Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

Jahncke, Raymond; Leblon, Brigitte; Bush, Peter J.; LaRocque, Armand

doi:10.1016/j.jag.2018.01.012

Cited by 51 publications

(48 citation statements)

References 51 publications

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“…Wetlands in the Atlantic Maritime ecozone comprise a small percentage of the total wetland area of Canada's forested ecosystems, but this ecozone had the largest relative decline in wetland extent in the analyzed period, with a 1.3% decrease in area per year. The reason for this steep decline is unclear; the combined effects of drought and urban expansion might be contributing to the higher rates of decline in this ecozone than in others [49,65,66], but the ecozone may also be very sensitive to mapping errors given the relatively small extent of wetlands compared with the national wetland coverage. Wetlands in mountainous ecozones (Montane, Boreal, and Taiga Cordillera) also comprised a small portion of total wetland area, but exhibited increases in wetland extent that may be related to increased snowmelt [67,68].…”

Section: Discussionmentioning

confidence: 97%

A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data

Wulder

Campbell

et al. 2018

Remote Sensing

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Wetlands are important globally for supplying clean water and unique habitat, and for storing vast amounts of carbon and nutrients. The geographic extent and state of wetlands vary over time and represent a dynamic land condition rather than a permanent land cover state. Herein, we combined a time series of land cover maps derived from Landsat data at 30-m resolution to inform on spatial and temporal changes to non-treed and treed wetland extents over Canada’s forested ecosystems (>650 million ha) from 1984 to 2016. Overall, for the period, 1984 to 2016, we found the extent of wetlands (non-treed and treed combined) in Canada’s forested ecosystems to be stable, with some regional variability, often resulting from offsetting decreases and increases within a given ecozone. Notwithstanding difficulties in using optical satellite data for mapping a land condition, by accumulating wetland evidence via earth observations consistently through multiple decades, our results capture the trends in wetland cover over a previously unmapped, national extent at a level of spatial detail and temporal reach suitable for further focused interpretations of wetlands and drivers and projections of wetland dynamics.

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Section: Discussionmentioning

confidence: 97%

A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data

Wulder

Campbell

et al. 2018

Remote Sensing

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“…LiDAR generates a very high resolution and good quality DEM that can be used in several applications like flood hazard zoning, forest mapping, bathymetry, coastal erosion monitoring, glacier studies etc (Jahncke et al, 2018;Joyce et al, 2014;Lennon et al, 2005). LiDAR data have limited spatial coverage.…”

Section: Lidarmentioning

confidence: 99%

“…Wetland mapping (Jahncke et al, 2018) Suitable for the mapping of vegetation condition and vegetation indices like NDVI.…”

Section: Optical Sar Lidarmentioning

confidence: 99%

A Comparative Assessment of Remote Sensing Imaging Techniques: Optical, Sar and Lidar

Agrawal

Khairnar

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing is a popular technique that is using in the mapping and monitoring of earth features. Early remote sensing was predominantly passive, i.e. it depends upon the sun for energy. Till the last decade, optical imaging is mainly used in remote sensing works. Over the time, significant innovations and improvements have been made in the active remote sensing which resulted in the form of sophisticated imaging techniques like Synthetic Aperture RADAR (SAR) and Light Detection And Ranging (LiDAR). These advancements promoted the use of multi-sensor and multi-source data in remote sensing projects. This highlights the need to review these imaging techniques. Therefore, this review work is carried out. In this review, different imaging techniques are discussed and compared. Optical, SAR and LiDAR techniques are first summarized and then they are compared on the basis of technical specification and working. Finally, an in-depth assessment is made on the applicability of these imaging techniques in different fields.

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“…Amani et al [46] classified a wetland in Newfoundland, Canada by using four spectral indexes of different data types, including RapidEye, Sentinel 2A, ASTER and Landsat 8, and made fully use of texture features and proportion features of each data. Jahncke et al [47] used RADARSAT-2, polSAR, LiDAR and QuickBird multi-source remote sensing data to divide a wetland in Nova Scotia, Canada into 5 types. Specifically, CNN algorithm has also been successfully used for multi-source remote sensing fusion classification in various applications.…”

Section: Introductionmentioning

confidence: 99%

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Liu

Tao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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It is significant for restoration and protection of natural resources and ecological services in coastal wetlands to map different land cover types with satellite remote sensing data. Considering difficulties of wetland species classification, hyperspectral images (HSI) with high spectral resolution and multispectral images (MSI) with high spatial resolution are considered to achieve complementary advantages of multi-source data. An effective approach, named as multi-stream convolutional neural network (CNN), is proposed to achieve fine classification of coastal wetlands. Firstly, regression processing is adopted to make chaotically scattered coastal wetland data more compact and different. Secondly, through appropriate feature extraction and feature fusion strategies, high-level information of multi-source data in regression domain is fused to distinguish different land cover. Experiments on GF-5 hyperspectral images and Sentinel-2 multispectral images are carried out in order to validate the classification performance of the proposed approach in two coastal wetlands of research value in China, i.e., Yellow River Estuary and Yancheng coastal wetland. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods in the field, especially when the number of sample size is extremely small. Index Terms-Coastal wetlands; data fusion; hyperspectral imagery (HSI); multispectral imagery (MSI); convolutional neural network (CNN); least squares regression (LSR).

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Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

Cited by 51 publications

References 51 publications

A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data

A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data

A Comparative Assessment of Remote Sensing Imaging Techniques: Optical, Sar and Lidar

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

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