Showing papers in "Photogrammetric Engineering and Remote Sensing in 1995"

Raster procedures for multi-criteria/multi-objective decisions

Abstract: Decisions about the allocation of land typically involve the evaluation of multiple criteria according to several, often conflicting, objective. With the advent of GIS, we now have the opportunity for a more explicity reasoned environmental decision making process. However, GIS has been slow to develop decision support tools, more typically relying on procedures outside the GIS software. In this paper the issues of multi-criteria/multi- objective decision making are discussed, along with an exploration of a new set of decision support tools appropriate for the large data-handling needs of raster GIS. A case study is used to illustrate these tools as developed for the IDRISI geographic analysis software system

Improved forest classification in the northern Lake States using multi-temporal Landsat imagery

Abstract: Forest classifications using single date Landsat TM data have been only moderately successful in separating forest cover types in the northern Lake States region. Few regional forest classifications have been presented that achieve genus or species level accuracy. We developed a more specific forest cover classification using TM data from early summer in conjunction with four MSS dates to capture phenological changes of different tree species. Among the 22 forest types classified, multi-temporal image analysis aided in separating 13 types. Of greatest significance, trembling aspen, sugar maple, northern red oak, northern pin oak, black ash, and tamarack were successfully classified. The overall classification accuracy was 83.2 percent and the forest classification accuracy was 80.1 percent. This approach may be useful for broad-scale forest cover monitoring in other areas, particularly where ancillary data layers are not available.

Tau coefficients for accuracy assessment of classification of remote sensing data

Abstract: The Kappa coefficient is generally used to assess the accuracy of image classifications. We introduce the Tau coefficient, which measures the improvement of a classification over a random assignment of pixels to groups, and we compare its performance with that of Kappa and percentage agreement. Not only does Tau better adjust percentage agreement than Kappa, but it is also easier to calculate and interpret. For these reasons, we believe that Tau is a better measure of classification accuracy for use with remote sensing data that either Kappa or percentage agreement

Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data

Abstract: Recent and historical satellite remote sensor data were used to inventory aquatic macrophyte (especially cattail and sawgrass) changes within the Florida Everglades Water Conservation Arae 2A using Landsat Multispectral Scanner (MSS) data (1973, 1976, and 1982) and SPOT High Resolution Visible (HRV) multispectral data (1987 and 1991). The method required a single base year of remotely sensed data with adequate ground reference information (1991). Histological remotely sensed data were normalized to the base year's radiometric characteristics. Statistical clusters extracted from each date of imagery were found in relatively consistent regions of multispectral feature space (using red and near-infrared bandes) and labeled using a core cluster approach. Wetland classification maps of each year were analyzed using post-classification comparison change detection technique to produce maps of (1) cattail change and (2) change in the sawgrass/cattail mixture class. The amount of hectares in each wetland class was tabulated by year. The spatial distribution of the wetland was then overlaid onto a soil porewater phosphorus statistical surface obtained through in situ investigation. The cattail and cattail/sawgrass mixture classes appear to be spatially associated with distribution of relatively high concentrations of porewater phosphorus in Water Conservation Area 2A

The integration of geographic data with remotely sensed imagery to improve classification in an urban area

Abstract: This paper investigates the incorporation of ancillary spatial data to improve the accuracy and specificity of a land-use classification from Landsat Thematic Mapper (TM) imagery for nonpoint source pollution modeling in a small urban area -- the city of Beaver Dam, Wisconsin. A post-classification model was developed to identify and correct areas of confusion in the Landsat TM classification. Zoning and housing density data were used to modify the initial classification. Land-use classification accuracy improved and the number of identifiable classes increased. Additionally, confusion between classes that were commonly misclassified (for example, commercial and industrial areas) was reduced.

