東華大學圖書館 |

Monitoring the Mangrove Species in Hong Kong with High Resolution Images Using Deep Learning Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Monitoring the Mangrove Species in Hong Kong with High Resolution Images Using Deep Learning Networks./
作者:	Wan, Luoma.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	162 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Natural resource management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29185905
ISBN:	9798209999706

Monitoring the Mangrove Species in Hong Kong with High Resolution Images Using Deep Learning Networks.
Wan, Luoma.

Monitoring the Mangrove Species in Hong Kong with High Resolution Images Using Deep Learning Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 162 p.

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.

This item is not available from ProQuest Dissertations & Theses.

Mangrove forest is a highly productive tropical and subtropical ecosystem, providing significant ecological functions and valuable social-economic services, including coastal protection, aquaculture, habitat provision, biodiversity protection, and carbon sequestration. Due to coastal development, mangroves have been suffering a significant loss in extent as well as species. Mangrove species richness has profound influences on the mangrove system in terms of robustness and health, but it cannot be restored by simply introducing exotic species, which may be invasive and threatening to local species and the ecosystem. Therefore, monitoring mangroves at the species level is necessary and useful for mangrove conservation. Remote sensing provides a cost-effective and feasible way to observe mangrove forests. The availability of high-resolution images makes monitoring fragmentized mangrove patches at the species level from regionality to globality possible. However, accurate mapping of mangrove species remains a challenge due to morphological and spectral similarity between different species and large inter-class variability within each species. This research aims to systematically explore the spatial and spectral information provided by high spatial resolution multispectral and hyperspectral satellite images for mangrove species mapping to facilitate mangrove management. First, the conventional methods of extracting spatial features, grey level co-occurrence matrix (GLCM) and Gaussian Markov Random Field (GMRF), are analyzed and compared, demonstrating the difficulties in feature design for mangroves without distinguished shapes, edges and texture. Instead of feature engineering, deep learning driven by a large amount of data is v introduced to extract mangroves' abstract features (high-level features). For patched mangroves, a small-patched convolutional neural network (CNN) is proposed to overcome the limitation of fixed large inputs for conventional CNNs. The proposed method obtains higher accuracy with modification in pooling layers, small kernels, and lightweight architecture. The results show that the abstract features from deep learning can improve the mapping of all mangrove species in the study sites, and the contextual information rather than mangroves per se making more contribution. Second, spectral features from hyperspectral images are also explored for mangrove species mapping, aiming to answer whether spectral increment can improve the performance of mangrove species mapping. Based on true Hyperion, the Hyperion with the same period as Gaofen 5 hyperspectral images and Landsat 8 was simulated to form a series of spectral features with different resolutions. The comparison using support vector machine and Random Forests demonstrates that significant improvement can be obtained from hyperspectral features over multispectral features (73.89% vs. 86.82%). A further improvement in spectral resolution from 10nm to 5nm brings limited effects (87.12% vs. 86.82%), in which the visible/near-infrared spectrum makes a significant contribution to better mapping, particularly mapping AC species. Third, to fully leverage the high spatial and high spectral information from multispectral and hyperspectral images for mangrove species mapping, a novel scheme is proposed to account for spectral variability between two images, and a lightweight 3D CNN was proposed to explore the fusion of spatial and spectral information. The synthetic high-resolution hyperspectral image can improve the problem of limited samples for deep learning and concurrence of high spatial and high spectral information, especially for species mapping of patched mangroves at the region level. Compared to hyperspectral and multispectral images alone, the results show that image fusion can greatly improve mangrove species mapping (91.25% vs. 73.05% and 85.52%), and 3D CNN excels SVM in processing high dimension data. Finally, a case study of individual invasive mangrove species of Sonneratia detection using RetinaNet based on CNN shows how to support mangrove management with high-resolution images using deep learning. Three thousand six hundred seventy-eight individuals were detected in Mai Po, and most of them are with the crown size of 5 by 5 meters. It can provide early warning vi for invasive species and support invader control. The primary outcome of this research provides a feasible way for accurate mapping of mangrove species in Hong Kong with up-to-date methods of deep learning. The method provides the baseline for extracting spatial and spectral information and fusing them for mangrove species mapping. The local government can get subtle information on mangroves timely and make quick responses to support effective and efficient conservation.

