Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data
The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and bota...
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1504 |
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| author | Sylwia Szporak-Wasilewska Hubert Piórkowski Wojciech Ciężkowski Filip Jarzombkowski Łukasz Sławik Dominik Kopeć |
| author_facet | Sylwia Szporak-Wasilewska Hubert Piórkowski Wojciech Ciężkowski Filip Jarzombkowski Łukasz Sławik Dominik Kopeć |
| author_sort | Sylwia Szporak-Wasilewska |
| collection | DOAJ |
| description | The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F<sub>1-SCORE</sub> equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F<sub>1-SCORE</sub> equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F<sub>1-SCORE</sub> equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy. |
| first_indexed | 2024-03-10T12:20:25Z |
| format | Article |
| id | doaj.art-8e19619939724419a05a73b2135f4c05 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:20:25Z |
| publishDate | 2021-04-01 |
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| series | Remote Sensing |
| spelling | doaj.art-8e19619939724419a05a73b2135f4c052023-11-21T15:30:29ZengMDPI AGRemote Sensing2072-42922021-04-01138150410.3390/rs13081504Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning DataSylwia Szporak-Wasilewska0Hubert Piórkowski1Wojciech Ciężkowski2Filip Jarzombkowski3Łukasz Sławik4Dominik Kopeć5Water Center, Warsaw University of Life Sciences—WULS, 02-776 Warsaw, PolandInstitute of Technology and Life Sciences, 05-090 Raszyn, PolandDepartment of Remote Sensing and Environmental Assessment, Institute of Environmental Engineering, Warsaw University of Life Science—WULS, Nowoursynowska 166, 02-787 Warsaw, PolandInstitute of Technology and Life Sciences, 05-090 Raszyn, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, PolandMGGP Aero Sp. z o.o., 33-100 Tarnów, PolandThe aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F<sub>1-SCORE</sub> equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F<sub>1-SCORE</sub> equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F<sub>1-SCORE</sub> equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy.https://www.mdpi.com/2072-4292/13/8/1504remote sensinghyperspectralAirborne Laser Scanningdata fusionRandom ForestRecursive Feature Elimination with Cross-Validation |
| spellingShingle | Sylwia Szporak-Wasilewska Hubert Piórkowski Wojciech Ciężkowski Filip Jarzombkowski Łukasz Sławik Dominik Kopeć Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data Remote Sensing remote sensing hyperspectral Airborne Laser Scanning data fusion Random Forest Recursive Feature Elimination with Cross-Validation |
| title | Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data |
| title_full | Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data |
| title_fullStr | Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data |
| title_full_unstemmed | Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data |
| title_short | Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data |
| title_sort | mapping alkaline fens transition mires and quaking bogs using airborne hyperspectral and laser scanning data |
| topic | remote sensing hyperspectral Airborne Laser Scanning data fusion Random Forest Recursive Feature Elimination with Cross-Validation |
| url | https://www.mdpi.com/2072-4292/13/8/1504 |
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