Relative radiometric normalization of landsat Multispectral Scanner (MSS) data using an Automatic Scattergram-Controlled Regression

Abstract: A relative radiometric normalization (RRN) based on an Automatic Scattergram-Controlled Regression (ASCR) has been developed to create radiometrically comparable multispectral data sets, compensating for radiometric divergence present in images acquired under different illumination, atmospheric, or sensor conditions. The ASCR procedure locates the statistical centers for stable land and stable water data clusters using the near-infrared date 1 versus date 2 scattergrams to establish an initial regression line. Thresholds are placed about the initial line to select a no-change pixel set, which is used in the regression analysis of each band to derive gains and off sets for the radiometric normalization. The ASCR procedure was designed for preparing large numbers of multitemporal Landsat data sets for digital detection of landcover change.

Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics

Abstract: Artificial neural networks have considerable potential for the classification of remotely sensed data. In this paper a feed-forward artificial neural network using a variant of the back-propagation learning algorithm was used to classify agricultural crops from synthetic aperture radar data. The performance of the classification, in terms of classification accuracy, was assessed relative to a conventional statistical classifier, a discriminant analysis. Classifications of training data sets showed that the artificial neural network appears able to characterize classes better than the discriminant analysis, with accuracies of up to 98 percent observed. This better characterization of the training data need not, however, translate into a significantly more accurate classification of an independent testing set. The results of a series of classifications are presented which show that in general markedly higher classification accuracies may be obtained from the artificial neural network, except when a priori information on class occurrence is incorporated into the discriminant analysis, when the classification performance was similar to that of the artificial neural network. These and other issues were analyzed further with reference to classifications of synthetic data sets. The results illustrate the dependency of the two classification techniques on representative training samples and normally distributed data

Incorporation of relief in polynomial-based geometric corrections

Abstract: This study focuses on the geometrical deformations introduced by relief in images captured by the TM sensor of Landsat satellites and by the HRV sensor of SPOT satellites. Different correction alternatives are presented in order to incorporate altitude data into correction procedures based on first-degree polynomial models. Column and row determinations from the corresponding map coordinates are carried out independently. Three different models for columns and two for rows are proposed. The results have been contrasted with those obtained using classic first- and second-degree polynomial calculations, and with those obtained using an orbital model (for SPOT images). The models presented are easy to implement and provide a level of precision similar to that of the orbital model used, while they are much more efficient in calculation time. In view of the results, the model which integrates altimetric data into a single first-degree polynomial seems of particular interest.

Multidate SAR/TM synergism for crop classification in western Canada

Abstract: Multidate synthetic aperture radar (SAR) and Thematic Mapper (TM) visible and near-infrared (VNIR) data were evaluated for classifying crops frequently grown in western Canada. The VNIR data were superior to the SAR data for single date classifications due to the multispectral information content. Multidate classifications with SAR data improved classification accuracy from 30 to 74 percent although multidate VNIR produced the highest single sensor result of 90 percent correct classification. This was slightly improved to 92 percent by including the SAR data with the VNIR data. However, transformed divergence statistics show that the SAR and VNIR channels are both found in the top eight channels, and, indeed, the best two SAR channels and the best two VNIR channels, based on their transformed divergence statistics, produced an overall classification accuracy of 85 percent. Furthermore, the May TM data combined with the SAR data yielded an 87 percent correct classification because the grain and alfalfa classes were much better separated when VNIR data was combined with SAR data. These results demonstrate significant synergism between the two sensors and suggest the need for a feature selection approach, or at least a knowledge based system incorporating the synergism effect, once multidate, multisensor data become available on a regular basis. The substitution of SAR data beneath cloud covered terrain in TM data is used to demonstrate another aspect of SAR/VNIR synergism.

Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the Northeast

Abstract: Classification of forest cover types in the Northeast is a difficult task. The complexity and variability in species composition makes various cover types arduous to define and identify. This project employed recent advances in spatial and spectral properties of satellite data, and the speed and power of computers to evaluate seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper (TM) data in New Hampshire. Data from May (bud break). September (leaf on), and October (senescene) were used to explore whether different leaf phenology would improve our ability to generate forest-cover-type maps. The study area covers three counties in the southeastern corner of New Hampshire. A modified supervised/unsupervised approach was used to classify the cover types. A detailed accuracy assessment was performed to evaluate the classification. The results indicate that specific northeast hardwood species can be identified and that time of the year can significantly affect the cover-type classification accuracy

Dealing with error in spatial databases: a simple case study

Abstract: There is now a considerable body of literature on the techniques available for modeling and communicating error in spatial databases. Some error models have solid statistical foundations, while the basis for others is not so strong. In this paper, three basic approaches to the problem are examined. The application investigated is a fundamental one ― to determine the position of a given terrain elevation value and to portray the resultant error of the answer. Such a problem can be of critical concern to communities in cases of flood plain mapping, determination of rising sea levels resulting from global warming, or delineation of the full supply level for a proposed reservoir. In this instance, the authors suggest that the application of simple probability theory, when combinede with the error estimates supplied by data producers and current computer graphics capabilities, can provide users with more meaningful information concerning the error of their spatial database products. In turn, this information may allow them to better deal with an issue of growing concer

A fractal approach to the classification of Mediterranean vegetation types in remotely sensed images

Abstract: A method is presented to assess fractal dimensions from remotely sensed images. The method is a three-dimensional version of the walking dividers method which has been applied to two digital images of southern France to distinguish various types of Mediterranean landscape units. The first image is a Landsat Thematic Mapper image, while the second image was acquired by the airborne Geophysical Environmental Research Imaging Spectrometer. The method has been tested on some artificial images to demonstrate procedures and results. The method can distinguish range-lands, maquis and closed garrigue and to a lesser extent agricultural regions on the TM image. Fractal dimensions for open garrigue and badlands are similar. However, the reflection properties of the land-cover units do not behave like real fractals at the scale considered, and different methods to compute the fractal dimension do not yield the same results. Results of the airborne image are disappointing, probably due to somewhat poor image quality. Finally, some advantages and disadvantages of the method are discussed.

Coniferous forest leaf area index estimation along the Oregon transect using compact airborne spectrographic imager data

Abstract: Leaf area index (LAI) is an important structural variable for quantitative analysis of the energy and mass exchange characteristics of a terrestrial ecosystem. Previous research on estimating forest LAI by remote sensing is limited to the use of conventional multispectral data. Methods used for LAI estimation involved primarily simple statistical relationships between LAI and vegetation indices (VI) derived from remote sensing data. In this paper, the potentials of an imaging spectrometer, the Compact Airborne Spectrographic Imager (CASI), have been studied for coniferous forest LAI estimation using three types of modeling techniques : univariate regression, multiple regression, and vegetation-index (VI) based LAI estimation. Four study sites have been selected along a forest transect in Oregon. LAI measurements were collected from these study sites. CASI data of two imaging modes - spatial and spectral - had been calibrated and corrected. The relationships between the LAI measurements and the corrected CASI data were then explored. Results indicate that the CASI data acquired with the two imaging modes have similar accuracies for LAI prediction. All three LAI estimation methods resulted in LAIs with reasonably low root-mean-squared errors (RMSEs). The use of the normalized difference vegetation index (NDVI) produced more accurate LAI estimates than did the use of channel ratio for the univariate regression and the VI-based LAI prediction methods. For the univariate regression, a non-linear hyperbola relationship between the LAI and the NDVI was the most appropriate for LAI estimation. In this study, the VI-based LAI estimation method has proven to be simple to use and effective.

What cell size does the computed slope/aspect angle represent?

