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

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.

[1]  Alisa L. Gallant,et al.  The Challenges of Remote Monitoring of Wetlands , 2015, Remote. Sens..

[2]  Guoqing Sun,et al.  Mapping biomass change after forest disturbance: Applying LiDAR footprint-derived models at key map scales , 2013 .

[3]  S. T. Seydi,et al.  Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands , 2021 .

[4]  Marguerite Madden,et al.  Hyperspectral image data for mapping wetland vegetation , 2003, Wetlands.

[5]  Brian Brisco,et al.  Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management , 2020, Remote. Sens..

[6]  A. O. Onojeghuo,et al.  Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta , 2021, GIScience & Remote Sensing.

[7]  Zhenhong Du,et al.  Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method , 2020, Remote. Sens..

[8]  Konstantinos P. Papathanassiou,et al.  Polarimetric SAR interferometry , 1998, IEEE Trans. Geosci. Remote. Sens..

[9]  Richard M. Lucas,et al.  The Remote Sensing and GIS Software Library (RSGISLib) , 2014, Comput. Geosci..

[10]  Laurence C. Smith,et al.  Amazon floodplain water level changes measured with interferometric SIR-C radar , 2001, IEEE Trans. Geosci. Remote. Sens..

[11]  L. Gill,et al.  A nested drone-satellite approach to monitoring the ecological conditions of wetlands , 2021 .

[12]  Floyd M. Henderson,et al.  Radar detection of wetland ecosystems: a review , 2008 .

[13]  Tao Liu,et al.  Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification , 2018 .

[14]  B. Noon,et al.  Vulnerability of Breeding Waterbirds to Climate Change in the Prairie Pothole Region, U.S.A , 2014, PloS one.

[15]  P. Zandbergen.  Accuracy Considerations in the Analysis of Depressions in Medium-Resolution Lidar DEMs , 2010 .

[16]  Aleksi Räsänen,et al.  Inventory and Connectivity Assessment of Wetlands in Northern Landscapes with a Depression-Based DEM Method , 2020 .

[17]  Waldir Renato Paradella,et al.  Use of RADARSAT-1 fine mode and Landsat-5 TM selective principal component analysis for geomorphological mapping in a macrotidal mangrove coast in the Amazon Region , 2005 .

[18]  Jinliang Huang,et al.  Monitoring Spatial and Temporal Dynamics of Flood Regimes and Their Relation to Wetland Landscape Patterns in Dongting Lake from MODIS Time-Series Imagery , 2015, Remote. Sens..

[19]  Brian Brisco,et al.  SAR and Lidar Temporal Data Fusion Approaches to Boreal Wetland Ecosystem Monitoring , 2019, Remote. Sens..

[20]  N. D. Niemuth,et al.  Spatial and Temporal Variation in Wet Area of Wetlands in the Prairie Pothole Region of North Dakota and South Dakota , 2010, Wetlands.

[21]  Qiusheng Wu,et al.  Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. , 2019, Remote sensing of environment.

[22]  R. Dial,et al.  Wetland drying and succession across the Kenai Peninsula Lowlands, south-central Alaska , 2005 .

[23]  Amr H. Abd-Elrahman,et al.  Analyzing fine-scale wetland composition using high resolution imagery and texture features , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Joseph F. Knight,et al.  Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota , 2013, Remote. Sens..

[25]  B. Wilkinson,et al.  A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds , 2020, Earth Surface Processes and Landforms.

[26]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[27]  Jeffrey W. Riley,et al.  Identifying Small Depressional Wetlands and Using a Topographic Position Index to Infer Hydroperiod Regimes for Pond-Breeding Amphibians , 2017, Wetlands.

[28]  G. Guntenspergen,et al.  Evidence for 20th century climate warming and wetland drying in the North American Prairie Pothole Region , 2013, Ecology and evolution.

[29]  Kazi Rifat Ahmed,et al.  A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms , 2021, Remote Sensing Applications: Society and Environment.

[30]  R. Tiner,et al.  Wetlands of the United States , 1993 .

[31]  F. Stecchi,et al.  Land use and land cover change analysis in predominantly man-made coastal wetlands: towards a methodological framework , 2017, Wetlands Ecology and Management.

[32]  Bunkei Matsushita,et al.  Impacts of Urbanization on the Muthurajawela Marsh and Negombo Lagoon, Sri Lanka: Implications for Landscape Planning towards a Sustainable Urban Wetland Ecosystem , 2021, Remote. Sens..

