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首页> 外文会议>Remote Sensing for Agriculture, Ecosystems, and Hydrology >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：使用高分辨率多光谱，热红外和激光雷达数据估算河岸植被的蒸发

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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 cottonwood 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：加利福尼亚州Mojave River的高分辨率空气传播和热红外图像获得了犹他州州立大学空气传播的遥感系统，与Lassi Maging Lidar集成在USU中。在预先建立的映射块中获得了数据，超过了2天的时间，覆盖了Mojave河洪泛区和河岸区域约1500米的大约144公里。多光谱图像（绿色，红色和近红外条带）通过直接地理参考技术使用LIDAR点云数据进行正常整理。热红外图像被整理到多光谱矫形器马赛克。 LIDAR点云数据被分类为分离植被返回的地面返回以及建筑物，桥梁等结构。从表面回报和植被冠层高度也在1米网格中产生单米DEM。使用遥感输入的两个表面能量平衡模型应用于高分辨率图像，即Sebal和两个源模型。略微修改模型参数，以接受高分辨率图像（1米）以及基于LIDAR的植被高度产品，用于估计空气动力学粗糙度长度。两种模型在潜热通量（LE）方面产生了非常相似的结果。使用参考ET分数将瞬时le值推断为每日蒸发率（et），从当地气象站获得的数据。通过根据不同物种的季节性生长习性来推断参考ET分数来获得季节性速率。从多光谱图像的分类获得植被物种分布和面积。结果表明，三角杨和盐雪松（Tamarisk）具有最高的蒸发率，然后是患蛋白酶，arundo，豆科和沙漠灌木。本研究表明，高分辨率空气传播多光谱和热红外图像与精确的全波形LIDAR数据集成，可用于估算河岸植被的蒸发蒸腾和水。

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