Remote Sensing and Geomatics. Université de Sherbrooke

Ph.D. Program in Remote Sensin

Analysis of Groundwater Depletion in the Saskatchewan River Basin in Canada from Coupled SWAT-MODFLOW and Satellite Gravimetry

The Saskatchewan River Basin (SRB) of central Canada plays a crucial role in the Canadian Prairies. Yet, climate change and human action constitute a real threat to its hydrological processes. This study aims to evaluate and analyze groundwater spatial and temporal dynamics in the SRB. Groundwater information was derived and compared using two different approaches: (1) a mathematical modeling framework coupling the Soil and Water Assessment Tool (SWAT) and the Modular hydrologic model (MODFLOW) and (2) gravimetric satellite observations from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO). Both methods show generalized groundwater depletion in the SRB that can reach −1 m during the study period (2002–2019). Maximum depletion appeared especially after 2011. The water balance simulated by SWAT-MODFLOW showed that SRB could be compartmented roughly into three main zones. The mountainous area in the extreme west of the basin is the first zone, which is the most dynamic zone in terms of recharge, reaching +0.5 m. The second zone is the central area, where agricultural and industrial activities predominate, as well as potable water supplies. This zone is the least rechargeable and most intensively exploited area, with depletion ranging from +0.2 to −0.4 m during the 2002 to 2011 period and up to −1 m from 2011 to 2019. Finally, the third zone is the northern area that is dominated by boreal forest. Here, exploitation is average, but the soil does not demonstrate significant storage power. Briefly, the main contribution of this research is the quantification of groundwater depletion in the large basin of the SRB using two different methods: process-oriented and satellite-oriented methods. The next step of this research work will focus on the development of artificial intelligence approaches to estimate groundwater depletion from a combination of GRACE/GRACE-FO and a set of multisource remote sensing data

Investigating Terrestrial Water Storage Response to Meteorological Drought in the Canadian Prairies

The Canadian Prairies region is considered a climate change hot spot due to the extreme drought events and their impacts on water resources. The overall goal of this research is to understand the linkage between meteorological droughts and Total Water Storage (TWS) variations in the Canadian Prairies. To achieve this goal, a diversified database is collected and analyzed by geostatistical tools and cross-wavelet transform approach. It concerns a multitude of climatic data (four CMIP6 multi-model datasets) and satellite observations (GRACE data). The results indicate that: (1) the models overestimate the precipitation rate over the Canadian Prairies, and the Norwegian Earth System Model version 2 (NorESM2–LM) is the most suitable model for the context of the Canadian Prairies; (2) Sen’s slope estimator of annual rainfall can reach −2.5 mm/year/year, with a decreasing magnitude of trends in the NE to SW direction; (3) the Standardized Precipitation Index (SPI) and the Modified China-Z Index (MCZI) demonstrate that, in the past, most of the climatological years were near normal with some extremely dry years (1952, 2000, 2003, and 2015) and one extremely wet year (1960); (4) the projections in the far future indicate an increase in the number of extremely dry years (2037, 2047, 2080, 2089, and 2095); (5) the combined analysis of GRACE-derived TWS and drought indices show the direct impact of the meteorological drought periods on the water resources. The TWS values decreased from 23 cm in 2002 to −54 cm in 2020, indicating a significant water reserve decline in the region. The results of this study are expected to provide a valuable perspective to understand the dynamic of hydrosystems in a climate change context in the Canadian Prairies

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

This study aimed to model the prospectivity for placer deposits using geomorphic and landscape parameters. Within a geographic information system (GIS), spatial autocorrelation analysis of 3709 geochemical samples was used to identify prospective and non-prospective targets for columbite-tantalite (Nb-Ta) placer deposits of Hana-Lobo (H-L) Geological Complex (West- Central Côte d’Ivoire, West Africa). Based on mineralization system analysis, hydrologic, geomorphologic and landscape parameters were extracted at the locations of the identified targets. Supervised automatic classification approaches were applied, including Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to find a prospectivity model complex enough to capture the nature of the data. Metrics such as cross-validation accuracy (CVA), Receiver Operating Characteristic (ROC) curves, Area Under Curve (AUC) values and F-score values were used to evaluate the performance and robustness of output models. Results of applying machine-learning algorithms demonstrated that predictions provided by the final RF and KNN models were very close (κ = 0.56 and CVA = 0.69; κ = 0.54 and CVA = 0.68, respectively) and those provided by the SVM models were slightly lower with κ = 0.46 and CVA = 0.63. Independent validation results confirmed the slightly higher performance of both KNN and RF prospectivity models, compared to final SVM. Sensitivity analyses of both KNN and RF prospectivity models for medium and high-grade Nb-Ta deposits show a prediction rate of up to 90%

Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach

The role of a service that is dedicated to road weather analysis is to issue forecasts and warnings to users regarding roadway conditions, thereby making it possible to anticipate dangerous traffic conditions, especially during the winter period. It is important to define pavement conditions at all times. In this paper, a new data acquisition approach is proposed that is based upon the analysis and combination of two sensors in real time by nanocomputer. The first sensor is a camera that records images and videos of the road network. The second sensor is a microphone that records the tire–pavement interaction, to characterize each surface’s condition. The two low-cost sensors were fed to different deep learning architectures that are specialized in surface state analysis; the results were combined using an evidential theory-based data fusion approach. This study is a proof of concept, to test an evidential approach for improving classification with deep learning, applied to only two sensors; however, one could very well add more sensors and make the nanocomputers communicate together, to analyze a larger urban environment

Impact of Uncertainty Estimation of Hydrological Models on Spectral Downscaling of GRACE-Based Terrestrial and Groundwater Storage Variation Estimations

