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Acid mine drainage resulting from mine tailings poses an environmental threat. An important initial step towards the reclamation of mine tailing sites is to detect the presence of acid-generating, sulphide-rich minerals and determine their spatial distribution. In this study, the potential of hyperspectral remote sensing for characterizing mine tailings is investigated. The study site is located in northern Ontario, Canada, and the data were collected with PROBE-1, an imaging spectrometer that covers the visible, near-infrared, and shortwave-infrared spectral ranges. The results indicate that using the weakly constrained linear spectral unmixing technique PROBE-1 data can provide information on mineral compositions of the tailing surface. The spatial locations and associations of acid-generating source minerals such as pyrite and pyrrhotite along with their oxidation products (e.g., copiapite, jarosite, ferrihydrite, goethite, and hematite) can provide information about the distribution of oxidation proce...

In recent years, remote-sensing scientists have been developing applications for hyperspectral remote sensing. Analysis techniques, such as linear spectral unmixing, have been used increasingly to solve real-world problems related to vegetation stress detection, mineral prospecting, and environmental monitoring. Information products such as end member fraction maps can be generated by interpreting unmixing results. However, the validity of these maps has not been fully examined. In this paper, two types of constrained linear spectral unmixing techniques are investigated, namely fully constrained and weakly constrained unmixing. Overall, this study revealed that the weakly constrained approach provides more realistic results than the fully constrained technique. From a data-analysis perspective, this study demonstrates that the weakly constrained unmixing can be used successfully when an incomplete list of end members is used in the unmixing, which is almost always the case in reality. In addition, this pa...

The overall goal of this study is to use medium-resolution satellite imagery to determine recent changes in the landscape of the coastal zone near Sanya in the Province of Hainan, China. A search for suitable satellite imagery revealed that the only way to identify the changes was to use data from three different sensors acquired over a 12-year time period: a 1987 Landsat 5 Thematic Mapper (TM) image, a 1999 Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image, and two SPOT 2 High Resolution Visible (HRV) images acquired in 1991 and 1997. Given that the Landsat and SPOT images have different spatial resolutions and that the spectral bands cover somewhat different spectral ranges, the challenge was how to combine the images in digital format to be able to detect subtle changes in the landscape. Measures of brightness, greenness, and the normalized difference vegetation index (NDVI) were explored using standardized principal components analysis (PCA). Approximately 38 percent of the scene was occupied by water, so tests were performed with the water included and also with the water masked out to remove these low-variance pixels. Factor loadings and input-band contributions were used to interpret component images. Results show that PCA of the visible bands, representing brightness, is the superior approach for identifying new urban features in the landscape. For identification of changes to vegetation, the near-infrared (NIR) bands outperformed NDVI. Selected standardized PCA images with visible and NIR bands are recommended for identifying general changes to an urban landscape using a time-series of imagery acquired by different satellite sensors. Benefits of using a mask are believed to be dependent upon study-site characteristics.

Shoreline wetlands in the Great Lakes basin are susceptible to frequent changes in vegetation composition due to fluctuations in climate and water level. Although water-level changes occur naturally and are essential to maintain productivity, the magnitude and rate of these changes can have a significant effect on the wetland ecosystems. From a management and scientific viewpoint, it is important to be able to map and monitor these long-term changes. Using Long Point on Lake Erie as a test site, the goal of this research study is to refine methods for using multiple dates of Landsat imagery to map and monitor wetlands over a relatively long period of time. Landsat images covering the period from 1976 to 1999 are used to produce multitemporal normalized difference vegetation index (NDVI) images. Two change-detection methods, postclassification comparison and multitemporal data clustering, were selected to determine patterns of change in the Long Point wetlands over the 23 year period. These patterns are co...

Detailed forest ecosystem classifications have been developed for large regions of northern Ontario, Canada. These ecosystem classifications provide tools for ecosystem management that constitute part of a larger goal of integrated management of forest ecosystems for long-term sustainability. These classification systems provide detailed stand-level characterization of forest ecosystems at a local level. However, for ecological approaches to forest management to become widely accepted by forest managers, and for these tools to be widely used, methods must be developed to characterize and map or model ecosystem classes at landscape scales for large regions. In this study, the site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20 000) forest ecosystem classification for the Rinker Lake Study Area located in the boreal forest north of Thunder Bay, Ontario. High spatial resolution remote sensing data were collected using the Compact Airborne Spectrographic Imager (CASI) and analyzed using geostatistical techniques to obtain an understanding of the nature of the spatial dependence of spectral reflectance for selected forest ecosystems at high spatial resolutions. Based on these analyses it was determined that an optimal size of support for characterizing forest ecosystems (i.e., optimal spatial resolution), as estimated by the mean ranges of a series of experimental variograms, differs based on (i) wavelength, (ii) forest ecosystem class, and (iii) mean maximum canopy diameter (MMCD). In addition, maximum semivariance as estimated from the sills of the experimental variograms increased with density of understory.

