Remote Sensing of Land Surface Information for Environmental Applications

全球环境变化影响了世界许多地区。目前，世界上生活在城市地区的人比生活在农村地区的人多。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）非常重要。另一方面，在未来二十年，全球对粮食的需求将翻一番。关于农作物的详细信息是可持续农业实践和满足人类粮食供应需求以及更好地适应全球气候变化所必需的。随着越来越多的航空和星载遥感数据以更高的空间分辨率记录不同可见光、红外和微波波长范围的信息，现在可以有效地提取有用的地表信息，用于城市和农田等各个地区的地球环境监测。与此同时，我国的城市和农业遥感知识还存在一定的空白。为了准确、自动地提取地表信息，需要新的、改进的方法和算法对多传感器数据进行处理和整合。在本提案中，我将重点填补利用遥感自动提取地表信息的空白。具体而言，在本提案中，我将开发基于多视图图像匹配的三维（3D）城市建筑建模方法；通过在颜色信息基础上增加结构和形状信息，提高城市树种分类的准确性；从光学和合成孔径雷达（SAR）数据（如加拿大Radarsat-2图像）中提取生物物理和地表变量，用于监测动态变化的农作物。在此资助期内，将培训四名博士、五名硕士及其他荣誉学生。拟议的研究将为遥感知识提供新的算法和想法，并有助于遥感产品在环境应用中的实际使用。城市规划者、市政府管理者和决策者、城市灾害管理团队、城市林务员、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领域的研究人员可以将所得信息作为模型的输入。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结果中受益。这项研究将使加拿大在全球环境监测和管理方面以及在目前和未来加拿大卫星的研究、开发和商业化方面受益。

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.