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Beauchemin M.,Canada Center For Remote Sensing
Neurocomputing | Year: 2015

In the first part of this paper, we present a method to build affinity matrices for spectral clustering from a density estimator relying on K-means with subbagging procedure. The approach is anchored in the theoretical works of Wong (1980, 1982a, b) [13-15] on the asymptotic properties of K-means as a density estimation method. The subbagging procedure is introduced to improve the density estimate accuracy. The behavior of the proposed method is analyzed on diverse data sets and two new mechanisms are suggested to improve clustering results on non-convex data. In the second part of the paper, we establish a link between the presented method and the evidence accumulation clustering (EAC) approach by showing that a normalized version of the density-based similarity matrix is approximately equal to a normalized version of the co-association matrix. The co-association matrix provides the co-occurrence probability of data pairs assigned to a same cluster over multiple K-means clustering partitions. Experimental results on artificial and real data demonstrate the effectiveness of the method and provide empirical support for the established link. © 2014.

Toutin T.,Canada Center For Remote Sensing
International Journal of Image and Data Fusion | Year: 2011

The three-dimensional (3D) geometric processing and ortho-rectification of remote sensing (RS) data becomes a key issue as being the first step in multi-source multi-format data fusion in geographic information systems. The fusion of image data (visible and microwave, panchromatic and multi bands, polarimetric bands, passive and active, etc.), their metadata (GPS, star trackers, inertial system, lens and focal plane, etc.), the associated 3D cartographic data (ground control points, contour lines, digital terrain model (DEM), planimetric features, etc.) is thus a requisite to perform first 3D precise geometric correction and then the ortho-rectification process with DEM. This article presents an update of the state-of-the-art of geometric correction with the source of geometric distortions, the different mathematical models, the methods, algorithms and processing steps to track finally the error propagation during the fusion of the different RS and cartographic data from the image acquisition to the ortho-rectification processes. © 2011 Her Majesty the Queen in Right of Canada.

Olthof I.,Canada Center For Remote Sensing | Pouliot D.,Canada Center For Remote Sensing
Remote Sensing of Environment | Year: 2010

Climate change is expected to have significant impacts on northern vegetation, particularly along transition zones such as the treeline. Studies of vegetation composition and change in this ecotone have largely focussed on local analysis of individual trees using labour intensive stand reconstruction techniques, which are spatially limited and do not consider vegetation types other than trees. Remote sensing may be well suited to monitoring recent changes across the treeline because it captures integrated changes of all vegetation life forms over large spatial extents. This research examines treeline vegetation composition and change along the western subarctic treeline mapped by Timoney et al. (1992) using a 1 km resolution, 22-year AVHRR archive from 1985-2006. While most remote sensing studies on vegetation change in arctic and subarctic regions only exploit information contained in the Normalized Difference Vegetation Index (NDVI), we examine long-term reflectance trends in AVHRR bands 1 and 2 in addition to NDVI. The GeoSail canopy reflectance model is used to map treeline composition by combining information from 22-year summertime and early springtime composite images. A set of spectral change vectors are then generated from GeoSail simulations and used to classify trends in AVHRR along the treeline to estimate vegetation change. Evaluation of vegetation composition against the MODIS Vegetation Continuous Fields (VCF) product that has been recently validated along the treeline reveals good spatial correspondence. Temporal trends are shown to agree with literature on tundra-taiga vegetation dynamics in recent decades. Evidence is presented that suggests replacement of bare surfaces with herb, conifer decline along the southern treeline, increased shrubiness, and increased conifer recruitment and growth in the north. Crown Copyright © 2009.