RESUMEDans cette etude, des donnees radar aeroportees en multipolarisation sont examinees pour leur potentiel dans la discrimination des classes de culture durant la periode initiale de developpement des cultures. Des donnees radar a faisceau etroit en bande C en polarisation HH, HV et VV ont ete acquises le 10 juillet 1990, au-dessus d'une region agricole dans le sud-ouest de l'Ontario, au Canada. Un classification dirigee basee sur la technique du k-plus proche voisin (k-NN) est appliquee aux caracteristiques de tonalite et de texture des images radar pour determiner le potentiel des donnees multipolarimetriques radar dans la classification des classes de culture tot dans la saison de croissance. Les techniques utilisees pour generer des mesures de texture incluent la matrice de dependance des niveaux de gris des pixels voisins (NGLDM) et la matrice de co-occurrences des niveaux de gris (GLCM). Diverses caracteristiques texturales sont extraites a partir de la matrice NGLDM (accentuation des valeurs ele...

Remote sensing has demonstrated wide applicability in the area of estimating and mapping forest physical and structural features. Focus in recent years has been directed towards measuring the biophysical/physiological character of forest ecosystems in order to estimate and predict forest ecosystem health and sustainability. The following reviews the relationship between forest condition and reflectance; remote-sensing measurements (and derivatives) that provide biophysical/physiological information; and the potential of hyperspectral sensors in the measurement of these parameters.

A method is presented for generating second-order spatial statistics (texture) from digital image data using the neighboring gray-level dependence matrix (NGLDM). Texture processing of a high-resolution airborne synthetic aperture radar image in an agricultural environment improved crop classification over the raw data. Classification of the large-number emphasis feature, based on the NGLDM (window size = 11 × 11, α = 24 and β = 1), showed a 30.5% improvement in validation accuracy over the tonal data.

RESUMEDes methodes operationnelles destinees a ameliorer la precision des resultats de classification des cultures sont evaluees a l'aide de donnees C-HH et C-HV recueillies le 10 juillet 1990 a partir d'un radar a antenne synthetique (RAS) aeroporte sur un site temoin situe dans le comte d'Oxford, dans le sud de l'Ontario. Les donnees RAS brutes, les donnees filtrees ainsi que des statistiques sur la texture des images ont ete classifiees a l'aide de methodes de classification utilisant l'une, les pixels et l'autre, les champs dans le but de determiner leur efficacite pour la classification des cultures. Les statistiques sur la texture qui sont derivees des densites de probabilite de la matrice de cooccurrence de niveaux de gris offrent, dans le cas des images RAS, un pouvoir de discrimination superieur a celui que presentent les statistiques des donnees brutes. La classification des champs permet, toutefois, d'obtenir des resultats nettement plus precis pour une foule de donnees d'entree.

The hypothesis tested in this study was that remote sensing constitutes a particular case of an arbitrary uniform spatial sampling grid used to obtain measurements about geographical entities that induces the scale and aggregation effect responsible for haphazard analysis results. The main objective was to evaluate the impact of measurement scale and spatial aggregation on the information content and classification accuracies of airborne MEIS-II data acquired over a midlatitude temperate forested environment. The original MEIS-II data were resampled to four spatial resolutions, namely 5 m, 10 m, 20 m, and 30 m. Forest classes were established according to three progressive levels of spatial aggregation. Descriptive statistics (Wald-Wolfowitz runs test, mean and variance) were calculated on transects of pixels representing each forest class delineated on the images at every spatial resolution. A maximum-likelihood classification was also performed for each combination of spatial resolution and aggregation level. The results reveal that, except for the mean, changing the measurement scale and the aggregation level of the classes greatly affects the values of the descriptive statistics. The Z value of the Wald-Wolfowitz runs test decreases with decreasing spatial resolution. The effect is more pronounced when the classes are progressively aggregated. For most classes, the variance decreases with the decrease of spatial resolution. In such cases, the impact of changing the measurement scale is greater than the change of aggregation level. Per-class accuracies are also considerably modified depending on the measurement scale and the aggregation level. Within a particular aggregation level, some classes are better classified at fine spatial resolutions, while others require coarser spatial resolutions. Three major conclusions can be stated from these results: 1) The information content of remote sensing images is dependent on the measurement scale determined by the spatial resolution of the sensor; 2) neglecting the scale and aggregation level when classifying remote sensing images can produce haphazard results having little correspondence with the objects of the scene; and 3) there is no unique spatial resolution appropriate for the detection and discrimination of all geographical entities composing a complex natural scene such as a forested environment. These conclusions provide a theoretical foundation from which original solutions to the problem of appropriate scales of measurement for geographical entities can be experimented. Logically, there exists an optimal spatial resolution for each entity of interest, corresponding to its intrinsic spatial and spectral characteristics.

