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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Fusion of optical and SAR data for forestry applications in the sierra Nevada of California
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Fusion of optical and SAR data for forestry applications in the sierra Nevada of California

机译：林业应用中的光学和SAR数据融合在加利福尼亚山脉内华达州

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Ecologists and forest managers commonly emphasize the description of forest structural features because these elements often serve as indicators of organisms and surrogates for processes that may be difficult to observe or measure directly, such as wildlife habitat suitability and the dynamics of forest ecosystems. As used here, the term structure refers to the numbers, sizes, and shapes of the vegetative components in a forest ecosystem and their spatial distribution. Key attributes of forest structure include above-ground biomass, canopy cover, tree height, large tree density, and three-dimensional structural complexity. Remote sensing is a particularly attractive alternative to ground-based measurements because data can be acquired repeatedly and across broad geographic areas that might otherwise be inaccessible. Mapped estimates of forest structural attributes are therefore considered critical to ongoing monitoring efforts requiring reliable inventories of forest resources and accurate assessments of species status and trend. Two pilot study areas were established in the Sierra Nevada mountain range of California for the purposes of characterizing the three-dimensional structure of selected Sierran forest vegetation types. Field measurements are being used to calibrate and validate estimates of forest structural attributes derived using remote-sensing techniques. Study area locations were selected to represent the pronounced elevational (hi/low) and latitudinal (north/south) gradients that distinguish the Sierra Nevada range. One study area, representing the southern Sierra, was established on the Sierra National Forest and includes the 60,000-ha King a River Sustainable Forest Ecosystem Project and the 1300-ha Teakettle Creek Experimental Forest. The second study area, representing the northern Sierra, was located on the Plumas National Forest. Within each study area, a stratified-random sampling scheme was used. A 3% sample resulted in a total of 500 1-ha sample plots. Remotely-sensed data that have been acquired for this project include SIR-C, JERS-1, interferometric JERS-1, RADARSAT-1, SRTM, Landsat ETM+, and LVIS. AirSAR, Hyperion, TOPSAR, and GeoSAR data have also been requested. These data include SAR, Lidar, and Optical modalities, which represent the variety of current remote-sensing platforms. The data sets have been rectified to overlay a detailed map of the area, using 10-m DEM data provided by the USDA Forest Service. The capabilities of these fused datasets to provide estimates of forest structural attributes is assessed.
机译：生态学家和森林经理通常强调森林结构特征的描述，因为这些元素通常用作有机体和工艺的指标，这些过程可能难以直接观察或衡量，例如野生动物栖息地适用性和森林生态系统的动态。如此在这里，术语结构是指森林生态系统中植物部件及其空间分布的数量，尺寸和形状。森林结构的关键属性包括上面的地面生物质，冠层盖，树高，树密度和三维结构复杂性。遥感是基于地面测量的特别有吸引力的替代品，因为可以重复获取数据，并且跨越可能无法访问的广泛的地理区域。因此，对森林结构属性的映射估计被认为是对需要可靠的森林资源清单和对物种状况和趋势准确评估的持续监测努力至关重要。在塞拉尼达达山脉的加利福尼亚山脉建立了两个试点研究领域，以表征选定的Sierran森林植被类型的三维结构。现场测量用于校准和验证使用远程传感技术导出的森林结构属性的估计。学习区域被选中以表示明显的高级（HI / LOW）和纬度（北/南）梯度，以区分塞拉尼达达范围。代表南部塞拉的一项研究区成立于塞拉国家森林，包括60,000张国王河流可持续森林生态系统项目和1300公顷Teakettle Creek实验森林。代表北部塞拉斯的第二学习区位于普鲁马斯国家森林。在每个研究区域内，使用分层随机抽样方案。 3％样品总共产生了500个1-HA样品图。已经为该项目获得的远程感测数据包括SiR-C，JERS-1，干涉测量JERS-1，RADARSAT-1，SRTM，LANDSAT ETM +和LVIS。还要求Airsar，Hyperion，Topsar和Geosar数据。这些数据包括SAR，LIDAR和光学模式，其代表了当前遥感平台的各种。数据集已被纠正以覆盖该区域的详细地图，使用由USDA林服务提供的10 M DEM数据。评估这些融合数据集的能力，以提供森林结构属性的估计。

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