Department of Geoinformatics Engineering, Inha University, Incheon, Republic of Korea.
Water Quality Control Center, National Institute of Environmental Research, Incheon, Republic of Korea.
J Environ Manage. 2018 Jan 15;206:587-601. doi: 10.1016/j.jenvman.2017.10.076. Epub 2017 Nov 9.
Impervious surfaces are mainly artificial structures such as rooftops, roads, and parking lots that are covered by impenetrable materials. These surfaces are becoming the major causes of nonpoint source (NPS) pollution in urban areas. The rapid progress of urban development is increasing the total amount of impervious surfaces and NPS pollution. Therefore, many cities worldwide have adopted a stormwater utility fee (SUF) that generates funds needed to manage NPS pollution. The amount of SUF is estimated based on the impervious ratio, which is calculated by dividing the total impervious surface area by the net area of an individual land parcel. Hence, in order to identify the exact impervious ratio, large-scale impervious surface maps (ISMs) are necessary. This study proposes and assesses various methods for generating large-scale ISMs for urban areas by using existing GIS data. Bupyeong-gu, a district in the city of Incheon, South Korea, was selected as the study area. Spatial data that were freely offered by national/local governments in S. Korea were collected. First, three types of ISMs were generated by using the land-cover map, digital topographic map, and orthophotographs, to validate three methods that had been proposed conceptually by Korea Environment Corporation. Then, to generate an ISM of higher accuracy, an integration method using all data was proposed. Error matrices were made and Kappa statistics were calculated to evaluate the accuracy. Overlay analyses were performed to examine the distribution of misclassified areas. From the results, the integration method delivered the highest accuracy (Kappa statistic of 0.99) compared to the three methods that use a single type of spatial data. However, a longer production time and higher cost were limiting factors. Among the three methods using a single type of data, the land-cover map showed the highest accuracy with a Kappa statistic of 0.91. Thus, it was judged that the mapping method using the land-cover map is more appropriate than the others. In conclusion, it is desirable to apply the integration method when generating the ISM with the highest accuracy. However, if time and cost are constrained, it would be effective to primarily use the land-cover map.
不透水表面主要是由屋顶、道路和停车场等不透水材料覆盖的人工结构。这些表面正在成为城市地区非点源（NPS）污染的主要原因。城市发展的迅速推进增加了不透水表面的总量和 NPS 污染。因此，世界上许多城市都采用了雨水利用费（SUF），以筹集管理 NPS 污染所需的资金。SUF 的金额是根据不透水率估算的，不透水率是通过将总不透水面面积除以单个土地地段的净面积来计算的。因此，为了确定准确的不透水率，需要有大规模的不透水面地图（ISM）。本研究提出并评估了利用现有 GIS 数据生成城市大规模 ISM 的各种方法。韩国仁川市的富平区被选为研究区域。收集了韩国国家/地方政府免费提供的空间数据。首先，使用土地覆盖图、数字地形图和正射影像图生成了三种 ISM，以验证韩国环境公司提出的三种概念方法。然后，为了生成更精确的 ISM，提出了一种使用所有数据的集成方法。制作了误差矩阵并计算了 Kappa 统计量来评估精度。进行了叠置分析以检查错误分类区域的分布。结果表明，与使用单一类型空间数据的三种方法相比，集成方法的精度最高（Kappa 统计量为 0.99）。然而，较长的生产时间和较高的成本是限制因素。在使用单一类型数据的三种方法中，土地覆盖图的精度最高，Kappa 统计量为 0.91。因此，判断使用土地覆盖图的制图方法比其他方法更合适。总之，生成精度最高的 ISM 时，应用集成方法是可取的。但是，如果时间和成本受到限制，主要使用土地覆盖图将是有效的。
