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

开发创新的融合策略和方法，以改善多传感器遥感数据的植被特征

• 批准号: RGPIN-2015-06563
• 负责人: Hu, Baoxin
• 金额: $ 1.6万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633971
• 关键词: Development; innovative; fusion; strategies; methods

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.

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。