Development of innovative fusion strategies and methods to improve vegetation characterization from multi-sensor remotely sensed data

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The long term goal of this proposed research program is to develop innovative integration strategies and fusion methods for the scientific advancement related to the accurate characterization of vegetation canopies from remotely sensed data. It is motivated and driven by technological developments in remote sensing and the demand for advancing science and innovation in vegetation characterization. Rapidly developed remote sensing technologies are making earth observation data widely available in unprecedented volume and detail; and meanwhile recent years have witnessed an increased demand for accurate determination of a growing number of attributes of vegetation canopies using remote sensing for sustainable management of forest resources, environment protection, and precision agriculture. The critical question we currently face is how to effectively utilize these data and intelligently integrate them together to improve vegetation characterization, such as the determination of vegetation types and conditions.*******Even though data/information fusion is not a new topic in the remote sensing community, the increasing number and heterogeneity of information sources, coupled with the complexity of vegetation canopies leads to an increasing demand for advanced methods  to fully exploit the available technologies, geospatial data, and accumulated knowledge. In this research, I propose innovative integration strategies that are active, intelligent and adaptive, and physics-and knowledge-based, representing a new paradigm in data fusion. These strategies deal with not only methodologies for information combination, but also mechanisms for information selection which actively control and manage the fusion process. With them, the whole scene is not necessarily analyzed in the same way; information and procedures used can be adaptive to local characteristics.*******The long-term research goal will be achieved through fulfilling the following short-term objectives addressing three important aspects in the characterization of vegetation canopies that are unique in term of algorithm development. (1) Developing intelligent integration approaches to improve individual tree crown delineation. (2) Developing innovative methods to adaptively combine all information obtained from different data sources and improve forest species classification. (3) Developing knowledge-based progressive inversion strategies to improve the retrieval of vegetation parameters using the multi-source remotely sensed data.*******The success of this program will have a great impact on research and development of information fusion and vegetation characterization. Research results will generate new knowledge and improve our understanding of different sensing technologies and their integrations in vegetation characterization and advance scientific and industrial applications.*********