Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expert...

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Vydané v:Environmental science and pollution research international Ročník 30; číslo 29; s. 73753 - 73779
Hlavní autori: Bindajam, Ahmed Ali, Mallick, Javed, Talukdar, Swapan, Shohan, Ahmed Ali A., Alshayeb, Mohammed J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Predmet:
Algorithms
Aquatic Pollution
Area
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Biodiversity
Biodiversity hot spots
Carbon sequestration
Coastal zone management
Computer vision
Connectivity
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental protection
Environmental science
habitat fragmentation
habitats
High resolution
Image resolution
Land cover
Land use
land use and land cover maps
Landscape
landscapes
Learning algorithms
Machine learning
Mangroves
Mapping
Marine organisms
Neural networks
Pattern analysis
Red Sea
Research Article
Saudi Arabia
Statistical analysis
statistics
Structural analysis
Support vector machines
Waste Water Technology
Water Management
Water Pollution Control
Red Sea
Saudi Arabia
Mangrove habitat
Saudi Arabia
Optimised machine learning algorithms
LULC
Landscape structure
Red Sea
Connectivity modelling
ISSN:1614-7499, 0944-1344, 1614-7499
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Popis
Shrnutí:Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expertise. In this study, an advanced machine learning algorithm was proposed to produce an accurate and precise high-resolution land use map that includes mangroves in the Al Wajh Bank habitat in northeastern Saudi Arabia. To achieve this, high-resolution multispectral images were generated using an image fusion technique, and machine learning algorithms were applied, including artificial neural networks, random forests and support vector machine algorithms. The performance of the models was evaluated using various matrices, and changes in mangrove distribution and connectivity were assessed using the landscape fragmentation model and Getis-Ord statistics. The research gap that this study aims to address is the lack of accurate and precise mapping and assessment of mangrove status in the Red Sea area, particularly in data-scarce regions. Our study produced high-resolution mobile laser scanning (MLS) imagery of 15-m length for 2014 and 2022, and trained 5, 6 and 9 models for artificial neural networks, support vector machines and random forests (RF) to predict land use and land cover maps using 15-m and 30-m resolution MLS images. The best models were identified using error matrices, and it was found that RF outperformed other models. According to the 15-m resolution map of 2022 and the best models of RF, the mangrove cover in the Al Wajh Bank is 27.6 km 2 , which increased to 34.99 km 2 in the case of the 30-m resolution image of 2022, and was 11.94 km 2 in 2014, indicating a doubling of the mangrove area. Landscape structure analysis revealed an increase in small core and hotspot areas, which were converted into medium core and very large hotspot areas in 2014. New mangrove areas were identified in the form of patches, edges, potholes and coldspots. The connectivity model showed an increase in connectivity over time, promoting biodiversity. Our study contributes to the promotion of the protection, conservation and planting of mangroves in the Red Sea area.
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ISSN:1614-7499
0944-1344
1614-7499
DOI:10.1007/s11356-023-27395-2