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.

Download Full-text

Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America

Remote Sensing ◽

10.3390/rs12111882 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1882 ◽

Cited By ~ 6

Author(s):

Masoud Mahdianpari ◽

Jean Elizabeth Granger ◽

Fariba Mohammadimanesh ◽

Bahram Salehi ◽

Brian Brisco ◽

...

Keyword(s):

Remote Sensing ◽

North America ◽

Spatial Resolution ◽

Meta Analysis ◽

Future Research ◽

Wetland Classification ◽

Classification Methods ◽

Wetland Ecosystems ◽

Sensing Technology ◽

Recreational Benefits

North America is covered in 2.5 million km2 of wetlands, which is the remainder of an estimated 56% of wetlands lost since the 1700s. This loss has resulted in a decrease in important habitat and services of great ecological, economic, and recreational benefits to humankind. To better manage these ecosystems, since the 1970s, wetlands in North America have been classified with increasing regularity using remote sensing technology. Since then, optimal methods for wetland classification by numerous researchers have been examined, assessed, modified, and established. Over the past several decades, a large number of studies have investigated the effects of different remote sensing factors, such as data type, spatial resolution, feature selection, classification methods, and other parameters of interest on wetland classification in North America. However, the results of these studies have not yet been synthesized to determine best practices and to establish avenues for future research. This paper reviews the last 40 years of research and development on North American wetland classification through remote sensing methods. A meta-analysis of 157 relevant articles published since 1980 summarizes trends in 23 parameters, including publication, year, study location, application of specific sensors, and classification methods. This paper also examines is the relationship between several remote sensing parameters (e.g., spatial resolution and type of data) and resulting overall accuracies. Finally, this paper discusses the future of remote sensing of wetlands in North America with regard to upcoming technologies and sensors. Given the increasing importance and vulnerability of wetland ecosystems under the climate change influences, this paper aims to provide a comprehensive review in support of the continued, improved, and novel applications of remote sensing for wetland mapping across North America and to provide a fundamental knowledge base for future studies in this field.

Download Full-text

Spaceborne remote sensing of the world’s protected areas

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133314561648 ◽

2014 ◽

Vol 39 (3) ◽

pp. 388-404 ◽

Cited By ~ 16

Author(s):

Thomas W. Gillespie ◽

Katherine S. Willis ◽

Stacey Ostermann-Kelm

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Protected Areas ◽

Natural Resource ◽

Series Data ◽

Pixel Resolution ◽

The World ◽

Status And Trends ◽

The Status

There has been a rapid evolution of satellites, sensors, and techniques to measure, monitor, and manage terrestrial protected areas. There are over 100,000 protected areas around the world and most lack important information on the status and trends of natural resource issues. We review advances and limitations in spaceborne remote sensing that can be applied to all terrestrial protected areas around the world. There have been significant advances in baseline vegetation mapping and land cover classifications by combining field data, data from multiple sensors, and classification techniques. However, global classifications on the extent of non-forest vegetation types (e.g. grasslands and shrublands) are still needed at 30 m pixel resolution. High spatial (< 1 m) and spectral (220 bands) resolution sensors have provided important data on environmental issues (e.g. invasive species, degradation) that are region or site specific. Advances in monitoring protected areas have primarily focused on forest ecosystems and land cover dynamics in and around protected areas using time series data. Landsat imagery can be used to monitor vegetation extent and dynamics at 30 m pixel resolution across the globe, while the MODIS sensors are more appropriate for monthly updates on trends of ecosystem health in protected areas. There has also been an increase in time series remote sensing datasets on anthropogenic impacts, such as light pollution, fire, and land surface temperature, that can be used for all protected areas. Future geographic research should focus on developing global protocols and incorporating near real time and annual metrics that can easily be used by natural resource managers to assess the status and trends of all protected areas.

