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Wetland Landscape Change Mapping Using Remote Sensing
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Wetland Landscape Change Mapping Using Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 70908

Special Issue Editors

Dr. Laura L. Bourgeau-Chavez

E-Mail Website
Guest Editor
Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, USA
Interests: SAR; optical; SAR–optical fusion; landscape ecology; peatlands; wetlands; hydrology; wildfire; soil moisture
Dr. Brian Brisco

E-Mail Website
Guest Editor
Sr. Scientist, Canada Center for Mapping and Earth Observation, Ottawa, ON, Canada
Interests: wetlands; SAR; surface water mapping
Special Issues, Collections and Topics in MDPI journals
Mr. Brian Huberty

E-Mail Website
Guest Editor
ASPRS WGL, 17246 Knox Path, Hastings, MN 55033, USA
Interests: SAR; optical; SAR–optical fusion; lidar; thermal; hyperspectral applications using drones, aircraft, and satellites for habitats including wetlands, forests, urban, and agriculture systems

Special Issue Information

Dear Colleagues,

Wetlands are four-dimensional, dynamic systems which need monitoring at high repeat intervals to capture the hydrologic and floristic changes that occur between and within a season. High repeat monitoring allows for understanding wetland vulnerability to climatic and anthropogenic change and improve our ability to manage, restore, and protect these valuable ecosystems. Too many wetlands have already been lost to draining, dredging, filling, peat mining, and more, while many others have been degraded by nutrient overload or other pollutants. This often results in reduced ecological function, reduced diversity of native plants, increased non-native plant invasions, and degraded habitats.

Many advances in wetland mapping and monitoring from remote sensing for a variety of applications are taking place through new technologies, innovative research, and improved computing capabilities. With large amounts of remote sensing data now available daily to weekly, our ability to monitor these vulnerable wetland systems more routinely is becoming a reality. We wish to capture these state-of-the-art advances in detecting changes in wetland extent, condition, and hydrologic features through optical, thermal, microwave sensing at fine to coarse scales in this Special Issue of Remote Sensing entitled “Wetland Landscape Change Mapping Using Remote Sensing.”

Dr. Laura L. Bourgeau-Chavez
Dr. Brian Brisco
Mr. Brian Huberty
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Seasonal dynamics in wetlands
  • Wetland hydrological monitoring
  • Wetland degradation
  • Invasive species
  • Wetland mapping
  • Wetland remote sensing
  • Optical
  • Radar
  • Lidar
  • Thermal
  • Hyperspectral
  • Digital surface models

Published Papers (15 papers)

