基于陆地卫星影像的罗马海岸线城市扩展的GEE和RF算法分析

Francesco Lodato, N. Colonna, G. Pennazza, S. Praticò, M. Santonico, L. Vollero, M. Pollino
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引用次数: 2

摘要

本研究通过遥感技术和创新的云服务分析了北罗马沿岸地区最近的土地利用动态,该地区的最新发展见证了纯农村地区向新的住宅和商业服务的重要重新转变。调查区域包括五个城市,包括重要的基础设施,如“达芬奇”机场和奇维塔韦基亚港。在高效的连接网络的支持下,邻近大都市已经改变了这些地区的城市和城郊结构,这些地区以前完全是农业。因此,通过使用云计算平台“谷歌地球引擎”(GEE)对Landsat卫星图像进行分类,对城市扩张进行量化。1985年至2020年的Landsat多光谱图像被用于历时分析,间隔为5年。为了获得较高的最终结果精度,我们沿着图像的时间维度进行工作,选择特定的时间窗口创建数据集,并根据NDVI指数随时间变化的相关信息对数据集进行调整。这种实现显示出每年模型性能的有趣改进,这表明NDVI标准差参数的重要性。结果表明,整体精度从90%提高到97%,在区分城市表面和不透水表面方面有所改善。最终结果表明,在35年的时间跨度内,“城市”和“林地”类别的研究面积显著增加,分别为67.4 km2和70.4 km2。获得的准确结果使我们能够量化和理解感兴趣地区的景观变化,特别是城市发展的动态。
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Analysis of the Spatiotemporal Urban Expansion of the Rome Coastline through GEE and RF Algorithm, Using Landsat Imagery
This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km2 and 70.4 km2, respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.
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