Abstract: The computation of slope and aspect angles for a cell is a common procedure in environmental studies and remote sensing applications in which topography is important. While the algorithm for computing slope/aspect angles requires either four or eight neighbors in a centered three by three window of cells, the estimated angles are used as if they depict the surface orientation of only the single central cell. Two questions result from this observation. What cell size does the slope and aspect angle derived from this window bes represent? How different is the actual surface angle of the central cell from the surface angle computed using the window of elevation values? Although this difference in computation versus use is somewhat known, it has never been documented. This article empirically demonstrates that the slope/aspect angle derived from the neighboring elevation points best depicts the surface orientation for a larger cell ― either 1.6 times or 2.0 times larger than the size of the central cell. It is suggested that, rather than first resampling elevation datasets of a finer resolution to a larger cell size commensurate with other data in a study and then deriving slope/aspect angles, a mean slope/aspect angular measurement be derived directly from the higher resolution data for each larger cell size

Knowledge formulation for supervised evidential classification

Abstract: The Dempster-Shafer Theory of Evidence provides an appropriate framework for overcoming problems associated with the analysis, integration, and classification of modern, multisource data sets. However, current methods for generating the prerequisite evidence are subjective and inconsistent. To address this, a more objective approach is presented for deriving evidence from histogram bin transformations of supervised training data frequency distributions. The procedure is illustrated by an example application in which evidential land-cover classification accuracy is increased from a kappa coefficient of 0.51 to 0.90 by appropriate use of bin transformation functions for a complex, mountainous environment in the Canadian sub-Arctic

Map projections for global and continental data sets and an analysis of pixel distortion caused by reprojection

Abstract: With growing emphasis on global monitoring, research using remotely sensed data and geographic information systems is increasingly focused on large regions studied at small scales. These global change studies require the integration of data sets from several sources that are reprojected to a common map base. In small-area, large-scale studies the choice of a map projection has little effect on data quality. In global change studies the effects of map projection properties on data quality are more apparent, and the choice of projection is more significant. To aid compilers of global and continental data sets, six equal-area projections were chosen : the interrupted Goode Homolosine, the interrupted Mollweide, the Wagner IV, and the Wagner VII for global maps ; the Lambert Azimuthal Equal-Area for hemisphere maps ; and the Oblated Equal-Area and the Lambert Azimuthal Equal-Area for continental maps. Distortions in small-scale maps caused by reprojection, and the additional distortions incurred when reprojecting raster images, were quantified and graphically depicted. For raster images, the errors caused by the usual resampling methods (pixel brightness level interpolation) were responsible for much of the additional error where the local resolution and scale change were the greatest.

A GIS-based sensitivity analysis of community vulnerability to hazardous contaminants on the Mexico/U.S. border

Abstract: Growing industrial development in the Mexico/U.S. border region is creating potential health risks for citizens of both nations. Planners and policy makers working in this region must prepare for hazardous material accidents in a situation of limited information. This research develops a geographic information system-based approach for estimating and determining community vulnerability to hazardous material releases in Nogales, Sonora/Arizona. A composite mapping analysis of human-related and hazard-related variables determines high vulnerability locations. In addition, a sensitivity analysis explores a full range of vulnerability scenarios based on different weighted combinations of the human-related and hazard-related factors. Results demonstrate that a GIS-based approach can effectively compensate for much of the inherent subjectivity in a composite mapping analysis.

Geometric calibration of zoom lenses for computer vision metrology

Abstract: Zoom lenses are used extensively in computer vision to overcome the limited resolution provided by the small focal planes of solid-state cameras. Laboratory studies of zoom lenses, with a focal range of 12.5 to 75 mm, showed that geometric distortions could amount to several tens of pixels across the focal plane, and that there were significant changes in the distortion patterns at different focal settings. Changes in position of the principal point amounting to as much as 90 pixels were measured. These changes were found to be highly systematic, repeatable, and stable over time. A mathematical model was developed to model the geometric distortions at a fixed focal setting with an RMS error better than ± 0.1 pixel. A method was devised to model the changes in the interior geometry of zoom lenses, with the resulting residual distortions amounting to less than ± 0.4 pixel (RMS)

Using aerial photography and GIS to map the forest-tundra ecotone in Rocky Mountain national park, Colorado, for global change research