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Research

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21 pages, 10348 KiB  
Article
Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program
by Colin Brooks, Charlotte Weinstein, Andrew Poley, Amanda Grimm, Nicholas Marion, Laura Bourgeau-Chavez, Dana Hansen and Kurt Kowalski
Remote Sens. 2021, 13(10), 1895; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101895 - 12 May 2021
Cited by 8 | Viewed by 2962
Abstract
Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least [...] Read more.
Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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38 pages, 12164 KiB  
Article
Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin
by Michael J. Battaglia, Sarah Banks, Amir Behnamian, Laura Bourgeau-Chavez, Brian Brisco, Jennifer Corcoran, Zhaohua Chen, Brian Huberty, James Klassen, Joseph Knight, Paul Morin, Kevin Murnaghan, Keith Pelletier and Lori White
Remote Sens. 2021, 13(4), 599; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040599 - 08 Feb 2021
Cited by 14 | Viewed by 3425
Abstract
Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to [...] Read more.
Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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23 pages, 6561 KiB  
Article
Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation
by Subrina Tahsin, Stephen C. Medeiros and Arvind Singh
Remote Sens. 2021, 13(3), 438; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030438 - 27 Jan 2021
Cited by 4 | Viewed by 2327
Abstract
Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis [...] Read more.
Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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20 pages, 5358 KiB  
Article
Wetland Hydroperiod Change Along the Upper Columbia River Floodplain, Canada, 1984 to 2019
by Chris Hopkinson, Brendon Fuoco, Travis Grant, Suzanne E. Bayley, Brian Brisco and Ryan MacDonald
Remote Sens. 2020, 12(24), 4084; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244084 - 14 Dec 2020
Cited by 10 | Viewed by 3547
Abstract
Increasing air temperatures and changing hydrological conditions in the mountainous Kootenay Region of British Columbia, Canada are expected to affect floodplain wetland extent and function along the Columbia River. The objective of this study was to determine the seasonally inundated hydroperiod for a [...] Read more.
Increasing air temperatures and changing hydrological conditions in the mountainous Kootenay Region of British Columbia, Canada are expected to affect floodplain wetland extent and function along the Columbia River. The objective of this study was to determine the seasonally inundated hydroperiod for a floodplain section (28.66 km2) of the Upper Columbia River wetlands complex using time series satellite image observations and binary open water mask extraction. A mid pixel resolution (30 m) optical satellite image time series of 61 clear sky scenes from the Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were used to map temporal variations in floodplain open water wetland extent during the April to October hydrologically active season from 1984 to 2019 (35 years). The hydroperiod from the first 31 scenes (T1: 18 years) was compared to the second 30 (T2: 16 years) to identify changes in the permanent and seasonal open water bodies. The seasonal variation in open water extent and duration was similar across the two time periods but the permanent water body extent diminished by ~16% (or ~3.5% of the floodplain). A simple linear model (r2 = 0.87) was established to predict floodplain open water extent as a function of river discharge downstream of the case study area. Four years of Landsat Multi-Spectral Scanner (MSS) data from 1992 to 1995 (12 scenes) were examined to evaluate the feasibility of extending the hydroperiod record back to 1972 using lower resolution (60 m) archive data. While the MSS hydroperiod produced a similar pattern of open water area to duration to the TM/OLI hydroperiod, small open water features were omitted or expanded due to the lower resolution. While MSS could potentially extend the TM/OLI hydroperiod record, this was not performed as the loss of features like the river channel diminished its value for change detection purposes. Radarsat 2 scenes from 2015 to 2019 were examined to evaluate the feasibility of continued mountain valley hydroperiod monitoring using higher spatial and temporal resolution sensors like the Radarsat Constellation Mission (RCM). From the available horizontal transmit/receive (HH) single polarization sample set (8 scenes), the hydroperiod pattern of open water extent to duration was similar to the longer Landsat time series and possessed greater feature detail, but it was significantly reduced in seasonal inundation area due to the systematic omission of open water areas containing emergent vegetation. However, accepting that differences exist in sensor-based hydroperiod attributes, the higher temporal resolution of RCM will be suited to mountain floodplain inundation monitoring and open water hydroperiod analysis. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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25 pages, 82744 KiB  
Article
Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape
by Daniel L. Peters, K. Olaf Niemann and Robert Skelly
Remote Sens. 2020, 12(22), 3819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223819 - 22 Nov 2020
Cited by 5 | Viewed by 3401
Abstract
A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor [...] Read more.
A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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34 pages, 3989 KiB  
Article
Investigating the Potential Use of RADARSAT-2 and UAS imagery for Monitoring the Restoration of Peatlands
by Lori White, Mark McGovern, Shari Hayne, Ridha Touzi, Jon Pasher and Jason Duffe
Remote Sens. 2020, 12(15), 2383; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152383 - 24 Jul 2020
Cited by 11 | Viewed by 2931
Abstract
The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial [...] Read more.
The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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19 pages, 11780 KiB  
Article
Automated SAR Image Thresholds for Water Mask Production in Alberta’s Boreal Region
by Craig Mahoney, Michael Merchant, Lyle Boychuk, Chris Hopkinson and Brian Brisco
Remote Sens. 2020, 12(14), 2223; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142223 - 11 Jul 2020
Cited by 15 | Viewed by 2874
Abstract
Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through [...] Read more.
Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through the application of thresholds to distinguish water from non-water. Despite improvements to threshold methods, threshold selection is traditionally manual, which introduces subjectivity and inconsistency over large scales. This study presents a novel method for objectively determining and applying a threshold to determine water masks from Synthetic Aperture Radar (SAR) imagery on a scene-by-scene basis. The method was applied to Radarsat-2 and simulated Radarsat Constellation Mission scenes, and validated against two independent validation sources with high accuracy (Kappa ranging from 0.85 to 0.93). Expectedly, greatest misclassification occurs near shorelines, which are often ecologically important zones. Comparisons between Radarsat-2 and Radarsat Constellation Mission thresholds and outputs suggest that the latter is a capable successor for surface water applications. This work represents a foundational step toward objectivity and consistency in large-scale water mapping and monitoring. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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29 pages, 6640 KiB  
Article
Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping
by Ian Olthof and Thomas Rainville
Remote Sens. 2020, 12(9), 1476; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091476 - 06 May 2020
Cited by 18 | Viewed by 3272
Abstract
When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be [...] Read more.
When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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25 pages, 5510 KiB  
Article
Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis
by Frank Thonfeld, Stefanie Steinbach, Javier Muro and Fridah Kirimi
Remote Sens. 2020, 12(7), 1057; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071057 - 25 Mar 2020
Cited by 38 | Viewed by 6349
Abstract
Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in [...] Read more.
Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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21 pages, 4928 KiB  
Article
Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data
by Megan W. Lang, Vincent Kim, Gregory W. McCarty, Xia Li, In-Young Yeo, Chengquan Huang and Ling Du
Remote Sens. 2020, 12(4), 707; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040707 - 21 Feb 2020
Cited by 18 | Viewed by 3329
Abstract
To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and [...] Read more.
To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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19 pages, 6669 KiB  
Article
Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks
by Ling Du, Gregory W. McCarty, Xin Zhang, Megan W. Lang, Melanie K. Vanderhoof, Xia Li, Chengquan Huang, Sangchul Lee and Zhenhua Zou
Remote Sens. 2020, 12(4), 644; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040644 - 15 Feb 2020
Cited by 37 | Viewed by 4532
Abstract
The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands [...] Read more.
The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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20 pages, 3234 KiB  
Article
Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada
by Evan R. DeLancey, John F. Simms, Masoud Mahdianpari, Brian Brisco, Craig Mahoney and Jahan Kariyeva
Remote Sens. 2020, 12(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010002 - 18 Dec 2019
Cited by 120 | Viewed by 9841
Abstract
Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use [...] Read more.
Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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Review

