This PDF file contains the errata for “JRS Vol. 1 Issue 01 Paper 2803347” for JRS Vol. 1 Issue 01

Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.

Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m^-1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.

We present an easy and efficient approach for remote sensing of ocean color, relevant for monitoring and management of kelp forest and bottom substrate with a cheap custom made hyperspectral imager. Remote sensing of ocean color was performed in the Kongsfjord, Spitsbergen (79 deg N and 12 deg E) from an airplane (2950 m altitude) equipped with a hyperspectral imager, giving monochromatic images (425-825 nm) using the push broom technique, captured with custom designed software in 5 nm steps. Synchronously in situ measurements of upwelling spectral irradiance, (E_u(λ))(λ= 350-950 nm) measured at 30 cm depth were performed as a reference for the remotely sensed images. Surface water samples were taken for enumeration and identification of organic (plankton), inorganic particles, and colored dissolved organic matter. For identification and classification of kelp and bottom substrate, Bayesian supervised classification and a differential histogram equalization technique were used and compared. Both techniques gave successful discrimination between kelp and bottom substrate in shallow water above the Secchi depth (<19 m). The imager could easily be implemented for other applications such as detection and monitoring of phytoplankton blooms, suspended matter, and colored dissolved organic matter in surface waters, especially in connection with environmental and aquaculture management.

Change in live coral cover within the nine different management zones of the Marine Protected Area (MPA) around Bunaken Island, Indonesia, was estimated using IKONOS satellite image data for 2001 and 2004. For both years, field data were used to classify image pixels on a semi-quantitative scale based mainly on coral cover. The resulting substrate maps had overall classification accuracies of 78% and 81%, respectively. Change was estimated for each zone using a post-classification comparison. Results indicated an increase in live coral cover in one zone, a decrease in two other zones, and no change in the remaining six zones. The results show no obvious influence of the MPA's zonation plan on changes in live coral cover in the three year period investigated, and the study demonstrates the use of remote sensing to determine change in live coral cover, to assess zonation effectiveness, and to guide management efforts.

Bathymetry and the spatial distribution of benthic cover in coastal waters are of key importance in managing and monitoring our coastal water environments. Currently very little of the Western Australian shallow coastal water habitats are mapped, and for those maps that do exist, the spatial resolution generally is poor and the information is dated. Aircraft and space-borne hyperspectral sensors have been shown to be useful in imaging substrate features in shallow coastal waters. This paper describes a method for quantitatively estimating both bathymetry and benthic cover in shallow waters from hyperspectral imagery. The method incorporates a shallow water reflectance model, which accounts for the water column absorption and backscattering, water depth and substrate reflectance. The model was tested against simulated reflectance data, demonstrating the models' ability to retrieve appropriate fractional coverage of sediment, sea grass and brown algae for depths ranging from 1 - 12 m. The model was applied to a HyMap image encompassing a portion of the Jurien Bay Marine Park off the coast of Western Australia. The retrieved benthic cover products were compared to underwater video observations sampled within the image. The comparison shows the method's great potential for characterizing key aspects of marine ecosystems from remotely sensed hyperspectral data.

This study describes the potential of multi-temporal Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) data for the operational monitoring of sediment plume produced by dredging between 8 May and 31 October 2006 at Hay Point, Northern Queensland, Australia. The methodology involved atmospheric correction of the TM, ETM+ and MODIS images, the development and verification of a Total Suspended Sediment (TSS) model, and the evaluation of the image derived TSS maps for tracking sediment plume movements and its influence on the fringing coral environment. TSS empirical models are developed from TM/ETM+ Band 3 with R² = 0.92 and from MODIS Band 12 with R² = 0.61. The accuracy of the TSS model for TM/ETM+ is {plus minus} 3.2 (mg l^-1) and for MODIS is {plus minus} 16.0 (mg l^-1). The estimated sediment plume displacement and rate of dispersion show high correlation with the tidal amplitude. Wind speed greater than 6 m s^-1 increases the sediment plume movements during the ebb tide. The frequency of the plume extending beyond the fringing coral islands during dredging operations is in agreement with the field observed coral mortality due to sediment accumulation.

Nowcasting of harmful algal blooms is important both for the public and for environmental management purposes. In the Baltic Sea summer blooms of nitrogen-fixing cyanobacteria are regular phenomena but the past years intense and widespread blooms have caused major environmental concern due to its nuisance, increased nitrogen input and toxicity. The Swedish Meteorological and Hydrological Institute have developed a monitoring application that use satellite data to detect blooms and that assembles essential sources of information at one website. A supervised classifications algorithm has been applied to NOAA-AVHRR data during 1997-2006 and the dataset collected has been evaluated and analyzed. Subsequently, definitions of normalized bloom duration, extent and intensity have been developed to enable comparison between different years. Results suggest that the most intense blooms during 1997-2006 were recorded in 2006, while both 2005 and 2006 had the longest duration. The largest extent was noted in 1998. The experience from the 10 years of monitoring has shown that the combination of satellite imagery, observations, forecasts and tools assembled at one website, is a powerful nowcasting tool for monitoring and prediction of cyanobacterial blooms.

A satellite sensor nonspecific operational advanced algorithm is developed to simultaneously retrieve the concentrations of phytoplankton chlorophyll (chl), dissolved organics (doc) and suspended minerals (sm) in turbid and strongly absorbing natural waters (i.e., case II waters). Also, a new interpolation procedure is developed and used jointly with the advanced bio-optical and standard window-split algorithms to generate from SeaWiFS and AVHRR data the time series of spatial and temporal (seasonal and interannual) variations of chl, sm, doc and water surface temperature (T_S) for the period 1998-2004 in Lake Ladoga, the largest European fresh water body. Obtained for the first time, the spaceborne fields of the above variables have revealed at an unprecedented time and space resolution some intrinsic features and interdependence of thermal and biogeochemical processes in the lake. Rates of thermobar displacement from the littoral zone to the central deep water area are quantified during periods of lake warming and cooling. From spring to mid-summer, the dynamics of phytoplankton biomass spatial distribution are evidenced to follow the retraction of the cold water zone bordered by the thermobar. Importantly, along with the thermobar dynamics, the zones of the most enhanced phytoplankton concentration are concurrently governed by the lake bathymetry, and thus gradually move from south to north along the eastern coast line. Brought with fluvial input, suspended minerals and allochthonous dissolved organics are not only restricted to the zones of major river deltas but also driven northward by coastal cyclonic currents prevailing in Lake Ladoga. The obtained space data allows the interplay of the above factors to be explicitly revealed and explains the observed interannual variations in the surficial expressions of biogeochemical processes inherent in Lake Ladoga.

