Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
Department of Information Technology, IGDTUW, Delhi, India.
Environ Monit Assess. 2024 Aug 3;196(9):782. doi: 10.1007/s10661-024-12928-0.
Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.
利用陆地卫星土地利用/土地覆盖（LULC）数据分析建立淡水湖泊的时空分布，可以为未来的生态和环境政策制定提供坚实的基础，以更好地管理生态系统。土地利用/土地覆盖变化分析是一种可以用来更多地了解人类与环境之间直接和间接相互作用的方法，以实现可持续性。神经网络技术极大地促进了非对称和高维数据之间的映射。本文提出了一种方法上的进展，即将 CA-ANN（元胞自动机-人工神经网络）技术与水体的动态特征相结合，以预测“乌勒湖”未来的水位及其空间分布。我们使用了 2001 年至 2021 年的遥感数据，间隔为 10 年，以预测时空变化和土地利用/土地覆盖模拟。对 2021 年预测和准确土地利用/土地覆盖图的校准进行验证，得到的最大kappa 值为 0.86。在过去的三十年中，研究区域的不透水面净变化百分比增加了 22.41%，农业用地增加了 52.02%，而水减少了 14.12%，树木/森林减少了 40.77%，灌木减少了 11.53%，水生植被减少了 4.14%。由于人类活动导致的大规模土地转化，克什米尔山谷环境可持续的乌勒湖面临着许多环境挑战，主要是负面的。这项研究承认了（LULC）分析的重要性，认为它是制定未来生态和环境政策框架的基本基石。
