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A conceptual model for identifying the risk susceptibility of urban green spaces using geo-spatial techniques

机译：使用地理空间技术识别城市绿地风险敏感性的概念模型

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Urban green spaces are often regarded as the harbinger of sustainability in the rapidly urbanizing world. This study forwards a conceptual framework towards urban green space (UGS) management by the quantification of risk susceptibility of the UGS ata neighborhood level by using remote sensing data and geo-spatial techniques. Objective measure of the UGS was performed using weighted evaluation of NDVI data at a 20 m x 20 m grid over the city of Kolkata. The normalized green index (NGI) was developedto quantify the implication of the built-up spaces in UGS risk susceptibility in the urban fabric. Both the satellite image data and the NGI values were spatially auto-correlated using bivariate Moran's I to derive the intricate relationship between built-up area and green spaces using LISA. It was observed that the low green-spaces were greatly influenced by the high built-up area around it. This inference was extended on the Kolk-ata's grid, the results revealed that the older part of the city had the most risk susceptible zones, whereas the area surrounded by wetlands were the most stable region bearing high resilience to urbanization. Hence, by determining the most risky zones for UGS degradation, planners and policy makers can efficiently allocate resources towards the sustainable development of the city by conserving and promoting UGS.
机译：在快速城市化的世界中，城市绿地通常被视为可持续发展的先驱。这项研究通过使用遥感数据和地理空间技术量化UGS ata邻域的风险敏感性，为城市绿地（UGS）管理提供了一个概念框架。使用加尔各答市上方20 m x 20 m网格上NDVI数据的加权评估，对UGS进行客观测量。开发归一化绿色指数（NGI）来量化建筑空间中UGS风险易感性的暗示。卫星图像数据和NGI值均使用二变量Moran's I在空间上自动关联，从而使用LISA推导建筑面积与绿地之间的复杂关系。据观察，低矮的绿色空间受到周围高建筑面积的很大影响。该推论在Kolk-ata的网格上进行了扩展，结果表明，该城市的较早部分具有最易受风险影响的区域，而被湿地包围的区域是最稳定的区域，对城市化具有较高的适应力。因此，通过确定UGS退化风险最高的区域，规划人员和政策制定者可以通过保护和推广UGS有效地为城市的可持续发展分配资源。

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