Temporal variation in the extent of submerged macrophytes along the littoral zone of Sirmione Peninsula in the southern part of Lake Garda (Northern Italy) was investigated using imaging spectrometry. Two images, with a spatial resolution of 5 m were acquired by the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) in the summers of 1997 and 2005. Image data were first geocoded and then corrected for both atmospheric and skylight reflection effects at the water surface using the 6S radiative transfer code. The two images were inverted using a bio-optical model, which was parameterised with the inherent optical properties of the lake. The inversion utilized the spectral range from 0.48-0.60 μm because it simultaneously provided the lowest environmental noise and the best atmospheric correction performances for the two scenes and produced images of bottom depth and of two substrate classes: bare sand and submerged vegetation, representing a mixture of valuable freshwater species. The MIVIS-derived bottom depth ranges and patterns were comparable to a bathymetry chart with a deviation less than 5%. In 2005, the image was consistent with contemporaneous in-situ derived knowledge on macrophyte distribution. In 1997, the substrate image map was deemed reasonable with respect to the macrophyte distribution documented in 2000. The comparison of the substrate products for the two dates showed a marked decrease in macrophyte beds, with a concomitant increase in sandy substrates. In the 8-year interval the extent of submerged macrophyte decreased from 72% to 52%. We expect that this study will contribute to increased knowledge of macrophyte colonisation patterns of the Sirmione Peninsula, where, despite their ecological significance, changes have been poorly documented.

This PDF file contains the editorial “Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management” for JRS Vol. 1 Issue 01

This paper reviews the science of sun photometry, tracing its origins back to research by Newton. Modern sun photometry originated through the works of the Angstrom family in Sweden and the Smithsonian Astrophysical Observatory's program started by Langley and energized over many years by Abbott. In the case of the Smithsonian's program, the objective was to search for evidence between fluctuations in climate and solar radiation. Multiwavelength sun photometry as a science came about primarily by turning this nuisance factor of atmospheric corrections into useful scientific information. Modern sun photometry uses solar radiometers calibrated to accuracies of one part in a thousand and routinely assess atmospheric spectral optical depths to high accuracy. There is presently a global network of sun photometers providing information about the spectral variation of aerosol optical depth and information mapping atmospheric trace constituents.

For more than 46 years, NOAA's environmental satellites have been an integral key to life-saving weather and climate forecasts for the United States. NOAA, the National Oceanic and Atmospheric Administration, has a rich history of successfully operating geostationary operational environmental satellites (GOES) and polar-orbiting environmental satellites (POES) that have helped take some of the mystery out of atmospheric phenomenon. Through the years, NOAA, with cooperation with NASA, has developed increasingly advanced satellites that have provided higher spatial and temporal resolution images, operational soundings, atmospheric temperature and moisture data in all weather situations. During those 46 years, NOAA has also built strong partnerships within the U.S. and throughout the world, leading to satellite-data sharing agreements that will strengthen the scientific community's understanding of the world's changing climate.The article, History of the NOAA Satellite Program, traces the development of the U.S. weather satellite system from an early commitment in 1953 by the Eisenhower administration to hoist an Earth satellite in orbit in 1958, to the next generation polar-orbiting system, the National Polar-orbiting Operational Environmental Satellite System, or NPOESS.

Investment in understanding the Earth pays off twice. It enables pursuit of scientific questions that rank among the most interesting and profound of our time. It also serves society's practical need for increased prosperity and security. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide raw information from which we derive Earth knowledge. This network has served us well in the development of science and the provision of operational services. In the next decade, the demand for such information will grow dramatically. New remote sensing capabilities will emerge. Rapid evolution of Internet geospatial and location-based services will make communication and sharing of Earth knowledge easier. Governments, businesses, and consumers will all benefit. But this exciting future is threatened from many directions. Risks range from technology and market uncertainties in the private sector to budget cuts and project setbacks in the public sector. The coming decade will see a dramatic confrontation between the vision of what needs to be accomplished in Earth remote sensing and the reality of our resources and commitment. The outcome will have long-term implications for both the remote sensing community and society as a whole.

Earth observation with Landsat and other moderate resolution sensors is a vital component of a wide variety of applications across disciplines. Despite the widespread success of the Landsat program, recent problems with Landsat 5 and Landsat 7 create uncertainty about the future of moderate resolution remote sensing. Several other Landsat-like sensors have demonstrated applicability in key fields of earth observation research and could potentially complement or replace Landsat. The objective of this paper is to review the range of applications of 5 satellite suites and their Landsat-like sensors: SPOT, IRS, CBERS, ASTER, and ALI. We give a brief overview of each sensor, and review the documented applications in several earth observation domains, including land cover classification, forests and woodlands, agriculture and rangelands, and urban. We conclude with suggestions for further research into the fields of cross-sensor comparison and multi-sensor fusion. This paper is significant because it provides the remote sensing community a concise synthesis of Landsat-like sensors and research demonstrating their capabilities. It is also timely because it provides a framework for evaluating the range of Landsat alternatives, and strategies for minimizing the impact of a possible Landsat data gap.

Watermarking represents a potentially effective tool for the protection and verification of ownership rights in remotely sensed imagery. Such data however cannot undergo critical quality degradation processes, for they must ensure accurate extraction of thematic and scientific information. In this paper, we propose to adapt a state-of-the-art spread spectrum method found to be particularly powerful for real world images to make the watermark insertion process near-lossless while preserving most of its effective robustness capability. This is done by embedding the watermark in the middle-frequency range of the discrete cosine transform (DCT) domain instead of the low-frequency range. The exact position is determined by a numerical root-finding method, targeted to achieve a userspecified minimum level of robustness against a given ensemble of attacks. In this way, an optimal trade-off can be obtained between quality and robustness. Thorough experimental tests over a multispectral remote sensing image show that the proposed method allows to trade a contained loss in robustness with a significant gain in image quality, quantified in terms of both peak signal-to-noise ratio (PSNR) and impact on classification performance. In addition, they reveal an interesting property of the watermark insertion shifting into the middle-frequency range, consisting of a sharp increase in robustness to the widely used cropping operation, which is considered the most critical attack in remote sensing imagery.

We present an optomechanical switching device (OSD) for a photopolarimetric lidar system with differential-absorption Mueller matrix spectroscopy standoff detection method. An output train of alternate continuous-wave CO2 laser beams [...L1:L2...] is directed onto suspect chemical-biological (CB) aerosol plume or the land mass it contaminates (S) vis-à-vis the OSD, where L1 [L2] is tuned on [detuned off] a resonant molecular absorption moiety of CB analyte. Moreover, both incident beams and their backscattered radiances, from S, are polarization-modulated synchronously so as to produce gated temporal voltage waveforms called scattergrams recorded on focus at the receiver end of polarization lidar sensor system. All 16 elements of the Mueller matrix (Mij) of S are measured via digital or analog filtration of constituent frequency components in these running scattergram data streams (phase-sensitive detection). A collective set of normalized differential elements {ΔMi,j} (ratioed to element M11) that are susceptible to the analyte, probed on-then-off its molecular absorption band, form a unique detection domain that is scrutinized. Any mapping onto this domain in Mueller-space, from incoming sensor scattergram data sets preprocessed by algorithm and forwarded through a trained neural network pattern recognition system, cues a standoff detection event.

