Critical assessment of vegetation indices from AVHRR in a semi-arid environment

The most frequently used vegetation index (VI), the Normalized Difference Vegetation Index (NDVI) and its variants introduced recently to correct for atmospheric and soil optical response such as Global Environment Monitoring Index (GEMI) and Modified Soil-Adjusted Vegetation Index (MSAVI) are evaluated over a Sahelian region. The usefulness and limitations of the various vegetation indices are discussed, with special attention to cloud contamination and green vegetation detection from space. The HAPEX Sahel database is used as a test case to compare these indices in arid and semi-arid environments. Selected sites are characterized by sparse vegetation cover and day-to-day variability in atmospheric composition. Simulated indices values behaviour at the surface level shows that these VIs were all sensitive to the presence of green vegetation but were affected differently by changes in soil colour and brightness. We showed that GEMI is less sensitive to atmospheric variations than both NDVI and MSAVI since it exhibits a high atmospheric transmissivity over its entire range for various atmospheric aerosol loadings and water vapour contents. These results were first tested on a vegetation gradient, and secondly evaluated on a transect which encompasses various soils formations. On the vegetation gradient, it was found that GEMI computed from measurements at the top of the atmosphere is invariable from one day to the next. On the bare soils transect, MSAVI calculated at the surface level, has shown a great insensitivity to soil optical responses modifications, while GEMI exhibits from space noticeable variability in this bright soil context. Finally, it was illustrated that GEMI exhibits interesting properties for cloud detection because of the strong decrease of its value on cloudy pixels.     

Temporal dynamics of soil and vegetation spectral responses in a semi-arid environment

This paper discusses several difficulties encountered in detecting and monitoring temporal changes in vegetation using multispectral imagery from airborne or spaceborne sensors. These difficulties are due to (1) temporal change in the vegetation state; (2) temporal change in the soil/rock signature; and (3) difficulty in discriminating vegetation from soil or rock background. The seasonal dynamics of soil and vegetation was investigated over two years on permanent sample plots in a natural fenced-off area in the semi-arid region (200 mm annual average rainfall) of the Northern Negev, Israel. Results show that temporal analysis of natural vegetation in semi-arid regions should take into account three ground features--perennials, annuals, and biological soil crusts; all having phenological cycles with the same basic elements--oscillation from null (or low) to full photosynthetic status. However, these cycles occur in successive periods throughout the year. The phenological cycle of perennial plants is related to the adaptation of desert plants to scarcity of water. Annuals are green only for a relatively short period during the wet season and turn into dry organic matter during the summer. The microphytic communities (lower plants) of the biological soil crusts are rapidly affected by moisture and turn green immediately after the first rain, in a timescale of minutes. In arid environments, where the higher plants are sparse, this type of plant has considerable importance in the overall production of the greenness signal. However, crust-covered areas are visually similar to bare soil throughout the dry period. This paper concludes that a priori knowledge of the phenological changes in desert plants (lower and higher) is valuable in the interpretation of remote sensing data of arid environments. It is shown that rainfall amount and regime are the keys for understanding the dynamic processes of the different ground features. Through polynomial fitting, simple functions describing the annual variations in the NDVI of the different cover types have been formulated and validated; showing the feasibility and viability of modelling the processes. Although fluctuations in the rainfall regime between years poses a problem to designing a unique model, it is believed that such a problem can be overcome with long-term observations.     

Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina

We used multiple regression analysis to relate evapotranspiration (ET), computed from a water balance technique, to both thermal infrared and normalized difference vegetation index data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board on the National Oceanic and Atmospheric Administration (NOAA) satellite. This approach, based on only remotely sensed data, provided a reliable estimate of ET over the Pampas, the main agricultural region of Argentina. The relationship between spectral data and ET was more sensitive to the dates than to the sites used to generate the models.     

