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As airborne laser scanning (ALS) gains wider adoption to support forest operations in Canada, the consistency and quality of derivative products that support long-term monitoring and planning are becoming a key issues for managers. The Canadian Consortium for Lidar Environmental Applications Research (C-CLEAR) has supported almost 200 projects across Canada since 2000, with forest-related studies being a dominant theme. In 2010 and 2011, field operations were mobilized to support 13 ALS projects spanning almost the full longitudinal gradient of Canada's forests. This paper presents case studies for seven plus an overview of some best practices and data processing workflow tools that have resulted from these consortium activities. Although the projects and research teams are spread across Canada, the coordination and decade of experience provided through C-CLEAR have brought common methodological elements to all. It is clear that operational, analytical and reporting guidelines that adhere to community accepted standards are required if the benefits promised by ALS forestry are to be realized. A national Lidar Institute that builds upon the C-CLEAR model and focuses on developing standards, guidelines, and certified training would address this need. [ABSTRACT FROM AUTHOR]

Change analysis is probably a natural step following the detection of changes using remote sensing data. One significant topic in change analysis is to model the changes in relation to their driving factors and to project future land-use patterns. While logistic regression (LR) has been widely used in change modeling, this paper presents an improved method, kernel logistic regression (KLR), to model the nonlinear relationship between land-use change and various causal factors such as population, distance to road and facilities, surrounding land-use, and others. Traditional KLR models contain one coefficient for each training sample, rendering it inappropriate for applications of land-use change analysis with more than a few thousand samples. A feature vectors selection method for the KLR model has therefore been proposed to impose sparsity and control complexity. To test the effectiveness of KLR, a case study was implemented to model rural-urban land-use conversion in the city of Calgary, Canada during the periods 1985 to 1990 and 1990 to 1999. The KLR model was compared with a commonly used KLR model in terms of the Percentage of Correct Prediction (PCP), Area under Curve (Auc), and McNamara's test, and the results consistently demonstrated the better performance of KLR. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a spatio-spectral template on Ikonos imagery to automatically detect small recreational boats. The spatio-spectral template is employed which is then enhanced through use of a weighted Euclidean distance metric adapted from the Mahalanobis distance metric. The purpose is to assist the Canadian Coast Guard in gathering data on recreational boating for the modeling of search and rescue incidence risk. To test the detection accuracy of the enhanced spatio- spectral template, a dataset was created by gathering position and attribute data for 53 recreation vessel targets purposely moored for this research within Cadboro Bay, British Columbia, Canada. The Cadboro Bay study site containing the targets was imaged using Ikonos. The overall detection accuracy was 77 percent. The targets were broken down into two categories: Category A-less than 6 m in length, and Category B-greater than 6 m long. The detection rate for the Category B targets was 100 per- cent, while the detection rate for the Category A targets was 61 percent. It is important to note that some Category A targets were purposely selected for their small size to specifically test the detection limits of the enhanced spatio-spectral template. The smallest target detected was 2.2 meters long and 1.1 meters wide. The analysis also revealed that the ability to detect targets between 2.2 meters and 6 meters long was diminished if the target was a dark color. [ABSTRACT FROM AUTHOR]

In forestry, the availability of high spatial resolution (<1 m/pixel) imagery from new earth observation satellites like Ikonos favours a shift in the image analysis paradigm from a pixel-based approach towards one dealing directly with the essential structuring element of such images: the individual tree crown (ITC). This paper gives an initial assessment of the effects of 1 m and 4 m/pixel spatial resolutions (panchromatic and multispectral bands, respectively) on the detection, delineation, and classification of the individual tree crowns seen in Ikonos images. Winter and summer Ikonos images of the Hudson plantation of the Petawawa Research Forest, Ontario, Canada were analyzed. The panchromatic images were resampled to 0.5 m/pixel and then smoothed using a 3 × 3 kernel mean filter. A valley-following algorithm and rule-based isolation module were applied to delineate the individual tree crowns. Local maxima within a moving 3 × 3 window (i.e., Tree Tops) were also extracted from the smoothed images for comparison. Crown delineation and detection results were summarised and compared with field tree counts. Overall, the ITC delineation and the local maxima approaches led to tree counts that were on average 15 percent off for both seasons. Visual inspection reveals delineation of clusters of two or three crowns as a common source of error. Crown-based species spectral signatures were generated for six classes representing conifer species, plus a hardwood class and a shrub class. After the ITC-based classification, classification accuracy was ascertained using separate test areas of known species. The overall accuracy was 59 percent. Important confusion exists between red and white spruces, and mature versus immature white pines, but post-classification regroupings into single spruce and white pine classes led to an overall accuracy of 67 percent. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at the 30th Canadian Symposium on Remote Sensing entitled, "Bridging Excellence" that was held at the University of Lethbridge in Lethbridge, Alberta from June 22-25, 2009 is presented. Topics include the academic, commercial and political aspects of remote sensing in Canada. It mentions that the symposium was attended by 200 participants with Dr. Derek Peddle as the conference director.

Landsat Thematic Mapper (TM) data have been used to detect the presence of altered rocks associated with ore mineral deposition. In arid environments, the spectral signatures of diagnostic minerals are often not masked by water, vegetation, or surficial materials. There are many studies in which TM data have been used to locate hydrothermallyaltered rocks in near desert regions, but few successful examples can be found that relate to glaciated, vegetated terrain at high latitudes. In this paper, TM data, in concert with lithogeochemical data and field observations, are used to detect and map altered rocks in theSulphurets-Brucejack Lake district of northwestern British Columbia.Areas of iron-pyrite and phyllic (sericitic) alteration as well as areas of intense silicification have been located using band ratioing techniques. The chemical nature of these alteration zones has been established through comparison with lithogeochemical data. These alteration styles reflect the underlying rock type, as would be expected, but also appear to reflect two different styles of alteration, perhapsreflecting different mineralizing systems. The western half of the study area, characterized by iron-pyritic alteration, reflects a base metal (Cu porphyry and minor Au) system while the eastern half, characterized by areas of silicification and clay alteration, may represent a precious metal (Au, As, Sb) system. [ABSTRACT FROM AUTHOR]