A remotely sensed method of assessing radiative-transfer processes which considers distinctive zones in the mountain-glacier drainage basin, increases the potential for comprehensive radiative-exchange analysis. By investigating terrain-reflected and terrain-emitted radiation using Landsat-5 Thematic Mapper images and digital elevation data, the objective of this study is to demonstrate the importance of local exchange in the computation of net shortwave and longwave radiation. The results show that terrain-reflected radiation estimates are required to calculate the total shortwave spectral irradiance in all parts of the basin This is necessary to compute accurate surface-cover reflectance and albedo values from the satellite imagery. Furthermore, the assessment of the terrain-emitted radiation explains why, especially on a clear day, the snow and ice covers in many parts of the basin have a very small longwave radiation deficit.

Standard error assessment techniques in image classification have been primarily concerned with identifying errors in individual pixel assignments. However, these techniques overlook a fundamental fact that image classification is basically a process of generalization. The outputs of this process are often intended to be cartographic objects (e.g., polygons) which are abstract models of reality and may not be verifiable at each pixel. Linking errors with cartographic objects in image classification is a challenging problem in remote sensing. This article proposes a new error assessment methodology for image classification (an error model) in which uncertainties involved in the classification process are estimated through simulations of various steps in image classification. Two error models have been developed to estimate the uncertainties involved in class modeling (training) and boundary generation (boundary pixel allocation). Results derived from two case studies show the validity of the proposed error concept for image classification and its potential for improving image classification.

An accurate evaluation of glacier energy balance requires a precise knowledge of surface-cover albedo and emittance. Usually these values are acquired from field measurements. However, the microclimatological effects in mountainous terrain greatly limit the potential for spatial extrapolation of such a set of values. This study overcomes the problem by using the upward radiance values registered on Landsat-5 Thematic Mapper (TM) images and information on the geometry of ter terrain extracted from a digital elevation model (DEM). Studies were performed in the Athabasca Glacier basin, one of the major glacier outlets of the Columbia Icefield in the Canadian Rocky Mountains. The methodology is composed, first, of an automated procedure for the physiographic description of the glacierized basin (cover type, elevation, slope, aspect, horizon profiles, sky-view factor, and the level of enclosing topography) in order to calculate the effects of topography on the radiation balance for each 30 m pixel. Because glacier surface covers usually have high reflective behaviors or distinct emission patterns, this study puts special emphasis on modelling the amount of terrain-reflected or terrain-emitted radiation received on a particular surface. Second, the topographic correction is applied to values of irradiance computed using the LOWTRAN-6 code, a spectrally based radiative transfer model, and atmospheric radiosonde measurements of the vertical temperature, air pressure, and relative humidity profiles. The measured upward radiance values from TM, corrected for path effects, are used to calculate surface-cover albedo and brightness temperature. Third, the daily net radiation field for snow and ice covers is computed to illustrate the contribution of the estimated surface radiative parameters to the glacier snow-and-ice melt analysis. The resultsshow that the irradiance values are accurately modelled within 10% of the radiattion values acquired from a field pyranometer. The calculated glacial surface-cover reflectance and albedo values compare favorably to published information, whereas the measured surface brightness temperature is well within the expected range of values.

