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

这一拟议研究计划的长期目标是开发创新的整合战略和融合方法，以促进与从遥感数据准确表征植被冠层相关的科学进步。它是由遥感技术的发展以及对推进植被特征的科学和创新的需求所推动和推动的。快速发展的遥感技术使对地观测数据以前所未有的数量和细节广泛可用；同时，近年来越来越多的人需要利用遥感准确确定越来越多的植被冠层属性，以实现森林资源的可持续管理、环境保护和精准农业。我们目前面临的关键问题是如何有效地利用这些数据，并将它们智能地整合在一起，以改进植被特征，如确定植被类型和条件。 尽管数据/信息融合在遥感领域并不是一个新话题，但信息源的数量和异质性，加上植被冠层的复杂性，导致对充分利用现有技术、地理空间数据和积累的知识的先进方法的需求越来越大。在这项研究中，我提出了主动、智能和自适应的创新整合策略，以及基于物理和知识的整合策略，代表了信息数据融合的新范式。这些战略不仅涉及信息组合的方法，而且还涉及积极控制和管理融合过程的信息选择机制。有了它们，整个场景不一定以相同的方式进行分析；所使用的信息和程序可以适应当地的特点。 长期研究目标将通过实现以下短期目标来实现，这些短期目标涉及在算法开发方面独一无二的植被冠层特征的三个重要方面。(1)开发智能集成方法，改进单株树冠划分。(2)开发创新方法，自适应地组合从不同数据源获得的所有信息，改进森林物种分类。(3)开发基于知识的渐进式反演策略，提高利用多源遥感数据反演植被参数的效果。 该项目的成功将对信息融合和植被表征的研究和发展产生重大影响。研究成果将产生新的知识，提高我们对不同遥感技术及其在植被表征中的集成的理解，并促进科学和工业应用。

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.