Abstract

Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expand LULC change analysis and provide a new dimension for UGS vulnerability assessment.

摘要

城市地区土地利用和土地覆盖 (LULC) 的变化会影响自然环境，尤其是城市绿地 (UGS)。本研究基于“压力-状态-响应”框架，研究了不同时期 LULC 转化导致的 UGS 损失，以预测 UGS 未来的脆弱区。为了计算每个因素的权重，使用了组合的层次分析法和模糊综合评价方法。基于集成多层感知器的人工神经网络和马尔可夫链 (MLP-ANN-MC) 模型已被用于预测加尔各答的 UGS 脆弱区域。结果表明，建成区增长率、土地利用动态程度、变化强度指数和邻近因素是导致UGS脆弱性的主要因素。应用 MLP-ANN-MC 模型，未来的脆弱区域被确定为 UGS 的管理和保护。本研究中开发和展示的方法扩展了 LULC 变化分析，并为 UGS 脆弱性评估提供了一个新维度。