High resolution digital data were collected over an ecologically varied area to determine the extent to which different field data can be used to enhance vegetation mapping through remote sensing. Field data, in the form of tree and shrub densities, and herb frequencies were recorded for 155 vegetation plots at Presqu'ile Provincial Park, Ontario, Canada. These field-plot data were analyzed using two-way indicator species cluster analysis (TWINSPAN) to produce an hierarchical, ecological classification scheme. A parallel classification scheme was also developed independently using only qualitative field notes. The results of field-plot data classifications were used to produce supervised classifications of Multi-detector Electro-optical Imaging Sensor (MEIS II) airborne digital data recorded in five spectral bands with a 5 m × 5 m spatial resolution. Accuracy assessments show a considerable range in the classification accuracies for different plant communities. In general, the more ecologically / spectrally unique the class is and the lower the species/spectral variations that occur, the higher the classification accuracy. In addition, a qualitative approach to field-plot description produces a more statistically accurate digital classification of MEIS II data than does the detailed quantitative ground information.

A two-stage classification procedure has been applied to extract land use in a rural-urban fringe environment from SPOT High Resolution Visible (HRV) multi-spectral data. In this procedure, the SPOT HRV data were first classified into twelve land-cover types using a supervised maximum-likelihood classification (MLC). In the second stage, cover frequencies were extracted by moving a pixel window over the land-cover map obtained at the first stage. These cover frequencies were then employed in the classification of 14 land-use classes using a supervised minimum-city-block classifier. Results obtained with the cover-frequency method have been compared with those obtained using the conventional MLC approach. The overall accuracy measured by the Kappa coefficient was 0·462 for the MLC method; it was significantly improved to 0·663 with the cover-frequency method.

Visual interpretation of high-resolution satellite data has been useful for mapping linear features, such as roads and updating land-use changes. However, it would be beneficial to map new road networks digitally from satellite data to update digital databases using semi-automated techniques. In this paper, an algorithm called Gradient Direction Profile Analysis (GDPA) is used to extract road networks digitally from SPOT High Resolution Visible (HRV) panchromatic data. The roads generated are compared with a visual interpretation of the SPOT HRV multispectral and panchromatic data. The technique is most effective in areas where road development is relatively recent. This is due to the spectral consistency of new road networks. As new road networks are those of most interest to the land manager, this is a useful technique for updating digital road network files within a geographical information system of urban areas.

RESUMEDans les etudes sur l'environnement aussi bien naturel qu'artificiel, la description structurale d'un patron de reseaux peut contribuer largement a notre comprehension du paysage; c'est les cas, par exemple, des etudes sur les reseaux routiers urbains, les reseaux fluviaux des bassins versants et les patrons de lineaments geologiques. Dans le present article, plusieurs mesures structurales pour l'analyse des reseaux d'elements lineaires, calculees directement a partir de l'imagerie satellite, sont presentees.Les donnees analysees proviennent de quatre sous-zones d'une image panchromatique SPOT (haute resolution visible) couvrant des zones urbaines et suburbaines situees au nord de Toronto. Differentes etapes ont ete suivies dans cette etude: choix des sous-zones et extraction des reseaux routiers; identification et numerotation des points caracteristiques (PC), y compris des points limites et des points de branchement; calcul des mesures structurales basees sur les PC, notamment la densite des resea...

Absfruct-Nine cover types have been classified using a textural/ spectral approach. The texture analysis is based on the grey-level cooccurrence matrix method. Texture features are created from a SPOT near-infrared image using four texture indices, seven window sizes, and two quantization levels. A supervised classification based on the maximum-likelihood algorithm is applied to the three SPOT multispectral bands combined with each texture image individually and to the three bands combined with all four texture images. Classification accuracy is measured by Kappa coefficients calculated from confusion matrices. A factor analysis, based on principal components, is performed to evaluate the contribution to the classification accuracy of each variable involved in the creation of the texture features. The addition of texture features provides a significant improvement in the classification accuracy of each cover type when compared with the results obtained from the multispectral analysis alone. The window size accounts for 90% of the classification variability, 7% is explained by the statistics used as texture measures, and only 3% by the quantization level. There is a window size that optimizes the discrimination of each cover type.

Most lineament mapping is done visually using enhanced images. To automate this procedure partially, algorithms have been developed to imitate some of the visual rules employed by geologists when mapping lineaments. The Hough transform is described, and its capabilities for automated lineament analysis are demonstrated using part of a Landsat TM image of the Canadian Shield near Sudbury, ON, Canada. The results of the automated analyses are compared with the major faults shown on a geologic map of the area and with a visual assessment of an image of the same area undertaken by two remote sensing / geology specialists. The results show that automated interpretation identifies more of the faults than visual interpretation.

