植被雷达遥感方法与应用

“植被雷达遥感方法与应用”项目针对我国西南多云多雨地区植被资源调查中光学遥感数据获取困难这一瓶颈问题，针对贵州特有的喀斯特地形地貌特点，充分发挥合成孔径雷达（SAR）不受光照和天气条件限制可以全天时、全天候获取遥感数据的优势，开展了多项关键技术攻关，为全极化SAR数据在贵州省森林资源调查、森林灾害监测、水稻识别中的应用找到了有效途径，为我国多云多雨地区植被资源调查提供了全天候的监测方法与技术。1.在国内外首次提出了基于DEM的双视向雷达几何校正技术，并形成了软件系统，修正了由于SAR侧视成像造成的山区图像几何畸变和后向散射系数失真，有效解决了山区SAR图像的信息丢失和变形问题。2.在国内首次针对喀斯特地区的森林，提取了能最大程度区分森林与其它典型地物的极化散射参数，形成了基于极化雷达数据进行森林类型识别与分类的技术流程，建立了典型森林类型的极化雷达图像识别标志，为遥感技术在喀斯特地区森林资源调查中的推广和应用起到了显著的促进作用。3.在国内外首次提出了综合利用多时相极化SAR数据和光学数据融合进行森林类型识别的方法，解决了多云多雨地区森林资源调查中光学遥感数据获取困难的瓶颈问题，积极推动了南方各省和东北地区的森林资源调查工作的开展。4.揭示了水稻极化响应特征、散射机制以及它们随着水稻生长发育变化的规律，为全极化SAR数据水稻识别找到了有效方法与途径 提出了紧致极化SAR的水稻识别方法，对加拿大雷达卫星星座的发展具有重要意义。5.揭示了雪灾破坏森林与正常森林后向散射系数、极化响应特征和散射机理的差异，构建了基于雷达与光学遥感数据融合识别森林雪灾破坏的技术流程，积极推动了2008年贵州和江西两省森林雪灾破坏调查工作的开展。

The project titled "Vegetation Radar Remote Sensing Methods and Applications" addresses the bottleneck problem of difficult optical remote sensing data acquisition in vegetation resource surveys in cloud-prone and rainy regions of southwest China. Taking into account the unique karst topography and landforms of Guizhou Province, it leverages the advantages of Synthetic Aperture Radar (SAR) — which can acquire remote sensing data all-day and all-weather without being restricted by lighting and weather conditions — to conduct research on multiple key technologies. It has identified effective approaches for applying fully polarimetric SAR data to forest resource surveys, forest disaster monitoring, and rice identification in Guizhou Province, providing all-weather monitoring methods and technologies for vegetation resource surveys in cloud-prone and rainy regions of China.
1. For the first time globally, it proposed a DEM-based dual-view radar geometric correction technology and developed a corresponding software system. This technology corrects the geometric distortion of mountainous images and the distortion of backscattering coefficients caused by SAR side-looking imaging, effectively solving the problems of information loss and deformation in mountainous SAR images.
2. For the first time in China, it extracted polarization scattering parameters that can maximally distinguish forests from other typical ground features for forests in karst areas, established a technical workflow for forest type recognition and classification based on polarimetric radar data, and built recognition markers of polarimetric radar images for typical forest types. This has significantly promoted the popularization and application of remote sensing technology in forest resource surveys in karst areas.
3. For the first time globally, it proposed a method for forest type recognition by comprehensively fusing multi-temporal polarimetric SAR data and optical data, solving the bottleneck problem of difficult optical remote sensing data acquisition in forest resource surveys in cloud-prone and rainy regions, and actively advancing the development of forest resource survey work in southern provinces and Northeast China.
4. It revealed the polarization response characteristics, scattering mechanisms, and their variation rules with the growth and development of rice, identifying effective methods and approaches for rice identification using fully polarimetric SAR data. It proposed a rice identification method based on compact polarimetric SAR, which is of great significance for the development of the Canadian Radar Satellite Constellation.
5. It revealed the differences in backscattering coefficients, polarization response characteristics, and scattering mechanisms between forests damaged by snow disasters and normal forests, constructed a technical workflow for identifying forest snow disaster damage based on the fusion of radar and optical remote sensing data, and actively promoted the forest snow disaster damage survey work in Guizhou and Jiangxi provinces in 2008.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于植被雷达遥感方法与应用，旨在解决我国西南多云多雨地区因光学遥感受限而难以进行植被资源调查的问题。通过利用合成孔径雷达（SAR）的全天候观测优势，项目开发了多项关键技术，包括山区图像校正、森林类型识别、水稻监测和雪灾评估，为森林资源调查和灾害监测提供了有效解决方案。