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首页> 外文学位 >Using LiDAR-Derived Information on the Vegetation Canopy Structure to Scale ET Estimates Beyond the Tower Footprint in the Western Boreal Plains, Alberta, Canada.
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Using LiDAR-Derived Information on the Vegetation Canopy Structure to Scale ET Estimates Beyond the Tower Footprint in the Western Boreal Plains, Alberta, Canada.

机译：在加拿大艾伯塔省西部北方平原，利用植被覆盖结构的LiDAR得出的信息来扩展ET估计值，使其超出塔架足迹。

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摘要

In Canada a large portion of the boreal forest is comprised of the Western Boreal Plains (WBP)—a region characterized by a mosaic of vegetation cover and persistent water deficit conditions experienced on a 10-15 year cycle. Owing to this, the WBP is a hydrologically sensitive region and future changes to the climate will significantly impact the region's water balance. The WBP is also an economic hub for the country's natural resource extraction, creating significant disturbances on a hydrologically sensitive landscape. Sparse environmental measurement stations also make it challenging to gain information on water balances within different land cover types used to drive and validate models outside of the tower-footprint scale. Remote sensing can provide spatial estimates of land cover type that can be used to better understand vegetation characteristics which influence the water balance of the WBP—though further investigation is needed to determine the most appropriate resolution at which remote sensing data is best collected. Additionally, large disparities in scale between point or tower validation data and coarse satellite-derived evapotranspiration (ET) estimates can introduce error into validation processes.;This study uses high resolution (1m x 1m) Light Detection and Ranging (LiDAR)derived vegetation parameters and measured hydro-meteorological variables to estimate spatially variable ET at a 5km x 5km study site in the WBP. High resolution ET estimates are scaled to lower resolutions to provide insight into the accuracy of ET estimated from low resolution satellite systems, as well as to bridge the gap between fine-scale validation data and coarse ET estimates characteristic of hydrologic components of GCMs. Environmental drivers and estimates of ET were scaled up (5km x 5km) from the point scale using spatially variable and spatially averaged methods. ET estimates scaled using spatially variable vegetation parameters overestimated measured values by 1.5mm, or 3% (r2 =0.51, RMSE = 0.14mm d-1). ET estimated scaled using averaged vegetation parameters overestimated measured values by 3.3mm, or 6% (r2 = 0.49, RMSE = 0.37mm d-1 ). Subsequent scaling of 1m x 1m spatially variable ET estimates to lower resolutions characteristic of global satellite systems—i.e. SPOT (10m), Landsat (30m), and MODIS (250m, 500m)—yielded increasingly inaccurate results ranging from overestimations of 3.2mm (7%) at 4m resolution, 3.4mm (8%) at 10m, 3mm (6%) at 25m, to underestimations of 3.5mm (7%) at 250m, and 4.5mm (9%) at 500m when compared to measured values.
机译：在加拿大，大部分的北方森林都由西部北方平原（WBP）组成，该地区的特征是植被覆盖的马赛克和持续10到15年周期的持续缺水状况。因此，WBP是一个水文敏感地区，未来气候变化将极大地影响该地区的水平衡。 WBP还是该国自然资源开采的经济枢纽，在水文敏感地区造成了严重干扰。稀疏的环境测量站也使获取不同土地覆盖类型（用于驱动和验证塔式足迹比例之外的模型）中水平衡的信息变得充满挑战。遥感可以提供土地覆盖类型的空间估计，这些估计可以用来更好地理解影响WBP水分平衡的植被特征，尽管还需要进一步研究以确定最适合收集遥感数据的分辨率。此外，点或塔的验证数据与粗卫星衍生的蒸散量（ET）估计值之间的规模差异较大，可能会在验证过程中引入误差。;本研究使用高分辨率（1m x 1m）的光探测和测距（LiDAR）得出的植被参数并测量水文气象变量以估算WBP中5km x 5km研究地点的空间变量ET。将高分辨率的ET估算值缩放到较低的分辨率，以洞悉从低分辨率的卫星系统估算的ET的准确性，并弥合精细尺度验证数据与GCM水文成分特征的粗略ET估算之间的差距。使用空间可变和空间平均的方法，从点尺度上将环境驱动因素和ET的估算进行了放大（5km x 5km）。使用空间可变植被参数将ET估计值高估测量值1.5mm或3％（r2 = 0.51，RMSE = 0.14mm d-1）。使用平均植被参数将ET估计值高估3.3mm或6％（r2 = 0.49，RMSE = 0.37mm d-1）。随后将1m x 1m空间变量ET估计值缩放到较低的全球卫星系统特征，即SPOT（10m），Landsat（30m）和MODIS（250m，500m）所产生的结果越来越不准确，从高估4m分辨率时为3.2mm（7％），10m时高估为3.4mm（8％），3mm（6％）与测量值相比，在25m处的估计值低了3.5mm（7％），在500m处的估计值低估了4.5mm（9％）。

著录项

  • 作者

    Sutherland, George.;

  • 作者单位

    Wilfrid Laurier University (Canada).;

  • 授予单位 Wilfrid Laurier University (Canada).;
  • 学科 Hydrology.;Remote Sensing.;Atmospheric Sciences.
  • 学位 M.Sc.
  • 年度 2013
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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