A method for evaluating the effectiveness of different feature combinations and training strategies is described. Preliminary tests have been made using two groups of feature combinations derived from SPOT High Resolution Visible (HRV) data and two sets of training samples. The method is objective, and needs no ground confirmation or interaction from the image analyst. It is recommended as a surrogate for detailed accuracy assessment when attempting to find an optimum set of training pixels or feature combinations for image classification.

An issue of concern when analyzing remotely sensed data is the impact of data resolution on the results of the analysis and on the conclusions that are subsequently drawn. In this paper, we investigate the effects of spatial resolution on fractal dimension of digital elevation models (DEMs) generated at various spatial resolutions. The DEMs used in this study were created from three sources: 2-m resolution derived from LiDAR altimetry data, 20-m resolution derived from 1:20000-scale topographic maps, and 100-m resolution derived from National Topographic Series (NTS) maps of Canada at 1:250000 scale. Directional variograms were used to determine the fractal characteristics of the study area for each scale. In addition, the 2-m LiDAR data, which show detailed lithological and structural textures, were resampled to simulate lower resolutions of 50-m and 100-m. The results of the study indicate that, while the three data sources display a multifractal surface, the fractal dimension (D) at range-scale shows a consistent decrease with increasing cell size. The lower resolution, therefore, appears to cause a smoothing of the topographic surface, thereby reducing the fractal dimension in all directions. In contrast, the results from the resampled LiDAR data are less consistent This suggests that the detailed lithological information present in the high-resolution LiDAR data is sufficiently retained, even at degraded resolutions, to influence the fractal characteristics

Three techniques for generating texture statistics are examined: the gray-level co-occurrence matrix (GLCM), the gray-level difference vector (GLDV) and the neighboring gray-level dependence matrix (NGLDM). The objective of these statistical approaches is to translate visual texture properties into quantitative descriptors in a manner that they can be used to discriminate relevant land features using additional image processing techniques. These second-order statistical methods are used to generate texture features from C-HH and C-HV airborne synthetic aperture radar (SAR) data collected on July 10, 1990 over an agricultural area in southern Ontario Canada. Texture features generated from the GLCM, GLDV and NGLDM are classified individually using a k-nearest neighbor (k-NN) supervised classifier. The greatest classification improvement (/spl ap/20%) was observed with the mean and correlation texture features derived from the GLCM. However, the selection of a specific second-order statistical technique may not be critical, since similar classification improvements were observed for the GLCM, GLDV and NGLDM statistical techniques. The results reported here highlight the importance of texture processing to methods of classifying agricultural crops using SAR data.

Waste from nickel mining comprises a mixture of fine silicates with a variable amount of sulphides, and iron oxides. Subsequent oxidation of the sulphides results in the formation of environmentally hazardous acid-rich leachate. Identification of the distribution of regions of higher sulphide concentration and resulting leachate ponding represents an important first step towards the implementation of an effective tailings management strategy. Synergistic information regarding the prevalence of Acid Mine Drainage can be derived from combining hyperspectral imagery and topographic data that describe near surface variations with airborne geophysical surveys that describe shallow subsurface variations. Preliminary investigations over the Copper Cliff tailings site, Sudbury Ontario reveal that these parameters can be used to separate tailings with varying compositional and alteration levels, and water bodies of varying acidic levels. On-going oxidation of older tailings and soil development is demonstrated in the hyperspectral data by increased vegetation coverage. Magnetic susceptibility changes are associated with the progressive alteration of magnetite and pyrrhotite to less magnetic hematite. Inductive conductivity variations in tailings are related to the combined effects of sulphide mineral content and the ionic concentration of acid in groundwater. Inversion of conductivity data shows lateral contrasts both between and within adjacent tailings basins.

Acid mine drainage (AMD), caused by mine tailings, poses an environmental threat. AMD control is a major challenge facing the mining industries worldwide. An important initial step towards the reclamation of mine tailings sites is to identify the presence of sulphide-rich minerals and their spatial distribution. This study investigated the potential of hyperspectral PROBE data for mine tailings characterization over the Copper Cliff's tailings site in northern Ontario, Canada. The results indicated that PROBE data could provide information on locating oxidation zonations of the tailings. More importantly, it revealed that library mineral spectra could replace the scene-derived endmember spectra to unmix the PROBE image.