In red-NIR reflectance space, the Modified Soil Adjusted Vegetation Index (MSAVI) isolines, representing similar vegetation biophysical quantities, are neither convergent to a point nor parallel to each other. Consequently, the treatment of the MSAVI isolines is distinctly different from those of other vegetation index isolines, such as the normalized difference vegetation index (NDVI), the perpendicular vegetation index (PVI), and the soil-adjusted vegetation index (SAVI). In this study, the MSAVI isolines are shown to be the tangent lines of the parabola, (NIR-0.5)2+2Red=0, and the values of the MSAVI isolines are equal to the ordinates of their tangent points plus 0.5. These findings provide a graphic interpretation of the MSAVI and are useful in understanding the biophysical characteristics of the MSAVI. The MSAVI isolines are shown to better approximate field data and simulated vegetation biophysical isolines than the other 2-band vegetation index isolines. As the treatment of the MSAVI isolines can be depicted by the parabola curve, the MSAVI can be referred to as a parabola-based vegetation index.

The Moderate Resolution Imaging Spectroradiometer (MODIS instruments onboard the NASA Terra and Aqua Spacecrafts have provided unprecedented capabilities in global observations of land, ocean, and atmosphere. In this paper, we report that, under certain atmospheric and surface conditions, several MODIS channels in the visible and near-IR spectral regions can saturate. The radiance dynamic ranges of similar channels for future satellite earth observing instruments should be increased. Instrument designers and MODIS data users should be aware of the saturation problems associated with these MODIS channels.

In light of recently documented hypotheses relating long-term trends in tropical cyclone (TC) activity and global warming, the need for consistent reanalyses of historical TC data records has taken on a renewed sense of urgency. Such reanalyses rely on satellite data, but until now, no comprehensive global satellite data set has been available for studying tropical cyclones. Here a new hurricane satellite (HURSAT) data record is introduced that will facilitate the reanalysis of TCs by providing satellite imagery in a standard format for the period of record 1983 to 2005. The data are collected from Japanese, European and U.S. geostationary satellites and the infrared window channel data, which are particularly relevant for TC analyses, have been recalibrated to reduce inter-satellite differences. Observations are provided on a 0.07{degree sign}×0.07{degree sign} (~8km) Lagrangian grid that follows the TC center at 3-hourly intervals. The data set will be updated annually and work is also underway to expand the data set backward to the late 1970s.

One of the goals of applied remote sensing is to map locations of invasive weeds. However, differences in plant cover and leaf area index (LAI) alter canopy reflectance, making detection of a single species difficult. Variation in canopy reflectance may be simulated using the Scattering by Arbitrarily Inclined Leaves (SAIL) model. Simulated reflectances are used to calculate spectral angles to determine the separability of an invasive weed from co-occurring vegetation. Leafy spurge is a noxious invasive weed with yellow-green flower-bracts. Spectral angles from SAIL model simulations show that flowering leafy spurge may be detected when LAI is greater than 1.0 and flower-bract cover is greater than 10%. A threshold of 3.5 deg (0.061 radians) was determined to provide the best separation between leafy spurge and co-occurring vegetation. To test this prediction, the Spectral Angle Mapper was used to classify leafy spurge using AVIRIS, Landsat ETM+ and SPOT data. Classification accuracy was inversely related to simulated spectral angles from the SAIL model analyses. Using canopy reflectance models and spectral angles may help identify those invasive species that are potentially detectable by remote sensing, and may indicate the conditions where detection will be problematic based on variation of LAI, cover and other variables.

The notion of the A-Train constellation of satellites is described. This constellation includes five satellites with EOS Aqua and EOS Aura at each end of the constellation and another small satellite, PARASOL, carrying the POLDER polarimeter inserted in the formation between the larger EOS spacecraft. This constellation also includes the CloudSat and CALIPSO spacecraft that were inserted into the Aqua orbit on April 28th, 2006. This newly formed constellation, together with the earlier NASA TRMM mission, represent a new age of remote sensing of clouds and precipitation in that we are now able to combine the observations of active radar and laser systems with passive radiometric observations. This paper reviews a few selected but commonly used remote sensing approaches for observing clouds and precipitation and highlights the potential of the new observing capabilities of the A-Train. It is also highlighted how this new era provides us with the opportunity to move away from present artificial practices of 'observing' and analyzing clouds and precipitation as separate entities toward a more unified approach to observe clouds and precipitation properties jointly.

Supporting U.S. military operations and homeland security requirements in and around coastal zones, geospatial intelligence must be quickly integrated by tactical commanders to meet mission demands. Increasingly, unclassified commercial imagery and data acquired from conventional aircraft, unmanned aerial vehicles and satellites are being used by military and civilian analysts to populate coastal zone databases. This study assessed the suitability of commercially available satellite images for littoral warfare and homeland security operations. From the study, data that show the probabilities for extracting mandatory shoreline features from various images for maps produced at varying scale is provided. A prioritized list of image resources was also created for use in assisting military and civilian analysts to rapidly collect feature data from commercially available resources.

In this paper, we study the Locally Linear Embedding (LLE) for nonlinear dimensionality reduction of hyperspectral data. We improve the existing LLE in terms of both computational complexity and memory consumption by introducing a spatial neighbourhood window for calculating the k nearest neighbours. The improved LLE can process larger hyperspectral images than the existing LLE and it is also faster. We conducted experiments of endmember extraction to assess the effectiveness of the dimensionality reduction methods. Experimental results show that the improved LLE is better than PCA and the existing LLE in identifying endmembers. It finds more endmembers than PCA and the existing LLE when the Pixel Purity Index (PPI) based endmember extraction method is used. Also, better results are obtained for detection.

This paper investigates the approaches of Saharan dust storm detection with the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and presents an index, thermal-infrared dust index, through quantitative analysis of MODIS data for major dust events over the Atlantic Ocean during year 2004-2006. It is found that aerosol optical thickness (AOT) at 550nm has close relationships with the brightness temperature of MODIS bands 20, 30, 31, and 32. The proposed thermal-infrared dust index matches MODIS AOT very well with a squared correlation coefficient of 0.7646. Case study and statistical analysis suggest the potential to use 1km thermal-infrared dust index data for detecting dust storms and evaluating the potential impacts of regional weather, such as Atlantic hurricanes, and climate in the near future.