[33]  A. V. D. Valk,et al.  Vegetation of wetlands of the prairie pothole region , 1989 .

[34]  L. Martz,et al.  Topographic analysis for the prairie pothole region of Western Canada , 2012 .

[35]  R. Rooney,et al.  Determinants of Wetland- Bird Community Composition in Agricultural Marshes of the Northern Prairie and Parkland Region , 2021 .

[36]  J. S. Lee,et al.  A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction , 2004 .

[37]  Rob Jamieson,et al.  Surface moisture and vegetation influences on lidar intensity data in an agricultural watershed , 2011 .

[38]  C. Hopkinson,et al.  A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta , 2015 .

[39]  Tao Liu,et al.  Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system , 2018 .

[40]  Jahan Kariyeva,et al.  Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada , 2019, Remote. Sens..

[41]  Hannah M. Cooper,et al.  Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data , 2018 .

[42]  Brian Brisco,et al.  Monitoring Hydro Temporal Variability in Alberta, Canada with Multi-Temporal Sentinel-1 SAR Data , 2018 .

[43]  Eric Pottier,et al.  One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images , 2012 .

[44]  Mohammed Dabboor,et al.  A Collection of SAR Methodologies for Monitoring Wetlands , 2015, Remote. Sens..

[45]  M. Tulbure,et al.  Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011 , 2013 .

[46]  Bai Zhang,et al.  Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data , 2017 .

[47]  Molly Reif,et al.  Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data , 2009, Wetlands.

[48]  Philippe C. Baveye,et al.  Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery , 2008 .

[49]  C. Schröder,et al.  Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. , 2019, Journal of environmental management.

[50]  Patrick Van Damme,et al.  Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland, Iran , 2013 .

[51]  J. Pomeroy,et al.  Quantifying the effects of Prairie depressional storage complexes on drainage basin connectivity , 2021 .

[52]  Heather McNairn,et al.  Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data , 2017 .

[53]  T. Dube,et al.  Use of multispectral satellite data to assess impacts of land management practices on wetlands in the Limpopo Transfrontier River Basin, South Africa , 2021, South African Geographical Journal.

[54]  C. Hopkinson,et al.  A Physically Based Terrain Morphology and Vegetation Structural Classification for Wetlands of the Boreal Plains, Alberta, Canada , 2016 .

[55]  M. Kelly,et al.  Prospective HyspIRI global observations of tidal wetlands , 2015 .

[56]  Benjamin F. Zaitchik,et al.  Monthly flooded area classification using low resolution SAR imagery in the Sudd wetland from 2007 to 2011 , 2017 .

[57]  Brian Brisco,et al.  Remote sensing for wetland classification: a comprehensive review , 2018 .

[58]  Laura Chasmer,et al.  Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment , 2016 .

[59]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[60]  Xiaoxue Wang,et al.  Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images , 2019, Remote. Sens..

[61]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[62]  Nicholas C. Coops,et al.  Lidar plots — a new large-area data collection option: context, concepts, and case study , 2012 .

[63]  Brindusa Cristina Budei,et al.  Identifying the genus or species of individual trees using a three-wavelength airborne lidar system , 2018 .

[64]  A. Harris,et al.  Microtopographic Drivers of Vegetation Patterning in Blanket Peatlands Recovering from Erosion , 2019, Ecosystems.

[65]  Jing Li,et al.  A Review of Wetland Remote Sensing , 2017, Sensors.

[66]  Natascha Kljun,et al.  Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data , 2012 .

[67]  Brian Brisco,et al.  Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration , 2017 .

[68]  D. M. Ghioca-Robrecht,et al.  Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal Marsh , 2008, Wetlands.

[69]  R. Nemani,et al.  Unmixing Algorithms: A Review of Techniques for Spectral Detection and Classification of Land Cover from Mixed Pixels on NASA Earth Exchange , 2017 .

[70]  S. Zang,et al.  Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques , 2015, Environmental Monitoring and Assessment.

[71]  Ross S. Lunetta,et al.  Application of multi-temporal Landsat 5 TM imagery for wetland identification , 1999 .

[72]  B. Scheuchl,et al.  Potential of RADARSAT-2 data for operational sea ice monitoring , 2004 .

