Présentation de l’article 2

Cet article aborde la problématique liée à la qualité des données d’élévation actuelles dans le Nord du Canada. Il décrit la réalisation du deuxième sous-objectif, visant à créer un modèle altimétrique optimal pour la zone d'intérêt. En effet, le Nord du Canada a un potentiel exceptionnel pour l'exploitation minière. Cependant, sa vaste étendue et les difficultés d'accès des sites d'exploitation potentiels présentent un obstacle à la profitabilité des projets miniers. Les modèles d'élévation sont très utiles pour cibler des sites d'exploitation potentiels, mais ceux actuellement disponibles ne sont pas optimaux principalement parce qu’ils ont étés dérivés de courbes de niveau à équidistance variable et avec une valeur au mètre. Le projet vise donc à améliorer la qualité des données d’élévation disponibles dans le Nord du Canada. Pour le site en étude situé au Territoire du Nord-Ouest, Canada, une méthode basée sur la stéréoradargrammetrie a été développée pour générer des modèles numériques de surface (MNS) à partir d'imagerie Radarsat-2 (RS-2). Des MNS préliminaires ont été générés, sans point de contrôle, et assemblés à partir de 136 images RS-2 acquises en mode wide-ultrafine. L'exactitude verticale moyenne de ces MNS préliminaires variait entre 9 et 11 m en fonction des configurations utilisées pour la stéréorestitution. En utilisant différentes combinaisons de MNS préliminaires, cinq MNS ont été assemblés à partir de ces MNS préliminaires pour permettre une couverture complète du site d'étude. Leur analyse a permis de constater une

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amélioration de 60% de l'exactitude, par rapport aux MNS préliminaires. Les MNS assemblés à partir de configuration stéréo de même côté ont produit la meilleure exactitude (4 m) tandis que le MNS assemblé à partir de visées opposées avait une exactitude légèrement inférieure (5 m) mais utilise moins de 70% des images RS-2 commandées initialement. Ces résultats démontrent clairement que l'assemblage a été essentiel à l'amélioration de l'exactitude des MNS produits. L’analyse locale a démontré qu’il n’y a pas de patron d’exactitude associé à une catégorie de configuration donnée. Cependant, il en ressort que l’utilisation d’information périphérique lors de l’assemblage permet d’augmenter l’exactitude du résultat final d’environ 30%.

Une phase d'analyse préliminaire a été réalisée pour comprendre le fonctionnement du logiciel Correlator3D, utilisé pour la stéréorestitution. Les détails concernant l’algorithme implanté dans ce logiciel n’ont pas pu être révélés pour des questions de propriété intellectuelles. L'algorithme requiert, d'une part l’utilisation d’une autre source d’élévation, ou élévation de base comme point de départ pour le calcul des valeurs altimétriques. D'autre part, il requiert un intervalle de recherche, par rapport à cette élévation de base, à l’intérieur duquel l’élévation recherchée devrait se trouver. Les résultats de la phase d'analyse préliminaire ont démontré une sensibilité importante à l’élévation de base et à l’intervalle de recherche considéré. En effet, des modifications apportées à l'un ou l'autre de ces paramètres entrainaient un impact important sur le résultat. Par exemple, l’augmentation de l’intervalle de recherche amplifiait considérablement les différences d’élévation observées entre l’élévation de base et l’élévation produite. Ce constat a permis de raffiner la méthode de stéréorestitution en utilisant une élévation de base la plus précise possible et de réduire l’intervalle de recherche afin d'éviter les aberrations et d'optimiser le résultat. Plus spécifiquement, un pré-traitement a été appliqué aux données numériques d’élévation du Canada (DNEC), utilisé comme élévation de base, pour corriger son biais par rapport aux données GLA14 filtrées dans le chapitre 3. Afin de fournir l’intervalle de recherche le plus petit possible, l’erreur quadratique moyenne à un niveau de confiance de 95% (RMSE95) a été calculée pour le DNEC, en utilisant les données GLA14 filtrées. Cette valeur fournit théoriquement la plus grande variabilité attendue par rapport à la vérité terrain. Ensuite,

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puisque le DNEC représente l’élévation au sol et que la stéréorestitution radar produit une valeur d’élévation de surface, il aurait été nécessaire de considérer cette différence dans l’intervalle de recherche. Mais, étant donné que l’occupation du sol de la zone d’étude n’aura généralement pas tendance à créer une différence entre l’élévation perçue par le capteur et l’élévation au sol et, considérant le fait que l’intervalle de recherche doit être restreint, cette différence n’a pas été considérée.

