Multi-source remote sensing data for monitoring natural reserves: a case study on the Shaanxi Hanzhong crested ibis National Nature Reserve, China
Establishing nature reserves is an effective measure to achieve SDG 15.5, but human disturbances have reduced their conservation efficacy, making it essential to closely monitor human activities and their impacts within these areas. The paper proposes a framework to monitor human activities and eval...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528655 |
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| Summary: | Establishing nature reserves is an effective measure to achieve SDG 15.5, but human disturbances have reduced their conservation efficacy, making it essential to closely monitor human activities and their impacts within these areas. The paper proposes a framework to monitor human activities and evaluate habitat suitability for the Crested Ibis in the Shaanxi Hanzhong Nipponia nippon National Nature Reserve using multi-source satellite data. Using a multimodal classification method based on Convolutional Neural Network (MDCNN), combined with GF-2, GF-5, and SDGSAT-1 data, the distribution of Land Use and Land Cover (LULC) was explored, with an average overall accuracy of 87.29%, higher than using only GF-2 data(78.37%). A hierarchical change detection method identified 701.25 ha of land cover changes, with 560.92 ha (79.99%) resulting from human activities, indicating that land cover changes in the study area were mainly affected by human activities. On this basis, the MaxEnt model was used to predict the distribution of feasible habitats, and a designated priority area with a total area of 25646 hectares was established. This study also validated the enormous potential of recently launched SDGSAT-1 satellite data in LULC classification, thus pioneering its application in nature reserve research. |
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| ISSN: | 1753-8947 1753-8955 |