Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery
Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery
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DOI：
10.1016/j.rse.2013.08.029
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发表时间：
2014-01-01
影响因子：
13.5
通讯作者：
Proud, Simon R.
中科院分区：
文献类型：
--
作者：
Feyisa, Gudina L.;Meilby, Henrik;Proud, Simon R.
地表覆盖类型分类和变化分析是遥感最常见的应用。最基本的分类任务之一是区分水体和干燥的陆地表面。陆地卫星图像是水资源遥感中使用最广泛的数据来源之一；虽然文献中描述了几种利用陆地卫星数据提取地表水的技术，但在各种情况下，它们的应用受到精度低的限制。此外，在使用单波段阈值和双波段指数等技术时，由于阈值随图像采集的位置和时间而变化，确定产生最高可能精度的适当阈值是一项具有挑战性和耗时的任务。因此，本研究的目的是设计一个指数，在存在各种环境噪声的情况下，持续提高水提取的准确性，同时提供一个稳定的阈值。因此，我们引入了一种新的自动水提取指数（Automated Water Extraction Index， AWEI），提高了包括阴影和暗表面在内的区域的分类精度，而其他分类方法通常无法正确分类。我们使用丹麦、瑞士、埃塞俄比亚、南非和新西兰几个水体的Landsat 5 TM图像测试了新方法的准确性和鲁棒性。计算Kappa系数、遗漏和佣金误差来评估准确性。将该分类器的性能与改进的归一化差分水指数（MNDWI）和最大似然（ML）分类器进行了比较。5个试验点中有4个awi的分类准确率显著高于MNDWI和ML （p值< 0.01）。与MNDWI相比，awi通过减少50%的错误和遗漏来提高准确率，与ML分类器相比，awi减少了25%的错误。此外，新方法具有相当稳定的最优阈值。因此，awi可以用于高精度提取水体，特别是在山区，地形造成的深阴影是分类误差的重要来源。(C) 2013 Elsevier Inc .版权所有
Classifying surface cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic classification tasks is to distinguish water bodies from dry land surfaces. Landsat imagery is among the most widely used sources of data in remote sensing of water resources; and although several techniques of surface water extraction using Landsat data are described in the literature, their application is constrained by low accuracy in various situations. Besides, with the use of techniques such as single band thresholding and two-band indices, identifying an appropriate threshold yielding the highest possible accuracy is a challenging and time consuming task, as threshold values vary with location and time of image acquisition. The purpose of this study was therefore to devise an index that consistently improves water extraction accuracy in the presence of various sorts of environmental noise and at the same time offers a stable threshold value. Thus we introduced a new Automated Water Extraction Index (AWEI) improving classification accuracy in areas that include shadow and dark surfaces that other classification methods often fail to classify correctly. We tested the accuracy and robustness of the new method using Landsat 5 TM images of several water bodies in Denmark, Switzerland, Ethiopia, South Africa and New Zealand. Kappa coefficient, omission and commission errors were calculated to evaluate accuracies. The performance of the classifier was compared with that of the Modified Normalized Difference Water Index (MNDWI) and Maximum Likelihood (ML) classifiers. In four out of five test sites, classification accuracy of AWEI was significantly higher than that of MNDWI and ML (P-value < 0.01). AWEI improved accuracy by lessening commission and omission errors by 50% compared to those resulting from MNDWI and about 25% compared to ML classifiers. Besides, the new method was shown to have a fairly stable optimal threshold value. Therefore, AWEI can be used for extracting water with high accuracy, especially in mountainous areas where deep shadow caused by the terrain is an important source of classification error. (C) 2013 Elsevier Inc All rights reserved.