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High resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes

机译:高分辨率多光谱卫星图像,用于提取南极浅水湖泊的浴室信息

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High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Larsemann Hills and Schirmacher oasis, east Antarctica. We digitized the lake features in which all the lakes from both the study areas were manually extracted. In order to extract the bathymetry values from multispectral imagery we used two different models: (a) Stumpf model and (b) Lyzenga model. Multiband image combinations were used to improve the results of bathymetric information extraction. The derived depths were validated against the in-situ measurements and root mean square error (RMSE) was computed. We also quantified the error between in-situ and satellite-estimated lake depth values. Our results indicated a high correlation (R = 0.60~0.80) between estimated depth and in-situ depth measurements, with RMSE ranging from 0.10 to 1.30 m. This study suggests that the coastal blue band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands. To test the effect of size and dimension of lake on bathymetry retrieval, we distributed all the lakes on the basis of size and depth (reference data), as some of the lakes were open, some were semi frozen and others were completely frozen. Several tests were performed on open lakes on the basis of size and depth. Based on depth, very shallow lakes provided better correlation (≈0.89) compared to shallow (≈0.67) and deep lakes (≈0.48). Based on size, large lakes yielded better correlation in comparison to medium and small lakes.
机译:来自WorldView-2的高分辨率Pansharpened图像用于距离东南南极洲的Larsemann Hills和Schirmacher Oasis周围的浴约定映射。我们数字化了湖泊特征,其中手动提取了研究区的所有湖泊。为了从多光谱图像中提取浴约定值我们使用了两种不同的模型:(a)stumpf模型和(b)Lyzenga模型。多频带图像组合用于改善碱基信息提取的结果。衍生深度针对原位测量验证,并计算了均方根误差(RMSE)。我们还量化了原位和卫星估计的湖泊深度值之间的误差。我们的结果表明估计深度和原位深度测量之间的高相关(R = 0.60〜0.80),RMSE范围为0.10至1.30米。本研究表明,与其他频带相比,WV-2图像中的沿海蓝色频段可以检索准确的浴约集信息。为了测试湖泊的尺寸和尺寸对沐浴浴检索的影响,我们根据大小和深度(参考数据)分发了所有湖泊,因为一些湖泊是开放的,有些湖泊已经半冷冻,其他湖泊被完全冷冻。基于大小和深度的开放湖泊进行了几次测试。基于深度,非常浅的湖泊提供了更好的相关性(≈0.89)与浅(≈0.67)和深湖(≈0.48)相比。基于尺寸,与中小湖泊相比,大湖泊产生了更好的相关性。

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