Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia
Mangrove ecosystems are increasingly threatened by climate change and coastal development, making precise ecological stress modeling essential for informing conservation strategies. This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023)...
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2025-01-01
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| author | Asma A. Al-Huqail Zubairul Islam Hanan F. Al-Harbi |
| author_facet | Asma A. Al-Huqail Zubairul Islam Hanan F. Al-Harbi |
| author_sort | Asma A. Al-Huqail |
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| description | Mangrove ecosystems are increasingly threatened by climate change and coastal development, making precise ecological stress modeling essential for informing conservation strategies. This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023) along the Jazan Coast, identifying a total mangrove area of 19.4 km<sup>2</sup>. The ensemble classifier achieved an F1 score of 95%, an overall accuracy of 93%, and a kappa coefficient of 0.86. Ecological stress was modeled via a generalized additive model (GAM) with key predictors, including trends in the NDVI, NDWIveg (vegetation water content), NDWIow (open water), and LST from 1991 to 2023, which were derived using surface reflectance (SR) products from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS sensors. The model exhibited strong performance, with an R<sup>2</sup> of 0.89. Model diagnostics using linear regression (R<sup>2</sup> = 0.86), a high F-statistic, minimal intercept, and 10-fold cross-validation confirmed the model’s robustness, with a consistent MSE (0.12) and cross-validated R<sup>2</sup> of 0.86. Moran’s I analysis also indicated significant spatial clustering. Findings indicate that mangroves in non-ravine, mainland coastal areas experience more ecological stress from disruptions in freshwater and sediment supply due to recent developments. In contrast, island coastal areas exhibit low stress levels due to minimal human activity, except in dense canopy regions where significant stress, likely linked to climate change, was observed. These results underscore the need for further investigation into the drivers of this ecological pressure. |
| format | Article |
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| institution | Kabale University |
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| language | English |
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| spelling | doaj-art-b687eb887fff4a3c84cac153f917e7572025-01-24T13:37:47ZengMDPI AGLand2073-445X2025-01-011417010.3390/land14010070Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi ArabiaAsma A. Al-Huqail0Zubairul Islam1Hanan F. Al-Harbi2Chair of Climate Change, Environmental Development and Vegetation Cover, Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Geography and Environmental Management, University of Abuja, Abuja 900105, NigeriaChair of Climate Change, Environmental Development and Vegetation Cover, Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaMangrove ecosystems are increasingly threatened by climate change and coastal development, making precise ecological stress modeling essential for informing conservation strategies. This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023) along the Jazan Coast, identifying a total mangrove area of 19.4 km<sup>2</sup>. The ensemble classifier achieved an F1 score of 95%, an overall accuracy of 93%, and a kappa coefficient of 0.86. Ecological stress was modeled via a generalized additive model (GAM) with key predictors, including trends in the NDVI, NDWIveg (vegetation water content), NDWIow (open water), and LST from 1991 to 2023, which were derived using surface reflectance (SR) products from Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS sensors. The model exhibited strong performance, with an R<sup>2</sup> of 0.89. Model diagnostics using linear regression (R<sup>2</sup> = 0.86), a high F-statistic, minimal intercept, and 10-fold cross-validation confirmed the model’s robustness, with a consistent MSE (0.12) and cross-validated R<sup>2</sup> of 0.86. Moran’s I analysis also indicated significant spatial clustering. Findings indicate that mangroves in non-ravine, mainland coastal areas experience more ecological stress from disruptions in freshwater and sediment supply due to recent developments. In contrast, island coastal areas exhibit low stress levels due to minimal human activity, except in dense canopy regions where significant stress, likely linked to climate change, was observed. These results underscore the need for further investigation into the drivers of this ecological pressure.https://www.mdpi.com/2073-445X/14/1/70ecological stress modelKendall’s tau-bmachine learningmangrovesGAM |
| spellingShingle | Asma A. Al-Huqail Zubairul Islam Hanan F. Al-Harbi Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia Land ecological stress model Kendall’s tau-b machine learning mangroves GAM |
| title | Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia |
| title_full | Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia |
| title_fullStr | Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia |
| title_full_unstemmed | Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia |
| title_short | Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia |
| title_sort | ecological stress modeling to conserve mangrove ecosystem along the jazan coast of saudi arabia |
| topic | ecological stress model Kendall’s tau-b machine learning mangroves GAM |
| url | https://www.mdpi.com/2073-445X/14/1/70 |
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