Growing degree days (GDD) is a simple temperature-based index of biological development. In this paper we evaluated the potential of using 2003-2005 MODIS-based 8-day and 16-day composites of daytime surface temperature (TS) and enhanced vegetation index (EVI) values at 250 m resolution for mapping GDD. The work was applied to the Canadian Atlantic Maritime Ecozone as a demonstration of the methodology. The work proceeded by establishing an empirical relationship between mean tower-based estimates of TS for the MODIS-acquisition period of 10:30 am-12:00 pm and the daily mean TS calculated from half-hourly emitted infrared/longwave radiation measurements taken from four flux sites in southern commercial forests of Canada. The relationship revealed a strong correlation between variables (r2=98.4%) and was central to the calculation of daily mean TS from MODIS-based estimates of TS. Since seasonally-based estimates of GDD and EVI were strongly correlated (r2=87%), data fusion techniques were applied to enhance the GDD map originally produced at 1 km resolution (from infrared emission band data), to 250 m. In general, the MODIS-derived map of GDD showed a positive constant offset of about 511 degree days from calculated long-term averages (1971 2000) based on temperatures collected at 101 Environment Canada climate stations.

The Planetary Fourier Spectrometer (PFS) onboard the Mars Express (MEX) Mission is a double pendulum interferometer working in two wavelength ranges (1.2 to 5.9 μm and 5.9 to 40 μm). These channels are indeed detected by two separate instruments one on top of the other, with different optical axis alignments. Analysis of in-flight measurements of Mars and Phobos indicates that pre-launch optical alignment measurements do not describe the in-flight pointing characteristics of the two PFS channels. Pre-launch pointing characterization measurements were made with the PFS alone prior to spacecraft integration. It is suspected that disturbances experienced by the PFS during spacecraft integration and especially during launch may have affected its optical alignment. Daily observations of the planet, its limb, and Phobos have been used to retrieve the real pointing directions for the two PFS channels, by applying a method based on the division of the field-of-view cone in 24 angular sectors.

Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.

Invasive plant species are causing severe environmental and ecological impacts. This study utilized airborne hyperspectral image data and digital image processing techniques to map one of the most aggressive weeds, kudzu (Pueraria montana), in western Georgia. Minimum Noise Fraction (MNF) transform followed by Spectral Angle Mapper (SAM) produced the best map results among several other procedures. Validation with field data show that this procedure delivered user's accuracy of 83.02% for kudzu-invaded plots and 95.90% for non-invaded plots, with Producer's accuracy of 73.26% and 82.47%, respectively. Further analysis using a GIS-based CART analysis indicates the importance of elevation in limiting the spatial distribution of kudzu.

Exploiting the inherent redundancy in hyperspectral observations, Principle Component Analysis (PCA) is a simple yet very powerful tool not only for noise filtering and lossy compression, but also for the characterization of sensor noise and other variable artifacts using Earth scene data. Our approach for dependent set PCA of radiance spectra from the Atmospheric Infrared Sounder (AIRS) on NASA Aqua is presented. Aspects of the analyses include 1) estimation of NEDT and comparisons to values derived from on-board blackbodies, 2) estimation of the signal dependence of NEDN, 3) estimation of the spectrally correlated component of NEDT, 4) investigation of non-Gaussian noise behavior, and 5) inspection of individual PCs. The results are generally consistent with results obtained pre-launch and on-orbit using blackbody and space view data. Specific findings include: 1) PCA estimates of AIRS spectrally random and spectrally correlated NEDN compare well with estimates computed from blackbody and space views, 2) the signal dependence of AIRS NEDN is accurately parameterized in terms of scene radiance, 3) examination of the reconstruction error allows non-Gaussian phenomenon such as popping to be characterized, and 4) inspection of the PCs and filtered spectra is a powerful technique for diagnosing variable artifacts in hyperspectral data.

The 3.75-micron and 11-micron channels on the polar orbiting NOAA Advanced Very High Resolution Radiometer (AVHRR) sensors have saturation temperatures of approximately 325 K. They allowed limited successes in estimating the sub-pixel fire temperature and fractional area coverage. The saturation problem associated with the 3.75-micron AVHRR channel greatly limited the ability for such estimates. In order to overcome this problem, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Terra and Aqua spacecrafts have both been equipped with a special fire channel centered at 3.95 microns with a specified saturation temperature of 500 K and a spatial resolution of 1 km. We have analyzed more than 40 sets of Terra and Aqua MODIS fire data acquired over different geographical regions, and found that very few fire pixels have the 3.95-micron fire channel brightness temperatures greater than 450 K. We suggest that the saturation temperature of fire channels near 4 microns for future satellite instruments with pixel sizes of about 1 km should be specified at about 450 K or even slightly lower in order to make the channels more useful for quantitative remote sensing of fires. A dual gain approach should also be considered for future satellite fire channels.

A comparative study between two approaches of subpixel classification, based on fuzzy set theory and statistical learning has been carried out. The fuzzy set classifiers investigated in this study are Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) in supervised modes. Further, Support Vector Machines (SVMs) have been used in this study for density estimation as a statistical learning subpixel classifier and Mean Field (MF) method has been used for easy and efficient learning procedure for the SVM. The three algorithms FCM, PCM and SVMs were evaluated in subpixel classification mode and accuracy assessment has been carried out using Fuzzy Error Matrix (FERM). Test on the two sub sets of LISS-III multi-spectral image from Resourcesat -1, (IRS-P6) satellite, indicates that density estimation based on SVM approach is consistent with different data sets and out performs both FCM as well as PCM approach.

The aerosol properties of urban atmospheric particles have been analyzed for radiometry data obtained using a multi-spectral photometer located at a NASA/AERONET station at Kinki University Campus, Higashi-Osaka, Japan, since 2002. The suspended particulate matter has been simultaneously measured at the same AERONET site since 2004. Our measurements and a clustering analysis reveal the aerosol types over the industrial city of Higashi-Osaka. It is shown that aerosols at Higashi-Osaka can be classified into three categories: (1) ordinary, which represents the background clear atmosphere of Higashi-Osaka, (2) anthropogenic aerosol events, and (2) dust episodes called Kosa, when large amounts of soil dust are transported to Higashi-Osaka from the Chinese mainland on westerly winds, especially in spring. We also found that the linear correlation exists between column aerosol optical thickness and PM2.5 concentration, and the correlation is better within each aerosol cluster than overall.

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different fields, can be used to locate spectral targets by modeling the scene background as either structured (i.e., geometric, with endmembers or basis vectors) or unstructured (i.e., stochastic, with a mean and covariance matrix). In unstructured techniques, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the appropriate choice of a subset of the image to be used as training data based on spatial or spectral characteristics. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub-pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques may increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well understood or documented. This work describes in detail several methods for unstructured background characterization, both new methods and those taken from the literature. A comparison of methods across a common set of targets is presented. Training data are chosen either spatially, spectrally, or both prior to estimation of the background statistics. Evaluation of the methods indicates that consistent improvements over scene-wide statistics can be achieved through such spatial or spectral subsetting of the image data. However, results are not conclusive as to the overall "best" method for all targets in the test set.