Download Full-text

Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery

Water ◽

10.3390/w14020178 ◽

2022 ◽

Vol 14 (2) ◽

pp. 178

Author(s):

Ali Jamali ◽

Masoud Mahdianpari

Keyword(s):

Remote Sensing ◽

Language Processing ◽

Coastal Wetland ◽

Remote Sensing Data ◽

Machine Learning Algorithms ◽

Coastal Marsh ◽

Forested Wetland ◽

Wetland Classification ◽

Sensing Applications

The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing.

Download Full-text

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

Remote Sensing ◽

10.3390/rs11050516 ◽

2019 ◽

Vol 11 (5) ◽

pp. 516 ◽

Cited By ~ 16

Author(s):

◽

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Newfoundland And Labrador ◽

Earth Observation ◽

Synthetic Aperture ◽

Conservation Strategies ◽

Wetland Classification ◽

Wetland Mapping ◽

Discrimination Capability ◽

Sar Data

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

Download Full-text

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Remote Sensing ◽

10.3390/rs12213511 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3511

Author(s):

Roghieh Eskandari ◽

Masoud Mahdianpari ◽

Fariba Mohammadimanesh ◽

Bahram Salehi ◽

Brian Brisco ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Environmental Monitoring ◽

Unmanned Aerial Vehicle ◽

Meta Analysis ◽

Learning Algorithms ◽

Effective Means ◽

Machine Learning Algorithms ◽

Sensor Technology ◽

Aerial Vehicle

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

Download Full-text

Interpretation of the Status Quo of Domestic Ecological Environment Quality Based on Remote Sensing Technology

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/692/4/042048 ◽

2021 ◽

Vol 692 (4) ◽

pp. 042048

Author(s):

Yanan Li

Keyword(s):

Remote Sensing ◽

Status Quo ◽

Ecological Environment ◽

Environment Quality ◽

Remote Sensing Technology ◽

Sensing Technology ◽

The Status

Download Full-text

Remote Sensing for International Peace and Security: Its Role and Implications

Remote Sensing ◽

10.3390/rs13030439 ◽

2021 ◽

Vol 13 (3) ◽

pp. 439

Author(s):

Ram Avtar ◽

Asma Kouser ◽

Ashwani Kumar ◽

Deepak Singh ◽

Prakhar Misra ◽

...

Keyword(s):

Remote Sensing ◽

Meta Analysis ◽

Ground Truth ◽

Ground Level ◽

Armed Conflicts ◽

International Peace ◽

Conflict Zones ◽

Sensing Technology ◽

Peace And Security ◽

Relief Operations

Remote sensing technology has seen a massive rise in popularity over the last two decades, becoming an integral part of our lives. Space-based satellite technologies facilitated access to the inaccessible terrains, helped humanitarian teams, support complex emergencies, and contributed to monitoring and verifying conflict zones. The scoping phase of this review investigated the utility of the role of remote sensing application to complement international peace and security activities owing to their ability to provide objective near real-time insights at the ground level. The first part of this review looks into the major research concepts and implementation of remote sensing-based techniques for international peace and security applications and presented a meta-analysis on how advanced sensor capabilities can support various aspects of peace and security. With key examples, we demonstrated how this technology assemblage enacts multiple versions of peace and security: for refugee relief operations, in armed conflicts monitoring, tracking acts of genocide, providing evidence in courts of law, and assessing contravention in human rights. The second part of this review anticipates future challenges that can hinder the applicative capabilities of remote sensing in peace and security. Varying types of sensors pose discrepancies in image classifications and issues like cost, resolution, and difficulty of ground-truth in conflict areas. With emerging technologies and sufficient secondary resources available, remote sensing plays a vital operational tool in conflict-affected areas by supporting an extensive diversity in public policy actions for peacekeeping processes.

Download Full-text

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

Remote Sensing ◽

10.3390/rs13214444 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4444

Author(s):

Canran Tu ◽

Peng Li ◽

Zhenhong Li ◽

Houjie Wang ◽

Shuowen Yin ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Coastal Wetlands ◽

Yellow River ◽

Yellow River Delta ◽

River Delta ◽

Machine Learning Algorithms ◽

Support Vector ◽

Wetland Classification

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

Download Full-text