首页> 外文会议>Fifth international airborne remote sensing conference and exhibition >INTEGRATION OF RADARSAT SAR AND AIRBORNE HYPERSPECTRAL IMAGERY: SODA BUTTE CREEK AND LAMAR RIVER VALLEYS, YELLOWSTONE NATIONAL PARK
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INTEGRATION OF RADARSAT SAR AND AIRBORNE HYPERSPECTRAL IMAGERY: SODA BUTTE CREEK AND LAMAR RIVER VALLEYS, YELLOWSTONE NATIONAL PARK

机译:RADARSAT SAR与机载高光谱影像的整合:黄石国家公园的SODA BUTTE CREEK和LAMAR RIVER VALLEYS

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Vegetation biomass in the Soda Butte Creek and Lamar River floodplains inrnnortheastern Yellowstone National Park was evaluated using Radarsat fine beam modernSAR imagery, integrated with land cover information derived from the airborne ESSIrnProbe-1 hyperspectral instrument. The SAR biomass algorithm is improved byrnincorporating the contribution to the backscatter of each vegetation class in the studyrnarea, based on the percent cover of each in 250 m square grid areas. The percent cover ofrnwoody vegetation (e.g. sagebrush, willow) and low backscatter vegetation types (e.g.rngrasses, lupine), along with bare soil areas are derived from classifications and thernapplication of vegetation indices to the high spatial (5 m) and spectral (128 bands, 0.4 torn2.5 ~m) resolution Probe-1 imagery. The 250 m square grids allow for Radarsat pixelrnaveraging, to reduce the effects of speckle and minor topographic undulations on thernbackscatter. In addition, two SAR images were acquired (17 August and 10 September,rn1999) with identical image pararneters to allow for further pixel averaging and to monitorrnsoil moisture effects. A field campaign during image acquisition was performed forrnground truth of the remotely sensed biomass and vegetation cover measurements. Thernmaps and processed imagery generated by this study are being incorporated into a GIS tornserve as a baseline for river and vegetation change studies, and to evaluate the effects ofrnfuture floods, vegetation disease, or fires on the riparian system.
机译:在东北黄石国家公园的苏打比尤特河和拉马尔河洪泛区,采用Radarsat细光束现代SAR图像,并结合从机载ESSIrnProbe-1高光谱仪获得的土地覆盖信息,对植被生物量进行了评估。 SAR生物量算法通过在250 m正方形网格区域中每种植被的覆盖百分比的基础上,结合了每种植被对反向散射的贡献来改进。木本植物(如鼠尾草,柳树)和低背向散射植被(如草,羽扇豆)以及裸露土壤的覆盖率是根据植被指数的分类及其对高空间(5 m)和光谱(128条带)的应用得出的,0.4 torn2.5〜m)分辨率的Probe-1图像。 250 m的正方形网格可对Radarsat像素进行平均,以减少斑点和较小的地形起伏对rnback散射的影响。另外,采集了两个SAR图像(1999年8月17日和1999年9月10日),它们具有相同的图像参数,以便进一步进行像素平均并监测土壤水分的影响。进行了图像采集期间的野外活动,以遥感生物量和植被覆盖度测量的真实性为前提。这项研究生成的地图和处理后的图像已被整合到GIS中,作为河流和植被变化研究的基准,并评估了洪水,植被疾病或火灾对河岸系统的影响。

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