Looking into the green roof scenario to mitigate flash flood effects in Mamak, Turkey, via classifying images of Sentinel-1, 2, and PlanetScope satellites with LibSVM algorithm in Google Earth Engine cloud platform

IF 0.9 4区社会学 Q3 GEOGRAPHY

Geografie-Sbornik CGS Pub Date : 2022-01-01 DOI:10.37040/geografie.2022.008

Sima Pouya, Majid Aghlmand, F. Karsli

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Abstract

This research aimed to increase the green space factor to mitigate flash flood effects on urban storm water runoff in the Ankara Mamak region and to minimize the damages by flash floods. The land use/cover map was first obtained by using the images of Sentinel-1, Sentinel-2, and PlanetScope satellites with the LIBSVM algorithm on the Google Earth Engine. The GSF value was then calculated and it was low (0.26) compared to world standards. This study was proposed as a solution for the flood disaster, using the extensive green roof scenario. After green roof conversion scenarios, the GSF value was recalculated. It was found to be above the minimum of green infrastructure that human settlements should achieve, regardless of density or land use (0.43). Offering high resolution images and the possibility of processing them via different algorithms of machine learning has revolutionized the environmental and urban-related studies as they help urban managers and planners to make decisions accurately and quickly.

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通过在Google Earth Engine云平台上使用LibSVM算法对Sentinel-1、2和PlanetScope卫星的图像进行分类，研究土耳其Mamak的绿色屋顶场景以减轻山洪影响

本研究旨在增加绿地因子，以减轻山洪对安卡拉Mamak地区城市雨水径流的影响，并最大限度地减少山洪造成的损失。首先利用Sentinel-1、Sentinel-2和PlanetScope卫星图像，在谷歌Earth Engine上使用LIBSVM算法获得土地利用/覆被图。计算GSF值，与世界标准相比，GSF值较低(0.26)。本研究是作为洪水灾害的解决方案提出的，使用广泛的绿色屋顶场景。在绿化屋顶转换场景后，重新计算GSF值。结果发现，无论密度或土地利用如何，它都高于人类住区应达到的最低绿色基础设施(0.43)。提供高分辨率图像以及通过机器学习的不同算法处理它们的可能性，已经彻底改变了环境和城市相关研究，因为它们可以帮助城市管理者和规划者准确、快速地做出决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Geografie-Sbornik CGS Social Sciences-Geography, Planning and Development

CiteScore

2.20

自引率

0.00%

发文量