Introduction to Wetland Mapping and Its Challenges

2015 ◽  
pp. 43-66 ◽  
Author(s):  
Ralph Tiner
Keyword(s):  
Wetland Mapping

scholarly journals Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Pilot Study ◽  
Data Processing ◽  
High Latitude ◽  
Hyperspectral Images ◽  
The Arctic ◽  
Wetland Mapping ◽  
Kappa Index ◽  
Spectral Angle Mapper ◽  
Spectral Angle

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine

2021 ◽  
Author(s):  
Mohammad Ali Hemati ◽  
Mahdi Hasanlau ◽  
Masaud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Google Earth ◽  
Wetland Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

Object-based random forest wetland mapping in Conne River, Newfoundland, Canada

2021 ◽  
Vol 15 (03) ◽  
Author(s):  
Jean Elizabeth Granger ◽  
Masoud Mahdianpari ◽  
Thomas Puestow ◽  
Sherry Warren ◽  
Fariba Mohammadimanesh ◽  
...  
Keyword(s):  
Random Forest ◽  
Wetland Mapping ◽  
Object Based

Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta

2021 ◽  
pp. 1-23
Author(s):  
Alex Okiemute Onojeghuo ◽  
Ajoke Ruth Onojeghuo ◽  
Michelle Cotton ◽  
Johnathan Potter ◽  
Brennan Jones
Keyword(s):  
Lidar Data ◽  
Wetland Mapping ◽  
Natural Region ◽  
Multi Temporal

scholarly journals Using Random Forest Classification and Nationally Available Geospatial Data to Screen for Wetlands over Large Geographic Regions

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1158 ◽  
Author(s):  
Benjamin R. Felton ◽  
Gina L. O’Neil ◽  
Mary-Michael Robertson ◽  
G. Michael Fitch ◽  
Jonathan L. Goodall
Keyword(s):  
Environmental Planning ◽  
False Negative ◽  
False Negative Rate ◽  
Geospatial Data ◽  
Wetland Mapping ◽  
Study Region ◽  
Random Forest Classification ◽  
Negative Rate ◽  
Forest Classification ◽  
Geographic Regions

Wetland impact assessments are an integral part of infrastructure projects aimed at protecting the important services wetlands provide for water resources and ecosystems. However, wetland surveys with the level of accuracy required by federal regulators can be time-consuming and costly. Streamlining this process by using already available geospatial data and classification algorithms to target more detailed wetland mapping efforts may support environmental planning efforts. The objective of this study was to create and test a methodology that could be applied nationally, leveraging existing data to quickly and inexpensively screen for potential wetlands over large geographic regions. An automated workflow implementing the methodology for a case study region in the coastal plain of Virginia is presented. When compared to verified wetlands mapped by experts, the methodology resulted in a much lower false negative rate of 22.6% compared to the National Wetland Inventory (NWI) false negative rate of 69.3%. However, because the methodology was designed as a screening approach, it did result in a slight decrease in overall classification accuracy compared to the NWI from 80.5% to 76.1%. Given the considerable decrease in wetland omission while maintaining comparable overall accuracy, the methodology shows potential as a wetland screening tool for targeting more detailed and costly wetland mapping efforts.

scholarly journals UAS for Wetland Mapping and Hydrological Modeling

2019 ◽  
Vol 11 (17) ◽  
pp. 1997 ◽  
Author(s):  
Justyna Jeziorska
Keyword(s):  
Spatial Data ◽  
Hydrological Modeling ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Regulatory Environment ◽  
Unmanned Aerial Systems ◽  
Wetland Mapping ◽  
Monitoring Strategies ◽  
Sensing Technology ◽  
Aerial Systems

The miniaturization and affordable production of integrated microelectronics have improved in recent years, making unmanned aerial systems (UAS) accessible to consumers and igniting their interest. Researchers have proposed UAS-based solutions for almost any conceivable problem, but the greatest impact will likely be in applications that exploit the unique advantages of the technology: work in dangerous or difficult-to-access areas, high spatial resolution and/or frequent measurements of environmental phenomena, and deployment of novel sensing technology over small to moderate spatial scales. Examples of such applications may be the identification of wetland areas and use of high-resolution spatial data for hydrological modeling. However, because of the large—and growing—assortment of aircraft and sensors available on the market, an evolving regulatory environment, and limited practical guidance or examples of wetland mapping with UAS, it has been difficult to confidently devise or recommend UAS-based monitoring strategies for these applications. This paper provides a comprehensive review of UAS hardware, software, regulations, scientific applications, and data collection/post-processing procedures that are relevant for wetland monitoring and hydrological modeling.

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