A5061974289- Climate change and permafrost
- Peatlands and Wetlands Ecology
- Cryospheric studies and observations
- Remote Sensing and LiDAR Applications
- Flood Risk Assessment and Management
- Arctic and Antarctic ice dynamics
- Remote Sensing in Agriculture
- Atmospheric and Environmental Gas Dynamics
- Methane Hydrates and Related Phenomena
- Soil Moisture and Remote Sensing
- Land Use and Ecosystem Services
- Soil erosion and sediment transport
- Species Distribution and Climate Change
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Oil Spill Detection and Mitigation
- Ocean Waves and Remote Sensing
- Advanced SAR Imaging Techniques
- Geographic Information Systems Studies
- Geology and Paleoclimatology Research
- Soil Geostatistics and Mapping
- Remote-Sensing Image Classification
- Rangeland and Wildlife Management
- Coastal wetland ecosystem dynamics
- Fire effects on ecosystems
Ducks Unlimited Canada
2016-2024
University of Guelph
2022-2024
Natural Resources Canada
2023
The following review is the second part of a two series on use remotely sensed data for quantifying wetland extent and inferring or measuring condition monitoring drivers change environments. In first part, we introduce policy makers non-users with an effective feasibility guide how can be used. current review, explore more technical aspects processing analysis using case studies within literature. Here describe: (a) technologies used assessment monitoring; (b) latest algorithmic...
The authors evaluated multiple remotely sensed datasets for their contributions to operational wetland mapping in a subarctic, boreal cordillera study site Yukon, Canada. They assessed Sentinel-2 optical imagery, Sentinel-1 C-band and ALOS PALSAR L-band synthetic aperture radar (SAR) topographical data from the territorial digital elevation model (DEM) using an object-based image analysis (OBIA) approach. Three machine-learning algorithms were tested, namely random forest (RF), support...
Wetlands have and continue to undergo rapid environmental anthropogenic modification change their extent, condition, therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use remote sensing technologies for ‘wise wetlands’, following Ramsar Convention protocols. The objectives review are provide: (1) synthesis history wetlands, (2) feasibility study quantify accuracy remotely sensed data products when compared field based...
The objective of this paper is to assess the accuracy C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region Canada's subarctic boreal zone. This assessed contributions quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude-Pottier, 2) Freeman-Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles 19.4°, 23.1°, 45.8°, 48.1°. influence combining...
The first Canadian wetland inventory (CWI) map, which was based on Landsat data, produced in 2019 using the Google Earth Engine (GEE) big data processing platform. proposed GEE-based method to create preliminary CWI map proved be a cost, time, and computationally efficient approach. Although initial effort produce valuable with 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training validation of map). Therefore, it important...
Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that in decline due to numerous anthropogenic pressures. Satellite Synthetic Aperture Radar a popular inexpensive solution such exercises over large scales through the application of thresholds distinguish from non-water. Despite improvements threshold methods, selection traditionally manual, which introduces subjectivity inconsistency scales. This study presents novel method...
Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by polarimetric SAR has proven characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there been increased interest exploiting novel interferometric (InSAR)...
The Arctic-Boreal zone (ABZ) covers over 26 million km2 and is home to numerous duck species; however, understanding the spatiotemporal distribution of their populations across this vast landscape challenging, in part due extent data scarcity. Species abundance models for ducks ABZ commonly use static (time invariant) habitat covariates inform predictions, such as wetland type maps. For first time region, we developed species using high-resolution, time-varying inundation produced satellite...
The objective of this study was to map wetlands representing 2017-2022 conditions in two areas Canada's Boreal Forest, specifically Nunavut's taiga shield ecozone and Saskatchewan's boreal ecozone. Wetland classification performed by leveraging machine learning (ML) modelling on the Google Earth Engine (GEE) JavaScript API, Python programming for model cross validation/optimization explainbility. Robust wetland coverage estimates were derived summarizing predictions several ML models (i.e.,...
The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek'èhodì region, Northwest Territories, while also enhancing previously established mapping workflow. Our methods used multidate optical and radar satellite imagery fused these data with ArcticDEM topographic variables. Additionally, few studies date have assessed for mapping; research helps fill this critical gap literature. A machine-learning, object-based approach employed classify stacks included...
Climate-driven permafrost degradation and an intensification of the hydrological cycle are rapidly altering intricate ecohydrological processes Arctic wetlands, threatening their long-term carbon sequestration capabilities. Addressing this concern through effective management holds immense potential for climate regulation, mitigation, adaptation efforts. As such, there is growing need timely spatial inventory data identifying wetlands with sufficient accuracy, resolution, detail. Wetland...
In this study, Sentinel-2 optical satellite imagery was acquired over the Peace Athabasca Delta and assessed for its open water classification capabilities using an object-oriented deep learning algorithm . The workflow involved segmenting data into meaningful image objects, building a Convolutional Neural Network (CNN), training CNN, lastly applying resulting in probability heat maps of (with score values ranging from 0–1). Using vector segmentation, were then iteratively assigned final...
Permafrost environments are increasingly threatened by warming temperatures. Active Layer Thickness (ALT) is a critical variable because it influences several environmental processes, particularly carbon storage and cycling. Remote Sensing (RS) techniques hold great potential for mapping ALT over remote, challenging, inaccessible permafrost regions, especially in comparison to situ field surveys. In this study, various RS sources (radar, optical, topographic) were assessed estimation....
The Arctic-Boreal zone (ABZ) is a vast landscape, supporting many waterfowl species. However, because of spatial extent and data scarcity, modelling populations across the ABZ challenging. Species abundance models (SAMs) for typically use time-static habitat covariates to make predictions, such as wetland type maps. Here, first time, we used remote sensing methods create time-varying inundation covariates, which helped improve our SAMs. SAMs were tested over Peace Athabasca Delta (PAD),...
This research presents the findings of a study where Sentinel-2 optical satellite imagery was assessed for its boreal open water mapping capabilities using deep learning algorithm and an object-oriented image analysis approach [1]. Key components within workflow this included following: 1) segmentation into meaningful relatively homogenous objects, 2) building Convolutional Neural Network (CNN) model, 3) training CNN, 4) applying CNN. produced probability heat maps water; object-based then...
Arctic environments are remote and inaccessible, making conventional field-based data collection challenging. Thus, this study describes an efficient desktop-based methodology for deriving reference to support large-scale sensing classification focusing on wetland ecosystems. Our area was Canada's Southern Ecozone. Various Earth observation (EO) datasets, including optical, multi-spectral, topographic, were used as a base photointerpretation process collecting data. Ten 30-by-30-kilometer...