Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods
Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods
复制标题
青藏高原湖泊面积测绘：数据和方法评估
DOI：
10.1080/01431161.2016.1271478
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发表时间：
2017
影响因子：
3.4
通讯作者：
Zheng Guoxiong
中科院分区：
文献类型：
--
作者：
Zhang Guoqing;Li Junli;Zheng Guoxiong
摘要湖泊面积是遥感数据的主要数据来源，因为湖泊数量和面积的变化是气候变化的敏感指标。当气候变化与直接人类活动的信号无关时，这些指标特别有用。用于湖泊面积绘图的数据很重要，可避免给湖泊面积估计的长期趋势带来不必要的不确定性。根据卫星数据确定水体的方法与地表水分化的质量和效率密切相关。然而，很少有研究全面考虑的影响因素选择的数据和方法在青藏高原湖泊面积制图（TP），也没有评估其后果。这项研究测试的主要数据集（陆地卫星和中分辨率成像光谱仪（MODIS）数据）和方法的自动水体测绘14个大型湖泊（>500平方公里）分布在不同的气候带的TP。湖泊面积的季节性变化和大地卫星图像数据的可用性进行了评价。10月份获得的数据是最佳的，因为在这个月湖泊面积相对稳定。如果10月份数据不足，数据窗口可以延长至9月和11月。将数据转换为三年的数据箱，可以减少每年季节性变化的影响，并提供适合于时间序列分析的长期趋势。陆地卫星数据（多光谱扫描仪、专题成像仪、增强型专题成像仪+和实用陆地成像仪）和中分辨率成像分光仪数据（MOD 09 A1）显示出良好的湖区制图性能。大津法被用来确定区分水和非水特征的最佳阈值。几个水提取指数，即NDWIMcFeeters，NDWIXu，AWEInon阴影，产生了较高的整体分类精度（92%），kappa系数（0.83），和用户的精度（~90%）的湖泊水分类使用Landsat数据。使用NDWIMcFeeters和NDWIXu的MODIS数据显示一致的湖泊面积（r2 = 0.99）与Landsat数据在相应的日期与均方根误差（RMSE）值为86.87和103.33 km 2和平均绝对误差（MAE）值分别为25.7和29.04 km 2。MODIS数据适用于大型湖泊制图，这是在TP的大型湖泊的情况。尽管自动水提取指数在区分水和非水方面表现出很高的准确性，但仍需要目视检查和手动编辑。这些遥感影像与中国高分辨率卫星遥感影像相结合，将为研究青藏高原湖泊动态和湖泊长期演化提供丰富的数据。
ABSTRACT Lake area derived from remote-sensing data is a primary data source, because changes in lake number and area are sensitive indicators of climate change. These indicators are especially useful when the climate change is not convoluted with a signal from direct anthropogenic activities. The data used for lake-area mapping is important, to avoid introducing unnecessary uncertainty into long-term trends of lake-area estimates. The methods for identifying waterbodies from satellite data are closely linked to the quality and efficiency of surface-water differentiation. However, few studies have comprehensively considered the factors affecting the selection of data and methods for mapping lake area in the Tibetan Plateau (TP), nor of evaluating their consequences. This study tests the dominant data sets (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data) and the methods for automated waterbody mapping on 14 large lakes (>500 km2) distributed across different climate zones of the TP. Seasonal changes in lake area and data availability from Landsat imagery are evaluated. Data obtained in October is optimal because in this month the lake area is relatively stable. The data window can be extended to September and November if insufficient data is available in October. Grouping data into three-year bins decreases the effects of year-to-year seasonal variability and provides a long-term trend that is suitable for time series analysis. The Landsat data (Multispectral Scanner, MSS; Thematic Mapper, TM; Enhanced Thematic Mapper Plus, ETM+; and Operational Land Imager, OLI) and MODIS data (MOD09A1) showed good performance for lake-area mapping. The Otsu method is used to determine the optimal threshold for distinguishing water from non-water features. Several water extraction indices, namely NDWIMcFeeters, NDWIXu, and AWEInon-shadow, yielded high overall classification accuracy (92%), kappa coefficient (0.83), and user’s accuracy (~90%) for lake-water classification using Landsat data. The MODIS data using NDWIMcFeeters and NDWIXu showed consistent lake area (r2 = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km2 and mean absolute error (MAE) values of 25.7 and 29.04 km2, respectively. The MODIS data is suitable for great lake mapping, which is the case for the large lakes in the TP. Although automated water extraction indices exhibited high accuracy in separating water from non-water, visual examination and manual editing are still necessary. Combined with recent Chinese high-resolution satellites, these remotely sensed imageries will provide a wealth of data for studies of lake dynamics and long-term lake evolution in the TP.