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首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIII >Estimating Evapotranspiration of Riparian Vegetation using High resolution Multispectral, Thermal Infrared and Lidar Data
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Estimating Evapotranspiration of Riparian Vegetation using High resolution Multispectral, Thermal Infrared and Lidar Data

机译：利用高分辨率多光谱，热红外和激光雷达数据估算河岸植被的蒸散量

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High resolution airborne multispectral and thermal infrared imagery was acquired over the Mojave River, California with the Utah State University airborne remote sensing system integrated with the LASSI imaging Lidar also built and operated at USU. The data were acquired in pre-established mapping blocks over a 2 day period covering approximately 144 Km of the Mojave River floodplain and riparian zone, approximately 1500 meters in width. The multispectral imagery (green, red and near-infrared bands) was ortho-rectified using the Lidar point cloud data through a direct geo-referencing technique. Thermal Infrared imagery was rectified to the multispectral ortho-mosaics. The lidar point cloud data was classified to separate ground surface returns from vegetation returns as well as structures such as buildings, bridges etc. One-meter DEM's were produced from the surface returns along with vegetation canopy height also at 1-meter grids. Two surface energy balance models that use remote sensing inputs were applied to the high resolution imagery, namely the SEBAL and the Two Source Model. The model parameterizations were slightly modified to accept high resolution imagery (1-meter) as well as the lidar-based vegetation height product, which was used to estimate the aerodynamic roughness length. Both models produced very similar results in terms of latent heat fluxes (LE). Instantaneous LE values were extrapolated to daily evapotranspiration rates (ET) using the reference ET fraction, with data obtained from a local weather station. Seasonal rates were obtained by extrapolating the reference ET fraction according to the seasonal growth habits of the different species. Vegetation species distribution and area were obtained from classification of the multispectral imagery. Results indicate that cotton wood and salt cedar (tamarisk) had the highest evapotranspiration rates followed by mesophytes, arundo, mesquite and desert shrubs. This research showed that high-resolution airborne multispectral and thermal infrared imagery integrated with precise full-waveform lidar data can be used to estimate evapotranspiration and water use by riparian vegetation.
机译：在犹他州立大学的机载遥感系统与LASSI成像激光雷达的集成下，在加利福尼亚州的莫哈韦河上获得了高分辨率的机载多光谱和热红外图像，该系统也在USU建造和运行。数据是在预先建立的地图块中进行的，历时2天，覆盖了约144公里的Mojave河洪泛区和河岸带，宽约1500米。使用激光雷达点云数据通过直接地理参考技术对多光谱图像（绿色，红色和近红外波段）进行了正射校正。将热红外图像校正为多光谱正交马赛克。对激光雷达点云数据进行分类，以将地表回波与植被回波以及建筑物，桥梁等结构分开。从地表回波以及1米网格处的植被冠层高度生成一米DEM。将使用遥感输入的两个表面能平衡模型应用于高分辨率图像，即SEBAL和“两个源”模型。对模型参数进行了稍微修改，以接受高分辨率图像（1米）以及基于激光雷达的植被高度积，该积用于估计空气动力学粗糙度长度。两种模型在潜热通量（LE）方面产生的结果非常相似。利用参考ET分数，将瞬时LE值外推至每日蒸散率（ET），并从当地气象站获得数据。通过根据不同物种的季节性生长习惯外推参考ET分数来获得季节性速率。从多光谱影像的分类中获得了植被的种类分布和面积。结果表明，棉木和雪松（ta柳）的蒸散速率最高，其次是中生植物，芦荟，豆科灌木和沙漠灌木。这项研究表明，高分辨率的机载多光谱和热红外图像与精确的全波形激光雷达数据相结合，可用于估算河岸植被的蒸散量和用水量。

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