地理空间技术在城市环境绿化中的应用——以泰国曼谷为例☆

Nargis Kamal , Muhammad Imran , Nitin Kumar Tripati
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引用次数: 13

摘要

城市化是引起土地利用变化的人为活动之一。近年来,曼谷的各种土地利用转型对城市的生态可持续性产生了全方位的影响,即减少了城市的耕地和绿化。这项研究调查了由于特大城市的极端城市增长而缺乏绿色空间。为此,首先,通过两个不同的图像对土地利用过渡进行建模;一张来自1994年的Landsat 5专题绘图仪,另一张来自2012年的mhj - 1a CCD。接下来,应用多层感知器马尔可夫模型(MLP-Markov)预测2030年的土地利用变化。MLP神经网络通过创建转换映射来训练建模和使用转换。采用马尔可夫链预测模型对预测过程中的过渡图进行处理,具有足够的精度。结果表明:1994-2012年期间,有348km2的绿地被改造为建成区,绿化面积损失相当大(42%);MLP模型预测显示,2012-2030年期间,建筑面积增加4%,绿化面积减少6%。该研究强烈建议在城市中保护绿色空间和绿色走廊。未来的研究可以包括分析在脆弱地点适宜的绿色斑块的绿积比。研究成果将有利于城市规划者实施保护特大城市自然环境的长期规划战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Greening the Urban Environment Using Geospatial Techniques, A Case Study of Bangkok, Thailand

Urbanization is one of human induced activities causing land use changes. In recent years, various land usetransformationsin Bangkok influenced the city's ecological sustainability in all means, i.e., diminishing the city's cultivated land and greenery. This study investigates lack of green spaces due to extreme urban growth in the mega city. To do so, first, land use transitions are modelled through two different images; one from Landsat 5Thematic Mapper for the year 1994, and second fromHJ-1A CCD for the year 2012. Next, theMulti-Layer Perceptron Markov Model (MLP-Markov) is applied to predict land usechange for the year 2030. The MLP neural network is trained to modelland usetransitionsthrough creating transition maps. Markov Chain predictive model is applied with sufficient accuracy to process the transition maps for the prediction process. The results indicate that 348km2of green areas are transformed into built-up areas for the period 1994-2012,witha considerable loss of greenery (42%). The MLP model predictions show 4% increase in built-up and 6% decrease in greenery for the period 2012-2030. The study highly recommends conservation of green spaces and green corridors in the city. Future research can includeanalysing greenplot ratio for suitablegreen patches in vulnerable sites. The research output will benefit urban planners to implement long term planning strategies forsecuring natural environment in mega cities.

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