[73]  Stefan Dech,et al.  Comparing four operational SAR-based water and flood detection approaches , 2015 .

[74]  H. Savenije,et al.  Hydrology and Earth System Sciences Hydrological Landscape Classification: Investigating the Performance of Hand Based Landscape Classifications in a Central European Meso-scale Catchment , 2022 .

[75]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[76]  Lars Brabyn,et al.  Effects of class granularity and cofactors on the performance of unsupervised classification of wetlands using multi-spectral aerial photography , 2014 .

[77]  Chunhua Li,et al.  Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China , 2021, Remote. Sens..

[78]  Michael A. Merchant,et al.  An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada , 2019, Canadian Journal of Remote Sensing.

[79]  M. Simard,et al.  Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM , 2013 .

[80]  B. McConkey,et al.  On the behaviour of dynamic contributing areas and flood frequency curves in North American Prairie watersheds , 2012 .

[81]  Steven E. Franklin,et al.  Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion , 2018 .

[82]  P. H. Crown,et al.  Application of Clustering to Landsat MSS Digital Data for Peatland Inventory , 1984 .

[83]  Masoud MahdianPari,et al.  Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review , 2020, Remote. Sens..

[84]  Bradley C. Rundquist,et al.  Terminal Lake Flooding and Wetland Expansion in Nelson County, North Dakota , 2004 .

[85]  Maggi Kelly,et al.  A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data , 2021 .

[86]  Brian K. Gelder,et al.  Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[87]  Sang-Hoon Hong,et al.  Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types , 2015, Remote. Sens..

[88]  B. Wilen,et al.  THE US FISH AND WILDLIFE SERVICE'S NATIONAL WETLANDS INVENTORY PROJECT , 1995 .

[89]  Weimin Huang,et al.  Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data , 2017 .

[90]  Xinwu Li,et al.  Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses , 2013, Remote. Sens..

[91]  Madhu Verma,et al.  Valuing ecosystem services of wetlands—a tool for effective policy formulation and poverty alleviation , 2011 .

[92]  B. Brisco,et al.  Evaluation of C-band polarization diversity and polarimetry for wetland mapping , 2011 .

[93]  Baoxin Hu,et al.  Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection , 2020 .

[94]  Dongqing Zhang,et al.  Constructed wetlands in China , 2009 .

[95]  E. Work,et al.  Utilization of satellite data for inventorying prairie ponds and lakes , 1976 .

[96]  M. Simard,et al.  A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia , 2008 .

[97]  S. Tesfamichael,et al.  Unmanned Aerial Vehicle (UAV) Photogrammetry Produces Accurate High-resolution Orthophotos, Point Clouds and Surface Models for Mapping Wetlands , 2016 .

[98]  J. Pomeroy,et al.  Hydrological regime changes in a Canadian Prairie basin , 2015 .

[99]  P. Chow-Fraser,et al.  Mapping Phragmites cover using WorldView 2/3 and Sentinel 2 images at Lake Erie Wetlands, Canada , 2021, Biological Invasions.

[100]  D. Alsdorf,et al.  Repeat-pass multi-temporal interferometric SAR coherence variations with Amazon floodplain and lake habitats , 2010 .

[101]  Kazuo Ouchi,et al.  Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics , 2013, Remote. Sens..

[102]  Joong-Sun Won,et al.  Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR) , 2010 .

[103]  Implications of Climate Change for Wetland-Dependent Birds in the Prairie Pothole Region , 2016, Wetlands.

[104]  Marcelle Grenier,et al.  Object-based classification of a SPOT-4 image for mapping wetlands in the context of greenhouse gases emissions: the case of the Eastmain region, Québec, Canada , 2008 .

[105]  Andy Hardy,et al.  Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands , 2020, Remote. Sens..

[106]  Xuefeng Chu,et al.  Dynamic puddle delineation and modeling of puddle‐to‐puddle filling‐spilling‐merging‐splitting overland flow processes , 2013 .

[107]  M. Hayashi,et al.  The Groundwater Recharge Function of Small Wetlands in the Semi-Arid Northern Prairies , 1998 .

[108]  E. Mcleod,et al.  Clarifying the role of coastal and marine systems in climate mitigation , 2017 .

[109]  L. Rebelo,et al.  Remote sensing and GIS for wetland inventory, mapping and change analysis. , 2009, Journal of environmental management.

