Abstract：
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.
摘要：
Landsat土地利用/土地覆盖(LULC)数据分析以建立淡水湖的时空分布可以为今后更好地管理生态系统的生态环境政策制定提供坚实的基础。LULC变化分析是一种可用于了解更多有关人类与环境的直接和间接相互作用以实现可持续性的方法。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。本文介绍了一种方法上的进步，该方法将CA-ANN（元胞自动机-人工神经网络）技术与水体的动态特性相结合，以预测沃尔湖中即将到来的水位及其空间分布。“我们使用2001年至2021年的遥感数据，间隔为10年，以预测时空变化和LULC模拟。2021年预测的和准确的LULC图的校准的验证产生了0.86的最大kappa值。在过去的三十年里，研究区域的不透水面净变化百分比增加了22.41%，农业用地净变化百分比增加了52.02%，而水减少了14.12%，树木/森林减少40.77%,灌木减少11.53%,水生植被减少4.14%。由于巨大的土地改造，在克什米尔山谷的环境可持续发展的Wular湖中出现了多种环境挑战，主要是由于人类活动，并且主要是负面的。研究承认(LULC)分析的重要性，认识到它是制定未来生态和环境政策框架的基本基石。
