Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network
Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network
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DOI：
10.1016/j.rse.2014.09.026
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发表时间：
2015-01-01
影响因子：
13.5
通讯作者：
Wisniewski, Wit T.
中科院分区：
文献类型：
--
作者：
Czyzowska-Wisniewski, Elzbieta H.;van Leeuwen, Willem J. D.;Wisniewski, Wit T.
毫无疑问，需要提高复杂地形地区积雪覆盖率（FSC）估计的准确性，特别是在依赖冬季积雪为其供水的大部分地区，如美国西部。本研究的主要目的是开发FSC估计在复杂的高山森林环境中使用的人工神经网络（ANN）方法之间的融合框架多传感器遥感数据在中等时间/空间分辨率（例如16天重访时间; 30米;陆地卫星），和高空间分辨率（例如1米; IKONOS）。这项研究是第一次已知的尝试，开发一个多尺度估计的FSC来自IKONOS多光谱数据的表面等效参考数据。这也是第一次奋进通过结合地形和雪/非雪反射率数据来估计FSC值。开发的人工神经网络Landsat-FSC模型的可塑性，适应高山森林的异质性，并呈现无偏，全面，精确的FSC估计。基于Landsat的ANN FSC的精度具有以下特点：（1）非常低的误差值（平均误差类似于0.0002; RMSE类似于0.10; MAE与0.08FSC相似），（2）与从1 m分辨率IKONOS图像导出的地面等效参考数据集高度相关（r（2）与0.9相似），（3）不受地形/植被高山异质性影响的稳健FSC估计。后者得到误差空间均匀分布的支持，并且地形（坡度、坡向、地形阴影分布）、归一化差异植被指数和误差（r（2）= 0）之间缺乏相关性。(C)2014爱思唯尔公司All rights reserved.
There is an undisputed need to increase accuracy of Fractional Snow Cover (FSC) estimation in regions of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their water supply, such as the western United States. The main aim of this research is to develop FSC estimation in complex alpine-forested environments using an Artificial Neural Network (ANN) methodology as a fusion framework between multi-sensor remotely sensed data at medium temporal/spatial resolution (e.g.16-day revisit time; 30 m; Landsat), and high spatial resolutions (e.g.1 m; IKONOS). This research is the first known attempt to develop a multi-scale estimator of FSC from surface equivalent reference data derived from IKONOS multispectral data. It is also the first endeavor to estimate FSC values by combining terrain and snow/non-snow reflectance data. The plasticity of the developed ANN Landsat-FSC model accommodates alpine-forest heterogeneity, and renders unbiased, comprehensive, and precise FSC estimates. The accuracy of the ANN Landsat based FSC is characterized by: (1) very low error values (mean error similar to 0.0002; RMSE similar to 0.10; MAE similar to 0.08 FSC), (2) high correlation with the ground equivalent reference datasets derived from I m resolution IKONOS images (r(2) similar to 0.9), and (3) robust FSC estimation that is independent of terrain/vegetation alpine heterogeneity. The latter is supported by a spatially uniform distribution of errors, and lack of correlation between terrain (slope, aspect, terrain shadow distribution), Normalized Difference Vegetation Index, and the error (r(2) = 0). (C) 2014 Elsevier Inc. All rights reserved.