La première partie de la méthodologie consistait donc à faire la stéréorestitution de paires d'images RS-2 pour produire les MNS préliminaires. Un total de 136 images RS-2 ont été utilisées pour générer 136 MNS préliminaires. L'exactitude de ces MNS préliminaires a ensuite été analysée à partir des données GLA14 filtrées, en tenant compte des angles d'incidences utilisés pour la stéréorestitution. Ensuite, des sélections de MNS préliminaires ont été assemblées afin de produire 5 MNS couvrant la zone d'étude en totalité. Ces MNS sont analysés afin d'identifier la sélection optimale pour la zone d'intérêt. Aucune stéréoconfiguration n’a pu être identifiée comme étant idéale pour la SRG, en raison de la grande variabilité des exactitudes verticales. Par contre, l'analyse a indiqué que les images devraient idéalement être acquises à des températures en dessous de 0°C, pour minimiser les disparités radiométriques entre les scènes. Les résultats ont aussi confirmé que la pente est le principal facteur d’influence sur l’exactitude de MNS produits par SRG. La meilleure exactitude verticale (4 m) a été obtenue avec des configurations de même direction de visées. L’assemblage des configurations de visées opposées a produit une exactitude du même ordre (5 m) tout en permettant de réduire le nombre d’images utilisées de 30% par rapport au nombre d'images acquises initialement. L'utilisation d'images de visées opposées pourrait permettre d’augmenter l’efficacité de réalisation de projets de SRG en diminuant la période d’acquisition.

L'analyse des MNS produits aura permis d'identifier le MNS optimal pour la zone d'étude. Par ailleurs, le projet aura conduit au développement d’une stratégie d'acquisition d'images stéréoradargrammétriques minimisant le nombre d’images à acquérir. Finalement, le projet aura mis en évidence l’importance de l’assemblage des MNS dans la production de données altimétriques les plus exactes à partir d'imagerie RS-2.

34 4.2. Article 2

Abstract :

Northern Canada has an exceptional mining potential. However, its vast extent combined with its remoteness is an obstacle for the profitability of mining projects. Elevation models are very useful for targeting potential mining areas, but the currently available models are not optimal primarily because they have been derived from contours with values at a metric precision. The objective of this project is to improve the quality of the elevation data available in Northern Canada. For the study site located in the Northwest Territories, Canada, a stereoradargrammetry method was developed to generate Digital Surface Models (DSMs) from Radarsat-2 (RS-2) images. Sub-DSMs were generated, without control points, and assembled from 136 RS-2 images acquired in the wide- ultrafine mode. The average vertical accuracy of the generated sub-DSMs was 9 to 11 m depending on the configuration used for stereorestitution. Using different sets of sub- DSMs, five DSMs were assembled from these sub-DSMs for full coverage of the study site. Analyses of the results indicated that they improved the vertical accuracy up to 60% compared to the accuracy of the sub-DSMs. The assembled DSM from same-side stereo configurations achieved the best accuracy (4 m), while the DSM assembled with opposite-side view had a slightly lower accuracy (5 m) but used less than 70% of the images originally ordered. These results clearly show that assembling was essential in improving the DSM accuracy. Local analysis showed no accuracy pattern associated with stereo configuration categories. However, it resulted from this analysis that additional peripheral information around the area of interest increases the assembled result’s accuracy of about 30%.

