Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India
Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India
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基于印度北方邦 Gautam Budh Nagar 的统计模型和地球观测数据集监测空间土地利用和覆盖变化及其增长预测
DOI：
10.1007/s10668-018-0234-8
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Prafull Singh
中科院分区：
文献类型：
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作者：
Shivangi S. Somvanshi;O. Bhalla;P. Kunwar;Madhulika Singh;Prafull Singh
众所周知，近年来城市化的发展和城市人口的增加，特别是在发展中国家，是城市规划人员和其他环境专业人员的主要关切。本研究涉及多时相卫星数据沿着与统计模型，以映射和监测的LULC变化模式和预测的城市扩展在未来几年的恒河冲积平原的重要城市之一。在我们的研究的帮助下，我们还试图描绘城市扩张对自然环境的影响。利用2001年至2016年的Landsat卫星数据进行了长期LULC和城市空间变化建模。对结果的评估表明，从2001年到2016年，城市建成区的增加有利于农业用地和农村建成区的大幅下降。香农熵指数也被用来衡量的空间增长模式在一段时间内在研究区的土地利用变化的统计数据的基础上。利用人工神经网络方法通过Quantum GIS软件对研究区2019年、2022年和2031年的未来土地利用增长进行了预测。模拟模型的结果显示，到2019年，城市建成区面积将增加14.7%，到2022年增加15.7%，到2031年增加18.68%。根据对卫星数据的长期分类、统计方法和实地调查，本研究收到的观察结果表明，该地区的土地利用变化预测图将为政策制定者和决策者提供宝贵的信息，以促进该地区的可持续城市发展和自然资源管理，促进粮食和水安全。
It is well known and witnessed the fact that in recent years the growth of urbanization and increasing urban population in the cities, particularly in developing countries, are the primary concern for urban planners and other environmental professionals. The present study deals with multi-temporal satellite data along with statistical models to map and monitor the LULC change patterns and prediction of urban expansion in the upcoming years for one of the important cities of Ganga alluvial Plain. With the help of our study, we also tried to portray the impact of urban sprawl on the natural environment. The long-term LULC and urban spatial change modelling was carried out using Landsat satellite data from 2001 to 2016. The assessment of the outcome showed that increase in urban built-up areas favoured a substantial decline in the agricultural land and rural built-up areas, from 2001 to 2016. Shannon’s entropy index was also used to measure the spatial growth patterns over the period of time in the study area based on the land-use change statistics. Prediction of the future land-use growth of the study area for 2019, 2022 and 2031 was carried out using artificial neural network method through Quantum GIS software. Results of the simulation model revealed that 14.7% of urban built-up areas will increase by 2019, 15.7% by 2022 and 18.68% by 2031. The observation received from the present study based on the long-term classification of satellite data, statistical methods and field survey indicates that the predicted LULC map of the area will be precious information for policy and decision-makers for sustainable urban development and natural resource management in the area for food and water security.
提高应急避难所容量和改善环境的新管理模式
DOI：
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发表时间：
2021
期刊：
影响因子：
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作者：
Kyoko Hirata;Takashie Ishikawa;Akiko Murata;Hiroaki Notake
通讯作者：
Hiroaki Notake