当前位置: X-MOL 学术Remote Sens. Environ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2018-05-01 , DOI: 10.1016/j.rse.2017.12.020
Giona Matasci , Txomin Hermosilla , Michael A. Wulder , Joanne C. White , Nicholas C. Coops , Geordie W. Hobart , Harold S.J. Zald

Abstract Passive optical remotely sensed images such as those from the Landsat satellites enable the development of spatially comprehensive, well-calibrated reflectance measures that support large-area mapping. In recent years, as an alternative to field plot data, the use of Light Detection and Ranging (lidar) acquisitions for calibration and validation purposes in combination with such satellite reflectance data to model a range of forest structural response variables has become well established. In this research, we use a predictive modeling approach to map forest structural attributes over the ~ 552 million ha boreal forest of Canada. For model calibration and independent validation we utilize airborne lidar-derived measurements of forest vertical structure (known as lidar plots) obtained in 2010 via a > 25,000 km transect-based national survey. Models were developed linking the lidar plot structural variables to wall-to-wall 30-m spatial resolution surface reflectance composites derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery. Spectral indices extracted from the composites, disturbance information (years since disturbance and type), as well as geographic position and topographic variables (i.e., elevation, slope, radiation, etc.) were considered as predictor variables. A nearest neighbor imputation approach based on the Random Forest framework was used to predict a total of 10 forest structural attributes. The model was developed and validated on > 80,000 lidar plots, with R2 values ranging from 0.49 to 0.61 for key response variables such as canopy cover, stand height, basal area, stem volume, and aboveground biomass. Additionally, a predictor variable importance analysis confirmed that spectral indices, elevation, and geographic coordinates were key sources of information, ultimately offering an improved understanding of the driving variables for large-area forest structure modeling. This study demonstrates the integration of airborne lidar and Landsat-derived reflectance products to generate detailed and spatially extensive maps of forest structure. The methods are portable to map other attributes of interest (based upon calibration data) through access to Landsat or other appropriate optical remotely-sensed data sources, thereby offering unique opportunities for science, monitoring, and reporting programs.

中文翻译:

使用 Landsat 复合材料和激光雷达图对加拿大北方森林覆盖率、高度、生物量和其他结构属性进行大面积制图

摘要 无源光学遥感图像(例如来自 Landsat 卫星的图像)能够开发支持大面积测绘的空间全面、校准良好的反射率测量。近年来,作为现场绘图数据的替代方案,将光探测和测距(激光雷达)采集用于校准和验证目的,并结合此类卫星反射率数据对一系列森林结构响应变量进行建模,已经非常成熟。在这项研究中,我们使用预测建模方法绘制了加拿大约 5.52 亿公顷北方森林的森林结构属性图。对于模型校准和独立验证,我们利用 2010 年通过 a > 25 获得的机载激光雷达衍生的森林垂直结构测量值(称为激光雷达图),000 公里基于样带的全国调查。开发的模型将激光雷达图结构变量与源自 Landsat Thematic Mapper 和 Enhanced Thematic Mapper Plus 图像的墙到墙 30 米空间分辨率表面反射复合材料联系起来。从复合材料中提取的光谱指数、扰动信息(自扰动以来的年份和类型)以及地理位置和地形变量(即海拔、坡度、辐射等)被视为预测变量。使用基于随机森林框架的最近邻插补方法来预测总共 10 个森林结构属性。该模型是在超过 80,000 个激光雷达图上开发和验证的,关键响应变量的 R2 值范围从 0.49 到 0.61,例如冠层盖度、林分高度、基部面积、茎体积、和地上生物量。此外,预测变量重要性分析证实,光谱指数、高程和地理坐标是关键的信息来源,最终为大面积森林结构建模提供了对驱动变量的更好理解。本研究展示了机载激光雷达和 Landsat 衍生反射率产品的集成,以生成详细且空间广泛的森林结构图。这些方法可移植到通过访问 Landsat 或其他适当的光学遥感数据源来绘制其他感兴趣的属性(基于校准数据),从而为科学、监测和报告程序提供独特的机会。地理坐标是关键的信息来源,最终为大面积森林结构建模提供了对驱动变量的更好理解。本研究展示了机载激光雷达和 Landsat 衍生反射产品的集成,以生成详细且空间广泛的森林结构图。这些方法可移植到通过访问 Landsat 或其他适当的光学遥感数据源来绘制其他感兴趣的属性(基于校准数据),从而为科学、监测和报告程序提供独特的机会。地理坐标是关键的信息来源,最终为大面积森林结构建模提供了对驱动变量的更好理解。本研究展示了机载激光雷达和 Landsat 衍生反射产品的集成,以生成详细且空间广泛的森林结构图。这些方法可移植到通过访问 Landsat 或其他适当的光学遥感数据源来绘制其他感兴趣的属性(基于校准数据),从而为科学、监测和报告程序提供独特的机会。本研究展示了机载激光雷达和 Landsat 衍生反射产品的集成,以生成详细且空间广泛的森林结构图。这些方法可移植到通过访问 Landsat 或其他适当的光学遥感数据源来绘制其他感兴趣的属性(基于校准数据),从而为科学、监测和报告程序提供独特的机会。本研究展示了机载激光雷达和 Landsat 衍生反射产品的集成,以生成详细且空间广泛的森林结构图。这些方法可移植到通过访问 Landsat 或其他适当的光学遥感数据源来绘制其他感兴趣的属性(基于校准数据),从而为科学、监测和报告程序提供独特的机会。
更新日期:2018-05-01
down
wechat
bug