Improving the characterization of permafrost using polarimetric SAR interferometry (pol-inSAR)

使用偏振 SAR 干涉测量 (pol-inSAR) 改善永久冻土的表征

基本信息

  • 批准号:
    513708-2017
  • 负责人:
  • 金额:
    $ 1.55万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Collaborative Research and Development Grants
  • 财政年份:
    2019
  • 资助国家:
    加拿大
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

The goal of this project is to exploit the Pol-InSAR (Polarimetric Synthetic Aperture RADAR Interferometry) to improve permafrost characterization. This project is motivated by the growing demands for accurate characterization of the spatial distribution and dynamics of permafrost in the Canadian North and is enabled by the availability of advanced remotely sensed data, such as those from RADASAT-2 and the RADARSAT Constellation Mission (RCM) to be launched in 2018. Accurate information and scientific knowledge of the status and future trends of permafrost in the North are fundamentally important. They are needed to determine the vulnerability of permafrost systems to global warming and to design measures and effective management policies for the protection of infrastructures and ecosystems and for the minimization of other adverse effects of permafrost thawing. To achieve the goal of this project, we will fulfill the following specific objectives: develop advanced methodologies to accurately characterize the topographic displacements due to the seasonal changes in permafrost regions by exploiting the Pol-InSAR technology, and carry out a comprehensive study on the prediction of active layer thickness using features derived from data acquired by RADARSAT data. The outcome of this project will advance the research and development of the Pol-InSAR technology using data sets from RADARSAT-2 and its application in permafrost characterization. It will significantly contribute to understanding the effects of global climate change on permafrost landscapes and associated ecosystems. The developed methodologies can be transferred to our industrial partner, PCI Geomatics Inc, for potential commercialization. Ultimately, this will lead to national and potentially international economic benefits. The project will also provide training opportunities for highly qualified personnel to enhance their prospects for employment upon the completion of their higher education programs.
该项目的目标是利用 Pol-InSAR(偏振合成孔径雷达干涉测量法)来改善永久冻土特征。该项目的动机是对加拿大北部永久冻土空间分布和动态的准确表征日益增长的需求,并通过先进遥感数据的可用性得以实现,例如来自 RADASAT-2 和 RADARSAT 星座任务 (RCM) 的数据将于 2018 年启动。有关北方永久冻土现状和未来趋势的准确信息和科学知识至关重要。 需要它们来确定永久冻土系统对全球变暖的脆弱性,并设计措施和有效的管理政策,以保护基础设施和生态系统,并最大限度地减少永久冻土融化的其他不利影响。为实现本项目的目标,我们将实现以下具体目标:利用Pol-InSAR技术,开发先进的方法来准确表征多年冻土区季节变化引起的地形位移,并对其预测进行综合研究。使用从 RADARSAT 数据获取的数据导出的特征来测量活性层厚度。该项目的成果将推动利用RADARSAT-2数据集的Pol-InSAR技术的研究和开发及其在永久冻土表征中的应用。 它将极大地有助于了解全球气候变化对永久冻土景观和相关生态系统的影响。 开发的方法可以转移给我们的工业合作伙伴 PCI Geomatics Inc,以实现潜在的商业化。最终,这将带来国家和潜在的国际经济利益。该项目还将为高素质人才提供培训机会,以提高他们完成高等教育课程后的就业前景。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Hu, Baoxin其他文献

An individual tree crown delineation method based on multi-scale segmentation of imagery
Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data
Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model
  • DOI:
    10.1016/j.rse.2010.01.004
  • 发表时间:
    2010-06-15
  • 期刊:
  • 影响因子:
    13.5
  • 作者:
    Liu, Jiangui;Pattey, Elizabeth;Hu, Baoxin
  • 通讯作者:
    Hu, Baoxin
Classification of tree species based on structural features derived from high density LiDAR data
  • DOI:
    10.1016/j.agrformet.2012.11.012
  • 发表时间:
    2013-04-15
  • 期刊:
  • 影响因子:
    6.2
  • 作者:
    Li, Jili;Hu, Baoxin;Noland, Thomas L.
  • 通讯作者:
    Noland, Thomas L.
Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data

Hu, Baoxin的其他文献

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{{ truncateString('Hu, Baoxin', 18)}}的其他基金

Smart deep learning by incorporating remote sensing domain knowledge in vegetation characterization
将遥感领域知识融入植被表征中的智能深度学习
  • 批准号:
    RGPIN-2021-03624
  • 财政年份:
    2022
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
Smart deep learning by incorporating remote sensing domain knowledge in vegetation characterization
将遥感领域知识融入植被表征中的智能深度学习
  • 批准号:
    RGPIN-2021-03624
  • 财政年份:
    2021
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
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
  • 财政年份:
    2019
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
Improving the characterization of permafrost using polarimetric SAR interferometry (pol-inSAR)
使用偏振 SAR 干涉测量 (pol-inSAR) 改善永久冻土的表征
  • 批准号:
    513708-2017
  • 财政年份:
    2018
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Collaborative Research and Development Grants
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
  • 财政年份:
    2018
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
A GIS-based system for assessing emerald ash borer infestation
基于 GIS 的白蜡虫侵染评估系统
  • 批准号:
    490711-2015
  • 财政年份:
    2018
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Collaborative Research and Development Grants
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
  • 财政年份:
    2017
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
A GIS-based system for assessing emerald ash borer infestation
基于 GIS 的白蜡虫侵染评估系统
  • 批准号:
    490711-2015
  • 财政年份:
    2017
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Collaborative Research and Development Grants
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
  • 财政年份:
    2016
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual
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
  • 财政年份:
    2015
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Discovery Grants Program - Individual

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相似海外基金

Improved characterization of permafrost vulnerability to support decision makers, infrastructure, and community stewardship in the Northwest Territories
改进永久冻土脆弱性的描述,以支持西北地区的决策者、基础设施和社区管理
  • 批准号:
    521584-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Strategic Projects - Group
Characterization of microbial assemblages associated with large-scale thawing of permafrost
与大规模永久冻土融化相关的微生物群落特征
  • 批准号:
    19K20462
  • 财政年份:
    2019
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  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Improved characterization of permafrost vulnerability to support decision makers, infrastructure, and community stewardship in the Northwest Territories
改进永久冻土脆弱性的描述,以支持西北地区的决策者、基础设施和社区管理
  • 批准号:
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Improved characterization of permafrost vulnerability to support decision makers, infrastructure, and community stewardship in the Northwest Territories******
改进对永久冻土脆弱性的描述,以支持西北地区的决策者、基础设施和社区管理*****
  • 批准号:
    521584-2018
  • 财政年份:
    2018
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Strategic Projects - Group
Improving the characterization of permafrost using polarimetric SAR interferometry (pol-inSAR)
使用偏振 SAR 干涉测量 (pol-inSAR) 改善永久冻土的表征
  • 批准号:
    513708-2017
  • 财政年份:
    2018
  • 资助金额:
    $ 1.55万
  • 项目类别:
    Collaborative Research and Development Grants
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