[110]  M. Watmough,et al.  Current approaches to wetland status and trends monitoring in prairie Canada and the continental United States of America , 2007 .

[111]  Min Feng,et al.  Demonstration of a conceptual model for using LiDAR to improve the estimation of floodwater mitigation potential of Prairie Pothole Region wetlands , 2011 .

[112]  Swapan Talukdar,et al.  Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh , 2021, Environmental Science and Pollution Research.

[113]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[114]  P. Sutton,et al.  Changes in the global value of ecosystem services , 2014 .

[115]  Gloria Bordogna,et al.  Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[116]  Simon Dufour,et al.  Monitoring restored riparian vegetation: how can recent developments in remote sensing sciences help? , 2013 .

[117]  Brian Brisco,et al.  Operational Surface Water Detection and Monitoring Using Radarsat 2 , 2016, Remote. Sens..

[118]  Lori White,et al.  Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands , 2017, Remote. Sens..

[119]  Brian Brisco,et al.  Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin , 2021, Remote. Sens..

[120]  Brian Brisco,et al.  Mapping and Monitoring Surface Water and Wetlands with Synthetic Aperture Radar , 2015 .

[121]  Nicholas C. Coops,et al.  Four steps to extend drone use in research , 2019, Nature.

[122]  Hongxing Liu,et al.  Multi-temporal Sub-pixel Landsat ETM+ Classification of Isolated Wetlands in Cuyahoga County, Ohio, USA , 2012, Wetlands (Wilmington, N.C.).

[123]  Patrick J. Cullen,et al.  UAV-hyperspectral imaging of spectrally complex environments , 2020 .

[124]  Brian Brisco,et al.  Wetland hydroperiod classification in the western prairies using multitemporal synthetic aperture radar , 2018 .

[125]  Qiang Zhou,et al.  Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data , 2019, Remote. Sens..

[126]  Elijah W. Ramsey,et al.  Resource Management of Forested Wetlands: Hurricane Impact and Recovery Mapped by Combining Landsat TM and NOAA AVHRR Data , 1998 .

[127]  B. Rundquist,et al.  Remote Detection of Prairie Pothole Ponds in the Devils Lake Basin, North Dakota , 2005 .

[128]  Brian D. Smerdon,et al.  An Initial Inventory and Indexation of Groundwater Mega-Depletion Cases , 2012, Water Resources Management.

[129]  Chengquan Huang,et al.  Automated Extraction of Surface Water Extent from Sentinel-1 Data , 2018, Remote. Sens..

[130]  Ridha Touzi,et al.  Wetland characterization using polarimetric RADARSAT-2 capability , 2007 .

[131]  Marc Simard,et al.  Continental-Scale Canopy Height Modeling by Integrating National, Spaceborne, and Airborne LiDAR Data , 2016 .

[132]  C. Westbrook,et al.  Simplified Volume-Area-Depth Method for Estimating Water Storage of Prairie Potholes , 2010, Wetlands.

[133]  M. Vanderhoof,et al.  Patterns and drivers for wetland connections in the Prairie Pothole Region, United States , 2016, Wetlands Ecology and Management.

[134]  Philip A. Townsend,et al.  Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal Radarsat SAR , 2001 .

[135]  Saheba Bhatnagar,et al.  Mapping Vegetation Communities inside Wetlands Using Sentinel-2 Imagery in Ireland , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[136]  Brian Brisco,et al.  The integration of optical, topographic, and radar data for wetland mapping in northern Minnesota , 2011 .

[137]  David S. Gilmer,et al.  Characteristics of central North Dakota wetlands determined from sample aerial photographs and ground study , 1981 .

[138]  Paul Bolstad,et al.  Mapping northern wetlands with high resolution satellite images and LiDAR , 2009, Wetlands.

[139]  Predictive Mapping of Solute‐rich Wetlands in the Canadian Prairie Pothole Region Through High‐resolution Digital Elevation Model Analyses , 2021 .

[140]  Andreas Schmitt,et al.  Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery , 2013 .

[141]  J. P. D. Simioni,et al.  A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation , 2020, Wetlands Ecology and Management.

[142]  Sasanka Ghosh,et al.  Wetland conversion risk assessment of East Kolkata Wetland: A Ramsar site using random forest and support vector machine model , 2020 .

[143]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[144]  F. Flores-Verdugo,et al.  Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands , 2020, Journal of Coastal Conservation.

