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

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

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, bu

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

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

Temporal data fusion approaches to remote sensing-based wetland classification

This thesis investigates the ecology of wetlands and associated classification in prairie and boreal environments of Alberta, Canada, using remote sensing technology to enhance classification of wetlands in the province. Objectives of the thesis are divided into two case studies, 1) examining how satellite borne Synthetic Aperture Radar (SAR), optical (RapidEye & SPOT) can be used to evaluate surface water trends in a prairie pothole environment (Shepard Slough); and 2) investigating a data fusion methodology combining SAR, optical and Lidar data to characterize wetland vegetation and surface water attributes in a boreal environment (Utikuma Regional Study Area (URSA)). Surface water extent and hydroperiod products were derived from SAR data, and validated using optical imagery with high accuracies (76-97% overall) for both case studies. High resolution Lidar Digital Elevation Models (DEM), Digital Surface Models (DSM), and Canopy Height Model (CHM) products provided the means for data fusion to extract riparian vegetation communities and surface water; producing model accuracies of (R2 0.90) for URSA, and RMSE of 0.2m to 0.7m at Shepard Slough when compared to field and optical validation data. Integration of Alberta and Canadian wetland classifications systems used to classify and determine economic value of wetlands into the methodology produced thematic maps relevant for policy and decision makers for potential wetland monitoring and policy development.Funding for this thesis was provided by the NSERC CREATE AMETHYST Program, and the Government of Alberta (Economic Development and Trade, Environment and Parks), Campus Alberta Innovates Program

Interferomeetriline tehisavaradar kui vahend turbaalade pinna dünaamika jälgimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsiooneSood on unikaalsed ökosüsteemid, kus turba ladestumise käigus seotakse pikaajaliselt süsinikku. Üleilmselt on soodes seotud süsiniku kogus, mis võrdub peaaegu poolega hetkel atmosfääris olevast. Tasakaalu süsiniku sidumise ja lendumise vahel mõjutab soodes kõige enam veetase, mistõttu veerežiimi muutudes võivad sood muutuda süsiniku talletajast kasvuhoonegaaside õhku paiskajaks. Tehisavaradar (SAR) on aktiivne mikrolainealas töötav kaugseiresüsteem, mille kasutamine võimaldaks turbaalade ülemaailmset seiret. SAR näeb läbi pilvede, katab korraga suure ala, on hea ruumilise lahutuse ja tiheda ajalise katvusega. Interferomeetriline SAR (InSAR) on uudne meetod, mis võimaldab mõõta maapinna kõrgusmuutusi, tuginedes radarisignaali pool läbitava teekonna pikkusete erinevusele kahest samast kohast, aga eri aegadel tehtud pildi vahel. Tulemuseks on kõrgusmuutuse pilt (interferogramm), kõrvalsaaduseks on koherentsuse pilt, mis kirjeldab võrreldavate piltide ruumimustrite sarnasust. Meetodi kitsaskohaks on suurte kõrgusmuutuste õigesti hindamine. Töö eesmärk oli katsetada InSAR meetodi kasutusvõimaluse piire ja rakendada uusi teadmisi rabade seirel. Uurisin: 1) raba veetaseme mõju koherentsusele; 2) freesturba tootmisega kaasnevat pinna muutuse mõju koherentsusele; 3) InSAR meetodi usaldusväärsust raba pinna kõrguse muutuse hindamisel. Tulemused näitavad, et koherentsustest on kasu soode veerežiimi uurimisel, kuid see ei sobi pinnase niiskuse otseseks mõõtmiseks. Koherentsust saab kasutada turba tootmise seireks, võttes arvesse SAR-ist ja turba tootmise protsessist tulenevaid piiranguid. Töös on visandatud seiremetoodika, mis võimaldab eristada aktiivseid turbatootmisalasid kasutuses välja jäänud aladest ja jälgida turba tootmise intensiivsust, edendamaks tõhusamat ressursikasutust. InSAR meetodil maapinna kõrguse mõõtmised tavapärase 5,6 sentimeetrise lainepikkuse juures ei ole rabas usaldusväärsed. Katsetatud InSAR meetodid ei suutnud kiiresti toimuvaid suuri kõrgusmuutusi õigesti hinnata. Sarnaselt varasematele uuringutele oleks selline viga jäänud avastamata, kui meil poleks võrdluseks olnud maapealseid kõrgusandmeid. Tõenäoliselt võiks soos maapinna kõrguse muutuse hindamiseks paremini sobida lähitulevikku planeeritud pikalainelised (24 cm) radarsatelliidi missioonid.  Peatlands are significant in regard to climate change because peatlands may switch from being a net carbon sink to an emitter of greenhouse gases. The delicate carbon balance in peatlands is controlled by the peatland water table. Peatland soils contain globally nearly as much carbon as a half of what is currently in the atmosphere. Synthetic Aperture Radar (SAR) is an active microwave remote sensing system which has potential for global peatland monitoring. SAR can penetrate through clouds, covers simultaneously a vast area at high spatial resolution and has a short revisit cycle. Interferometric SAR (InSAR) is an emerging technique to measure surface height changes utilising the difference in the path length that the signal travels between SAR acquisitions of the same target from the same orbital position at different times. The resultant deformation image does not show the absolute change in the path length but the result is ambiguously wrapped in cycles corresponding to half of the signal wavelength, complicating estimation of larger changes. A co-product of InSAR processing is the coherence image, describing the similarity of the spatial patterns in the images. The objective of my dissertation is testing the limits of InSAR and, built on it, improving peatland monitoring. It was studied: 1) coherence response to the water table in raised bogs; 2) coherence response to peat surface alteration caused by the milled peat production; 3) reliability of InSAR deformation estimates in open bogs. Based on the results, coherence could be used as aid to understanding of hydrologic conditions in bogs but it is unsuitable for direct moisture retrieval. Coherence can be used to monitor peat extraction, considering intrinsic limitations posed by the SAR and the peat extraction process. The ambiguity problem makes displacement measurements at the conventional 5.6 cm wavelength unreliable in bogs. A solution could be the planned long wavelength (24 cm) SAR missions.https://www.ester.ee/record=b550580

Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions

Spaceborne microwave remote sensing (300 MHz–100 GHz) provides a valuable method for characterizing environmental changes, especially in Arctic–boreal regions (ABRs) where ground observations are generally spatially and temporally scarce. Although direct measurements of carbon fluxes are not feasible, spaceborne microwave radiometers and radar can monitor various important surface and near-surface variables that affect terrestrial carbon cycle processes such as respiratory carbon dioxide (CO2) fluxes; photosynthetic CO2 uptake; and processes related to net methane (CH4) exchange including CH4 production, transport and consumption. Examples of such controls include soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties and land cover. Microwave remote sensing also provides a means for independent aboveground biomass estimates that can be used to estimate aboveground carbon stocks. The microwave data record spans multiple decades going back to the 1970s with frequent (daily to weekly) global coverage independent of atmospheric conditions and solar illumination. Collectively, these advantages hold substantial untapped potential to monitor and better understand carbon cycle processes across ABRs. Given rapid climate warming across ABRs and the associated carbon cycle feedbacks to the global climate system, this review argues for the importance of rapid integration of microwave information into ABR terrestrial carbon cycle science.</p