Status and trends of wetland studies in Canada using remote sensing technology with a focus on wetland classification: a bibliographic analysis

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.Peer ReviewedPostprint (published version

Synthetic aperture radar and optical remote sensing image fusion for flood monitoring in the Vietnam lower Mekong basin: a prototype application for the Vietnam Open Data Cube

Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system

Ecological applications of remote sensing data in neotropical rainforests

Understanding species' distributions is a central theme of biodiversity studies. A combination of data derived from moderate and high spectral resolution satellite imagery (vegetation indices and hyperspectral narrow bands, respectively) was used to address questions regarding tree species' distributions, vegetation phenology, and influences on bird seasonal movements in tropical rainforests. Vegetation indices were used in ecological niche modeling to predict movement patterns of a tropical canopy frugivorous bird in Central America: the predicted distributions generally recovered observed non-breeding ranges, but estimated lowland areas for the breeding range, which is restricted to middle elevations. Hyperspectral imagery provided sufficient spectral information to discriminate crowns of five different tree taxa that represent food resources for macaws and peccaries in southeastern Peru. Tree spectra showed significant temporal variation, suggesting that it is possible to study tree phenology remotely. Current and future developments of remote sensing techniques permit regional studies of ecosystem functions and structure

Application of RADARSAT-2 Polarimetric Data for Land Use and Land Cover Classification and Crop monitoring in Southwestern Ontario

Timely and accurate information of land surfaces is desirable for land change detection and crop condition monitoring. Optical data have been widely used in Land Use and Land Cover (LU/LC) mapping and crop condition monitoring. However, due to unfavorable weather conditions, high quality optical images are not always available. Synthetic Aperture Radar (SAR) sensors, such as RADARSAT-2, are able to transmit microwaves through cloud cover and light rain, and thus offer an alternative data source. This study investigates the potential of multi-temporal polarimetric RADARSAT-2 data for LU/LC classification and crop monitoring in the urban rural fringe areas of London, Ontario. Nine LU/LC classes were identified with a high overall accuracy of 91.0%. Also, high correlations have been found within the corn and soybean fields between some polarimetric parameters and Normalized Difference Vegetation Index (NDVI). The results demonstrate the capability of RADARSAT-2 in LU/LC classification and crop condition monitoring

Iceberg topography and volume classification using TanDEM-X interferometry

Icebergs in polar regions affect water salinity, alter marine habitats, and impose serious hazards on maritime operations and navigation. These impacts mainly depend on the iceberg volume, which remains an elusive parameter to measure. We investigate the capability of TanDEM-X bistatic single-pass synthetic aperture radar interferometry (InSAR) to derive iceberg subaerial morphology and infer total volume. We cross-verify InSAR results with Operation IceBridge (OIB) data acquired near Wordie Bay, Antarctica, as part of the OIB/TanDEM-X Antarctic Science Campaign (OTASC). While icebergs are typically classified according to size based on length or maximum height, we develop a new volumetric classification approach for applications where iceberg volume is relevant. For icebergs with heights exceeding 5 m, we find iceberg volumes derived from TanDEM-X and OIB data match within 7 %. We also derive a range of possible iceberg keel depths relevant to grounding and potential impacts on subsea installations. These results suggest that TanDEM-X could pave the way for future single-pass interferometric systems for scientific and operational iceberg mapping and classification based on iceberg volume and keel depth

Investigating Full-Waveform Lidar Data for Detection and Recognition of Vertical Objects

A recent innovation in commercially-available topographic lidar systems is the ability to record return waveforms at high sampling frequencies. These “full-waveform” systems provide up to two orders of magnitude more data than “discrete-return” systems. However, due to the relatively limited capabilities of current processing and analysis software, more data does not always translate into more or better information for object extraction applications. In this paper, we describe a new approach for exploiting full waveform data to improve detection and recognition of vertical objects, such as trees, poles, buildings, towers, and antennas. Each waveform is first deconvolved using an expectation-maximization (EM) algorithm to obtain a train of spikes in time, where each spike corresponds to an individual laser reflection. The output is then georeferenced to create extremely dense, detailed X,Y,Z,I point clouds, where I denotes intensity. A tunable parameter is used to control the number of spikes in the deconvolved waveform, and, hence, the point density of the output point cloud. Preliminary results indicate that the average number of points on vertical objects using this method is several times higher than using discrete-return lidar data. The next steps in this ongoing research will involve voxelizing the lidar point cloud to obtain a high-resolution volume of intensity values and computing a 3D wavelet representation. The final step will entail performing vertical object detection/recognition in the wavelet domain using a multiresolution template matching approach

Water Body Distributions Across Scales: A Remote Sensing Based Comparison of Three Arctic Tundra Wetlands

Water bodies are ubiquitous features in Arctic wetlands. Ponds, i.e., waters with a surface area smaller than 104 m2, have been recognized as hotspots of biological activity and greenhouse gas emissions but are not well inventoried. This study aimed to identify common characteristics of three Arctic wetlands including water body size and abundance for different spatial resolutions, and the potential of Landsat-5 TM satellite data to show the subpixel fraction of water cover (SWC) via the surface albedo. Water bodies were mapped using optical and radar satellite data with resolutions of 4mor better, Landsat-5 TM at 30mand the MODIS water mask (MOD44W) at 250m resolution. Study sites showed similar properties regarding water body distributions and scaling issues. Abundance-size distributions showed a curved pattern on a log-log scale with a flattened lower tail and an upper tail that appeared Paretian. Ponds represented 95% of the total water body number. Total number of water bodies decreased with coarser spatial resolutions. However, clusters of small water bodies were merged into single larger water bodies leading to local overestimation of water surface area. To assess the uncertainty of coarse-scale products, both surface water fraction and the water body size distribution should therefore be considered. Using Landsat surface albedo to estimate SWC across different terrain types including polygonal terrain and drained thermokarst basins proved to be a robust approach. However, the albedo–SWC relationship is site specific and needs to be tested in other Arctic regions. These findings present a baseline to better represent small water bodies of Arctic wet tundra environments in regional as well as global ecosystem and climate models

Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization

This dissertation describes three contributions in the characterization of vegetation canopies using remote sensing data, with a focus on hyperspectral and very high spatial resolution imagery. The new and innovative methods developed are: 1) integration of contribution theory into a model inversion approach to obtain high accuracy in canopy biophysical parameter estimation; 2) exploration and adoption of tree crown longitudinal profiles to achieve high accuracy in tree species classification; and 3) evaluation of canopy health state for Emerald Ash Borer (EAB) infestation assessment by intelligent combination of multi-sourced data