Jimmy Poulin

Monitoring volumetric surface soil moisture content at the La Grande basin boreal wetland by radar multi polarization data.

Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is imperative to develop remote sensing monitoring techniques to retrieve hydrological parameters. The main objective of this study is to find out if multi-date and multi-polarization Radar Satellite 2 (RADARSAT-2) (C-band) image analysis could detect seasonal variations of surface soil moisture conditions of the acrotelm. A change detection approach through the use of multi temporal indexes was chosen based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of surface soil moisture (Δ-Index is based on a reference image that represents dry soil, in order to maximize the sensitivity of σ° to changes in soil moisture with respect to the same location when soil is wet). The Δ-Index approach was tested with each polarization: σ° for fully polarimetric mode (HH, HV, VV) and the cross-polarization coefficient (HV/HH). Results show that the best regression adjustment with regard to surface soil moisture content in boreal wetlands was obtained with the cross-polarization coefficient. The cross-polarization multi-temporal index enables precise volumetric surface soil moisture estimation and monitoring on boreal wetlands, regardless of the influence of vegetation cover and surface roughness conditions (bias was under 1%, standard deviation and RMSE were under 10% for almost all estimation errors). Surface soil moisture estimation was more precise over permanently flooded areas than seasonally flooded ones (standard deviation is systematically greater for the seasonally flooded areas, at all analyzed scales), although the overall quality of the estimation is still precise. Cross-polarization ratio image analysis appears to be a useful mean to exploit radar data spatially, as we were able to relate changes in wetland eco-hydrological dynamics to variations in the intensity of the ratio.

Comparison of TerraSAR-X and ALOS PALSAR Differential Interferometry With Multisource DEMs for Monitoring Ground Displacement in a Discontinuous Permafrost Region

Differential synthetic aperture radar interferometry (DInSAR) has shown its capability in monitoring ground displacement caused by the freeze-thaw cycle in the active layer of permafrost regions. However, the unique landscape in the discontinuous permafrost zone increases the difficulty of applying DInSAR to detect ground displacements. In this study, datasets from two radar systems, X-band TerraSAR-X and L-band ALOS PALSAR, were used to evaluate the influencing factors and application conditions for DInSAR in the discontinuous permafrost environment based on a large number of analyzed interferograms. Furthermore, the impact of different DEMs on the application of DInSAR was illustrated by comparing the high-resolution LiDAR-DEM, TanDEM-X DEM, and SRTM DEM. The results demonstrate that temporal decorrelation and strong volume decorrelation in areas with developed vegetation highly constrains the application of X-band data. In terrain with more developed vegetation (such as shrubs and spruce), the X-band differential phase becomes linked to the canopy rather than the topography, whereas L-band data show promising results in retrieving topography-related displacement. By comparing the displacement velocity maps of the two sensors and referencing in situ measurements, we demonstrated that the ALOS PALSAR results capture the permafrost-induced terrain movement characteristics and values in the correct range. Moreover, the influence of soil moisture and vegetation phenology on the accuracy of displacement retrievals using the L-band data are illustrated and discussed. The analyses confirm that the L-band has strong advantages over the X-band in monitoring displacements in discontinuous permafrost environments.

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

Using available time series of Passive and Active Microwave to develop SMAP Freeze/Thaw algorithms adapted for the canadian subarctic

Seasonal terrestrial Freeze/Thaw cycle in Northern Quebec Tundra (Nunavik) determined and evaluated with Passive and Active Microwave Observations. SMOS time series data were analyzed to examine seasonal variations of soil freezing, and to assess the impact of snow cover and land cover on freeze-thaw cycle. Further, the soil freezing maps derived from SMOS observations compared to microwave active images in the region near Umiujaq and Field survey data. The objective is to develop algorithms to follow the seasonal cycle of freezing and thawing of the soil in the Tundra and Boreal forest. Field data shows that freezing and thawing dates vary much spatially at the local scale in the Boreal Forest and the Tundra. Therefore, the field validation of the F/T state maps at the regional scale will be very important. Agreement Factor derived from comparison of SMOS FT maps with daily in-situ data shows low values which does not seems to be acceptable. New parameters such as lake and pond as well as vegetation type and height present on surface have to be introduced in the algorithm to obtain more realistic estimations.

Development of a methodology for flood hazard detection in urban areas from RADARSAT-2 imagery

Due to their capacity do acquire data day-and-night, whatever the meteorological conditions may be, and to the improved spatial resolution of the new generation of SAR sensors such as RADARSAT-2, COSMO-SkyMed or TerraSAR-X (~ 1 to 3 m with fine acquisition modes), SAR imagery is the most suitable tool for the detection of flooded areas in a context of crisis management. However, due to the complexity of the backscattered signal in urban environments, the development of precise and automated methods for flood detection in urban areas with SAR imagery is still challenging. A new approach for flood delineation and mapping in urban areas, combining RADARSAT-2 imagery and probability of flooding data simulated by a flood dynamic risk mapping tool is presented in this paper.