A digital elevation model (DEM), derived from light imaging detection and ranging (LiDAR) altimetry data, is used to investigate the fractal characteristics and structural trends of a topographic surface. The study area is located on the southeastern rim of the Sudbury Basin, a meteorite impact feature in Ontario, Canada. Visual interpretation and statistical analysis of fractal dimension (D) indicate that the geological units in the study area are characterized by statistically different distributions of D. In addition, the landscape exhibits two predominant aspect directions: 127° and 290° or ∼SE and NW. High values of D correspond to these predominant aspect directions. Topographic ridgelines exhibit a NE-SW trend, which is perpendicular to that of the highest fractal dimensions and the predominant aspect directions. It is suggested that these ridgelines are topographic expressions of tectonic fabrics present in the various geological units. Genesis of the tectonic fabric is related to the NW-SE–directed shortening of the Sudbury Basin impact crater.

RESUMEDes images obtenues a l'aide d'un scanneur aeroporte et ayant une limite de resolution spatiale de 2,5 metres ont ete utilisees pour evaluer leur contenu informationnel en fonction de la limite de resolution spatiale pour une region peri-urbaine situee dans le Sud de l'Ontario.Cette image de base a ete ‘degradee’ a l'aide de convolutions cubiques afin de simuler des limites de resolution spatiale de 10, 20 et 50 metres.Par contenu informationnel, on entend l'information thematique pouvant etre extraite des donnees de teledetection sous forme de classes d'occupation et d'utilisation du sol. L'information thematique a ete extraite au moyen d'une classification non dirigee en vue de produire des classes d'occupation et d'utilisation du sol pour chaque serie de donnees correspondant a une limite de resolution spatiale. Une evaluation quantitative de la precision de ces classifications a ete realisee en comparant les images classifiees avec les donnees de terrain.Les resultats indiquent que plus la limit...

Airborne multispectral scanner (MSS) digital data (3.8 m resolution) and MEIS II digital data (10 ;m resolution) have been used to investigate the effects of anomalous metal concentrations in the soil on the spectral reflectance of selected tree species. Analysis of on-site spectral measurements for 31 samples indicated that trees growing in soil with anomalously high concentrations of metals have higher reflectances at all wavelengths between 400 ;nm and 950 ;nm. However, statistical analysis of pixel values from individual airborne channels and some combinations/enhancements of channels revealed that detection of metal-stressed trees is generally not possible, except in some areas of glacio-lacustrine deposits.

Studies are being undertaken to determine the capabilities of SPOT multispectral (XS) imagery for providing information on rural-to-urban land conversion. Four procedures are tested. These are visual analysis of two images, visual analysis of a multidate image consisting of two XS2 bands, supervised classification of the two images and supervised classification of the multidate image. Results show that visual analysis of two images and supervised classification of the multidate image provide the best overall classification accuracies at approximately 80%. The best change-detection accuracy of 60% is achieved with supervised classification of the multidate image. Change/no change accuracies are greater than 90%. Although classification accuracies are slightly lower than those achieved with Landsat multispectral scanner imagery, on the SPOT imagery changes in much smaller parcels of land can be observed with greater spatial precision.

The extensive Peace—Athabasca Delta has experienced major changes in water boundaries and vegetation types due to flooding. To determine the feasibility of Landsat digital data to monitor such changes, comparisons of parts of the delta under normal and flooded conditions have been made. The analysis involved supervized classification of the digital data on two dates followed by post-classification change detection. Comparison of the digital analysis results with aerial photography and existing maps showed good classification accuracy, although a considerable number of pixels remained unclassified. Change detection involved registration of the images on the Image 100 monitor followed by class comparison on a pixel-by-pixel basis. This demonstrated not only that changes had occurred, but also the nature of the changes. Evaluation of the original reflectance values for each class helped in the explanation of the known water and vegetational changes. Results of the study indicate that this type of wetland environment can be effectively mapped and changes readily determined using digital Landsat data. Care must be taken, however, in the analysis of statistical information generated on a pixel-by-pixel basis, particularly for linear features where there are frequent boundaries between classes.

Routine surface wind speed data, vital for the study of drifting pack ice, are not available for the polar oceans. Over sea, it has been demonstrated by Hasse that estimates of surface wind speed may be obtained from the geostrophic wind speed using linear formulae. Comparing estimated with observed data for six sites in Canada, it is found that the formulae may also be applied to obtain estimates of surface wind speed over pack ice.

