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Efficient Coral Survey Using Aerial Remote Sensing and Multi-modal Segmentation for Large-Scale Ecological Assessment.

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Title: Efficient Coral Survey Using Aerial Remote Sensing and Multi-modal Segmentation for Large-Scale Ecological Assessment.
Authors: Jiangying Qin1,2, Ming Li1,2 lisouming@whu.edu.cn, Armin, Gruen3, Jianya Gong1, Jiageng Zhong1, Xuan Liao1,4
Source: Photogrammetric Engineering & Remote Sensing. Aug2025, Vol. 91 Issue 8, p509-516. 8p.
Subjects: Coral reefs & islands, Ecological mapping, Ecological surveys, Optical radar, Machine learning, Deep learning
Abstract: Due to the ecological pressures of global warming and human activities in coastal regions, coral reef ecosystems, predominantly located in shallow marine areas, are facing severe threats to their survival. Scientists and governmental managers are eager to leverage novel aerial remote sensing technologies to address the challenges of acquiring accurate, comprehensive, and timely data on coral reef health, structural complexity, and spatial distribution. This study aims to tackle these challenges by using precise and accurate aerial remote sensing data to support the restoration and sustainable prosperity of coral reef systems. Specifically, this study develops and applies an efficient coral survey method based on aerial remote sensing. The method integrates aerial imagery and bathymetric lidar (light detection and ranging) point cloud data and uses advanced photogrammetric computer vision and deep learning algorithms. Using a state-of-the-art multi-modal neural network segmentation technique, the proposed method enables high-precision and intelligent identification of coral reefs, facilitating detailed habitat mapping. Furthermore, by accurately delineating the habitat range and geometric structures of reefs, this approach allows for precise measurements of coral biomass production and skeletal calcification. These metrics help assess coral reef structural complexity and their adaptability to environmental stressors, providing robust scientific data for conservation strategies and policy making. The use of advanced multi-modal aerial remote sensing data not only enhances monitoring reliability and accuracy but also offers a cost-effective and flexible tool for coral reef ecological mapping. This approach effectively addresses challenges encountered in coastal ecological surveys, particularly in areas where direct human access or boat entry is difficult. [ABSTRACT FROM AUTHOR]
Copyright of Photogrammetric Engineering & Remote Sensing is the property of ASPRS: The Imaging & Geospatial Information Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 186829338
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Label: Title
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  Data: Efficient Coral Survey Using Aerial Remote Sensing and Multi-modal Segmentation for Large-Scale Ecological Assessment.
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  Data: <searchLink fieldCode="AR" term="%22Jiangying+Qin%22">Jiangying Qin</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ming+Li%22">Ming Li</searchLink><relatesTo>1,2</relatesTo><i> lisouming@whu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Armin%2C+Gruen%22">Armin, Gruen</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Jianya+Gong%22">Jianya Gong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Jiageng+Zhong%22">Jiageng Zhong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xuan+Liao%22">Xuan Liao</searchLink><relatesTo>1,4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Photogrammetric+Engineering+%26+Remote+Sensing%22">Photogrammetric Engineering & Remote Sensing</searchLink>. Aug2025, Vol. 91 Issue 8, p509-516. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Coral+reefs+%26+islands%22">Coral reefs & islands</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+mapping%22">Ecological mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+surveys%22">Ecological surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+radar%22">Optical radar</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Due to the ecological pressures of global warming and human activities in coastal regions, coral reef ecosystems, predominantly located in shallow marine areas, are facing severe threats to their survival. Scientists and governmental managers are eager to leverage novel aerial remote sensing technologies to address the challenges of acquiring accurate, comprehensive, and timely data on coral reef health, structural complexity, and spatial distribution. This study aims to tackle these challenges by using precise and accurate aerial remote sensing data to support the restoration and sustainable prosperity of coral reef systems. Specifically, this study develops and applies an efficient coral survey method based on aerial remote sensing. The method integrates aerial imagery and bathymetric lidar (light detection and ranging) point cloud data and uses advanced photogrammetric computer vision and deep learning algorithms. Using a state-of-the-art multi-modal neural network segmentation technique, the proposed method enables high-precision and intelligent identification of coral reefs, facilitating detailed habitat mapping. Furthermore, by accurately delineating the habitat range and geometric structures of reefs, this approach allows for precise measurements of coral biomass production and skeletal calcification. These metrics help assess coral reef structural complexity and their adaptability to environmental stressors, providing robust scientific data for conservation strategies and policy making. The use of advanced multi-modal aerial remote sensing data not only enhances monitoring reliability and accuracy but also offers a cost-effective and flexible tool for coral reef ecological mapping. This approach effectively addresses challenges encountered in coastal ecological surveys, particularly in areas where direct human access or boat entry is difficult. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Photogrammetric Engineering & Remote Sensing is the property of ASPRS: The Imaging & Geospatial Information Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.14358/PERS.25-00007R2
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 509
    Subjects:
      – SubjectFull: Coral reefs & islands
        Type: general
      – SubjectFull: Ecological mapping
        Type: general
      – SubjectFull: Ecological surveys
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      – SubjectFull: Optical radar
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: Efficient Coral Survey Using Aerial Remote Sensing and Multi-modal Segmentation for Large-Scale Ecological Assessment.
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            NameFull: Jiangying Qin
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            NameFull: Ming Li
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            NameFull: Armin, Gruen
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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