Abstract: Ecotones could be useful locations to monitor the potential effects of global change on the biosphere. The GRASS GIS and scanned, orthorectified aerial photography were used in combination with extensive ground-truthing to map and analyze the major zones (e.g., patch forest, krummholz) and zone limits within the forest-tundra ecotone of Rocky Mountain National Park in the Colorado Front Range. Only a small percentage of the 1,092-km length of zone limit-lines, and the 19,520 ha within the patch forest and krummholz zones, bears evidence of recent disturbance, in contrast to forest-tundra ecotones in arctic locations. The ecotone is patchy and the scale of patchiness is similar in the krummholz and patch forest zones, although krummholz patchiness is derived more from rock outcrops and meadow/wetland areas and less from natural disturbance than is the case for the patch forest zone. Scanned aerial photography may be useful for GIS analyses of ecotones and detection of global change, but spectral variation among photographs, the need for adequate ground control and DEM precision for accurate orthorectification, and the errors introduced through digitizing and interpretation are limitations

Spatial characteristics of forest clearing and vegetation regrowth as detected by Landsat Thematic Mapper imagery

Abstract: Spatial characteristics of forest clearing and vegetation regrowth in the Peten Region of northern Guatemala were analyzed. Forest change detection procedures were applied to compare size and shape of forest clearing, fallow clearing, and regrowth patches between two study sites. The results revealed that there were only half as many new forest clearings in 1990 along the Guatemala border as in the vicinity of Carmelita; they were, however, twice as large (x = 3.98 ha) as the forest clearings at Carmelita (x = 2.04 ha). The mean size of regrowth patches along the border were less than half the size of new clearings ; however, the clearing and regrowth patches at Carmelita were approximately equal in size and number. The ratio of forest clearing to regrowth area was one to one at Carmelita but nearly 3 to 1 along the border. Over 90 percent of all new clearings and regrowth patches were within 3 km of known roads at both study sites. These results indicate that spatial characteristics of forest clearing can be monitored by multi-date Landsat imagery and suggest differences in socio-economic factors operating at the two study sites. A method of separating fallow clearing from deforestation estimates is presented and discussed.

The design and implementation of an integrated geographic information system for environmental applications

Abstract: Environmental monitoring and restoration at the U.S. Department of Energy's Savannah River Site (SRS) requires efficient access to large amounts of diverse spatial information. These geographic information system (GIS) and remotely sensed data are related to both physical and man-made features. In order to handle this task, the Environmental Sciences Section (ESS) of the Westinghouse Savannah River Company (WSRC) created the Environmental Data Atlas (EDA) that uses spatial keys to link all data sources to a common geographical data base. Furthermore, it was important that all of the data be readily accessible on the desktop of scientists regardless of the type of computer platform they used. This paper describes the creation of a comprehensive computing environment that utilizes a geographical data browsing system as the core for meeting these requirements. It also describes how bibliographic search and aerial photography browsing functions were incorporated into the system. Finally, it describes a sophisticated modeling system that has been integrated into the system to support site selection activities. By taking advantage of the latest advancements in geographical data browsing systems, fourth generation procedural programming languages, and network communications, the integrated system represents an important step in the evolution of GIS.

Algorithm development for recovering chlorophyll concentrations in the Chesapeake Bay using aircraft remote senging, 1989-91

Abstract: A study using aircraft remote sensing of chlorophyll concentrations was conducted in the Chesapeake Bay from 1989 to 1991. The goal was to improve spatial and temporal resolution of the distribution of phytoplankton in this highly dynamic and variable estuary. The focus of the study was on improving our ability to estimate chlorophyll a [Chl a] from aircraft by developing local algorithms for individual years, and by exploring the use of seasonally and spatially specific algorithms. Our findings suggest that an overall, multi-year algorithm can be used predictively to estimate the distributio of Chl a i.e, the location, duration, and spatial estent of phytoplankton blooms -in near the real time -. Refinements that improve the recoverny of Chl a include the separation of spring data from the data for other seasons, and the use of separate local algorithms for regions of low and high turbidity. These developments improve the accuracy with which we recover Chl a in the Chesapeake Bay using aircraft remote sensing, and have implications for the detection of changes in algal biomass that are expected to accompany nutrient reductions between now and turn of the century. Our results suggest that the shipboard sampling of Monitoring Program may underestimate the biomass of phytoplankton blooms and, hence, the amount of particulate carbon produced in the Bay. This finding has ramifications for detecting changes in phytoplankton abundance that are expected to accompagny nutrient reductions, and for processess such as hypoxia (i.e., low oxygen concentrations) that are driven by organic material derived from the spring phytoplankton bloom in the mesohaline of the Chesapeake Bay

