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Forest structure estimation using SAR, LiDAR, and optical data in the Canadian Boreal forest

机译:使用SAR,LiDAR和光学数据估算加拿大北方森林的森林结构

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One of the most fundamental new technical challenges of a DESDynI space-borne mission is the fusion of the several sensor modalities - LiDAR, SAR, InSAR, and Optical - in order to accurately estimate desired 3D vegetation structures and biomass parameters in areas where the sensors overlap, and to extrapolate them over continuous areas where lidar data is absent. The objective of this paper is to use measured datasets in conjunction with our sensor forward models to develop and validate an estimation algorithm that fuses various remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure, including biomass, canopy height, and tree species.
机译:DESDynI星载任务的最根本的新技术挑战之一是融合几种传感器模式-LiDAR,SAR,InSAR和光学-以便准确估计传感器所在区域所需的3D植被结构和生物量参数重叠,并在没有激光雷达数据的连续区域上推断它们。本文的目的是将测得的数据集与我们的传感器正向模型结合使用,以开发和验证一种估计算法,该算法将各种遥感技术与最少的地面信息融合在一起,并能对森林结构(包括生物量,冠层)进行准确的估计。高度和树种。

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