In recent years, the UAV light-weighted hyperspectral imaging system has become more available. It can provide high spatial and spectral resolution images for environment monitoring. Mangrove forest interspecies distribution map is important data source for the government to design the protection policy. A research on UAV hyperspectral remote sensing surveying of mangrove forest is implemented in Futian Nature Reserve area located In Shenzhen, China. The UAV-based hyperspectral images were collected and processed to get the geometrically corrected mosaicked reflectance data that was latter used to extract the mangrove interspecies map. Based on the hyperspectral data, the supervised pixel-based classification methods were implemented. Firstly, NDVI is used to distinguish mangrove forest from other ground object features based on the decision tree classification method with expert knowledge. Then MNF transformation is performed on the hyperspectral data to get the transformed feature that represents the most information of the hyperspectral bands. Next, the best exponential factor formula (OIF) method is used to analyze the first 10 band features chosen by the MNF transformation to get the optimal band combination for classification. Finally, the pixel-based maximum likelihood, minimum distance classification methods are used to classify the mangrove interspecies. The classification results show the distribution of mangrove areas. The pixel-based minimum distance method performs better and the overall classification accuracy can reach to 98.21%.
| Published in | Earth Sciences (Volume 12, Issue 6) |
| DOI | 10.11648/j.earth.20231206.16 |
| Page(s) | 244-248 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Mangrove, Hyperspectral, Information Extraction, Futian Nature Reserve
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APA Style
Yi, L., Zhang, G., Lu, F., Zhou, Y. (2023). Mangrove Interspecies Classification Based on UAV Hyperspectral Images. Earth Sciences, 12(6), 244-248. https://doi.org/10.11648/j.earth.20231206.16
ACS Style
Yi, L.; Zhang, G.; Lu, F.; Zhou, Y. Mangrove Interspecies Classification Based on UAV Hyperspectral Images. Earth Sci. 2023, 12(6), 244-248. doi: 10.11648/j.earth.20231206.16
AMA Style
Yi L, Zhang G, Lu F, Zhou Y. Mangrove Interspecies Classification Based on UAV Hyperspectral Images. Earth Sci. 2023;12(6):244-248. doi: 10.11648/j.earth.20231206.16
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Download@article{10.11648/j.earth.20231206.16,
author = {Lina Yi and Guifeng Zhang and Feiyang Lu and Yongcha Zhou},
title = {Mangrove Interspecies Classification Based on UAV Hyperspectral Images},
journal = {Earth Sciences},
volume = {12},
number = {6},
pages = {244-248},
doi = {10.11648/j.earth.20231206.16},
url = {https://doi.org/10.11648/j.earth.20231206.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20231206.16},
abstract = {In recent years, the UAV light-weighted hyperspectral imaging system has become more available. It can provide high spatial and spectral resolution images for environment monitoring. Mangrove forest interspecies distribution map is important data source for the government to design the protection policy. A research on UAV hyperspectral remote sensing surveying of mangrove forest is implemented in Futian Nature Reserve area located In Shenzhen, China. The UAV-based hyperspectral images were collected and processed to get the geometrically corrected mosaicked reflectance data that was latter used to extract the mangrove interspecies map. Based on the hyperspectral data, the supervised pixel-based classification methods were implemented. Firstly, NDVI is used to distinguish mangrove forest from other ground object features based on the decision tree classification method with expert knowledge. Then MNF transformation is performed on the hyperspectral data to get the transformed feature that represents the most information of the hyperspectral bands. Next, the best exponential factor formula (OIF) method is used to analyze the first 10 band features chosen by the MNF transformation to get the optimal band combination for classification. Finally, the pixel-based maximum likelihood, minimum distance classification methods are used to classify the mangrove interspecies. The classification results show the distribution of mangrove areas. The pixel-based minimum distance method performs better and the overall classification accuracy can reach to 98.21%.
},
year = {2023}
}
TY - JOUR T1 - Mangrove Interspecies Classification Based on UAV Hyperspectral Images AU - Lina Yi AU - Guifeng Zhang AU - Feiyang Lu AU - Yongcha Zhou Y1 - 2023/12/14 PY - 2023 N1 - https://doi.org/10.11648/j.earth.20231206.16 DO - 10.11648/j.earth.20231206.16 T2 - Earth Sciences JF - Earth Sciences JO - Earth Sciences SP - 244 EP - 248 PB - Science Publishing Group SN - 2328-5982 UR - https://doi.org/10.11648/j.earth.20231206.16 AB - In recent years, the UAV light-weighted hyperspectral imaging system has become more available. It can provide high spatial and spectral resolution images for environment monitoring. Mangrove forest interspecies distribution map is important data source for the government to design the protection policy. A research on UAV hyperspectral remote sensing surveying of mangrove forest is implemented in Futian Nature Reserve area located In Shenzhen, China. The UAV-based hyperspectral images were collected and processed to get the geometrically corrected mosaicked reflectance data that was latter used to extract the mangrove interspecies map. Based on the hyperspectral data, the supervised pixel-based classification methods were implemented. Firstly, NDVI is used to distinguish mangrove forest from other ground object features based on the decision tree classification method with expert knowledge. Then MNF transformation is performed on the hyperspectral data to get the transformed feature that represents the most information of the hyperspectral bands. Next, the best exponential factor formula (OIF) method is used to analyze the first 10 band features chosen by the MNF transformation to get the optimal band combination for classification. Finally, the pixel-based maximum likelihood, minimum distance classification methods are used to classify the mangrove interspecies. The classification results show the distribution of mangrove areas. The pixel-based minimum distance method performs better and the overall classification accuracy can reach to 98.21%. VL - 12 IS - 6 ER -
School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, China
Key Laboratory of Computational Optics Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; School of Opto-Electronics, University of Chinese Academy of Sciences, Beijing, China
