Scholarlink

<p>The Gulf Cooperation Council (GCC) countries have experienced rapid coastal development over the past decades, significantly impacting their marine ecosystems. This study aimed to study the land use and land cover change for coastal vegetation in GCC countries from 2000 to 2023 using remote sensing and machine learning techniques, and to identify the impact of climate and anthropogenic factors on coastal vegetation cover of the GCC. We used Landsat satellite imagery and a Random Forest classification algorithm to map various land cover classes along the GCC coastline. Our results revealed significant changes in land cover, as seen by an increase in artificial built-up areas by 15.5% in two decades and a corresponding decrease in bareland and dense vegetation. We also observed an increase in mangrove and seagrass areas, likely due to recent conservation and afforestation efforts. The spatiotemporal analysis showed trends in land cover changes, with agricultural areas generally increasing (21.6%) and bareland steadily decreasing (−&#xa0;21.2%). Dense vegetation declined by 35% from 2003 to 2023, while mangroves increased by 6%. A multiple linear regression analysis among the various climatic and anthropogenic factors showed that higher temperature positively affected mangroves and seagrass while having a negative relation to dense vegetation. Anthropogenic factors such as urban expansion and agricultural growth negatively impacted dense vegetation. Our findings underscore the need for integrated coastal management strategies balancing economic development with environmental conservation. Further research using higher resolution imagery and advanced classification techniques could improve accuracy and use of the results on a localized level. Our results also provide a baseline for future monitoring and management of coastal ecosystems in the GCC region.</p> Graphical Abstract <p>The significance of this research lies in its ability to quantify the impact of both climatic and anthropogenic factors on vegetation patterns through multiple linear regression analysis. The graphical abstract provides a structured workflow for analyzing vegetation dynamics and land cover changes from 2000 to 2023, integrating remote sensing techniques, machine learning classification, and regression analysis. The left section outlines a methodological framework, beginning with Landsat data collection and preprocessing, followed by the extraction of spectral indices and compositing image collections to ensure consistent analysis. A key step involves training and validating a Random Forest classification model, which is refined through classifier comparison and hyperparameter tuning. The analysis further extends to accuracy assessment, temporal mapping of land cover changes, and climate data integration, enabling a robust evaluation of long-term environmental transformations. The right section presents the spatiotemporal outcomes of the classification, showing mapped land cover classes across the Arabian Peninsula, focusing on urbanization, vegetation shifts, and water bodies. The maps highlight key land cover categories such as mangroves, dense vegetation, agricultural land, and built-up areas, offering critical insights into the effects of anthropogenic activities and climate variability. The integration of multiple linear regression enables a deeper understanding of the relationship between climatic drivers, human interventions, and ecosystem transformations. This comprehensive approach provides valuable tools for environmental monitoring, sustainable land management, and policy planning, particularly in regions vulnerable to climate change and rapid urban expansion.</p> <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Analyzing the Influence of Climate and Anthropogenic Development on Vegetation Cover in the Coastal Ecosystems of GCC

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月18日

查看全文
  • Abhilash Dutta Roy,
  • Midhun Mohan,
  • Aaron Althauser,
  • Amare Gebrie,
  • Meshal Abdullah,
  • Talal Al-Awadhi,
  • Ahmed M. El Kenawy,
  • Ammar Abulibdeh

摘要

The Gulf Cooperation Council (GCC) countries have experienced rapid coastal development over the past decades, significantly impacting their marine ecosystems. This study aimed to study the land use and land cover change for coastal vegetation in GCC countries from 2000 to 2023 using remote sensing and machine learning techniques, and to identify the impact of climate and anthropogenic factors on coastal vegetation cover of the GCC. We used Landsat satellite imagery and a Random Forest classification algorithm to map various land cover classes along the GCC coastline. Our results revealed significant changes in land cover, as seen by an increase in artificial built-up areas by 15.5% in two decades and a corresponding decrease in bareland and dense vegetation. We also observed an increase in mangrove and seagrass areas, likely due to recent conservation and afforestation efforts. The spatiotemporal analysis showed trends in land cover changes, with agricultural areas generally increasing (21.6%) and bareland steadily decreasing (− 21.2%). Dense vegetation declined by 35% from 2003 to 2023, while mangroves increased by 6%. A multiple linear regression analysis among the various climatic and anthropogenic factors showed that higher temperature positively affected mangroves and seagrass while having a negative relation to dense vegetation. Anthropogenic factors such as urban expansion and agricultural growth negatively impacted dense vegetation. Our findings underscore the need for integrated coastal management strategies balancing economic development with environmental conservation. Further research using higher resolution imagery and advanced classification techniques could improve accuracy and use of the results on a localized level. Our results also provide a baseline for future monitoring and management of coastal ecosystems in the GCC region.

Graphical Abstract

The significance of this research lies in its ability to quantify the impact of both climatic and anthropogenic factors on vegetation patterns through multiple linear regression analysis. The graphical abstract provides a structured workflow for analyzing vegetation dynamics and land cover changes from 2000 to 2023, integrating remote sensing techniques, machine learning classification, and regression analysis. The left section outlines a methodological framework, beginning with Landsat data collection and preprocessing, followed by the extraction of spectral indices and compositing image collections to ensure consistent analysis. A key step involves training and validating a Random Forest classification model, which is refined through classifier comparison and hyperparameter tuning. The analysis further extends to accuracy assessment, temporal mapping of land cover changes, and climate data integration, enabling a robust evaluation of long-term environmental transformations. The right section presents the spatiotemporal outcomes of the classification, showing mapped land cover classes across the Arabian Peninsula, focusing on urbanization, vegetation shifts, and water bodies. The maps highlight key land cover categories such as mangroves, dense vegetation, agricultural land, and built-up areas, offering critical insights into the effects of anthropogenic activities and climate variability. The integration of multiple linear regression enables a deeper understanding of the relationship between climatic drivers, human interventions, and ecosystem transformations. This comprehensive approach provides valuable tools for environmental monitoring, sustainable land management, and policy planning, particularly in regions vulnerable to climate change and rapid urban expansion.