Remote Sensing of Land Surface Information for Environmental Applications

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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.