Acharya Tri Dev, Lee Dong Ha, Yang In Tae, Lee Jae Kang
Department of Civil Engineering, Kangwon National University, Chuncheon 200-701, Korea.
LX Korea Cadastral Surveying Corporation, 141 Uisadang-daero Yeodeungpo-gu, Seoul 150-911, Korea.
Sensors (Basel). 2016 Jul 12;16(7):1075. doi: 10.3390/s16071075.
Water bodies are essential to humans and other forms of life. Identification of water bodies can be useful in various ways, including estimation of water availability, demarcation of flooded regions, change detection, and so on. In past decades, Landsat satellite sensors have been used for land use classification and water body identification. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed by Landsat 8 has improved, enabling better characterization of land cover and increased data size. Therefore, it is necessary to explore the most appropriate and practical water identification methods that take advantage of the improved image quality and use the fewest inputs based on the original OLI bands. The objective of the study is to explore the potential of a J48 decision tree (JDT) in identifying water bodies using reflectance bands from Landsat 8 OLI imagery. J48 is an open-source decision tree. The test site for the study is in the Northern Han River Basin, which is located in Gangwon province, Korea. Training data with individual bands were used to develop the JDT model and later applied to the whole study area. The performance of the model was statistically analysed using the kappa statistic and area under the curve (AUC). The results were compared with five other known water identification methods using a confusion matrix and related statistics. Almost all the methods showed high accuracy, and the JDT was successfully applied to the OLI image using only four bands, where the new additional deep blue band of OLI was found to have the third highest information gain. Thus, the JDT can be a good method for water body identification based on images with improved resolution and increased size.
水体对人类和其他生命形式至关重要。水体识别在多种方面都很有用，包括水资源可用性估计、洪水区域划分、变化检测等。在过去几十年中，陆地卫星传感器一直用于土地利用分类和水体识别。由于陆地卫星8号引入了具有高光谱分辨率和改进信噪比的新型业务陆地成像仪（OLI）传感器，陆地卫星8号所感测图像的质量得到了提升，能够更好地描述土地覆盖情况并增加了数据量。因此，有必要探索利用改进后的图像质量且基于原始OLI波段使用最少输入的最合适、最实用的水体识别方法。本研究的目的是探索J48决策树（JDT）利用陆地卫星8号OLI图像的反射波段识别水体的潜力。J48是一种开源决策树。该研究的测试地点位于韩国江原道的北汉江流域。使用单个波段的训练数据来开发JDT模型，随后将其应用于整个研究区域。使用kappa统计量和曲线下面积（AUC）对模型的性能进行了统计分析。使用混淆矩阵和相关统计数据将结果与其他五种已知的水体识别方法进行了比较。几乎所有方法都显示出高准确率，并且JDT仅使用四个波段就成功应用于OLI图像，其中发现OLI新增的深蓝色波段具有第三高的信息增益。因此，JDT可以成为基于分辨率提高和数据量增加的图像进行水体识别的一种好方法。