Introduction

While Northern Canada has an exceptional mining potential, the logistical challenges of locating resources in such remote regions can reduce the profitability of mining projects because of the high cost of exploration. To help targeting potential areas of interest for the mining industry, scientists from the Geological Survey of Canada (GSC) developed Remote Predictive Mapping (RPM), a method to create maps of geological potential

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(Schetselaar et al., 2007). In this process, elevation information is a key input. However, north of the 60th parallel, the currently available Digital Elevation Models (DEMs) have two limitations: 1) they are often the result of the interpolation of contours and, consequently, some of the terrain textures are missed between contour lines; 2) they demonstrate a stepping effect due to the storage of elevation values as integers. Removing this effect and using non-interpolated elevation data could improve the reliability of the geological potential maps generated from RPM.

To generate elevation data, several remote sensing approaches, with different limitations, can be found in the literature (Toutin et al., 2013; Beaulieu & Clavet, 2009). Optical sensors can only be used during cloud-free and daytime conditions. LIDAR is the most precise solution, but with the highest acquisition cost. As for Synthetic Aperture Radar (SAR), it is independent of solar radiation, weather conditions and is available at relatively reasonable cost. Further, it provides good geographic coverage at decent spatial resolution. RADARSAT-2 (RS-2) SAR images were used in this project because the images could be ordered on demand and this allowed us to cover the study site with specific beams and swath positions. While better accuracy could be achieved using interferometry (Toutin & Gray, 2000), stereoradargrammetry (SRG) was deemed more appropriate due to the RS-2 revisit time (24 days). Considering that the elevation obtained using interferometry is susceptible to temporal decorrelation (Toutin & Gray, 2000), SRG is more stable (Balz et al., 2013) and, consequently, it is more suitable for mapping large areas with heterogeneous land cover and relief. However, since the RADAR signal is sensitive to surface components, it must be emphasized that SRG generates Digital Surface Models (DSMs).

SRG extracts elevation from a pair of radar images. Theoretically, it leads to optimal results when the right trade-off is achieved between radiometric coherency and parallax (Toutin, 2012). In practice, reaching this trade-off can be challenging because the two conditions are contradictory: good parallax is achieved with opposite-side views, while radiometric coherency is maximized when both images are acquired from the same point of view (Toutin & Gray, 2000).

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In this paper, we aim to improve the quality of the elevation data available in Northern Canada through the generation of DSMs using SRG for applications such as RPM. The study was conducted using an extensive amount of RS-2 images over a large study area. While focusing not on the model used for the stereorestitution but rather on the influence of the stereo configurations on accuracy, their impact was analyzed by comparing the accuracy resulting from different stereo configurations. Radiometric disparities were also considered in this analysis. Then, results from different assembling strategies were compared in order to identify which one provided the optimal result over the study site.

Background on Stereoradargrammetry

The configuration of a stereographic image pair can be described by two factors: the difference in the acquisition angles between images and their relative perspective of the scene (same-side or opposite-side view). Figure 4-1 illustrated the influence of stereo configuration on parallax (ρ). Previous studies aimed to correlate the vertical accuracy of extracted elevation data with stereo configurations. Results, summarized by Toutin (1999), showed that: 1) optimum differences in acquisition angles are about 40°-45°; 2) higher accuracy is consistently achieved with opposite-side stereo viewing (see also Balz

et al., 2013); 3) higher accuracy is not correlated with higher ground resolution and is not

associated with the largest difference between acquisition angles. In addition, Table 4-1 presents the optimal configurations with respect to the terrain reliefs (Toutin, 1999).

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Figure 4-1: Illustration of the components of stereo-configuration (the difference

between acquisition angles and their relative perspective) and their influence on parallax (ρ) (Toutin, 1999).

Table 4-1: Optimal configurations of stereographic pairs for different terrain reliefs

(Toutin, 1999). "Look angles" refer to the angular offset from the horizontal.