The main objective of the study is to estimate snow wetness using advanced synthetic aperture radar alternative polarization single look complex data. A software has been developed for backscattering coefficient image generation and advanced synthetic aperture radar, single look complex data have been processed for backscattering coefficient image generation for dual (HH and VV) polarization data. Incidence angle image was extracted from the advanced synthetic aperture radar header data using interpolation method. These mages were multi-looked 5 times in azimuth and 1 time in range direction. Multi-looked backscattering image was despeckled using Frost filter technique. Advanced synthetic aperture radar backscattering coefficient image has been calibrated and processed into terrain corrected image in Universal Transverse Mercator (UTM), zone 43 north and WGS-84 datum map projection using ERDAS imagine software. In this study, we have used an integral equation method based inversion model for snow wetness estimation. The combined correlation coefficient between measured and estimated snow wetness was observed to be 0.89 at 95% confidence interval at Dhundi observatory and individual correlation coefficient between both measurements were observed as 0.78, 0.68,0.6 and 0.66 for 24th Feb., 27th Feb., 4th March, and 7th March, 2006 respectively at 95% confidence interval and average absolute error 2.52%. The snow wetness range varies from 0-15% by volume.

This paper discusses the methodology of Synthetic Aperture Radar (SAR) data analysis for studying snow porosity and its effect on electric property of snow. ENVISAT- Advanced Synthetic Aperture Radar (ASAR) single polarized, single look complex (SLC) data have been processed for backscattering coefficient image generation. Incidence angle image has been extracted from the ASAR header data using interpolation method. These images were multi-looked 5 times in azimuth and 1 time in range direction. ASAR Backscattering coefficient images have been calibrated. The estimation of snow porosity is optimal at different incidence angles. The effect of snow porosity on snow wetness, snow density and backscattering coefficient is studied in detail. The correlation coefficient between estimated and measured porosity is observed to be 0.88 and absolute error was 0.045.

Present paper proposes an algorithm for hotspot (sub-surface fire) detection in NOAA/AVHRR images in Jharia region of India by employing Principal Component Analysis (PCA) and fusion technique. Proposed technique is very simple to implement and is more adaptive in comparison to thresholding, multi-thresholding and contextual algorithms. The algorithm takes into account the information of AVHRR channels 1, 2, 3, 4 and vegetation indices NDVI and MSAVI for the required purpose. Proposed technique consists of three steps: (1) detection and removal of cloud and water pixels from preprocessed AVHRR image and screening out the noise of channel 3, (2) application of PCA on multichannel information along with vegetation index information of NOAA/AVHRR image to obtain principal components, and (3) fusion of information obtained from principal component 1 and 2 to classify image pixels as hotspots. Image processing techniques are applied to fuse information in first two principal component images and no absolute threshold is incorporated to specify whether particular pixel belongs to hotspot class or not, hence, proposed method is adaptive in nature and works successfully for most of the AVHRR images with average 87.27% detection accuracy and 0.201% false alarm rate while comparing with ground truth points in Jharia region of India.

In the domain of modern space-borne remote sensing in optical band, the operation mode of multiple-element or linear sensor with plane mirror's two-dimensional (2-D) scanning is popular and has been applied in some real applications successfully. Three basic image rotation models of different 2-D plane mirror scanning modes are analyzed in mathematic detail, and their performances are also compared in the aspects of scanning coverage, observing efficiency and degree of image rotation respectively. It is shown that two of them are feasible for real applications and have some advantages with each other. On the other hand, some raw observed sub-image series of Indian National Satellite (INSAT) Charge Coupled Device (CCD) imager, whose plane mirror uses one of the modes to complete the full-disc view of the Earth, are used to validate the authenticity of the image rotation models above. It is shown that, the accuracy of image positioning (about 2~3 pixels) is some better than that (about 5 pixels) achieved by the investigators who cared the launch and the first accuracy assessment of this satellite. Moreover, both the image rotation model based on the plane mirror's 2-D scanning and the corresponding image derotation method are useful for end-user who wants to utilize the images quantitatively on ground.

This paper presents several methods for change detection in a pair of multi-temporal synthetic aperture radar (SAR) images of the same scene. Several techniques which vary in complexity were implemented and compared. Among the simple methods that were implemented are differencing, Euclidean distance, and image mean ratioing. These methods require minimal processing time, with little computational complexity, and incorporate no statistical information. These methods have demonstrated some degree of accuracy in detecting changes in SAR imagery. However, the presence of highly correlated speckle noise, misregistration errors, and nonlinear variations in SAR images motivated us to seek more sophisticated methods of change detection in order to obtain more favorable results. Therefore, methods were implemented which incorporated second order statistic calculations in making a change decision in efforts to mitigate false alarms arising from the aforementioned causes. Pre-whitened the data was created and then a Wiener prediction-based method, Euclidean distance measure and subspace projection method was implemented. The performance of these methods were compared using multi-look SAR images containing several targets (mines). The results are presented in the form of receiver operating characteristics (ROC) curves.

This study adapts a variety of multi-spectral image classification techniques to generate supervised object detection algorithms for hyperspectral imagery, and compares and quantitatively tests them against the Adaptive Cosine Estimator (ACE) and the standard, matched filter (MF). A new search algorithm, Regularized Maximum Likelihood Clustering (RMLC), uses only pixels for the covariance matrix (CV) computation associated with the object after "regularizing" the matrix to avoid singularities and mitigate statistical degradation due to undersampling for small objects. The searches are applied to both visible/near IR and short wave IR data collected from forested areas. This study tests the detection sensitivity by using object signatures and CVs taken directly from the scene and from temporally transformed signatures and object CVs. This study adds simple, high performing algorithms to the small object search arsenal.

The Ecuadorian Andes feature a hotspot of biodiversity. At the same time Ecuador has the highest deforestation rate of South America. In order to develop sustainable counteractive measures, it is important to locate and quantify the ongoing changes on a regional scale. In this study we present a cost-effective remote sensing based change detection algorithm suitable for application in a tropical mountain rainforest landscape. We show that the deforestation in the study area is closely linked to the existence of roads but topography also influences the spatial distribution of land cover change.

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) is an imaging infrared spectrometer designed for atmospheric soundings. It measures the infrared spectrum in two spectral bands (14.6 to 8.8 μm, 6.0 to 4.4 μm) using two 128×128 detector arrays with an unapodized spectral resolution of 0.57 cm-1 (i.e., with a maximum optical path difference of ~0.88 cm) and a full spectral resolution scan duration of ~11 seconds. From a geosynchronous orbit, the instrument will have the capability of taking successive measurements of different geographical areas to scan any desired region of the globe, from which atmospheric temperature and moisture soundings, cloud and surface parameters, wind profiles, as well as other derived products can be retrieved. The GIFTS EDU provides a flexible and accurate testbed for the new challenges of the emerging hyperspectral era. The EDU ground-based measurement experiment, held in Logan, Utah during September 2006, demonstrated extensive capabilities and the potential for geosynchronous satellite and other platform Earth observing environmental measurements. This paper addresses the ground-based experiment objectives and provides "first-look" results illustrating the overall performance of the sensor system with a focus on the GIFTS EDU imaging capability and proof of the GIFTS measurement concept.

