810ed2da8f1ac50d8ee9711995977f40 remotesensing_a2021v13n6art1178.pdf e1c8492f6946fbb3c274661707fa3f177f6c0bb0 remotesensing_a2021v13n6art1178.pdf 2110f5a0a0dffc85e6b5f219d0f6c441d8f09c365461d2aab3b1691552ffb0a0 remotesensing_a2021v13n6art1178.pdf Title: Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping Subject: 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. Keywords: HySpex; hyperspectral image processing; classification; wetlands mapping; Arctic Author: Jordi Cristóbal, Patrick Graham, Anupma Prakash, Marcel Buchhorn, Rudi Gens, Nikki Guldager and Mark Bertram Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Mar 24 13:40:41 2021 CET ModDate: Wed Mar 24 14:03:13 2021 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 26 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 9886216 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- DDMOHK+VnURWPalladioL Type 1 Custom yes yes no 10 0 MRLFWH+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 YVOJFH+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 PEIVZS+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 SOZKOC+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 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IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Study Area Destination: section.2 Item: Title: HySpex System Commissioning and Data Acquisition Destination: section.3 Children: Item: Title: HySpex Hyperspectral Imaging System Destination: subsection.3.1 Item: Title: Integration of HySpex into Aircraft Destination: subsection.3.2 Item: Title: Flight Planning and Data Acquisition Destination: subsection.3.3 Item: Title: Hyperspectral Data Processing Destination: section.4 Children: Item: Title: Raw Images to At-Sensor Radiance Images Destination: subsection.4.1 Item: Title: Image Orthorectification Destination: subsection.4.2 Item: Title: Radiometric Correction Destination: subsection.4.3 Item: Title: Spectral Binning and Final Mosaic Destination: subsection.4.4 Item: Title: Wetland Mapping Destination: section.5 Children: Item: Title: Category Definition Destination: subsection.5.1 Item: Title: Training and Test Areas Selection and Band Selection Destination: subsection.5.2 Item: Title: Image Classification Methods: Hybrid Classification, Maximum Likelihood and Spectral Angle Mapper (SAM) Destination: subsection.5.3 Item: Title: Results and Discussion Destination: section.6 Children: Item: Title: Commissioning and Data Acquisition Destination: subsection.6.1 Item: Title: Image Processing Destination: subsection.6.2 Children: Item: Title: Systematic VNIR Sensor Response Drop Correction and Systematic Stripping in VNIR and SWIR Spectral Bands Destination: subsubsection.6.2.1 Item: Title: Geometric and Radiometric Corrections Destination: subsubsection.6.2.2 Item: Title: Image Classification: Results Destination: subsection.6.3 Item: Title: Conclusions Destination: section.7 Item: Title: Hyperspectral Data Processing Workflow Destination: section.A. Item: Title: Destination: section.B. Item: Title: References Destination: specialtable.caption.24 Info: Title: Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping Author: Jordi Cristóbal, Patrick Graham, Anupma Prakash, Marcel Buchhorn, Rudi Gens, Nikki Guldager and Mark Bertram Subject: 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. 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