Use of Synthetic Aperture Radar (SAR) images for fuel moisture modelling and land cover mapping in the case of boreal forests and natural grasslands

使用合成孔径雷达 (SAR) 图像进行北方森林和天然草原的燃料湿度建模和土地覆盖绘图

基本信息

批准号：
170378-2012
负责人：
Leblon, Brigitte
金额：
$ 1.97万
依托单位：
University of New Brunswick
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=633954
关键词：
Use Synthetic Aperture Radar SAR

项目摘要

Boreal forests and natural grasslands are two important ecosystems in the world, but they are also prone to fires. The Fire Weather Index (FWI) system of the Canadian Fire Danger rating system is used in many places in the world to rate fire dangers based primarily on weather data, which are point-source measurements. A promising alternative is using satellite data. The long term goal of my research program is to map Fire Weather Index codes for boreal forests and natural grasslands using remote sensing. In the short term, using images and data available through on-going collaborations on Canadian/South African natural grasslands and Alaska boreal forests, we will (i) compare two classifiers that simultaneously use polarimetric synthetic aperture radar (polSAR), optical and other geospatial data for land cover mapping; (ii) develop an empirical model to estimate the FWI drought code (DC) for natural grasslands; and (iii) test a physics-based DC model using polSAR images. The study will use both RADARSAT-2 C-band and ALOS-PALSAR L-band polSAR images. Our research team has built unique expertise on the use of SAR images for mapping FWI codes and indices. The proposed research continues this innovative trend in many ways. From the classifier point of view, it is the first time that the proposed genetic algorithm-neural network classifier will be use to classify boreal forest and natural grassland ecosystems. From the DC modeling point of view, all the soil moisture models using polSAR data were mainly developed for agricultural fields. It is the first time that DC of natural grasslands will be modeled using polSAR data and that a physics-based model will be proposed to estimate DC from polSAR images acquired over both boreal forest and natural grasslands. In Canada, the proposed research addresses fire research priorities of the Canadian Council of Forest Ministers. Across the world, better prediction of FWI codes and indices using remote sensing will have significant benefits both from the economical and the human safety points of views. The proposed research has a potential for significant economic advancement for Canada, as it will produce new market opportunities for MDA Inc., the R-2 owner.

北方森林和天然草原是世界上两个重要的生态系统，但它们也容易发生大火。加拿大火灾危险评级系统的火灾天气指数（FWI）系统在世界许多地方都使用，以评估主要基于天气数据的火灾危险，这是点源测量。一个有希望的替代方法是使用卫星数据。我的研究计划的长期目标是使用遥感绘制北方森林和天然草原的火灾天气指数代码。在短期内，使用对加拿大/南非天然草原和阿拉斯加北非北非森林的持续合作使用图像和数据，我们将（i）比较两个同时使用偏光层合成孔径雷达（POLSAR）的分类器（POLSAR），光学和其他地理空间土地覆盖地图的数据；（ii）开发一种经验模型，以估算天然草原的FWI干旱法规（DC）；（iii）使用POLSAR图像测试基于物理的DC模型。该研究将同时使用Radarsat-2 C波段和Alos-Palsar L波段图像。我们的研究团队在映射FWI代码和索引方面建立了独特的专业知识。拟议的研究在许多方面都延续了这种创新的趋势。从分类器的角度来看，这是提出的遗传算法 - 神经网络分类器首次用于对北方森林和天然草原生态系统进行分类。从直流建模的角度来看，所有使用POLSAR数据的土壤水分模型主要用于农业领域。这是第一次使用Polsar数据对天然草原的DC进行建模，并将提出基于物理的模型来估算从北方森林和天然草地上获得的Polsar图像的DC。在加拿大，拟议的研究涉及加拿大森林部长理事会的消防研究重点。在世界范围内，使用遥感的FWI代码和索引更好地预测将从经济和人类安全的观点的观点带来重大好处。拟议的研究有可能对加拿大进行重大的经济发展，因为它将为R-2所有者MDA Inc.带来新的市场机会。