In this paper, a simple surface dryness index (Vegetation Condition Albedo Drought Index, VCADI) based on the spectral patterns of surface moisture in two dimensional spectral space of vegetation index versus broadband albedo is suggested. VCADI derived from multi-sources remote sensing data including the Thematic Mapper (TM), the Enhanced Thematic Mapper Plus (ETM+) and the MODerate Resolution Imaging Spectroradiometer (MODIS) images are significantly related to field measured soil moisture over different eco-systems. Spatio-temporal patterns of VCADI are further analyzed using time series of MODIS data over Ningxia Huizu Autonomous Region of China. Results indicate that VCADI variations are accordant with regional rainfall dynamics and the index has a potential in drought estimation as a simple satellite derived method completely independent of surface ancillary data.

Improved accuracy in the retrieval of crop biophysical variables is needed to enable a greater contribution of hyperspectral remote sensing data to site-specific crop management. One season of maize and two seasons of wheat field experiments were used to explore the potential of multivariate data analysis for retrieving crop biophysical variables from spectroscopic data. Canopy spectral data at 350-2500 nm were collected during the experiments, in which various seeding densities, fertilization, and irrigation treatments were applied to generate dry biomass, water-content and nitrogen-content variability. Partial least squares (PLS) models that considered the reflectance derivatives (1st and 2nd) and that used only significant wavelengths were evaluated. As the measurements were conducted throughout the whole season, a wide variability was observed, which was critical for obtaining a good calibration model. The use of derivatives and selection of the most significant wavelengths were found to be the best pre-processing methodologies to increase prediction accuracy. Significant predictive power was achieved for the validation dataset, especially for the wheat dry biomass (R2 ~0.75), for which similar results were obtained, even with data from a different season (R2 ~0.70). PLS-predicted wheat water content had a correlation of R2 ~0.60 with the measured values. An advantage was found in the use of PLS models, compared to common vegetation indices. Based on ground spectral measurements, this study confirms the potential of multivariate-data-analysis procedures for the interpretation of hyperspectral remote sensing data.

The MODerate Resolution Imaging Spectroradiometer (MODIS) is a key instrument for the NASA Earth Observing System (EOS) mission. It was successfully launched onboard the Terra satellite in December 1999 and Aqua satellite in May 2002. MODIS senses the Earth's surface in thirty-six spectral bands which are distributed on four Focal Plane Assemblies (FPAs): Visible (VIS), Near-Infrared (NIR), Short-and Middle-wavelength IR (SMIR), and Long-wavelength IR (LWIR). It was found from sensor pre-launch measurements that Aqua MODIS SMIR/LWIR FPAs had a large misalignment or misregistration relative to the VIS/NIR FPAs in both along-scan and along-track directions. The misregistration of the two FPA groups has remained nearly the same during its on-orbit operation. Consequently this has been a major concern for Aqua MODIS performance since it could affect the quality of MODIS products which utilize bands from both the VIS/NIR and SMIR/LWIR FPAs, for example, the snow index. This paper focuses on investigating the impact of Aqua MODIS FPA-to-FPA or band-to-band misregistration on its snow index (NDSI) derived from measurements made by VIS band 4 and SWIR band 7. Preliminary results show that shifting one pixel (500 m) forward in the along-track direction of band 7 can improve the band-to-band registration between bands 4 and 7 and, therefore, the quality of Aqua MODIS snow mapping. This study will help MODIS data users to understand the potential impact of band-to-band misregistration on MODIS science products, and also be useful for the future sensor design.

This paper deals with the simulations of scattering by the sea surface using Two- Scale Model (TSM) and the realistic unified sea spectrum in various bistatic configurations. After a brief theoretical presentation, our numerical results (scattering matrix coefficients) are compared with measured data, with a semi-empirical model (CMOD5) and with other analytical models (Small Slope Approximation) in backscattering configuration. Then, we study the influence of sea parameters (wind speed and wind direction) upon the co- and cross-polarization signature in various bistatic configurations. Finally, we present the simulation of the signal received by an observer above the sea when a plane incident wave impinges on the ocean surface.

The goal of this research was to explore the intra-annual water quality variations using airborne hyperspectral remote sensing data in two lakes of Iowa, USA. Hyperspectral images were collected using the Airborne Imaging Spectroradiometer for Applications (AISA) on five dates from June through October 2004. Water samples and location information using Global Positioning System (GPS) were also collected nearly simultaneously with the hyperspectral images for these lakes. By analyzing relationships between principal components of reflectance at specific wavelength intervals and water quality data from on site sampling, prediction algorithms were developed for three major water quality constituents, chlorophyll a (Chl), turbidity and Secchi disk depth (SDD). The prediction algorithms were applied to the hyperspectral images to create spatially continuous water quality maps for all five months. Maps of Chl, turbidity and SDD were created for both lakes with an accuracy of R2 = 0.8911, 0.9373 and 0.8551 respectively. Using this data, it was found that Silver Lake was hypereutrophic on June 3, July 8 and July 31 and was borderline eutrophic/hypereutrophic on September 2 and October 5. Casey Lake was eutrophic on June 3, July 8 and July 31 and was borderline eutrophic/hypereutrophic on September 2 and October 5.

Urban forest is of great interest to a variety of scientific and urban planning applications. This paper presents a strategy for monitoring urban forest using Landsat TM/ETM+ (Thematic Mapper / Enhanced Thematic Mapper Plus) imagery time series and calculating its ecological benefits. And the strategy is applied to the temporal analysis of the zone inside the 4th Ring Road in Beijing. The analysis consists of two key steps in: the first is to extract urban forest from Landsat images; the second is to calculate the ecological benefits of urban forest. The extraction of urban forest from Landsat imagery is accomplished implementing classification models, which are based on empirical relationships between forest coverage and the spectrum on Landsat imagery, and are generated using regression tree techniques. Quickbird images and field investigations are applied to generate classification models and to assess their accuracies. Subsequently, the ecological benefits calculation about urban forest is carried out introducing CITYgreen model. This paper mainly concerns carbon storage and the function in air pollution reduction. In the following part, the results of the analysis are presented, as well as the figures that illustrate their variations. At the end, the advantages and disadvantages of this strategy are discussed.

Surface radiative fluxes and their diurnal cycles are very important components in the surface energy budget of the Arctic, and also critical forcing variables for land surface models. An approach has been developed to derive diurnal cycles of the surface radiative fluxes for the Arctic from 6-hourly accumulated surface downward shortwave and longwave radiative fluxes in the European Center for Medium Range Weather Forecasts (ECMWF) 40- year reanalysis (ERA-40) products by "correcting" interpolated hourly values with twice-daily satellite-derived radiative fluxes. Ground-based measurements of the surface radiative fluxes at an Arctic meteorological station are used to validate the approach. Results show that the blended satellite-model products are in good agreement with the ground-based measurements. Based on this blended product, diurnal cycles of surface radiative fluxes are examined over time and space. It clearly shows that satellite-retrieved products in high spatial and temporal resolutions are more realistic and accurate, and can be used to adjust or "correct" the reanalysis products, especially for the polar region where there exist few ground based measurements for diurnal cycle studies.

