• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPIE Conference on Remote Sensing Technologies and Applications in Urban Environments >Investigating the suitability of Sentinel-2 data to derive the urban vegetation structure
【24h】

Investigating the suitability of Sentinel-2 data to derive the urban vegetation structure

机译：调查Sentinel-2数据衍生城市植被结构的适用性

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Urban green is indispensable from an urban ecological and social point of view and fulfils important functions such as dust binding, temperature reduction, wind damping or groundwater recharge. Especially for bioclimatic modeling, knowledge of size, structure and green volume of the urban vegetation is essential. Manual mapping of vegetation structures is time-consuming and cost-intensive and can only ever be carried out in locally limited study areas. Active and passive remote sensing technologies in combination with automated methods for information extraction offer the opportunity to record the green structure in urban areas differentiated according to vegetation types. The new globally and freely available data provided by the European Copernicus Program raises the question whether these data are suitable for mapping and quantifying the urban green structure, including an accuracy estimation. Previous studies on the usability of Sentinel-2 data for vegetation analysis were essentially limited to crop and tree species classification in open space. The approach presented here thus considers for the first time the application of this data in a purely urban environment. Here we present a modeling approach based on multiple regression models. A Sentinel-2A scene from July 4, 2015 covering the greater Dresden area served as the input data set. After atmospheric correction of the satellite image scene 10 spectral channels were available. A high-resolution vegetation cover model with a grid width of 50 cm was available as a reference data set for the entire study area (City of Dresden, Germany). This takes into account the vegetation classes deciduous trees, conifers, shrubs, low (grassy) vegetation and arable land. Thus the area share of these vegetation types could be determined aggregated for each pixel of the satellite image scene. In addition, vegetation indices (NDVI and others) were calculated using suitable channels. For the prediction of each vegetation class,
机译：城市绿色是城市生态和社会观点中不可或缺的，实现重要的功能，如灰尘绑定，降温，风阻尼或地下水补给。特别是对于城市植被的尺寸，结构和绿色体积的知识至关重要。植被结构的手动映射是耗时和成本密集的，只能在当地有限的研究领域进行。主动和被动遥感技术与信息提取的自动化方法组合提供了根据植被类型鉴定城市地区的绿色结构的机会。欧洲哥白尼程序提供的新全球和自由提供的数据提出了这些数据是否适合绘制和量化城市绿色结构的问题，包括准确性估算。以前关于植被分析的Sentinel-2数据的可用性的研究基本上限于开放空间中的作物和树种分类。此处呈现的方法是首次考虑在纯粹的城市环境中应用此数据。在这里，我们提出了一种基于多元回归模型的建模方法。 2015年7月4日的Sentinel-2a场景覆盖了较大的德累斯顿地区作为输入数据集。在卫星图像场景的大气校正之后，可以使用10个光谱通道。具有50厘米的电网宽度为50厘米的高分辨率植被覆盖模型作为整个研究区（德累斯顿市）的参考数据设置。这考虑到植被患者落叶树，针叶树，灌木，低（草地）植被和耕地。因此，可以针对卫星图像场景的每个像素来聚合这些植被类型的面积份额。此外，使用合适的通道计算植被指数（NDVI和其他）。为了预测每个植被阶级，

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号