[145]  Kevin L. Erwin.  Wetlands and global climate change: the role of wetland restoration in a changing world , 2009, Wetlands Ecology and Management.

[146]  Lei Jing,et al.  Exploring Wetland Dynamics in Large River Floodplain Systems with Unsupervised Machine Learning: A Case Study of the Dongting Lake, China , 2020, Remote. Sens..

[147]  Alfredo Huete,et al.  Land surface phenological response to decadal climate variability across Australia using satellite remote sensing , 2014 .

[148]  Thomas Blaschke,et al.  Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[149]  Andreas Schmitt,et al.  SAR polarimetric change detection for flooded vegetation , 2013, Int. J. Digit. Earth.

[150]  Bruce W. Pengra,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[151]  Li Wen,et al.  Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques , 2020, Remote. Sens..

[152]  O. Mutanga,et al.  Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review , 2010, Wetlands Ecology and Management.

[153]  Benoit Montpetit,et al.  Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests , 2019, Remote. Sens..

[154]  M. Vanderhoof,et al.  Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States , 2016, Landscape Ecology.

[155]  Randolph H. Wynne,et al.  Mapping wetlands using ASTER data: a comparison between classification trees and logistic regression , 2009 .

[156]  Thuy Le Toan,et al.  Dependence of radar backscatter on coniferous forest biomass , 1992, IEEE Trans. Geosci. Remote. Sens..

[157]  Peter Bauer-Gottwein,et al.  Hydrologic Dynamics of the Ground-Water-Dependent Sian Ka’an Wetlands, Mexico, Derived from InSAR and SAR Data , 2010, Wetlands.

[158]  I. Creed,et al.  Understanding variation in trophic status of lakes on the Boreal Plain: A 20 year retrospective using Landsat TM imagery , 2007 .

[159]  M. Dubé,et al.  Essential components and pathways for developing Indigenous community‐based monitoring: Examples from the Canadian oil sands region , 2021, Integrated environmental assessment and management.

[160]  Chengquan Huang,et al.  Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks , 2020, Remote. Sens..

[161]  Craig Mahoney,et al.  Automated SAR Image Thresholds for Water Mask Production in Alberta's Boreal Region , 2020, Remote. Sens..

[162]  Alan A. Thompson,et al.  Overview of the RADARSAT Constellation Mission , 2015 .

[163]  Jennifer N. Hird,et al.  Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping , 2017, Remote. Sens..

[164]  Cui Yuan,et al.  Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China , 2020, Remote. Sens..

[165]  G. Cui,et al.  Analysis of the causes of wetland landscape patterns and hydrological connectivity changes in Momoge National Nature Reserve based on the Google Earth Engine Platform , 2021, Arabian Journal of Geosciences.

[166]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[167]  Laura Chasmer,et al.  Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment , 2005 .

[168]  Charlotte MacAlister,et al.  Mapping wetlands in the Lower Mekong Basin for wetland resource and conservation management using Landsat ETM images and field survey data. , 2009, Journal of environmental management.

[169]  P. M. Mather,et al.  A spectral library for a semi-arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data , 2004 .

[170]  Aaron J. Smith,et al.  A Semi-Automated, Multi-Source Data Fusion Update of a Wetland Inventory for East-Central Minnesota, USA , 2015, Wetlands.

[171]  Amr H. Abd-Elrahman,et al.  odeling soil parameters using hyperspectral image reflectance in ubtropical coastal wetlands aveen , 2014 .

[172]  Qiusheng Wu,et al.  Delineation and Quantification of Wetland Depressions in the Prairie Pothole Region of North Dakota , 2016, Wetlands.

[173]  M. Mahdianpari,et al.  Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery , 2017 .

[174]  P. Milly,et al.  Potential evapotranspiration and continental drying , 2016 .

[175]  Laurence Hubert-Moy,et al.  Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities , 2019, Remote Sensing of Environment.

[176]  Laura L. Bourgeau-Chavez,et al.  Improving Wetland Characterization with Multi-Sensor, Multi-Temporal SAR and Optical/Infrared Data Fusion , 2009 .

[177]  V. Klemas,et al.  Remote sensing of emergent and submerged wetlands: an overview , 2013 .

[178]  Catherine Ticehurst,et al.  The potential of L‐band SAR for quantifying mangrove characteristics and change: case studies from the tropics , 2007 .