ISBN: 9798209999706Subjects--Topical Terms:

589570
Natural resource management.
Subjects--Index Terms:

Mangroves

Monitoring the Mangrove Species in Hong Kong with High Resolution Images Using Deep Learning Networks.
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300 $a 162 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
500 $a Advisor: Ma, Peifeng;Zhang, Hongsheng.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Mangrove forest is a highly productive tropical and subtropical ecosystem, providing significant ecological functions and valuable social-economic services, including coastal protection, aquaculture, habitat provision, biodiversity protection, and carbon sequestration. Due to coastal development, mangroves have been suffering a significant loss in extent as well as species. Mangrove species richness has profound influences on the mangrove system in terms of robustness and health, but it cannot be restored by simply introducing exotic species, which may be invasive and threatening to local species and the ecosystem. Therefore, monitoring mangroves at the species level is necessary and useful for mangrove conservation. Remote sensing provides a cost-effective and feasible way to observe mangrove forests. The availability of high-resolution images makes monitoring fragmentized mangrove patches at the species level from regionality to globality possible. However, accurate mapping of mangrove species remains a challenge due to morphological and spectral similarity between different species and large inter-class variability within each species. This research aims to systematically explore the spatial and spectral information provided by high spatial resolution multispectral and hyperspectral satellite images for mangrove species mapping to facilitate mangrove management. First, the conventional methods of extracting spatial features, grey level co-occurrence matrix (GLCM) and Gaussian Markov Random Field (GMRF), are analyzed and compared, demonstrating the difficulties in feature design for mangroves without distinguished shapes, edges and texture. Instead of feature engineering, deep learning driven by a large amount of data is v introduced to extract mangroves' abstract features (high-level features). For patched mangroves, a small-patched convolutional neural network (CNN) is proposed to overcome the limitation of fixed large inputs for conventional CNNs. The proposed method obtains higher accuracy with modification in pooling layers, small kernels, and lightweight architecture. The results show that the abstract features from deep learning can improve the mapping of all mangrove species in the study sites, and the contextual information rather than mangroves per se making more contribution. Second, spectral features from hyperspectral images are also explored for mangrove species mapping, aiming to answer whether spectral increment can improve the performance of mangrove species mapping. Based on true Hyperion, the Hyperion with the same period as Gaofen 5 hyperspectral images and Landsat 8 was simulated to form a series of spectral features with different resolutions. The comparison using support vector machine and Random Forests demonstrates that significant improvement can be obtained from hyperspectral features over multispectral features (73.89% vs. 86.82%). A further improvement in spectral resolution from 10nm to 5nm brings limited effects (87.12% vs. 86.82%), in which the visible/near-infrared spectrum makes a significant contribution to better mapping, particularly mapping AC species. Third, to fully leverage the high spatial and high spectral information from multispectral and hyperspectral images for mangrove species mapping, a novel scheme is proposed to account for spectral variability between two images, and a lightweight 3D CNN was proposed to explore the fusion of spatial and spectral information. The synthetic high-resolution hyperspectral image can improve the problem of limited samples for deep learning and concurrence of high spatial and high spectral information, especially for species mapping of patched mangroves at the region level. Compared to hyperspectral and multispectral images alone, the results show that image fusion can greatly improve mangrove species mapping (91.25% vs. 73.05% and 85.52%), and 3D CNN excels SVM in processing high dimension data. Finally, a case study of individual invasive mangrove species of Sonneratia detection using RetinaNet based on CNN shows how to support mangrove management with high-resolution images using deep learning. Three thousand six hundred seventy-eight individuals were detected in Mai Po, and most of them are with the crown size of 5 by 5 meters. It can provide early warning vi for invasive species and support invader control. The primary outcome of this research provides a feasible way for accurate mapping of mangrove species in Hong Kong with up-to-date methods of deep learning. The method provides the baseline for extracting spatial and spectral information and fusing them for mangrove species mapping. The local government can get subtle information on mangroves timely and make quick responses to support effective and efficient conservation.
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