Accurately estimating hydrological parameters is crucial for comprehending global water resources and climate dynamics. This study addresses the challenge of quantifying uncertainties in the global land data assimilation system (GLDAS) model and enhancing the accuracy of downscaled gravity recovery and climate experiment (GRACE) data. Although the GLDAS models provide valuable information on hydrological parameters, they lack uncertainty quantification. To enhance the resolution of GRACE data, a spectral downscaling approach can be employed, leveraging uncertainty estimates. In this study, we propose a novel approach, referred to as method 2, which incorporates parameter magnitudes to estimate uncertainties in the GLDAS model. The proposed method is applied to downscale GRACE data over Alberta, with a specific focus on December 2003. The groundwater storage extracted from the downscaled terrestrial water storage (TWS) are compared with measurements from piezometric wells, demonstrating substantial improvements in accuracy. In approximately 80% of the wells, the root mean square (RMS) and standard deviation (STD) were improved to less than 5 mm. These results underscore the potential of the proposed approach to enhance downscaled GRACE data and improve hydrological models

New spectro-spatial downscaling approach for terrestrial and groundwater storage variations estimated by GRACE models

The study proposes a new mathematical method, referred to as spectral combination, to downscale Gravity Recovery And Climate Experiment (GRACE) observations. The goal is to improve the spatial resolution of GRACE from 1̊ to 0.25̊, based upon available hydrological variables. First, a new approach based upon condition adjustment is proposed to estimate uncertainties related to hydrological variables. Second, a spectral-spatial estimator is developed to derive downscaled Total Water Storage Anomalies (TWSA) by optimally combining GRACE models and hydrological variables. Last, groundwater storage anomalies (GWSA) are derived from the downscaled TWSA. The proposed spectral combination approach was tested over the Canadian Prairies by considering GRACE data and required Global Land Data Assimilation System (GLDAS) variables for February 2003 to December 2016. The results reveal greater details in TWSA after spatial downscaling. Quantitatively, retrieved downscaled GWSA were validated using 75 unconfined in situ piezometric wells that were distributed across the Province of Alberta. A correlation of 0.80, with an RMSE of 11 mm, was obtained after downscaling with all wells over the validation area. These results are better than those obtained before downscaling (correlation of 0.42, with an RMSE of 21.4 mm), demonstrating that the proposed approach is successful. This study was funded by the Université de Sherbrooke (Excellence Scholarship Program), and the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant Number: RGPIN-2018- 06101; NSERC Create Grant: 543360-2020). We thank all data and products providers, University of Texas at Austin, Natural Resources Canada, and the Goddard Earth Sciences Data and Information Services Center. We gratefully thank for all valuable suggestions from two reviewers, and JOH editorial team, which help us improve the manuscript significantly. We thank W.F.J. Parsons for correcting the English.CC-BY 4.0</p

Incertitudes des niveaux d’eau dérivés de l’altimétrie satellitaire pour des étendues d’eau soumises à l’action de la glace

La présence de cibles hétérogènes, comme la glace, reste un défi majeur pour l’utilisation des données altimétriques au-dessus des plans d’eau continentaux. Les satellites Jason-2 et SARAL/Altika utilisent des algorithmes de retraitement conçus pour traiter les formes d’onde non continentales afin d’obtenir des estimations améliorées. Dans cette étude, nous analysons le potentiel des produits dérivés de ces algorithmes pour estimer le niveau d’eau de 20 plans d’eau couverts par la glace répartis à travers le Canada. Les estimations de niveaux d’eau des algorithmes de retraitement sont comparées aux mesures in situ pendant deux périodes: la période entièrement couverte par chacun des deux satellites dans l’étude (2008–2016 pour Jason-2, et 2013–2016 pour SARAL/Altika); ainsi que les périodes de gel-dégel incluses dans les séries chronologiques. Les algorithmes produisent des incertitudes très variables, en fonction de la taille des cours d’eau et des conditions de la glace. Dans l’ensemble, l’algorithme ICE-1 utilisé par Jason-2 fournit les meilleures estimations de niveau d’eau, avec des erreurs RMSE non biaisées ≤0.3 m et des R2 ≥ 0.8 pour 90% des plans d’eau. Tous les algorithmes de retraitement utilisés par SARAL/Altika donnent des résultats très comparables aux observations in situ, démontrant les bonnes performances de la technologie SARAL

Prospectivity Mapping of Heavy Mineral Ore Deposits Based upon Machine-Learning Algorithms: Columbite-Tantalite Deposits in West- Central Côte d’Ivoire

This study aimed to model the prospectivity for placer deposits using geomorphic and landscape parameters. Within a geographic information system (GIS), spatial autocorrelation analysis of 3709 geochemical samples was used to identify prospective and non-prospective targets for columbite-tantalite (Nb-Ta) placer deposits of Hana-Lobo (H-L) Geological Complex (West- Central Côte d’Ivoire, West Africa). Based on mineralization system analysis, hydrologic, geomorphologic and landscape parameters were extracted at the locations of the identified targets. Supervised automatic classification approaches were applied, including Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to find a prospectivity model complex enough to capture the nature of the data. Metrics such as cross-validation accuracy (CVA), Receiver Operating Characteristic (ROC) curves, Area Under Curve (AUC) values and F-score values were used to evaluate the performance and robustness of output models. Results of applying machine-learning algorithms demonstrated that predictions provided by the final RF and KNN models were very close (κ = 0.56 and CVA = 0.69; κ = 0.54 and CVA = 0.68, respectively) and those provided by the SVM models were slightly lower with κ = 0.46 and CVA = 0.63. Independent validation results confirmed the slightly higher performance of both KNN and RF prospectivity models, compared to final SVM. Sensitivity analyses of both KNN and RF prospectivity models for medium and high-grade Nb-Ta deposits show a prediction rate of up to 90%

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data

The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R2 > 0.85)