The performances of two probabilistic relaxation (PR) classification methods, a standard and a modified version, are assessed in terms of classification accuracy measured by the Kappa coefficient and the CPU time required to carry out the computation. The classification results obtained with these methods are compared with results obtained using conventional maximum-likelihood classification (MLC). Experiments indicate that the modified PR method significantly improves upon the classification results generated by the MLC method. The modified PR method saves up to 70% of the CPU time, compared with the standard PR method, and also gives slightly better classification accuracy.

RESUMELes autorites municipales et les entreprises chargees d'etablir des donnees socio-economiques ont besoin d'informations sur l'expansion urbaine. Une etude a ete effectuee sur une partie du Toronto metropolitain en vue de determiner dans quelle mesure il est possible d'obtenir, a partir de classifications dirigees et non dirigees de donnees multibandes SPOT, des informations sur la conversion de territoires ruraux en territoires urbains.Les resultats demontrent que les images SPOT ne donnent pas de meilleures classifications que celles obtenues avec des donnees LANDSAT MSS. Meme en regroupant les classes d'utilisation du sol en trois groupes, a savoir secteur en developpement, zone urbaine et aire de vegetation, l'exactitude de la classification obtenue a partir des images SPOT n'etait environ que de 80 pour cent. La confusion entre les classes d'utilisation du sol est due au fait que les classes d'occupation du sol, a la limite du perimetre urbain, ne sont pas rattachees de facon unique a des classe...

Recently, there has been much interest in the incorporation of ancillary data into digital image analysis. One important type of ancillary data is the digital elevation model (DEM) and data sets derived from it. However, the software required to use these data is not available always as part of existing image-analysis systems. In this paper, an overview of the role of DEMs in the digital analysis of remotely sensed data is presented. An operational software package (PRODEM) for the manipulation and processing of DEMs for remote-sensing applications then is described. An important feature of this package is that it contains data operation and format conversion modules which allow it to be interfaced directly with an image-analysis system. An implementation of PRODEM in a research environment also is described and examples of research currently being conducted are used to illustrate the versatility and flexibility of this system.

Comparison of Landsat digital classifications from two dates has met with limited success for change detection in urban environments. The capabilities of digital enhancements for displaying change were therefore investigated as an alternative procedure for this task. Three different color enhancements, an overlay of band 5, ratios of bands 5 and 7, and a vegetation index, were generated using 1974 and 1978 Landsat data of Hamilton, Ontario. The ratios only emphasize major changes. The band 5 overlay shows change most clearly, but the vegetation index enhancement is almost as good and in addition to showing change also emphasizes the urban boundary and the major road network. It is suggested that change enhancements could be used effectively by agencies responsible for monitoring urban development over large areas.

Resource management agencies are interested in knowing when and how satellite data can be effectively used to monitor environmental change and what information can be expected from remote sensing techniques. In this paper the procedures that might be followed in applying the complementary methods of band ratioing and post-classification change detection to monitor a large remote area are outlined. Examples from research in the Peace-Athabasca Delta are used to illustrate the procedures and expected results.

Vertical aerial photography of Breidamer- changes have occurred as a result of the meltkurjokull flown in 1945 and 1965 has shown ing of buried ice. The plan shape of the ridge that an area containing a sandur in 1945 has appears to be controlled by ice structures giving developed into an esker ridge surrounded by a distinct rectilinear pattern to the esker. A kettle topography. Photogrammetric measure- cross section dug through the ridge revealed ments from this photography indicate that the that the gravels were underlain by ice, indicatesker was produced by the melting out of ing an englacial origin for at least part of buried ice from beneath sandur deposits on the esker. This cross section, as well as others, either side of the ridge. have shown varations in the shape of the tunField mapping of a second esker which was nel containing the gravels. The mechanism by melting out of the glacer during the period of which the esker is lowered to the proglacial study has shown that planimetric and height surface to form a single ridge is also discussed.

Observations on Tunsbergdalsbreen show that an ice-dammed lake, Store Brimkjelen, overflowed on to the surface of the glacier. The lake lifted a section of the glacier and formed a long irregular crevasse along the line of dislocation.

Methods based on remotely-sensed data are needed to predict motions of drifting open pack ice and to determine sea-ice parameters associated with these motions. The method presented here is able: (a) to predict the motions of groups of wind-driven detached ice floes over periods of 12, 36, and 60 h; (b) to determine sea-ice thickness and the surface and sub-surface drag coefficients associated with these motions.