; however the efficiency and the success of phase unwrapping cannot be guaranteed. We present here an alternative approach – temporarily coherent point (TCP) InSAR (TCPInSAR) – to estimate the long term deformation rate without the need of phase unwrapping. The proposed approach has a series of innovations including TCP identification, TCP network and TCP least squares estimator. We apply the proposed method to the Los Angeles Basin in southern California where structurally active faults are believed capable of generating damaging earthquakes. The analysis is based on 55 interferograms from 32 ERS-1/2 images acquired during Oct. 1995 and Dec. 2000. To evaluate the performance of TCPInSAR on a small set of observations, a test with half of interferometric pairs is also performed. The retrieved TCPInSAR measurements have been validated by a comparison with GPS observations from Southern California Integrated GPS Network. Our result presents a similar deformation pattern as shown in past InSAR studies but with a smaller average standard deviation (4.6 mm) compared with GPS observations, indicating that TCPInSAR is a promising alternative for efficiently mapping ground deformation even from a relatively smaller set of interferograms.

Remote Sensing of Environment↗
USGS2012

Regional scale impacts of Tamarix leaf beetles ( Diorhabda carinulata ) on the water availability of western U.S. rivers as determined by multi-scale remote sensing methods

Tamarix leaf beetles ( Diorhabda carinulata ) have been widely released on western U.S. rivers to control introduced shrubs in the genus Tamarix . Part of the motivation to control Tamarix is to salvage water for human use. Information is needed on the impact of beetles on Tamarix seasonal leaf production and subsequent water use overwide areas andmultiple cycles of annual defoliation.Herewe combine ground data with high resolution phenocam imagery and moderate resolution (Landsat) and coarser resolution (MODIS) satellite imagery to test the effects of beetles on Tamarix evapotranspiration (ET) and leaf phenology at sites on six western rivers. Satellite imagery covered the period 2000 to 2010 which encompassed years before and after beetle release at each study site. Phenocam images showed that beetles reduced green leaf cover of individual canopies by about 30% during a 6-8 week period in summer, but plants produced new leaves after beetles became dormant in August, and over three years no net reduction in peak summer leaf production was noted. ETwas estimated by vegetation index methods, and both Landsat and MODIS analyses showed that beetles reduced ET markedly in the first year of defoliation, but ET recovered in subsequent years. Over all six sites, ET decreased by 14% to 15% by Landsat and MODIS estimates, respectively. However, resultswere variable among sites, ranging fromno apparent effect on ET to substantial reduction in ET. Baseline ET rates before defoliation were low, 394 mmyr -1 by Landsat and 314 mm yr -1 by MODIS estimates (20-25% of potential ET), further constraining the amount of water that could be salvaged. Beetle- Tamarix interactions are in their early stage of development on this continent and it is too soon to predict the eventual extent towhich Tamarix populationswill be reduced. The utility of remote sensing methods for monitoring defoliation was constrained by the small area covered by each phenocamimage, the low temporal resolution of Landsat, and the lowspatial resolution ofMODIS imagery. Even combined image sets did not adequately reveal the details of the defoliation process, and remote sensing data should be combined with ground observations to develop operational monitoring protocols.

Colorado;Nevada;Utah;Wyoming↗
USGS2012

A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions

Spatially explicit representations of vegetation canopy height over large regions are necessary for a wide variety of inventory, monitoring, and modeling activities. Although airborne lidar data has been successfully used to develop vegetation canopy height maps in many regions, for vast, sparsely populated regions such as the boreal forest biome, airborne lidar is not widely available. An alternative approach to canopy height mapping in areas where airborne lidar data is limited is to use spaceborne lidar measurements in combination with multi-angular and multi-spectral remote sensing data to produce comprehensive canopy height maps for the entire region. This study uses spaceborne lidar data from the Geosciences Laser Altimeter System (GLAS) as training data for regression tree models that incorporate multi-angular and multi-spectral data from the Multi-Angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging SpectroRadiometer (MODIS) to map vegetation canopy height across a 1,300,000 km 2 swath of boreal forest in Interior Alaska. Results are compared to in situ height measurements as well as airborne lidar data. Although many of the GLAS-derived canopy height estimates are inaccurate, applying a series of filters incorporating both data associated with the GLAS shots as well as ancillary data such as land cover can identify the majority of height estimates with significant errors, resulting in a filtered dataset with much higher accuracy. Results from the regression tree models indicate that late winter MISR imagery acquired under snow-covered conditions is effective for mapping canopy heights ranging from 5 to 15 m, which includes the vast majority of forests in the region. It appears that neither MISR nor MODIS imagery acquired during the growing season is effective for canopy height mapping, although including summer multi-spectral MODIS data along with winter MISR imagery does appear to provide a slight increase in the accuracy of resulting height maps. The finding that winter, snow-covered MISR imagery can be used to map canopy height is important because clear sky days are nearly three times as common during the late winter period as during the growing season. The increased odds of acquiring cloud-free imagery during the target acquisition period make regularly updated forest height inventories for Interior Alaska much more feasible. A major advantage of the GLAS–MISR–MODIS canopy height mapping methodology described here is that this approach uses only data that is freely available worldwide, making the approach potentially applicable across the entire circumpolar boreal forest region.

Remote Sensing of Environment↗
USGS2012

Complementarity of ResourceSat-1 AWiFS and Landsat TM/ETM+ sensors

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.

Remote Sensing of Environment↗
USGS2012

Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity

Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes > 8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R 2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.

Maine↗