Forest mapping at Lassen volcanic national park, California, using Landsat TM data and a geographical information system

Abstract: Knowledge of forest species composition is an integral part of designing and implementing resource management policies in a national park. Managers must rely on cost-effective methods of vegetation mapping, namely, use of remotely sensed data coupled with digital geographic data, to help them meet their management goals. In this study, we demonstrate that genus-level maps can be generated from unsupervised classifications of Landsat TM data at an accuracy level of 73 percent. Species-level maps can be created to an accuracy level of 58 percent by post-stratification of the spectral classification with topographic data in a geographic information system (GIS). This modification method is a rule-based system whereby spectral forest classes are sorted based on elevation and soil-moisture gradients established for each species through ecological research. Our observations illustrate that spectral classification is optimized by using all six reflective TM bands and that classification accuracy is affected by canopy cover and understory vegetation. Modifying spectral classifications by environmental data in a GIS is a useful way of defining species composition of forests in an area where access to forests is limited but need for map information is great

Photolineament Factor: A New Computer-Aided Method for Remotely Sensing the Degree to which Bedrock is Fractured.

Abstract: Photolineaments are often utilized during exploration for groundwater resources in fractured bedrock; photolineaments are thought to denote areas where the bedrock may be relatively more fractured and, therefore, capable of storing and transproting significant volumes of groundwater. It is suggested that three key characteristics can be used to rank an area's potential to store and transmit large volutmes of groundwater: (1) the number of photolineaments, (2) the number of directional photolineament families, and (3) the total length of photolineaments which occur within or traverse an area of defined radius. The normalized sum of these three photolineament parameters is referred to here as a photolineament factor value. A new computerized approach for processing the thousands of photolineaments typically collected for large strudy sites (>10 km 2 or 2500 acres) yields a contourable grid of photolineament factor values. Such a contour map facilitates rapid quantitative ranking and selection of discrete areas for further evaluation. Results from a 900km 2 (220,000 acre) study area in the Geargia Piedmont illustrate this new approach

Remote sensing of forest stand age using airborne spectrometer data.

Abstract: Analysis of airborne imaging spectrometer data acquired over stands of Douglas-fir suggests that separation of age classes based on the spectral response curve is not feasible. Ratios used to further investigate the possibilities to isolate age classes suggested that a limited separation based on age was possible. Of the original five age classes used, only three were found to be separable. There was no differentiation between stands older than 40 years. This separation is, based on the previous work, thought to be related to the canopy structure, including LAI. The effects of changes in stand age do not translate into variations in the position of the red edge. Analysis of the second derivative of the radiance curves does not indicate that any in the total chlorophyll at the stand level could be mapped through the use of imaging spectrometer data.

State-of-the-art of digital photogrammetric workstations for topographic applications

Abstract: Digital Photogrammetric Workstations (DPWS) have become a major focus of research within the photogrammetric community in the last few years due to an increasing availability of digital imagery and a revolutionary hardware progress in computer science. Today more than a dozen DPWS are offered on the market and they are on the verge of replacing the analytical plotter as the main photogrammetric instrument for evaluating imagery. This paper presents the state-of-the-art of DPWS. A DPWS is the main component of a Digital Photogrammetric System (DPS). A DPS is defined as hardware and software for deriving input data for Geographic Information Systems (GIS) as well as for Computer Aided Design (CAD) systems and other photogrammetric products from digital imagery using manual and automatic techniques. Besides the DPWS itself, a DPS also includes A/D and D/A converters for the imagery (digital cameras, film scanners, and output devices for producing film and paper hardcopies). First, design issues of a DPWS are addressed. Then, the question of automation versus interaction is discussed, and it is pointed out where automation is possible in the chain of processing digital imagery. Subsequently, a classification of the different kinds of DPWS according to the products which can be derived is given. Then first experiences and results obtained by civil mapping organizations involved in digital photogrammetry and using DPWS are described. Finally, requirements for a broader use in practice and trends for further development in digital photogrammetry and in DPS are pointed out

