Bert Guindon

On the Slope-Aspect Correction of Multispectral Scanner Data

SUMMARYThe effects of topography on the radiometric properties of multispectral scanner (MSS) data are examined in the context of the remote sensing of forests in mountainous regions. The two test areas considered for this study are located in the coastal mountains of British Columbia, one at the Anderson River near Boston Bar and the other at Gun Lake near Bralorne. The predominant forest type at the former site is Douglas fir, whereas forest types at the latter site are primarily lodgepole pine and ponderosa pine. Both regions have rugged topography, with elevations ranging from 330 to 1100 metres above sea level at Anderson River and from 750 to 1300 metres above sea level at Gun Lake.Lambertian and non-Lambertian illumination corrections are formulated, taking into account atmospheric effects as well as topographic variations. Terrain slope and aspect values are determined from a digital elevation model and atmospheric parameters are obtained from a model atmosphere computation for the solar angles an...

An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images

A haze optimized transformation (HOT) is developed and assessed for the detection and characterization of haze/cloud spatial distributions in Landsat scenes. The transformation is derived from an analysis of a visible-band space where spectral response to diverse surface cover classes under clear-sky conditions is highly correlated, but spectral response to haze is highly sensitive to both optical wavelength and haze optical depth. The robustness of the detection algorithm is demonstrated through its application to visible band imagery of seven Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scenes that encompass diverse surface cover and atmospheric characteristics. A methodology for utilizing the transformed image to radiometrically compensate visible band imagery is presented and quantitatively tested in the correction of an example ETM+ scene.

Haze detection and removal in high resolution satellite image with wavelet analysis

A procedure for haze detection and removal from high-resolution satellite images using wavelet analysis (HAWAT) has been developed. It involves the analysis of the low spatial frequency information content of a scene. The image contaminated by haze is decomposed into different spatial layers with wavelet transforms. Although haze is distributed in the lower frequency layer, this layer may also contain a component of land cover that is spatially and temporally relatively stable. A haze-free reference image of the same area is used to characterize land cover. The component of the characterized land cover is then subtracted with wavelet analysis. The residual wavelet coefficients are used to construct a spatially varying mask for subsequent haze detection and removal. After smoothing, the mask is subtracted from the contaminated image to obtain a corrected image with haze-off characteristics. Both visual inspection and statistical accuracy assessment show that the haze calibration is valid and robust.

The CCRS SAR/MSS Anderson River Data Set

Technical Committee no. 7 of the International Association of Pattern Recognition is seeking test data sets that would further research into pattern recognition for remote sensing. Such data sets are usually expensive to acquire and are rarely made available. The Canada Centre for Remote Sensing (CCRS) has chosen to make the SAR/MSS Data Set for Anderson River available. This paper describes the contents and structure of the data set. Several major studies were conducted using these data by the authors and their colleagues. This paper will also summarize the results of these investigations conducted over four years. Studies included classification accuracies with and without terrain slope and aspect corrections, optimum sensor and feature selection, texture features, and multisensor data integration. Finally, the authors describe the procedure whereby other scientists can gain access to the data set.

Using satellite remote sensing to survey transport-related urban sustainability: Part II. Results of a Canadian urban assessment

The growing importance of urbanization in Canada highlights the need for nationally consistent information on major cities to support effective policy development. A spatially-explicit database, the Canadian Urban Land-Use Survey (CUrLUS), is described. It is a comprehensive source of integrated contemporary land-cover/land-use, demographic and socio-economic information as well as historic land use characterizations from earlier federal initiatives. Satellite remote sensing plays a key role in the form of provision of Landsat-based thematic classifications. The utilization of CUrLUS is illustrated in the quantification of transportation-related energy sustainability indicators, namely, density, urban compactness and land-use mix. The latter shows the greatest promise, being significantly correlated to both work-related median travel distance and percent private vehicle use. Urban transportation is complex and it is argued that indicators based solely on statistical and spatial analysis methodologies are limited in abilities to directly address specific components of this issue, for example, energy consumption. It is recommended that more sophisticated, model-enhanced indicators be developed. We also demonstrate that the land-use/urban-form information of CUrLUS will be a cornerstone in this endeavour.

A Robust Approach for Object-Based Detection and Radiometric Characterization of Cloud Shadow Using Haze Optimized Transformation

Cloud shadows in satellite imagery hinder understanding of ground surface conditions due to reduced illumination and the potential for confusion with illuminated low-reflectance objects such as water bodies. This paper extends the application of the haze optimized transform (HOT) from haze mapping to include object-oriented detection of clouds and cloud shadows. An integrated processing chain encompassing these tasks has been implemented and successfully applied to Landsat Enhanced Thematic Mapper Plus and Multispectral Scanner imagery covering a variety of land covers and landscapes. The results confirm that the HOT-based method for cloud shadow detection is robust and effective. Cloud shadows have been identified and extracted with overall accuracy of about 95.3%. Clear-sky dark pixels (e.g., small lakes) are well separated from cumulus cloud shadow pixels. The spatial distribution of HOT response in a given cloud patch can be used to estimate the extent and variation of incoming visible radiation reduction in its corresponding shadow patch. This information, in turn, has been used to apply a radiometric gain to compensate for the shadowing effect on the land. The HOT response has been tested for radiometric characterization of cloud shadows and subsequent shadow illumination compensation.

Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover

Abstract A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.

Landsat urban mapping based on a combined spectral-spatial methodology

As part of a program to monitor the evolution of urban growth and to encapsulate aspects of urban sustainability of major Canadian cities, we have developed a new methodology for improved urban delineation. This approach involves the generation of two independent land cover products based on pixel-based and segment-based classifications. These classifications are merged through a rule-based approach to exploit the fundamental advantages of each product. It is shown that a relatively simple rule set can be used both to infer new land cover and land use classes and to simplify the complex land cover class mix that is characteristic of low density residential areas. This paper presents an overview of the methodology and quantitative assessment of its performance. A study has also been conducted to quantify a building density detectability threshold for TM data. Example results are major Canadian cities including Ottawa, Calgary and urban centres in southwestern Ontario that have recently experienced rapid growth.

Forest Classification Using Simulated Landsat-D Thematic Mapper Data

SUMMARYThe expected performance of the LANDSAT-D Thematic Mapper (TM) in the forestry context has been evaluated from airborne Multi-Spectral Scanner (MSS) data and compared with the performance of simulated and actual data of the LANDSAT MSS. Specific comparisons are also made to examine the separate effects of the differences in spectral bands, radiometric resolution, and spatial resolution. We find that simulated TM data are significantly better than LANDSAT MSS data for the spectral classification of forest types. If TM imagery is used, the average misclassification error is reduced from 33% to 17%. Most of the improvement is attributable to the number and better wavelength characteristics of the TM spectral bands and, to a lesser extent, the improved TM radiometric resolution. No significant improvement in spectral classification was found as a result of the increased spatial resolution.

Urban land use mapping using high resolution SAR data based on density analysis and contextual information

This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building–road, urban block, and suburban commercial–industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial–industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%.