A GIS-based system for assessing emerald ash borer infestation
基于 GIS 的白蜡虫侵染评估系统
基本信息
- 批准号:490711-2015
- 负责人:
- 金额:$ 1.33万
- 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:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The goal of this project is to develop a Geographic Information System (GIS)-based system to characterize and model the spread of the invasive Emerald Ash Borer (EAB, Agrilus planipennis Fairmaire) insect infestation currently rife in numerous parts of the province of Ontario. We plan to develop innovative methods to derive up-to-date information on ash tree locations and the extent (presence/absence) of EAB infestation. An object-oriented and knowledge-based hierarchical framework will be designed and implemented to identify individual ash trees from multi-source remotely sensed data using ArcGIS software, the most widely used GIS platform by Canadian municipalities. The spatial patterns of the EAB spread at both regional and local scales will be characterized and the key factors controlling the spread of the EAB beetles will be identified and explicitly included in the EAB spread model. An EAB risk map will be generated for the study sites and management options will be suggested. ****The outcome of this project will result in a better understanding of the risk associated with the spread of this destructive insect, and allow better informed decisions to be made regarding its detection, monitoring, and control, which will help to inhibit its spread and allow local municipalities to preserve biomass in their urban tree inventory. The methodology and framework that will be developed in this project will advance remote sensing science in individual tree crown delineation and species identification and geospatial modelling, and will be transferred to our industrial partner, Esri Canada, for potential commercialization among its municipal clients. 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 education programs.
该项目的目标是开发一个基于地理信息系统(GIS)的系统,以描述和模拟入侵的翡翠灰螟(EAB,Agrilus planipennis Fairmaire)昆虫侵扰的蔓延,目前在安大略省的许多地方盛行。 我们计划开发创新的方法,以获得最新的信息灰树的位置和程度(存在/不存在)的EAB侵扰。将设计和实施一个面向对象和以知识为基础的分层框架,利用加拿大各城市使用最广泛的地理信息系统平台-ArcGIS软件,从多源遥感数据中识别单个白蜡树。EAB蔓延在区域和地方尺度的空间格局的特点和控制EAB甲虫的蔓延的关键因素将被确定,并明确包括在EAB蔓延模型。将为研究中心生成EAB风险图,并建议管理选项。* 该项目的结果将使人们更好地了解与这种破坏性昆虫传播相关的风险,并就其检测、监测和控制做出更明智的决策,这将有助于抑制其传播并允许当地城市保护其城市树木库存中的生物量。 该项目将开发的方法和框架将推进单个树冠划定和物种识别以及地理空间建模方面的遥感科学,并将转让给我们的工业合作伙伴加拿大ESRI,以便在其市政客户中进行潜在的商业化。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员提供培训机会,以提高他们在完成教育方案后的就业前景。
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
- DOI:
10.1016/j.isprsjprs.2012.04.003 - 发表时间:
2012-06-01 - 期刊:
- 影响因子:12.7
- 作者:
Jing, Linhai;Hu, Baoxin;Li, Jili - 通讯作者:
Li, Jili
Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data
- DOI:
10.1016/j.jag.2013.06.003 - 发表时间:
2014-02-01 - 期刊:
- 影响因子:7.5
- 作者:
Hu, Baoxin;Li, Jili;Judah, Aaron - 通讯作者:
Judah, Aaron
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
Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data
- DOI:
10.1080/01431160802558659 - 发表时间:
2009-01-01 - 期刊:
- 影响因子:3.4
- 作者:
Fan, Wenyi;Hu, Baoxin;Li, Mingze - 通讯作者:
Li, Mingze
Automated Delineation of Individual Tree Crowns from Lidar Data by Multi-Scale Analysis and Segmentation
- DOI:
10.14358/pers.78.11.1275 - 发表时间:
2012-12-01 - 期刊:
- 影响因子:1.3
- 作者:
Jing, Linhai;Hu, Baoxin;Noland, Thomas - 通讯作者:
Noland, Thomas
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:
$ 1.33万 - 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:
$ 1.33万 - 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:
$ 1.33万 - 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:2019
- 资助金额:
$ 1.33万 - 项目类别:
Collaborative Research and Development Grants
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:
$ 1.33万 - 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:
$ 1.33万 - 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:2017
- 资助金额:
$ 1.33万 - 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:
$ 1.33万 - 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:
$ 1.33万 - 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:
$ 1.33万 - 项目类别:
Discovery Grants Program - Individual
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