Introduction to the Use of Remote Sensing for Wetland Mapping

2015 ◽  
pp. 1-2
Keyword(s):  
Remote Sensing ◽  
Wetland Mapping

Remote Sensing for Wetland Mapping and Inventory

1981 ◽  
Vol 6 (4) ◽  
pp. 177-185 ◽  
Author(s):  
Virginia Carter
Keyword(s):  
Remote Sensing ◽  
Wetland Mapping

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

2019 ◽  
Vol 11 (5) ◽  
pp. 516 ◽  
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.

scholarly journals Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

2021 ◽  
Vol 13 (20) ◽  
pp. 4025
Author(s):  
S. Mohammad Mirmazloumi ◽  
Armin Moghimi ◽  
Babak Ranjgar ◽  
Farzane Mohseni ◽  
Arsalan Ghorbanian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Meta Analysis ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Sensing Technology ◽  
Status And Trends ◽  
The Status ◽  
Supervised Classifiers

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.

scholarly journals THE CONTRIBUTION OF RADAR REMOTE SENSING VIA SENTINEL-1 DATA AND PHOTO-INTERPRETATION BY GOOGLE EARTH IMAGES FOR WETLAND MAPPING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 45-51
Author(s):  
M. Benchelha ◽  
F. Benzha ◽  
H. Rhinane ◽  
A. Zilali
Keyword(s):  
Remote Sensing ◽  
A Priori ◽  
European Space Agency ◽  
Bare Soil ◽  
Google Earth ◽  
Support Vector ◽  
Natural Environments ◽  
Wetland Mapping ◽  
Radar Remote Sensing ◽  
Water Surfaces

Abstract. Wetlands are considered as sensitive ecosystems exposed and threatened by climate change and the urbanization of natural environments. In the purpose of managing these sensitive areas and conservatizing their biodiversity, remote sensing is an efficient way to track environmental variables over large areas as wetlands. However, when it comes to the study of hydrologic dynamics, high temporal and spatial resolutions are essential. Since the access to optical satellite imagery is restrictive because of the large cloud cover that masks the ground, radar sensors that are working in the microwave field, are particularly suited to the characterization of hydrological dynamics due to the sensitivity of their measurements in the presence of water, regardless of the vegetation in place. Recently, radar remote sensing has experienced a real revolution with the launch of the Sentinel-1A satellite in 2014, followed by its twin Sentinel-1B two years later by the European Space Agency as part of the Copernicus program. These sensors acquire C-band data (λ = 5.6 cm) with a temporal resolution of 12 days by satellite and their distribution is open and free. This article aims to assess the potential of Sentinel A1 SAR data for wetland mapping in the city of Benslimane (Central Morocco). The first part is explaining the methodology for mapping water surfaces. We identified a confusion of the C-band radar response of water surfaces and that of certain bare soils. We then showed that the VH polarization is the most suitable for the mapping of water surfaces, comparing four methods of detecting areas in water. It. The second part is discussing the use of unsupervised methods without a priori data demonstrating that the methods taking into account the spatial neighborhood give better results. Temporal filtering has been developed and has made it possible to improve detection and to overcome confusion between bare soil and permanent water surfaces. Water surfaces larger than 0.5 ha are at 80% detected. Classification was performed using the SVM (Support Vector Machine) algorithm. This latter information was then implemented into the thematic map derived from SPOT-4 images to obtain the final weltands map.

Protocols for Riverine Wetland Mapping and Classification Using Remote Sensing and GIS

2017 ◽  
Vol 112 (07) ◽  
pp. 1544 ◽  
Author(s):  
Rajiv Sinha ◽  
Shivika Saxena ◽  
Manudeo Singh
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Wetland Mapping ◽  
Riverine Wetland

scholarly journals Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

2017 ◽  
Vol 9 (11) ◽  
pp. 1919 ◽  
Author(s):  
Laurel Ballanti ◽  
Kristin Byrd ◽  
Isa Woo ◽  
Christopher Ellings
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
River Delta ◽  
Historical Change ◽  
Wetland Mapping

scholarly journals Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques

2020 ◽  
Vol 12 (10) ◽  
pp. 1683
Author(s):  
Li Wen ◽  
Michael Hughes
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Coastal Wetland ◽  
River Estuary ◽  
Gradient Boosting ◽  
Wetland Mapping ◽  
Ensemble Classifiers ◽  
Coastal Landscape ◽  
Extreme Gradient Boosting ◽  
Wetland Distribution

Coastal wetlands are a critical component of the coastal landscape that are increasingly threatened by sea level rise and other human disturbance. Periodically mapping wetland distribution is crucial to coastal ecosystem management. Ensemble algorithms (EL), such as random forest (RF) and gradient boosting machine (GBM) algorithms, are now commonly applied in the field of remote sensing. However, the performance and potential of other EL methods, such as extreme gradient boosting (XGBoost) and bagged trees, are rarely compared and tested for coastal wetland mapping. In this study, we applied the three most widely used EL techniques (i.e., bagging, boosting and stacking) to map wetland distribution in a highly modified coastal catchment, the Manning River Estuary, Australia. Our results demonstrated the advantages of using ensemble classifiers to accurately map wetland types in a coastal landscape. Enhanced bagging decision trees, i.e., classifiers with additional methods to increasing ensemble diversity such as RF and weighted subspace random forest, had comparably high predictive power. For the stacking method evaluated in this study, our results are inconclusive, and further comprehensive quantitative study is encouraged. Our findings also suggested that the ensemble methods were less effective at discriminating minority classes in comparison with more common classes. Finally, the variable importance results indicated that hydro-geomorphic factors, such as tidal depth and distance to water edge, were among the most influential variables across the top classifiers. However, vegetation indices derived from longer time series of remote sensing data that arrest the full features of land phenology are likely to improve wetland type separation in coastal areas.

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