Spatial and temporal patterns in Arctic river ice breakup observed with MODIS and AVHRR time series

The timing of spring river-ice breakup, a major annual event for physical, biological, and human systems on Arctic rivers, has been used to infer regional climate variations over the past century or more. Most observations of ice breakup are recorded as point data taken from selected ground-based stations. It is unknown whether these point observations are fully representative of breakup patterns elsewhere along the course of a river. Here, daily time series of moderate resolution imaging spectroradiometer (MODIS) and advanced very high resolution radiometer (AVHRR) satellite images are used to remotely sense spatial and temporal patterns in ice breakup along 1600-3300 km lengths of the Lena, Ob', Yenisey, and Mackenzie Rivers. The first day of predominantly ice-free water is visually identified and mapped for ten years (1992-1993, 1995-1998, and 2000-2003), with a mean precision of ±1.75 days. The derived breakup dates show high correlation with ground-based observations, although a slight trend towards earlier satellite-derived dates can be traced to differences in the way ice breakup date is defined. Large ice jams are often observed, particularly at confluences, although smaller ice jams may not be visible due to the limited spatial resolution of the imagery used. At the watershed scale, spatial patterns in breakup seem to be primarily governed by latitude, timing of the spring flood wave, and location of confluences with major tributaries. Interestingly, channel-scale factors such as slope, width, and radius of curvature, which are known to influence ice breakup at the reach scale, do not appear to be major factors at the scale observed here. The degree of similarity between interannual trends in breakup date at distant points along a river is generally high, which supports the use of point-scale data to infer regional climate variations. This similarity does not hold true for the Mackenzie River, where substantial spatial differences in breakup trends are observed. A new variable, spatially integrated breakup date (d_i), uses weighted spatial averaging to provide a more encompassing measure of breakup timing. The Ob' and Yenisey Rivers show similar trends in spatially integrated breakup date from year to year. In contrast, the Mackenzie and Lena show a remarkably consistent negative correlation, here attributed to sea surface temperature anomalies associated with the Pacific Decadal Oscillation Index.     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns

We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a ∼400,000 ha region of Michigan's Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird, were known to be associated with forest understory features during breeding. We examined the influences of varying two spatially explicit classification parameters on prediction accuracy: 1) the window size used to average spectral values in signature creation and 2) the threshold distance required for bird detections to be counted as present. Two accuracy measurements, proportion correctly classified (PCC) and Kappa, of maps predicting species' occurrences were calculated with ground data not used during classification. Maps were validated for all three species with Kappa values > 0.3 and PCC > 0.6. However, PCC provided little information other than a summary of sample plot frequencies used to classify species' presence and absence. Comparisons with rule-based maps created using the approach of Gap Analysis showed that spectral information predicted the occurrence of these species that use forest subcanopy components better than could be done using known land cover associations (Kappa values 0.1 to 0.3 higher than Gap Analysis maps). Accuracy statistics for each species were affected in different ways by the detection distance of point count surveys used to stratify plots into presence and absence classes. Moderate-to-large detection distances (100 m and 180 m) best classified maps of Black-throated Green Warbler and Nashville Warbler occurrences, while moderate detection distances (50 m and 100 m), which ignored remote observations, provided the best source of information for classification of Ovenbird occurrence. Window sizes used in signature creation also influenced accuracy statistics but to a lesser extent. Highest Kappa values of majority maps were typically obtained using moderate window sizes of 9 to 13 pixels (0.8 to 1.2 ha), which are representative of the study species territory sizes. The accuracy of wildlife occurrence maps classified from spectral data will therefore differ given the species of interest, the spatial precision of occurrence records used as ground references and the number of pixels included in spectral signatures. For these reasons, a quantitative examination is warranted to determine how subjective decisions made during image classifications affect prediction accuracies.     

A new approach for the detection of major fires caused by industrial accidents,using NOAA/AVHRR imagery

The Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites may be used to detect major fires caused by industrial accidents through the combined use of middle (3.55-3.93 mum) and thermal infrared (10.5-11.3 mum, 11.5-12.5 mum) channels. An algorithm was developed to identify the pixels which correspond to the sites of the industrial accidents where a major fire had developed. The algorithm was applied for two industrial accidents; Lyon, France on 2 June 1987 and Kalohori, Greece on 24 February 1986. The algorithm was based on the analysis of the differences between the brightness temperatures resulting from Channels 3 (3.55-3.93 mum) and 4 (10.5-11.3 mum) of AVHRR and of two masking filters for clouds. The first filter takes advantage of the information provided by Channels 1 (0.58-0.68 mum) and 5 (11.5-12.5 mum) of AVHRR, whereas the second introduces a threshold value for cloud top temperature. The use of the algorithm for the selected industrial accidents demonstrates its capability to detect major fires.     

