Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery
Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (<i>Euphydryas aurinia</i>), an endangered...
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2025-01-01
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| Series: | Remote Sensing |
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| author | Steffen Dietenberger Marlin M. Mueller Andreas Henkel Clémence Dubois Christian Thiel Sören Hese |
| author_facet | Steffen Dietenberger Marlin M. Mueller Andreas Henkel Clémence Dubois Christian Thiel Sören Hese |
| author_sort | Steffen Dietenberger |
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| description | Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (<i>Euphydryas aurinia</i>), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of <i>E. aurinia</i> and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of <i>E. aurinia</i>. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species. |
| format | Article |
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| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Remote Sensing |
| spelling | doaj-art-c1cd4987bc6541fd9e189d94ad6006c92025-01-10T13:20:24ZengMDPI AGRemote Sensing2072-42922025-01-0117114910.3390/rs17010149Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution ImagerySteffen Dietenberger0Marlin M. Mueller1Andreas Henkel2Clémence Dubois3Christian Thiel4Sören Hese5Institute of Data Science, German Aerospace Center (DLR), Mälzerstraße 3-5, 07745 Jena, GermanyInstitute of Data Science, German Aerospace Center (DLR), Mälzerstraße 3-5, 07745 Jena, GermanyNature Protection and Research, Hainich National Park Administration, Bei der Marktkirche 9, 99947 Bad Langensalza, GermanyInstitute of Data Science, German Aerospace Center (DLR), Mälzerstraße 3-5, 07745 Jena, GermanyInstitute of Data Science, German Aerospace Center (DLR), Mälzerstraße 3-5, 07745 Jena, GermanyDepartment of Earth Observation, Institute of Geography, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, GermanyAnalyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (<i>Euphydryas aurinia</i>), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of <i>E. aurinia</i> and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of <i>E. aurinia</i>. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species.https://www.mdpi.com/2072-4292/17/1/149habitat suitability model (HSM)biotope classificationvery high-resolution (VHR) imageryconvolutional neural networks (CNN)marsh fritillaryHainich National Park |
| spellingShingle | Steffen Dietenberger Marlin M. Mueller Andreas Henkel Clémence Dubois Christian Thiel Sören Hese Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery Remote Sensing habitat suitability model (HSM) biotope classification very high-resolution (VHR) imagery convolutional neural networks (CNN) marsh fritillary Hainich National Park |
| title | Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery |
| title_full | Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery |
| title_fullStr | Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery |
| title_full_unstemmed | Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery |
| title_short | Mapping Habitat Structures of Endangered Open Grassland Species (<i>E. aurinia</i>) Using a Biotope Classification Based on Very High-Resolution Imagery |
| title_sort | mapping habitat structures of endangered open grassland species i e aurinia i using a biotope classification based on very high resolution imagery |
| topic | habitat suitability model (HSM) biotope classification very high-resolution (VHR) imagery convolutional neural networks (CNN) marsh fritillary Hainich National Park |
| url | https://www.mdpi.com/2072-4292/17/1/149 |
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