Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

Q2 Environmental Science Environmental Challenges Pub Date : 2024-09-24 DOI:10.1016/j.envc.2024.101018
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Abstract

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.
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利用马尔可夫链模型和人工神经网络评估土地利用和土地覆被变化对登苏三角洲湿地的影响
本研究调查了加纳重要生态系统登苏三角洲湿地的土地利用和土地覆被 (LULC) 变化动态。研究利用 1998 年至 2023 年的卫星图像,分析了 Densu 三角洲湿地 LULC 变化的时空模式及其对水体、湿地、植被、裸地和城市地区的影响。研究采用马尔可夫链建模和人工神经网络(ANN)等先进技术,对 LULC 的变化进行了评估和预测。值得注意的是,在登苏三角洲湿地观察到的 LULC 损失最大,那里的湿地过渡到了水体覆盖类型(14.02 平方公里)。2023 年的模型验证证明了该模型的准确性,正确率为 70%,卡帕值为 0.74。深入分析探讨了登苏三角洲湿地的区域差异,揭示了 2013 年前后 LULC 变化率的不同模式。值得注意的是,城市化是 2013 年后的一个突出因素,城市地区的湿地变化率显著。过渡矩阵凸显了不同土地覆被等级在这些年里错综复杂的相互作用。对 2033 年和 2043 年土地覆被类型的模拟预测显示,城市土地覆被类型的正向变化最大,登苏三角洲湿地的正向变化率约为 0.39%。电须三角洲湿地的湿地土地覆被则出现了约-0.52%的负变化。土地利用、土地利用变化和林业数据的综合分析增强了我们对塑造这些关键生态系统的复杂相互作用的理解。这项研究为可持续环境保护提供了宝贵的见解,强调了知情城市规划政策的关键作用。它还揭示了气候变化带来的潜在挑战,倡导采用综合方法保护这些重要的湿地生态系统。
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来源期刊
Environmental Challenges
Environmental Challenges Environmental Science-Environmental Engineering
CiteScore
8.00
自引率
0.00%
发文量
249
审稿时长
8 weeks
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