Airborne remote sensing could play a more integrative role in regional ecosystem studies if the information derived from airborne observations could be readily converted to physical and chemical quantities representative of ecosystem processes and properties. We have undertaken an effort to specify, deploy, and apply a new system - the Carnegie Airborne Observatory (CAO) - to remotely measure a suite of ecosystem structural and biochemical properties in a way that can rapidly advance regional ecological research for conservation, management and resource policy development. The CAO "Alpha System" provides in-flight fusion of high-fidelity visible/near-infrared imaging spectrometer data with scanning, waveform light detection and ranging (wLiDAR) data, along with an integrated navigation and data processing approach, that results in geo-orthorectified products for vegetation structure, biochemistry, and physiology as well as the underlying topography. Here we present the scientific rationale for developing the system, and provide sample data fusion results demonstrating the potential breakthroughs that hybrid hyperspectral-wLiDAR systems might bring to the scientific community.

The vertical extent of waveforms collected by the Geoscience Laser Altimeter System (onboard ICESat - the Ice, Cloud, and land Elevation Satellite) increases as a function of terrain slope and footprint size (the area on the ground that is illuminated by the laser). Over sloped terrain, returns from both canopy and ground surfaces can occur at the same elevation. As a result, the height of the waveform (waveform extent) is insufficient to make estimates of tree height on sloped terrain, and algorithms are needed that are capable of retrieving information about terrain slope from the waveform itself. Early work on this problem used a combination of waveform height indices and slope indices from a digital elevation model (DEM). A second generation algorithm was developed using datasets from diverse forests in which forest canopy height has been estimated in the field or by via airborne lidar. Forest types considered in this paper include evergreen needleleaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil. The algorithm described eliminates the need for a DEM, and estimates forest canopy height with an RMSE of 5 m (83% of variance in forest canopy height explained).

This paper describes and illustrates frameworks for societal benefits associated with data products from the Multi-angle Imaging SpectroRadiometer (MISR), one of five instruments launched into polar orbit aboard NASA's Terra spacecraft in December 1999. The objective of framing and illustrating benefits is impelled by the January, 2007, U.S. National Research Council's seminal decadal survey of U.S. Earth science. The survey urges that all missions explicitly identify potential societal benefits in mission planning and operation. In this paper, our retrospective look at MISR offers approaches which mission planning for future instruments could operationalize. "Societal benefit" generally refers to practical applications of data and data products beyond their intrinsic science merit. The paper demonstrates how societal benefit frameworks work, highlights some of these benefits in the case of MISR, and seeks to provide useful guidance for benefit descriptions in future multi-angle and other Earth observation research programs.

This paper describes a procedure for mapping long-term average, growing season-accumulated growing degree days at an enhanced spatial resolution of 28.5 m. GDD-product enhancement is based on augmenting a previously developed 1 km resolution map of GDD described in Hassan et al. [J. Applied Remote Sens., 1, 013511, 12p (2007)] using data from a series of scene- and date-specific Landsat-7 ETM+ images (at 28.5 m resolution) from the 1999-2002 data collection period and a chronological series of standard MODIS 16-day composites of enhanced vegetation index (EVI; at 250 m resolution) spanning the 2003-2005 growing periods (April-October). Surface reflectances from the Landsat-7 ETM+ images are used to derive fine-scale estimates of EVI, which are then transformed into long-term averages by taking into account growing-season specific, temporal trends in the series of MODIS-EVI images. As values from the 8-day accumulated GDD and 16-day composites of EVI have been shown to be strongly correlated, a new data-fusion method based on the mean and instantaneous values of fine-grain long-term average EVI is used to augment the resolution of the initial GDD map. As a demonstration, we apply the procedure to satellite and climate station data for the Canadian Province of Nova Scotia.

Stratospheric ozone column data was acquired during four recent aircraft-based validation missions for the Aura satellite flown in years 2004-2006. The data was retrieved by the spectrally-resolved actinic flux measurements of the charge-coupled-device scanning actinic flux spectroradiometer (CAFS) instrument carried on board the NASA WB-57 and DC-8 aircrafts. Each dataset contains information on temporal and spatial variability in the stratospheric ozone column. Analyses of the CAFS datasets provide guidance for assimilation of data from individual satellite orbits into the global maps of stratospheric ozone. Moreover, the 10-second samplings of the CAFS data supply information on spatial variability of stratospheric ozone column across the footprint of a satellite measurement. The CAFS data is available as a function of altitude and geo location of the aircraft. This paper describes the algorithm for the retrieval of an ozone column above the aircraft level, along with validation of the CAFS retrieved ozone product. A discussion of the retrieval uncertainty is provided with emphasis on the algorithm's assumptions and instrumental uncertainties. Sensitivity of the ozone retrieval to fundamental atmospheric parameters is discussed in detail, and the range of uncertainties is estimated under a variety of observational conditions. The characteristic model uncertainty of the CAFS partial ozone column retrieval is better than 3%, whereas the CAFS measurement precision contributes less than 1% to the retrieval uncertainty.

In this paper, we evaluate the possibility of a wireless sensor network concept operating in the oceanic environment. A population of underwater sensor nodes, termed "optical plankton," form a distributed sensor network characterized by the deployment of many miniature and low-cost sensors capable of profiling the particulate composition in the immediate marine environs. The stimulation of backscatter signals from the surrounding medium is one principle of operation studied, but the potential for additional probing methods is discussed. The optically probed signal is communicated to a base station, where the large number of received signals is fused to obtain an accurate estimate of the nature of the local aqueous medium. The many similarities and differences between the atmospheric and oceanic sensing and wireless communication environments are discussed and the distinctive features of an oceanic probing system are underlined. Specific scientific applications are briefly reviewed. The challenges to be met are addressed in general and a focused analysis of the specific issue of multi-access interference (MAI), common to all optical wireless-based sensor networks, is presented. Novel analytical approaches have been employed to evaluate and quantify the MAI. It is the object of this paper to assess the feasibility of a novel miniaturized oceanic probing system and explore some of the challenges. It would appear that the proposed scheme could be the basis of an innovative oceanic probing method and fill a niche not as yet catered for with existing technologies.

Water strongly absorbs mid-infrared (1300-2500 nm) radiation, resulting in this region of the spectrum being sensitive to the water content within features. Little information is available on using an electronic digital camera filtered to this region of the spectrum to assess natural resources. The objectives of this study were to assemble an electronic digital camera system obtaining mid-infrared imagery and to show practical applications of the system for assessing natural resources. The system consists of a near-infrared to mid-infrared light sensitive camera (900-1700 nm, 14-bit radiometric resolution) containing a 640 by 512 indium gallium arsenide area array, a computer, an image acquisition card, a camera-link cable, a keyboard, a color monitor, a camera tripod, a generator, a camera mount, and a mid-infrared narrow bandpass filter (1632-1648 nm). The components of the system allow users to obtain ground or airborne imagery. Ground-based imagery and data were useful for separating succulent from nonsucculent plant parts. Airborne imagery provided useful information for differentiating vegetation, soil, and water and for identifying management practices such as irrigation. Findings of this study indicate that an electronic digital camera filtered to the mid-infrared region of the optical spectrum has high potential for assessing natural resources.