Zhang Y.,Canada Center For Remote Sensing
Journal of Geophysical Research: Earth Surface | Year: 2013

Although studies agree that climate warming will cause permafrost thaw, projected permafrost conditions differ widely, and most projections use half degree latitude/longitude or coarser spatial resolution. Using a process-based model, this study projected changes of permafrost from 2010 to 2200 at 30 m by 30 m resolution for a region in the northwest of the Hudson Bay Lowlands in Canada. This long-term spatially detailed modeling revealed some general features of permafrost dynamics with climate warming. Temporally, permafrost degradation at a site can be divided into five stages: gradual-thawing stage, increased-thawing stage, frequent-talik stage, isothermal-permafrost stage, and permafrost-free stage. This study determined the beginning or ends of the stages for each grid cell and mapped the degradation stages in this region. Spatially, permafrost was predicted to become increasingly discontinuous with climate warming. By the end of the 22nd century, only 20% to 65% of the land area in this region will be underlain by permafrost. With the formation of taliks, the maximum summer thaw depth will increase significantly, and near-surface permafrost will disappear in many areas while permafrost at depth can persist for decades. Thus, the spatial distribution of near-surface permafrost and permafrost at depth can be very different. This study also shows that climate scenarios, the depth of permafrost considered, spatial resolution and associated ground conditions used for modeling could cause significant differences in permafrost projections. Key Points Permafrost degradation at a site can be divided into five stages Permafrost is predicted to become increasingly discontinuous The distributions differ for permafrost in near-surface and at depth ©2013. Her Majesty the Queen in Right of Canada.

Toutin T.,Canada Center For Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

The new high-resolution mode of Radarsat-2 was evaluated for digital elevation model (DEM) generation using stereo-radargrammetry with Toutin's 3-D physical model as part of the Canadian Space Agency's programs. In addition, the impact of radar parameters on stereo-extracted DEMs was evaluated. Three stereo pairs using different radar parameters (resolutions; HH and VV polarizations; slant and ground range; image spacing) from ultrafine-mode Radarsat-2 data acquired over a test site north of Qubec City, Canada, were formed to generate DEMs, which were thus compared to 15-cm-accurate lidar elevation data. Results showed a good accuracy for the three stereo-extracted DEMs: less than 0.8 m for the horizontal positioning and around 3 m (1σ) for the elevation on bare soils, with small 3-D biases (2-3 m). The HH stereo pair in slant-range format slightly but consistently achieved the best results. © 2006 IEEE.

Toutin T.,Canada Center For Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

Digital surface models (DSMs) extracted from high-resolution Radarsat-2 stereo-images using different geometric modeling (deterministic, new hybrid, and empirical) are evaluated. The 3-D deterministic models are Toutin's and hybrid Toutin's models (TM and HTM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). TM is computed with one and eight ground control points (GCPs), HTM without GCP and RFM supplied by MacDonald, Dettwiler and Associates Ltd. is postprocessed with 3-9 GCPs depending of degrees of 2-D polynomial functions. The DSMs are then generated and compared to 0.2-m accurate lidar elevation data. Because DSMs included the height of land covers, elevation linear errors with 68% and 90% confidence level (LE68 and LE90) are computed and compared over bare surfaces only. LE90 results are: TM with eight GCPs achieves the best results (6.3 m), then HTM with no GCP (7 m), TM with one GCP (8.6 m), and finally RFM the worst (9.7 m) whatever the polynomial degree and GCP number. HTM is the only modeling not using any GCP, which offers a strong advantage in operational environments. © 2012 IEEE.

Canisius F.,Canada Center For Remote Sensing | Fernandes R.,Canada Center For Remote Sensing
Remote Sensing of Environment | Year: 2012

Substantial research has been conducted to derive Leaf Area Index (LAI), an essential climate variable, from satellite imageries acquired by moderate resolution optical sensors. The Medium Resolution Imaging Spectrometer (MERIS) is unique among such sensors in that it provides relatively high spectral (15 bands) and spatial (~. 300. m resolution) sampling within visible and near infrared wavelengths. A recent evaluation of four operational MERIS LAI algorithms found that they did not consistently meet accuracy targets typical of operational requirements. One explanation for the mixed performance of these algorithms may be that they do not suitably exploit the enhanced spectral sampling of MERIS. We exploit this enhanced spectral sampling to estimate several (80) narrow-band vegetation indices (VIs) by interpolating MERIS surface reflectance. The interpolation accuracy was evaluated using Hyperion imagery. Regressions were then calibrated between estimated VIs and in-situ LAI over a range of land cover types. The strongest performance (root mean squared error < 0.92 and relative root mean squared error < 0.38) was observed for two selected VIs (the NDVI8 and the CTR) based on both training and validation data. This study demonstrates that MERIS has the information content to meet typical operational performance specifications for LAI retrieval within the 1 unit error margin for the given atmospheric, environmental, soil and plant cover conditions on the day of the overpass and using locally derived relationships. Therefore the development of robust algorithms for retrieving LAI using these VIs is recommended. © 2012.