Weathering process effects on spectral reflectance of rocks in a semi-arid environment

Spectral properties of rocks are mainly dependent on their mineralogical composition, which produces characteristic absorption features in different wavelength regions. This can be considered as a tool to recognise and discriminate different lithological units of an area by remotely-sensed data. Nevertheless, physical and chemical natural processes produce changes that modify to a considerable extent the mineralogical composition of the rock surface (weathered surface) which mask some of the spectral properties of the original surface (fresh surface). In the present study, various rock types (gypsum, carbonate, sandstone, lamproites, phyllite, and quartzite) were selected from a semi-arid region (SE Spain), pilot zone for MEDALUS Project, and their bidirectional reflectance factors were measured under laboratory conditions over the spectral region between 400 and 2500 nm. The study reveals that reflectance differences between the fresh and weathered surfaces (in brightness and presence of characteristic absorption features) are highly significant in that spectral region, being the effect introduced by the iron oxides the most important.     

The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models

Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.     

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m² nested in macroplots of 10,000 m². Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image.Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r = 0.69) while coarse resolution images showed contrasting results (r = 0.43, r = 0.67, and r = 0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity.Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one.     

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery.The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.     

A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

We analyzed hyperspectral airborne imagery (CASI 2 with 46 contiguous VIS/NIR bands) that was acquired over a Lake Huron coastal wetland. To support detailed Great Lakes coastal wetland mapping, the optimal spatial resolution of imagery was determined to be less than 2 m. There was a 23% change in classification resiliency using the SAM classifier upon resampling the original 1-meter, 18-band imagery to 2-meter pixels, and further classifications with larger pixels (4 and 8 m) increased overall classification change to 35% and 50%, respectively.We performed a series of image classification experiments incorporating three independent band selection methodologies (derivative magnitude, fixed interval and derivative histogram), in order to explore the effects of spectral resampling on classification resiliency. This research verified that a minimum of seven, strategically located bands in the VIS-NIR wavelength region (425.4 nm, 514.9 nm, 560.1 nm, 685.5 nm, 731.5 nm, 812.3 nm and 916.7 nm) are necessary to maintain a classification resiliency above the 85% threshold. Significantly, these seven bands produced the highest classification resiliency using the fewest number of bands of any of the 63 band-reduction strategies that were tested.Analyzing only derivative magnitudes proved to be an unreliable tool to identify optimal bands. The fixed interval method was adversely influenced by the starting band location, making its implementation problematic. The combined use of derivative magnitude and frequency of occurrence appears to be the best method to determine the “optimal” bands for a wetland mapping hyperspectral application.     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Atmospheric optical depth and water vapour effects on the angular characteristics of surface reflectance in NOAA AVHRR

The effects of atmospheric optical depth and water vapour content on the bidirectional surface reflectance in channel 1 (visible) and channel 2 (near-infrared) of NOAA AVHRR have been analysed using a coupled surface atmosphere reflectance model. Two different cases of surface: (i) bare soil, and (ii) vegetation cover have been considered. In the case of bare soil, both the amplitude and angular distribution of the bidirectional reflectance of the surface are modified at satellite altitude due to scattering caused by atmospheric molecules and aerosols in the two channels and thereby, the directional properties of the surface are smoothed. Whereas, in the case of lawn, in channel 1, the angular variation of surface reflectance is enlarged together with a large augmentation in reflectance amplitude, and in channel 2, a small reduction in amplitude as well as a variation in angular distribution of reflectances are caused due to scattering particularly over large viewing angles and thereby, the directional variations are smoothed. In channel 1, atmospheric scattering reduces the contrast between the soil and vegetation and is very much significant for medium to high aerosol loadings. Atmospheric water vapour reduces the amplitude of the surface bidirectional reflectance without introducing any significant changes in angular distribution of the surface reflectance for both bare soil and vegetation canopy in channel 2.     

Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece

Satellite data can significantly contribute to agricultural monitoring. The reflected radiation, as recorded by satellite sensors, provides an indication of the type, density and condition of canopy. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI) derived from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data provided in high temporal resolution. An extension of the NDVI is the Vegetation Condition Index (VCI). VCI is a tool for monitoring agrometeorological conditions, providing a quantitative estimation of weather impact to vegetation. The primary objective of this paper is the quantitative assessment of the cotton yield before the end of the growing season by examining the weather effects as they are depicted by the VCI. The study area comprises several cotton producing areas in Greece. Ten-day NDVI maximum value composites (MVC) are initially utilized for the period 1982–1999. The correlation between VCI images as extracted from NDVI and the 10-day intervals during the growing season is examined to identify the critical periods associated mostly with the yield. Empirical relationships between VCI and yield are developed. The models are tested on an independent dataset. The results show that an early estimation of the cotton yield trend is feasible by the use of the VCI.     

Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

Cloud top phase determination from the fusion of signatures in daytime AVHRR imagery and HIRS data

An improved methodology for the retrieval of water vapour profiles from DMSP SSM /T-2 microwave sounder data has been demonstrated using cloud-top temperatures derived from NOAA AVHRR imagery as a constraint. However, the automated analysis of cloud-top temperature in AVHRR imagery is complicated by the presence of optically-thin cirrus clouds, since a component of the upwelling radiation from below passes unatttenuated to space. Therefore, cloud-top phase must first be determined to ensure the accurate specification of cloud-top temperature. In this paper, a new approach is presented for the specification of cloud-top phase in an operational environment. The methodology combines results from bi-spectral cloud tests for ice and water clouds in daytime AVHRR imagery with cloud-top pressure analyses based upon the CO2 slicing of HIRS data. The accuracy of the automated cloud-top phase analyses is measured quantitatively against manual analyses of the AVHRR imagery. It is concluded that the fusion of cloud signatures in AVHRR imagery and HIRS data improves the specification of cloud-top phase in the higher resolution imagery and reduces the ambiguity inherent in analyses based solely upon bi-spectral techniques.     

Classification of sea ice types in the Ross Sea,Antarctica from SAR and AVHRR imagery

ERS-2 synthetic aperture radar (SAR) and Advanced Very High Resolution Radiometer (AVHRR) imagery are used to examine spectral characteristics of late winter/early spring ice in the Ross Sea, Antarctica. The combined spectral signatures are used to distinguish six ice types: fast ice, new ice, smooth first year ice, rough first year ice, thin new ice/wind roughened open water and glacial ice. The procedure firstly involves 'picking' class boundaries from SAR imagery based on the morphology of a speckle reduced backscatter spectrum. These class boundaries are then used as input to an iterative segmentation procedure that involves the repeated application of a speckle reduction filter to the image. For an image from late September 1996 the segmentation procedure enabled separation of five general ice categories each with a characteristic backscatter range. However because of the combined contributions of ice thickness, surface roughness, salinity and water content to the SAR backscatter, further decision criteria are required to separate some physical ice types unable to be resolved individually using this method. Coincident and co-registered infrared data from the AVHRR sensor are used to extract spectral characteristics for the final ice classes. Using this procedure we were able to distinguish floating glacier ice from thin new ice/wind roughened open water and new ice from nearshore fast ice. These ice types were unable to be separated using SAR backscatter intensity alone. In addition image subtraction was also able to clearly delineate areas of shore fast ice.     

Application of spectral features’ ratios for improving classification in partially calibrated hyperspectral imagery: a case study of separating Mediterranean vegetation species

Efficient real-time discrimination of image objects is greatly affected by their radiometry, which is only partly accounted for by image scene calibration. Such calibration treats mainly variations in flux density in the generalized imaged scene plane rather than on the objects’ surface. The proposed methodology uses ratios between secondary parameterizations: e.g., absorption features and spectral derivatives. Clustering in the ratios’ parameter space may allow differentiation between image objects despite limitations regarding their relative calibration. The usefulness of this approach was demonstrated in the challenging task of separating Mediterranean vegetation species using imaging spectroscopy.     

Variability of the Columbia River plume observed in visible and infrared satellite imagery

Abstract

Variability of the Columbia River plume in coastal waters off the northwestern United States, 1979-1985, was observed in sea surface temperature and phytopiankton pigment images derived from Advanced Very High Resolution Radiometer and Coastal Zone Colour Scanner data. The orientation, shape, intensity and relative temperature of the plume vary in response to coastal winds and wind-driven surface currents. From October to April, plume water is oriented northward along the coast. Following the spring transition in April or May, the plume is oriented southward, either adjacent to the coast or offshore. Transition between the winter and summer forms can be observed in the satellite imagery. Brief reversals of the prevailing seasonal winds cause rapid changes in the orientation and shape of the plume. Remote sensing of the Columbia River plume offers valuable information for oceanographic studies and fisheries management in the region. Derivation of an appropriate visible-infrared signature for plume waters and tracking of tidal pulses in the plume is suggested as a promising direction for future research.