Remote Sensing of Land Surface Information for Environmental Applications

用于环境应用的地表信息遥感

• 批准号: RGPIN-2015-06453
• 负责人: Wang, Jinfei
• 金额: $ 1.6万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=591073
• 关键词: Remote; Sensing; Land; Surface; Information

Global environmental change has impacted many areas in the world. Currently more people live in urban areas than rural areas in the world. Monitoring of urban areas is important for sustainable urban development and adaptation to global environmental change, for example sea level changes and emergency response to disasters. On the other hand, in the next two decades, the demand for food will double globally. Detailed information on agricultural crops are required for sustainable agricultural practice and to meet the demand of human food supplies and better adapt to the global climate change. With the increasing availability of aerial and spaceborne remotely sensed data that record information from different visible, infrared and microwave wavelength ranges, with much higher spatial resolution, it is now possible to effectively extract useful land surface information for earth environmental monitoring in various areas such as urban and agricultural croplands. At the same time, there are some gaps in knowledge in the urban and agricultural remote sensing. New and improved methods and algorithms are required to process and integrate the multi-sensor data to extract the land surface information accurately and automatically. In this proposal, I will focus on filling the gaps in the automatic extraction of land surface information using remote sensing. Specifically, in this proposal, I will develop methodology for three dimensional (3D) urban building modelling based on multi-view image matching; urban tree species classification by adding structural and shape information to the colour information in order to improve the accuracy; and biophysical and surface variable extraction from optical and Synthetic Aperture Radar (SAR) data (such as the Canadian Radarsat-2 imagery) for monitoring the dynamically changing agricultural crops. During this grant funding period, four PhDs, five Master's and additional Honours students will be trained. The proposed research will contribute new algorithms and ideas to the knowledge of remote sensing, and contribute towards the operational use of remote sensing products in environmental applications. Urban planners, municipal government managers and decision makers, urban disaster management teams, urban foresters, hydrologists and ecologists will benefit from the research results of 3D building models and urban tree species classification. Researchers in urban climate, hydrology, energy consumption and environmental changes can use the resultant information as inputs to their models. Farmers, farming companies, Agriculture and Agri-food Canada, and researchers in agriculture will benefit from our agricultural remote sensing results. The research will benefit Canada in terms of global environmental monitoring and management, and in terms of the research, development and commercialization of current and future Canadian satellites.

全球环境变化影响了世界许多地区。目前，世界上居住在城市地区的人口多于农村地区。监测城市地区对于可持续城市发展和适应全球环境变化，例如海平面变化和对灾害的应急反应，都很重要。另一方面，在未来20年，全球对粮食的需求将翻一番。为了可持续农业做法和满足人类粮食供应的需求以及更好地适应全球气候变化，需要关于农作物的详细信息。随着航空和空间遥感数据的日益增多，这些数据记录了不同可见光、红外线和微波波长范围内的信息，空间分辨率也高得多，现在可以有效地提取有用的地表信息，用于城市和农田等不同地区的地球环境监测。与此同时，在城市和农业遥感方面还存在一些知识空白。因此，需要新的和改进的方法和算法来处理和集成多传感器数据，以准确和自动地提取地表信息。在本提案中，我将着重于填补利用遥感自动提取地表信息方面的空白。具体而言，在本提案中，我将开发基于多视角图像匹配的三维（3D）城市建筑建模方法;通过将结构和形状信息添加到颜色信息以提高准确性的城市树种分类;以及从光学和合成孔径雷达（SAR）数据中提取生物物理和表面变量（如加拿大雷达卫星2号图像），用于监测不断变化的农作物。在此赠款资助期间，将培训四名博士，五名硕士和其他荣誉学生。拟议的研究将为遥感知识提供新的算法和想法，并有助于在环境应用中实际使用遥感产品。城市规划者、市政府管理者和决策者、城市灾害管理团队、城市林业工作者、水文学家和生态学家将受益于3D建筑模型和城市树种分类的研究成果。城市气候、水文、能源消耗和环境变化方面的研究人员可以将由此产生的信息作为其模型的输入。农民、农业公司、加拿大农业和农业食品部以及农业研究人员将从我们的农业遥感成果中受益。这项研究将使加拿大在全球环境监测和管理方面以及在加拿大现有和未来卫星的研究、开发和商业化方面受益。