Advances in Earth observation and machine learning for quantifying blue carbon

Pham, Tien Dat; Ha, Nam Thang; Saintilan, Neil; Skidmore, Andrew K.; Phan, Duong Cao; Le, Nga Nhu; Viet, Hung Luu; Takeuchi, Wataru; Friess, Daniel A.

doi:10.1016/j.earscirev.2023.104501

Cited by 15 publications

(6 citation statements)

References 168 publications

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“…This technology overcomes limitations of traditional methods like Structure from Motion (SfM), providing improved canopy structural information and opportunities for highresolution, temporal mapping (Duffy et al, 2021). Threedimensional LiDAR measurements offer insights into plant-habitat interactions and biological adaptations to environmental stress, facilitating seasonal change assessments and carbon stock estimations (Omasa et al, 2007;Tempest et al, 2015;Pham et al, 2023). In our research, UAV-based LiDAR formed the basis for understanding canopy height and variations along main axes, representing a significant step towards comprehending the threedimensional structure of saltmarsh patches, particularly vertical variations.…”

Section: Advantages Of Lidar In Measuring Canopy Morphologymentioning

confidence: 94%

Measuring canopy morphology of saltmarsh plant patches using UAV-based LiDAR data

Hong,

Ge,

Wang

et al. 2024

Front. Mar. Sci.

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Plant patches play a crucial role in understanding the biogeomorphology of saltmarshes. Although two-dimensional optical remote sensing has long been applied to the study of saltmarsh plant patches, studies focusing on the canopy features at a patch-scale remain limited. Therefore, a simple and efficient method is needed to capture three-dimensional patch features and their relationship to habitat. This study utilized UAV-based LiDAR to obtain three-dimensional patch features of the native species S. mariqueter and the invasive species S. alterniflora in Andong Shoal, Hangzhou Bay, and examine the relationship between patch distribution and geomorphological characteristics. A workflow was established to overcome the inability of low-cost LiDAR sensor to penetrate dense vegetation, resulting in no ground return. Results showed that S. alterniflora patches were smaller in size but taller in canopy height than S. mariqueter patches. Regarding morphological patterns of patch canopy, S. alterniflora exhibited single-arch patterns (29%) and double-arch patterns (16%), whereas S. mariqueter exhibited only single-arch patterns (83%). The presence of double-arch patches suggested the development of fairy circles, indicating that the invasive S. alterniflora exhibits greater ecological resilience compared to the native S. mariqueter. Furthermore, this study explored the ecological niches of the two species in the pioneer zone of Andong Shoal. The ecological niches for S. alterniflora were 2.00-2.25 m, whereas that for S. mariqueter were 1.85-2.00 m and 2.25-2.40 m. Distance from the tidal creeks significantly moderated the number and area of patches for both species. This study demonstrated that UAV-based LiDAR technology can provide high-quality three-dimensional information about the pioneer zone of saltmarsh, thus helping to understand biogeomorphological processes in this region.

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Section: Advantages Of Lidar In Measuring Canopy Morphologymentioning

confidence: 94%

Measuring canopy morphology of saltmarsh plant patches using UAV-based LiDAR data

Hong,

Ge,

Wang

et al. 2024

Front. Mar. Sci.

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“…As a result, the processing of such data requires advanced methods of satellite image processing, e.g., removing noise, highlighting edges, and improving image contrast through the correction of atmospheric effects. This is possible using computer vision and machine learning (ML) algorithms for data processing and visualization due to their impressive computational and spatial analysis capabilities [29][30][31]. The most important advantage of ML techniques is that they are capable of automagically deriving information from existing datasets.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Lemenkova

2024

Coasts

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Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

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“…These areas often have fragile ecosystems such as coral reefs, seagrass beds, and mangroves and are significant [2] carbon sinks in the ocean, making them ecologically significant. Moreover, the increasing tourism development, aquaculture, and other human activities have made nearshore regions highly valuable in terms of social and economic benefits [3,4].…”

Section: Introductionmentioning

confidence: 99%

PhotonBathymetry: An Interactive Software for Nearshore Bathymetry Using ICESat-2 Data and Multispectral Images

Xing,

Guo,

Zhang

et al. 2024

IEEE Access

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In coastal regions, the increasing human activities have made it increasingly important to gain an understanding of the water depths in these areas. Although conducting field measurements for nearshore water depths on a global scale is quite challenging, the fusion of active and passive remote sensing data, utilizing laser altimetry data and multispectral imagery, allows us to obtain valuable nearshore bathymetric information without the need for on-site measurements. In this study, we have developed an interactive software named PhotonBathymetry (PBathy) with the aim of supporting nearshore bathymetry retrieval efforts. The construction of this platform encompasses four key levels: data storage, software workflows, functional plugins, and user services. These levels are designed to ensure the efficiency of data loading, browsing, and processing. To cater to the diverse needs of developers, PBathy is built using C++ and provides standardized interfaces for different programming languages. Furthermore, PBathy has undergone meticulous integration and optimization of its user interface and feature pages to offer an exceptional user experience. To validate PBathy's capability for nearshore bathymetry retrieval, we conducted a case study on Culebra Island in Puerto Rico. The results demonstrate that PBathy not only successfully performs bathymetric retrieval but also offers additional features such as visual querying of laser altimetry data and multispectral imagery. The introduction of PBathy addresses the limitations of current photon data processing software, providing a viable solution and a solid foundation for nearshore bathymetry retrieval efforts.INDEX TERMS photon-counting LiDAR, Ice Cloud and Land Elevation Satellite-2 (ICESat-2), nearshore bathymetry, interactive software, multispectral image.

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Advances in Earth observation and machine learning for quantifying blue carbon

Cited by 15 publications

References 168 publications

Measuring canopy morphology of saltmarsh plant patches using UAV-based LiDAR data

Measuring canopy morphology of saltmarsh plant patches using UAV-based LiDAR data

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

PhotonBathymetry: An Interactive Software for Nearshore Bathymetry Using ICESat-2 Data and Multispectral Images

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