Terrain Relief Slope Flat 0°-10° Rolling 10°-30° Mountainous 30°-50° Trade-off Opposite-side with

steep look angles large intersection Same-side with

or

Opposite-side with shallow look angles

Same-side with small intersection

angles (steep or shallow)

Furthermore, the accuracy is expected to drop when increasing the difference between view angles due to an increase in radiometric disparities (Toutin, 1999). Conversely, vertical accuracy should be more homogeneous with small differences between acquisition angles, as a consequence of smaller radiometric discrepancies (Toutin, 2002).

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This shows that vertical accuracy is not only dependent on parallax, but it is also influenced by radiometric coherency between the scenes, especially when the scenes are autocorrelated. Since the two images of a given pair must have different acquisition angles, targets characterized by a high dynamic in their angular responses will produce radiometric discrepancies between images. For a radar operating in C-band, such as Radarsat-2, vegetation can generate radiometric discrepancies due to their angular dynamic which depends on vegetation type (shrubs, low-lying vegetation, trees, etc.) and amount (Ulaby, 1989). Also, because images of a stereo-pair are often acquired at two different dates, changes in vegetation phenological development or changes in snow thickness or in temperature (T>0°C vs T<0°C) and wetness, are additional sources of radiometric discrepancies (Ulaby, 1986).

Study Site and Data

The study site is located in the Northwest Territories, Canada, and covers 23 000 km² (Figure 4-2). About 70% of the land cover is bare land, water and low-lying vegetation. The remaining 30% is covered by isolated tree patches and shrubs. Snow cover is generally present from October to April. The relief is flat; almost all slopes are within 0- 10% grade, with altitude ranging from 260 m to 440 m. Figure 4-2 presents the study site, its elevation and hydrography with the National Topographic System units at the 1:50000 scale (NTS50K) subdivisions that consist of 32 even sub-areas. The latter were used to perform local analyses over the study site.

To achieve the objective of this paper, the following data were used: Radarsat-2 images, Geoscience Laser Altimeter System (GLAS) from the Ice Cloud and land Elevation Satellite (ICESat), Canadian Digital Elevation Data (CDED) and other auxiliary data such as hydrography, meteorology, and land cover.

To provide complete coverage of the study site, 136 RS-2 images in wide-ultrafine mode were acquired, from October through December 2012. These images, with a nominal resolution of 3 m and incidences angles ranging from 30° to 49°, were acquired in both ascending and descending overpasses. Table 4-2 presents the amount of RS-2 images available for each beam.

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Figure 4-2: Location of the study site with elevation and hydrography. The subdivisions

correspond to the NTS50K.

Table 4-2: Beams (look angle ranges) and number of available RS-2 images for this

study.

Beams Number of images

U3W2 (U3) (30.4°-33.8°) 36

U8W2 (U8) (34.6°-37.8°) 34

U19W2 (U19) (42.7°-45.4°) 32

U25W2 (U25) (46.6°-49°) 34

Data from the Geoscience Laser Altimeter System (GLAS) of the Ice Cloud and land Elevation Satellite (ICESat) was used as a reference to evaluate the vertical accuracy of generated DSM (Beaulieu & Clavet, 2009). With a sub-metric accuracy, this data is considered to be sufficiently accurate for the evaluation of stereorestituted DSM accuracy (Beaulieu & Clavet, 2009; NDEP, 2004; Proulx-Bourque et al., 2015). The laser beam, with a wavelength of 1064 nm, produces a spot of ~70 m diameter every ~170 m along the orbit (Brenner et al. 2007). The satellite was launched in 2003 and operated for 18

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laser-operation campaigns until 2010. All available level-2 Global Land Altimetry (GLA14) elevation points within the study site were used. While GLA14 is meant to provide elevation at ground level, the signal is sensitive to surface and atmospheric components, such as trees and boulders or clouds, within the footprint. Consequently, GLA14 data was filtered (Proulx-Bourque et al., 2015) before being used as control points. A total of 18523 filtered GLA14 elevation points were used for the evaluation of the generated DSM. Their spatial distribution over the study site is shown in Figure 4-3.