Classifying remotely sensed images from urban environments is challenging. Urban land cover classes are spectrally heterogeneous and materials from different classes have similar spectral properties. Image segmentation has become a common preprocessing step that helped to overcome such problems. However, little attention has been paid to impacts of segmentation on the data's spectral information content. Here, urban hyperspectral data is spectrally classified using support vector machines (SVM). By training a SVM on pixel information and applying it to the image before segmentation and after segmentation at different levels, the classification framework is maintained and the influence of the spectral generalization during image segmentation hence directly investigated. In addition, a straightforward multi-level approach was performed, which combines information from different levels into one final map. A stratified accuracy assessment by urban structure types is applied. The classification of the unsegmented data achieves an overall accuracy of 88.7%. Accuracy of the segment-based classification is lower and decreases with increasing segment size. Highest accuracies for the different urban structure types are achieved at varying segmentation levels. The accuracy of the multi-level approach is similar to that of unsegmented data but comprises the positive effects of more homogeneous segment-based classifications at different levels in one map.

High-resolution spectroscopy using the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) was applied to the problem of detecting potentially harmful algae blooms in the coastal environment. Data were collected on two aircraft passes, 30-min apart, over the tidally influenced part of the Potomac River. Use of two wavelengths, 0.676 and 0.700 μm, permitted the detection of surface algae accumulations while avoiding the need for atmospheric corrections, which are problematic in Case-2 water. The analysis identified algal accumulations derived from frontal processes, and narrow, linearly coherent streaks, derived from Langmuir circulation. The streaks increased markedly in number between the two passes and formed a two-dimensional pattern across the river, consistent with the advection time of surface material into windrows. The effect of wind on the patches is primarily a local reorganization of the algal material into new streaks. Spectra from within the streaks compared to those from ambient water showed absorption characteristics consistent with the presence of cyanobacteria. This interpretation is reinforced by available in-situ data. This study illustrates the value of high spectral and temporal resolutions in observing the spatial distribution of the algae, in identifying dominant functional groups, and in understanding the response of the algae to physical forcing.

The Moderate Resolution Imaging Spectroradiometer (MODIS) data offers a unique combination of spectral, temporal, and spatial resolution in comparison to other global sensors. The MODIS Enhanced Vegetation Index (EVI) product has several advantages, which make it suitable for regional land cover mapping. This paper investigates the application of MODIS EVI time-series data for mapping temperate arid and semi-arid land cover at a moderate resolution (500 m), for regional land-cover/land-use monitoring purposes. A 16-day composite EVI time-series data for 2003 (22 March 2003 - 30 September 2003) was adopted for the study. A land cover map was generated for the Inner Mongolia Autonomous Region using 7 tiles of MODIS EVI time-series data and Self-Organizing Map (SOM) neural network classification. Land-use GIS data, Landsat TM/ETM, and ASTER data were employed as reference data. The results show that the overall accuracy of land cover classification is about 84% with a Kappa coefficient of 0.8170. These results demonstrate that the SOM neural network model could work well for the multi-temporal MODIS EVI data, and suggest a potential of using MODIS EVI time-series remote sensing data to monitor desertification in Inner Mongolia with limited ancillary data and little labor-input in comparison with using high-spatial resolution remote sensing data.

Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapping waterhyacinth infestations on Lake Corpus Christi in south Texas. Hyperspectral imagery with bands in the visible to near-infrared region of the spectrum was acquired from two study sites and minimum noise fraction (MNF) transformation was used to reduce the spectral dimensionality of the imagery. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the MNF-transformed imagery for distinguishing waterhyacinth from associated plant species (waterlettuce, mixed herbaceous species, and mixed woody species) and other cover types (bare soil and water). Accuracy assessment showed that overall accuracy varied from 79% for SAM to 96% for maximum likelihood for site 1 and from 84% for minimum distance to 95% for maximum likelihood for site 2. Kappa analysis showed that maximum likelihood was significantly better than the other three methods and that there were no significant differences in overall classifications among the other three methods. Producer's and user's accuracies for waterhyacinth based on maximum likelihood were 94% and 100%, respectively, for site 1 and 100% and 95% for site 2. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping waterhyacinth infestations.

A low-altitude remote sensing (LARS) system with an unmanned radio-controlled helicopter platform was used to acquire high-quality images of land and crop properties with higher spatial and temporal resolution. It is vital to visualize the relationship of LARS-based images with crop parameters, such as crop nutrient levels, etc. Five N-treatment (0, 33, 66, 99 and 132 kg ha^-1) rates with three replications each were arranged in a randomized manner for testing the LARS image acquisition system. Images were taken by the image acquisition unit of the system operated at a height of 20 m over the experimental plots. The coefficient of determination (r²) between N-treatments against NDVI_lars, NDVI_spectro, GNDVI_lars, and chlorophyll content estimated from leaf radiance values were in the range from 0.70 to 0.90, showing a high level of correlation between them. The test to verify the suitability of LARS-based images against spectrophotometer readings showed linear variation for the NDVI index with r² of 0.70 and 0.80 for 45-day-old and 65-day-old crops, respectively, Linear models were also developed to estimate chlorophyll content from NDVIlars and GNDVIlars index values, from the images, with better correlation for the latter (r² ≈ 0.82) and subsequently could determine the nitrogen deficiency level. The yield estimation model, with higher r² values of 0.95 and 0.98 for NDVI_lars and GNDVI_lars, respectively, further justified the suitability of the LARS system.

Natural resource managers have an interest in locating red harvester ant (Pogonomyrex barbatus) mounds because of the negative impact that heavy infestations have on pastures and because of the importance of the ants to the survival of the threatened Texas horned lizard (Phrynosoma cornutum). We evaluated panchromatic QuickBird imagery (450-900 nm; 0.6 m spatial resolution) subjected to computer classification as a tool for detecting red harvester ant mounds. The study focused on two sites located at the Welder Wildlife Refuge (28° 07' 21" N, 97° 21' 51" W). User's accuracy and producer's accuracy of the thematic maps were greater than or equal to 94.0% for the ant mound class, indicating that it is possible to use thematic maps generated from panchromatic QuickBird imagery and computer classification to detect red harvester ant mounds. Natural resource managers can use this imagery to determine the severity of infestations, which should lead to better management decisions.

This PDF file contains the editorial “Editorial: Meeting the challenges in remote sensing science and technology” for JRS Vol. 1 Issue 01