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17 pages, 945 KiB  
Review
Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions
by Siyamthanda Gxokwe, Timothy Dube and Dominic Mazvimavi
Remote Sens. 2020, 12(24), 4190; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244190 - 21 Dec 2020
Cited by 29 | Viewed by 6358
Abstract
Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic [...] Read more.
Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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48 pages, 6189 KiB  
Review
Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change
by Laura Chasmer, Craig Mahoney, Koreen Millard, Kailyn Nelson, Daniel Peters, Michael Merchant, Chris Hopkinson, Brian Brisco, Olaf Niemann, Joshua Montgomery, Kevin Devito and Danielle Cobbaert
Remote Sens. 2020, 12(8), 1321; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081321 - 22 Apr 2020
Cited by 39 | Viewed by 8028
Abstract
The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy [...] Read more.
The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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50 pages, 4807 KiB  
Review
Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management
by Laura Chasmer, Danielle Cobbaert, Craig Mahoney, Koreen Millard, Daniel Peters, Kevin Devito, Brian Brisco, Chris Hopkinson, Michael Merchant, Joshua Montgomery, Kailyn Nelson and Olaf Niemann
Remote Sens. 2020, 12(8), 1320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081320 - 22 Apr 2020
Cited by 20 | Viewed by 6143
Abstract
Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for [...] Read more.
Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale. Full article
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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