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

Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)

The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes

A Collection of Novel Algorithms for Wetland Classification with SAR and Optical Data

Wetlands are valuable natural resources that provide many benefits to the environment, and thus, mapping wetlands is crucially important. We have developed land cover and wetland classification algorithms that have general applicability to different geographical locations. We also want a high level of classification accuracy (i.e., more than 90%). Over that past 2 years, we have been developing an operational wetland classification approach aimed at a Newfoundland/Labrador province-wide wetland inventory. We have developed and published several algorithms to classify wetlands using multi-source data (i.e., polarimetric SAR and multi-spectral optical imagery), object-based image analysis, and advanced machine-learning tools. The algorithms have been tested and verified on many large pilot sites across the province and provided overall and class-based accuracies of about 90%. The developed methods have general applicability to other Canadian provinces (with field validation data) allowing the creation of a nation-wide wetland inventory system

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Wetland mapping and monitoring using polarimetric and interferometric synthetic aperture radar (SAR) data and tools

Wetlands are home to a great variety of flora and fauna species and provide several unique environmental functions, such as controlling floods, improving water-quality, supporting wildlife habitat, and shoreline stabilization. Detailed information on spatial distribution of wetland classes is crucial for sustainable management and resource assessment. Furthermore, hydrological monitoring of wetlands is also important for maintaining and preserving the habitat of various plant and animal species. This thesis investigates the existing knowledge and technological challenges associated with wetland mapping and monitoring and evaluates the limitations of the methodologies that have been developed to date. The study also proposes new methods to improve the characterization of these productive ecosystems using advanced remote sensing (RS) tools and data. Specifically, a comprehensive literature review on wetland monitoring using Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques is provided. The application of the InSAR technique for wetland mapping provides the following advantages: (i) the high sensitivity of interferometric coherence to land cover changes is taken into account and (ii) the exploitation of interferometric coherence for wetland classification further enhances the discrimination between similar wetland classes. A statistical analysis of the interferometric coherence and SAR backscattering variation of Canadian wetlands, which are ignored in the literature, is carried out using multi-temporal, multi-frequency, and multi-polarization SAR data. The study also examines the capability of compact polarimetry (CP) SAR data, which will be collected by the upcoming RADARSAT Constellation Mission (RCM) and will constitute the main source of SAR observation in Canada, for wetland mapping. The research in this dissertation proposes a methodology for wetland classification using the synergistic use of intensity, polarimetry, and interferometry features using a novel classification framework. Finally, this work introduces a novel model based on the deep convolutional neural network (CNN) for wetland classification that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PolSAR) imagery. The results of the proposed methods are promising and will significantly contribute to the ongoing efforts of conservation strategies for wetlands and monitoring changes. The approaches presented in this thesis serve as frameworks, progressing towards an operational methodology for mapping wetland complexes in Canada, as well as other wetlands worldwide with similar ecological characteristics

Studies of the Deepwater Horizon Oil Spill With the UAVSAR Radar

On 22- 23 June 2010, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L band radar imaged the Deepwater Horizon oil spill and the effects of oil that was transported within the Gulf of Mexico. We describe the campaign and discuss the unique contributions of the UAVSAR radar to the study of the detection, migration, and impact of oil from the spill. We present an overview of UAVSAR data analyses that support the original science goals of the campaign, namely, (1) algorithm development for oil slick detection and characterization, (2) mapping of oil intrusion into coastal wetlands and intercoastal waterways, and (3) ecosystem impact studies. Our study area focuses on oil-affected wetlands in Barataria Bay, Louisiana. The results indicate that fine resolution, low-noise, L band radar can detect surface oil-on-water with sufficient sensitivity to identify regions in a slick with different types of oil/emulsions and/or oil coverage; identify oil on waters in inland bays and differentiate mixed/weathered oil from fresh oil as it moves into the area; identify areas of potentially impacted wetlands and vegetation in the marshes; and support the crisis response through location of compromised booms and heavily oiled beaches

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version