Figure 4-3: Filtered GLA14 elevation points used as control points over the study site.

With a vertical accuracy of 6 m over the study site (Proulx-Bourque et al., 2015), Canadian Digital Elevation Data (CDED) were used as an altitude baseline for the stereorestitution. The National Hydrographic Network (NHN) was used to locate water bodies in the study site. The meteorological data (Environment Canada, 2014) provided temperature and snow cover information at the acquisition time of each RS-2 image. Finally, the land cover information was gathered from the circa 2000 product for the study site.

41 Method

The methodology consists of automated sub-DSMs generation and their assembling. The vertical accuracy of the generated sub-DSMs is computed and analyzed through the impacts of radiometric disparity sources identified in the background section. Different sets of criteria are used for assembling the generated sub-DSMs. The resulting DSMs are analyzed in terms of vertical and horizontal accuracy. Then, using the NTS50K subdivision, they are compared to the sub-DSM, at the local scale. Figure 4-4 outlines the methodology.

RS-2 images were geopositioned with Rapid Positioning Capabilities (RPC), a rational function based model (RFM), equivalent to the range-Doppler model in terms of accuracy (Toutin, 2012). Overlapping image pairs from different same-side and opposite-side stereo configurations were identified. Only those with the maximum surface area were selected to provide full coverage of the study site. The sub-DSMs were generated with Correlator 3D (Simactive Inc.). The selected stereo-pairs were autocorrelated and stereorestituted using the CDED as a baseline. The result was comparable to those generated with other commercial softwares and Correlator3D was found to achieve better results in terms of completeness, smoothness and accuracy. It is important to highlight that the stereorestitution was conducted without control points. The generated sub-DSMs were converted from ellipsoidal height to orthometric elevations using the reference model EGM96. No pre-processing was applied to the images prior to the stereo- restitution.

42 GLA14 (filtered) RS-2 Images CDED Denoising & Lake flattening Sub- DSMs Assembling Assembled DSMs NHN Stereo- restitution Slope Land- cover Meteo data NTS50K Optimal DSM STEREO CONFIGURATION ANALYSIS ASSEMBLED DSMs ANALYSIS

Figure 4-4: Flowchart of optimal DSM generation from RS-2 images.

Sub-DSM Analysis

Vertical accuracy is defined as the Root Mean Square Error on elevation with a confidence level of 95% (RMSE95) with respect to the filtered GLA14 data. The averaged RMSE95 provided the accuracy level of a given configuration, while its standard deviation described its variability. For relevant statistics, they were calculated for each sub-DSM including at least 30 reference points (GLA14 data).

The vertical accuracy of the sub-DSMs was also analyzed with respect to the sources of radiometric disparities previously identified: temperature differences, snow cover, land cover, phenological changes, and slope. Thus, for each configuration category, the sub-

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DSMs with the lowest vertical accuracy were identified and studied to determine if the radiometric disparities could account for the poor accuracy, when compared with sub- DSMs with the same configuration but higher accuracy.

Assembled DSM Analysis

Sub-DSMs were assembled to generate five full DSMs, each covering the study site completely. Correlator 3D was also used for the assembling process which is based on a weighted average approach where the higher weights are assigned to pixels closer to the center of the sub-DSMs. Further details about the assembling algorithm used were not disclosed for intellectual properties reasons. This method was selected because, compared to bilinear and bicubic interpolation methods, it was found to achieve better vertical accuracy and esthetics. The selection of sub-DSMs for the assembling is described as follow:

 U3-U19 and U8-U25 same-side stereo configurations only;  All same-side stereo configurations;

 All opposite-side stereo configurations;

 All generated sub-DSMs for both same-side and opposite-side configurations;  Sub-DSMs with highest local vertical accuracy.

U3-U19 and U8-U25 same-side configurations correspond to the trade-off for rolling terrain in Table 4-1. They provided a good trade-off between parallax and radiometric discrepancies and showed that a result could be generated over the whole study site. The assembled DSM resulting from these configurations was used as a baseline for the