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Landsat-derived bathymetry of lakes on the Arctic Coastal Plain of northern Alaska

机译:北极沿海平原北极沿海平原湖泊衍生的浴室

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The Pleistocene sand sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20?% of the Pleistocene sand sea, are relatively deep (up to 25?m). In addition to the natural importance of ACP sand sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP sand sea lakes have received little prior study. Here, we collect in situ bathymetric data to test 12 model variants for predicting sand sea lake depth based on analysis of Landsat-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in midsummer 2017 using a Humminbird 798ci HD SI Combo automatic sonar system. The field-measured data points were compared to red–green–blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and map bathymetry. Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8?% of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056 × 10 - 3 to 57.416 × 10 - 3 ?km 3 . Due to variations in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.
机译:阿拉斯加北部北极沿海平原(ACP)的全冬烯砂海是由古老的沙丘领域下划线,这是一种影响区域湖泊特色的地质特征。许多这些湖泊,覆盖了大约20?%的全粒藻砂海,相对深(最多25μm)。除了ACP砂海湖的自然重要性外,能量平衡和生态栖息地,冬季水的工业发展需求和勘探活动还需要在该地区成为宝贵的资源。然而,ACP沙海湖曾经接受过小先生。在这里,我们以原位碱基数据收集以测试12模型变体,用于基于Landsat-8运营陆地成像器(OLI)图像的分析来预测沙海湖深度。使用Humminbird 798Ci HD SI Combo自动声纳系统在仲夏2017年湖泊深度梯度测量湖泊深度梯度。将现场测量的数据点与2016年8月8日获取的Landsat-8 Oli图像的红绿蓝(RGB)频带进行比较,以选择和校准每个研究湖泊和地图浴的最精确的光谱深度模型。使用简单频带比的指数函数(带基于湖泊浊度和床基板选择的带)产生了最成功的模型变体。对于每个湖泊,最准确的模型将平均解释了81.8?%的变化,平均。模型湖沐浴浴与远程感测的湖面区域集成,以量化湖泊储水量,范围为1.056×10-3至57.416×10 - 3?Km 3。由于深度最大值,衬底和湖泊之间的浊度变化,区域模型目前是不可行的,渲染必要的额外地获取额外的原位数据,用于开发区域模型解决方案。使用遥感估算利用湖水卷将有助于更好地管理扩大发展活动,并作为基准,以评估未来对北极地区的持续和快速气候变化的反应。所有声纳深度数据和建模湖沐浴栅可以在https://doi.org/10.18739/2Sn01440(辛普森和arp,2018)和https://do.org/10.18739/2ht2gc6g(Simpson,2019),分别。

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