Validating a Florida Scrub Jay habitat suitability model, using demography data on Kennedy Space Center

Abstract: A habitat suitability index (ust) model for the Florida Scrub/ay (Aphelocoma coerulescens coerulescens] wos tested usinga geographic information system for the Tel-4 study site onKennedy Space Center, Florida. The model used suitabilitygraphs that quantify habitat preference with respect to agiven variable to produce spatial estimates of Florida Scrublay habitat suitability. Habitat suitability of each habitatpatch was dependent on its characteristics ond the charac-teristics of its sunoundings. A covetage containing threeyears of demographic data was overlaid on the HSI coverage.Areal correspondence measures and statistical testing werethen performed. Conelation coefficients between modeleddata and demographic data rcnged between 0.60 and 0.87.Spatial residual analysis also showed agteement between themodel and demography data. AII measures of model per-formance suggested that the model accurately predicted hab-itat suitability for the Tel-4 study site. lntroduction The main purpose of geographic information systems (crs)is to process spatial information (Berry, t093). Ecologistshave been modeling processes that involve spatial informa-tion since the early development of their discipline (Hold-ridge, 1,947; Whittaker, 1956; Curtis, 1g5S). However, use ofGIS technology by ecologists has been limited and repre-sents a relatively untapped potential for ecological model-ing of spatial processes (Hunsaker ef d1., 1993). With theaid of today's remote sensing and cIS technology, opportu-nities exist to further develop and test environmental mod-eling techniques that can readily be applied toenvironmental management.Many threatened and endangered wildlife species andtheir habitats are being adversely affected by human distur-bance. It is increasingly important to understand the habitatrequirements, delineate the remaining suitable habitat, andeffectively manage those units for the survival of these spe-cies. Habitat-based modeling techniques can identify remain-ing potential habitat and predict spatial habitat suitability.The modeling techniques should incorporate existing knowl-edge of species-habitat relationships, be reproducible, andprovide a quantitative measure of habitat suitability.Habitat suitability index (Hst) modeling is a common ap-proach to modeling wildlife-habitat relationships (Morrisonet al., 'l,sgz). The methodology was developed to supporthabitat evaluation procedures (ure) used by the United

Lineament anlysis of Landsat Thematic Mapper images, Northern Territory, Australia

Abstract: Lineament analysis of the edge-enhanced Palm Valley Land-sat TM images has defined major E-W, N-S, and a NNW-SSE, NNE-SSW conjugate set of lineaments. Two stages of deformation are proposed to have resulted in the development of the Palm Valley arcuate anticline. The stage-one deformation, with the maximum principal stress 1 oriented in the N-S direction, generated the major gently plunging, E-W trending anticline. The stage-two deformation, with the maximum principal stress 1 oriented in the E-W direction, was responsible for transforming the gently plunging, E-W trending anticline into an arcuate anticline. The western limb was rotated form E-W trending to WSW-ENE and the eastern limb from E-W trending to NW-SE trending. The first-stage shear fractures on the convex side of the arcuate anticline were transformed into extension fractures in this second stage. The surface and drill logging fractures study of do Rozario and Baird (1987) indicates that surface and subsurface fractures are related. Hence, the fracture system of the Palm Valley arcuate anticline can aid subsurface gas exploration in the area

The reversibility of six geometric color spaces

Abstract: Color coordinate systems provide a way to address, to describe, and to manipulate colors. In the field of image processing and computer graphics, several color models in common use include RGB, HVC, HSV, HLS, and ISH. In this study, the reversibility of several color coordinate systems and their numerical characteristics are studied. All these models are classified as user-oriented and related to the perceptual color space.