Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Fu, Bolin; He, Xu; Liang, Yiyin; Deng, Tengfang; Li, Huajian; He, Hongchang; Jia, Mingming; Fan, Donglin; Wang, Feng

doi:10.1016/j.ecolind.2023.110870

Ecological Indicators

2023

DOI: 10.1016/j.ecolind.2023.110870

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Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Bolin Fu,

Xu He,

Yiyin Liang

et al.

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2024

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Cited by 3 publications

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Potential applications of remote sensing and Earth observation approaches to monitor mangrove ecosystem services: A review

Roslani,

Ismail,

Kamarudin

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Remote sensing technologies offer significant potential for monitoring mangrove ecosystems, which serve as invaluable hubs of biodiversity and providers of crucial ecosystem services (ESs). In the face of mounting threats from human activities and climate change, effective monitoring becomes paramount to safeguarding their health and the services they offer. Remote sensing and Earth observation techniques present exceptional opportunities for monitoring mangrove ecosystems and their ESs. Despite the successful use of remote sensing and Earth observation-based technologies in mapping and monitoring mangrove ecosystems, few studies have been undertaken to utilize them for assessing mangrove ESs. This paper explores the vast potential of remote sensing and Earth observation in monitoring mangrove ecosystems and assessing their ESs. Through a comprehensive review and discussion of relevant scientific literature, researchers also have employed various sensors to study carbon stocks, species diversity, biomass, and related topics. These findings provide a set of values data essential for protecting, preserving, and conserving these ecosystems and their resources, thereby facilitating better management, planning, and policymaking. By leveraging these technologies, policymakers, scientists, and conservationists can make informed decisions to conserve mangrove ecosystems and ensure their long-term viability.

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Potential applications of remote sensing and Earth observation approaches to monitor mangrove ecosystem services: A review

Roslani,

Ismail,

Kamarudin

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using Machine Learning

Yang,

Meng,

et al. 2024

Remote Sensing

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Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats.

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A Systematic Review and Global Trends on Blue Carbon and Sustainable Development: A Bibliometric Study from 2012 to 2023

Pang,

Abdul Majid,

Perera

et al. 2024

Sustainability

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Halfway through Transforming Our World: The 2030 Agenda for Sustainable Development, only 15 percent of the goals have been reached. As a carbon storage and climate change mitigation mechanism, blue carbon is closely related to sustainable development goals and plays an important role in the global carbon cycle. In spite of its great potential, blue carbon still faces several challenges in terms of achieving the Sustainable Development Goals. Herein, this review aims to retrieve all known impacts of blue carbon on sustainable development through research published on the Web of Science from 2012 to 2023 using a sequence of bibliometric analyses. Keywords such as “blue carbon” and “sustain*” (including “sustainability”, “sustainable”, etc.) were used for article extraction. CiteSpace, a science mapping tool, was used to capture and visually present the bibliometric information in the research about blue carbon and sustainable development. Upon reviewing the existing literature, no study has concentrated on bibliometrically analyzing and visualizing studies about blue carbon and sustainable development. This study sets out to fill this gap by examining the key areas of concentration in published works on blue carbon and sustainable development from 2012 to date. Moreover, the integration of blue carbon and sustainable development may help to develop supportive policies for marine carbon sinks. Despite the valuable contribution of this study to the blue carbon and sustainable development body of knowledge, generalizations of the results must be made cautiously due to the use of a single database, which in this case is the Web of Science.

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Cited by 3 publications

References 60 publications

Potential applications of remote sensing and Earth observation approaches to monitor mangrove ecosystem services: A review

Potential applications of remote sensing and Earth observation approaches to monitor mangrove ecosystem services: A review

Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using Machine Learning

A Systematic Review and Global Trends on Blue Carbon and Sustainable Development: A Bibliometric Study from 2012 to 2023

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