The impact of water stress on plant stomatal conductance (g) has been widely studied but with little consensus as to the processes governing its responses. The photosynthesis-driven stomatal conductance models usually employ constant model parameters and attribute the decrease of g from water stress to the reduction of leaf photosynthesis. This has been challenged by studies showing that the model parameter values decrease when the plant is under water stress. In this study, the impact of plant water stress on the parameter values in stomatal conductance models is evaluated using the approach recently developed by S. Wang et al. and the tower flux measurements at a Canadian boreal aspen forest. Results show that the slope parameter (a) in the stomatal conductance models decreases substantially with the development of plant water stress. The magnitude of this reduction is dependent on how plant water stress is represented. Overall, the relative reduction of α from its maximum value is 28% when soil water content decreases from 0.38 to 0.18 m 3 m -3, and is 38% when Bowen ratio increases from 0.25 to 3.5. Equations for α correction to account for water stress impacts are proposed. Further studies on different ecosystems are necessary to quantify the parameter variations with water stress among different climate regions and plant species. © 2012 American Meteorological Society.

Beauchemin M.,Canada Center For Remote Sensing
Pattern Recognition Letters | Year: 2013

In this paper, an algorithm for image thresholding based on semivariance analysis is presented. The rationale of the approach is to binarize an image such that it best reproduces the original image variation across several spatial scales. The method can be alternatively viewed as one identifying the binary image that best approximate the overall level of edgeness measured across multiple scales in the original image. A comparison with seven other thresholding methods is presented for 2 synthetic images and 22 Non-Destructive Testing (NDT) grey level images. The results indicate that the proposed method is highly competitive. Performance of the proposed method in relation to the image content is also discussed. Copyright © 2012 Published by Elsevier B.V. All rights reserved.

Huang J.,Canada Center For Remote Sensing | Veronneau M.,Canada Center For Remote Sensing
Journal of Geodesy | Year: 2013

A new gravimetric geoid model, Canadian Gravimetric Geoid 2010 (CGG2010), has been developed to upgrade the previous geoid model CGG2005. CGG2010 represents the separation between the reference ellipsoid of GRS80 and the Earth's equipotential surface of W0=62,636,855.69 m2s-2. The Stokes-Helmert method has been re-formulated for the determination of CGG2010 by a new Stokes kernel modification. It reduces the effect of the systematic error in the Canadian terrestrial gravity data on the geoid to the level below 2 cm from about 20 cm using other existing modification techniques, and renders a smooth spectral combination of the satellite and terrestrial gravity data. The long wavelength components of CGG2010 include the GOCE contribution contained in a combined GRACE and GOCE geopotential model: GOCO01S, which ranges from -20.1 to 16.7 cm with an RMS of 2.9 cm. Improvement has been also achieved through the refinement of geoid modelling procedure and the use of new data. (1) The downward continuation effect has been accounted accurately ranging from -22.1 to 16.5 cm with an RMS of 0.9 cm. (2) The geoid residual from the Stokes integral is reduced to 4 cm in RMS by the use of an ultra-high degree spherical harmonic representation of global elevation model for deriving the reference Helmert field in conjunction with a derived global geopotential model. (3) The Canadian gravimetric geoid model is published for the first time with associated error estimates. In addition, CGG2010 includes the new marine gravity data, ArcGP gravity grids, and the new Canadian Digital Elevation Data (CDED) 1:50K. CGG2010 is compared to GPS-levelling data in Canada. The standard deviations are estimated to vary from 2 to 10 cm with the largest error in the mountainous areas of western Canada. We demonstrate its improvement over the previous models CGG2005 and EGM2008. © 2013 Her Majesty the Queen in Right of Canada.

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