Gültürk Doğruyol Pınar
Faculty of Fine Arts, Design and Architecture Department of Landscape Architecture, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
Integr Environ Assess Manag. 2025 Jan 1;21(1):93-104. doi: 10.1093/inteam/vjae015.
Wetlands provide necessary ecosystem services, such as climate regulation and contribution to biodiversity at global and local scales, and they face spatial changes due to natural and anthropogenic factors. The degradation of the characteristic structure signals potential severe threats to biodiversity. This study aimed to monitor the long-term spatial changes of the Göksu Delta, a critical Ramsar site, using remote sensing techniques. It seeks to analyze the relationship between these changes and land surface temperature (LST) and predict future land use patterns through machine learning (ML) methods. In this context, the normalized difference vegetation index, modified normalized difference water index, normalized difference bareness index, and normalized difference moisture index remote sensing spectral index analyses and LST maps were generated using Landsat 8 Operational Land Imager (OLI) satellite imagery for 1985, 2000, 2015, and 2023. Kappa accuracy assessments demonstrated a high level of agreement between the generated maps and ground truth data. Pearson correlation analysis was used to assess the consistency of the relationship between spectral index analyses and LST, revealing a statistically significant correlation at the 0.01 level. The study revealed that Lake Akgöl lost 58.85% of its water body over the 38 years of monitoring the delta. This loss was primarily attributed to increased LST and human activities. The land use land cover model for the year 2031, developed using artificial neural networks and cellular automata from ML methods, projected a 7.50% decrease in total water bodies, a 46.94% reduction in vegetated areas, and a 36.85% increase in nonvegetated areas. In conclusion, it was emphasized that the adverse land use trends within the Göksu Delta are expected to persist, degrading its ecosystem services and values. In this context, the study's findings can be utilized to identify strong strategies for protecting the delta.
湿地提供了必要的生态系统服务，比如在全球和地方尺度上的气候调节以及对生物多样性的贡献，并且由于自然和人为因素，它们面临着空间变化。其特征结构的退化预示着对生物多样性的潜在严重威胁。本研究旨在利用遥感技术监测拉姆萨尔重要湿地戈克苏三角洲的长期空间变化。它试图分析这些变化与地表温度（LST）之间的关系，并通过机器学习（ML）方法预测未来的土地利用模式。在此背景下，利用1985年、2000年、2015年和2023年的陆地卫星8号业务陆地成像仪（OLI）卫星图像生成了归一化植被指数、改进型归一化差异水体指数、归一化裸土指数和归一化湿度指数遥感光谱指数分析图以及地表温度图。卡帕精度评估表明生成的地图与地面真值数据之间具有高度一致性。采用皮尔逊相关分析来评估光谱指数分析与地表温度之间关系的一致性，结果显示在0.01水平上具有统计学显著相关性。研究表明，在对三角洲进行监测的38年里，阿克戈尔湖失去了58.85%的水体。这种损失主要归因于地表温度升高和人类活动。利用机器学习方法中的人工神经网络和细胞自动机开发的2031年土地利用土地覆盖模型预测，水体总量将减少7.50%，植被面积将减少46.94%，非植被面积将增加36.85%。总之，强调了戈克苏三角洲内不利的土地利用趋势预计将持续，这会使其生态系统服务和价值退化。在此背景下，该研究结果可用于确定保护三角洲的有力策略。
