Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada

LaRocque, Armand; Phiri, Chafika; Leblon, Brigitte; Pirotti, Francesco; Connor, Kevin; Hanson, Alan

doi:10.3390/rs12132095

Cited by 41 publications

(34 citation statements)

References 71 publications

(114 reference statements)

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“…Supervised classifications (including machine and deep learning) are one of the most common classifiers for land cover and wetland applications [259,[264][265][266][267]. Supervised classifiers require a reference dataset, referred to as 'training data', correlate class labels with pixel-associated data, and are used to train the algorithm to classify all pixels within the image.…”

Section: Supervised Classificationmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

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Section: Supervised Classificationmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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“…Therefore, the results show that wetland studies using remotely sensed data in Zimbabwe mainly rely on free software, ILWIS in particular. To improve the EO‐based analyses of wetland ecological studies, there is a need to explore utilisation of a wide range of other free software that have been applied in other countries such as Geographic Resources Analysis Support System (GRASS) GIS (Neteler et al., 2012), Google Earth Engine (Hardy et al., 2020), System for Automated Geoscientific Analyses (SAGA) GIS (LaRocque et al., 2020) and Water Observation and Information System (WOIS; Guzinski, Kass, Huber, Bauer‐Gottwein, Jensen & Naeimi, 2014; Guzinski, Kass, Huber, Bauer‐Gottwein, Jensen, Naeimi, Doubkova, et al, 2014; Makapela et al., 2015).…”

Section: Resultsmentioning

confidence: 99%

Trends in use of remotely sensed data in wetlands assessment and monitoring in Zimbabwe

Ngwenya

Marambanyika

2021

Afr J Ecol

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The paper assesses trends in use of earth observation data in wetland conditions monitoring and assessment in Zimbabwe from 1980 to 2019. Reviewed literature shows academia and research institutions (69.8%), government agencies (14%) and international development partners (16.3%) as the main users of remotely sensed data in wetland ecological assessments and monitoring. There is more reliance by the abovementioned stakeholders on freely available low‐cost resolution imagery from Landsat (62.9%) and Moderate Resolution Imaging Spectroradiometer (14.3%). Other stakeholders, however, are reliant on high‐resolution imagery like Rapid Eye (5.7%) and aerial photography (11.4%). Satellite images in wetland management in Zimbabwe are used for land use land cover change detection (42.1%), vegetation health monitoring (21.1%), water quantity monitoring (5.3%), water quality monitoring (13.2%) and wetland mapping (18.4%). The identified challenges faced by different stakeholders to effectively utilise EO data include high cost of high‐resolution imageries, limited expertise, inadequate equipment and software. Since the cost of high‐resolution satellite imagery mainly constraints the acquisition of suitable satellite data to assess the small wetlands that dominate Zimbabwe's landscape, there is need to promote use of recently launched freely available high‐resolution Sentinel data to improve the ecological assessment of wetland conditions.

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“…ML, SVM, k-NN, DT, NN, and ISODATA with the median overall accuracies between 83% and 85% were the mid-range classifiers. The best (97.67%) and worst (62.40%) overall accuracies were achieved by RF [117] and Other [118] classifiers, respectively. There are different wetland classification strategies.…”

Section: Table A1mentioning

confidence: 99%

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

et al. 2021

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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.

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Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada

Cited by 41 publications

References 71 publications

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Trends in use of remotely sensed data in wetlands assessment and monitoring in Zimbabwe

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

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