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Determining depth and ice thickness of shallow sub-Arctic lakes using space-borne optical and SAR data

机译:利用星载光学和SAR数据确定北极北极浅湖的深度和冰厚

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摘要

An approach to determine depth and ice thickness of shallow lakes and ponds using Landsat Thematic Mapper (TM) and European Remote Sensing (ERS)-l Synthetic Aperture Radar (SAR) data is presented. A summertime Landsat TM image is used to map lake bathymetry and multi-date ERS-1 images acquired during winter are utilized to determine when and which lakes freeze to the bottom during winter. The two remotely sensed derived products are then combined to estimate ice thickness from lakes and ponds on a monthly basis. The approach has been developed and tested successfully in a sub-Arctic tundra―forest landscape in the Hudson Bay Lowland near Churchill, Manitoba, Canada. Lake depth estimates derived from Landsat TM band 2 generally compared well with measurements obtained in the field, especially in the tundra zone [rms error (RMSE)= 15cm]. Maximum ice thickness estimates were also within the range of those typically measured during winter in this study area (tundra and forest―tundra zones ≈ 1.6m; open forest zone ≈ 1.2m). Results indicate that the approach is particularly well suited for estimating depth and ice thickness of shallow oligotrophic and ultra-oligotrophic lakes that are widespread in many regions above treeline. However, the results also suggest that the Landsat-based approach will require further testing and improvement if one wishes to map bathymetry for shallow lakes in which large nutrient concentrations or amounts of suspended sediments are found.
机译:提出了一种利用Landsat专题测绘仪(TM)和欧洲遥感(ERS)-1合成孔径雷达(SAR)数据确定浅水湖泊和池塘的深度和冰厚的方法。夏季的Landsat TM图像用于绘制湖泊测深图,冬季使用的多日期ERS-1图像用于确定冬季何时以及哪些湖泊冻结到底部。然后将这两种遥感衍生产品组合起来,以每月估算湖泊和池塘的冰层厚度。该方法已经在加拿大曼尼托巴省丘吉尔附近的哈德逊湾低地的亚寒带苔原森林景观中成功开发和测试。通常,从Landsat TM波段2得出的湖泊深度估算值与在野外获得的测量值(尤其是在苔原带[rms误差(RMSE)= 15厘米])相吻合。最大冰厚度估算值也在该研究区域冬季通常测得的范围内(冻原和森林-冻原带≈1.6m;开放森林带≈1.2m)。结果表明,该方法特别适合于估算浅层营养盐湖和超营养盐湖的深度和冰厚,而浅层营养盐湖和超营养盐湖分布在林线以上的许多区域。但是,结果还表明,如果人们希望绘制浅层湖泊的测深图,在浅层湖泊中发现大量营养物或大量悬浮沉积物,那么基于Landsat的方法将需要进一步测试和改进。

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