[179]  Georgia Fotopoulos,et al.  Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection , 2017, Remote. Sens..

[180]  Stephen V. Stehman,et al.  International Journal of Applied Earth Observation and Geoinformation: Time-Series Analysis of Multi-Resolution Optical Imagery for Quantifying Forest Cover Loss in Sumatra and Kalimantan, Indonesia , 2011 .

[181]  K. Millard,et al.  Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR–SAR fusion using a random forest classifier , 2013 .

[182]  B. Brisco,et al.  A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland , 2020 .

[183]  D. Mason,et al.  Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model , 2001 .

[184]  Tao Liu,et al.  Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery , 2017 .

[185]  Qiusheng Wu,et al.  Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. , 2017, Hydrology and earth system sciences.

[186]  Qiusheng Wu,et al.  Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes , 2017, Remote. Sens..

[187]  Tamás Szirányi,et al.  Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery , 2017 .

[188]  R. D. Nelson,et al.  Long-term changes in pond permanence, size, and salinity in Prairie Pothole Region wetlands: The role of groundwater-pond interaction , 2018, Journal of Hydrology: Regional Studies.

[189]  K. Olaf Niemann,et al.  Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change , 2020, Remote. Sens..

[190]  Francesco Pirotti,et al.  Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada , 2020, Remote. Sens..

[191]  J. Zyl,et al.  Unsupervised classification of scattering behavior using radar polarimetry data , 1989 .

[192]  Brian Brisco,et al.  Detecting Emergence, Growth, and Senescence of Wetland Vegetation with Polarimetric Synthetic Aperture Radar (SAR) Data , 2014 .

[193]  Lei Wang,et al.  Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery , 2014 .

[194]  Brian Brisco,et al.  Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study , 2017, Remote. Sens..

[195]  Wetland‐Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers , 2021 .

[196]  C. Jarchow,et al.  Using Full and Partial Unmixing Algorithms to Estimate the Inundation Extent of Small, Isolated Stock Ponds in an Arid Landscape , 2019, Wetlands.

[197]  Mercedes Vélez-Nicolás,et al.  Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review , 2021, Remote. Sens..

[198]  T. Dixon,et al.  Space-Based Detection of Wetlands' Surface Water Level Changes from L-Band SAR Interferometry , 2008 .

[199]  Brian Brisco,et al.  Water resource applications with RADARSAT-2 – a preview , 2008, Int. J. Digit. Earth.

[200]  D. Pennock,et al.  Prairie Pothole Wetlands – Suggestions for Practical and Objective Definitions and Terminology , 2016, Wetlands.

[201]  T. C. Winter,et al.  Hydrologic studies of wetlands in the northern prairie. , 1989 .

[202]  M. Fortin,et al.  Inland surface waters in protected areas globally: Current coverage and 30-year trends , 2019, PloS one.

[203]  R. Zarkami,et al.  Modelling Occurrence of Invasive Water Hyacinth (Eichhornia crassipes) in Wetlands , 2021 .

[204]  Junhua Li,et al.  A rule-based method for mapping Canada's wetlands using optical, radar and DEM data , 2005 .

[205]  Michael J. Anteau,et al.  Generating Nested Wetland Catchments with Readily-Available Digital Elevation Data May Improve Evaluations of Land-Use Change on Wetlands , 2014, Wetlands.

[206]  Wetland Losses Due to Row Crop Expansion in the Dakota Prairie Pothole Region , 2013, Wetlands.

[207]  J. Thayn.  Monitoring Narrow Mangrove Stands in Baja California Sur, Mexico Using Linear Spectral Unmixing , 2020 .

[208]  T. Nudds,et al.  Prairie Landscape Change and Flooding in the Mississippi River Valley , 1996 .

[209]  John K. Pattison-Williams,et al.  Wetlands, flood control and ecosystem services in the Smith Creek drainage basin: a case study in Saskatchewan, Canada. , 2018 .

[210]  Brian Brisco,et al.  Seasonal Change in Wetland Coherence as an Aid to Wetland Monitoring , 2017, Remote. Sens..

[211]  I MohdHasmadi,et al.  Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data , 2009 .

[212]  LiJonathan,et al.  Detecting Water Bodies on RADARSAT Imagery , 2011 .

[213]  Qiusheng Wu,et al.  GIS and Remote Sensing Applications in Wetland Mapping and Monitoring , 2018 .