Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods

• Published in: International journal of remote sensing 2017-02, Vol.38 (3), p.742-772
• Main Authors: Zhang, Guoqing, Li, Junli, Zheng, Guoxiong
• Format: Article
• Language: English
• Subjects: Accuracy; Anthropogenic factors; Area; Automation; Bins; Classification; Climate change; Data; Differentiation; Dynamics; Editing; Evaluation; Evolution; Feature extraction; Freshwater; High resolution; Identification methods; Imagery; Imaging techniques; Indicators; Lake dynamics; Lakes; Landsat; Landsat satellites; Mapping; Mean square values; Methods; MODIS; Multispectral scanners; Remote sensing; Resolution; Root-mean-square errors; Satellite data; Satellite imagery; Satellites; Seasonal variability; Seasonal variation; Seasonal variations; Spaceborne remote sensing; Surface water; Time series; Trends; Water
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2016.1271478

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 km 2 ) 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 NDWI McFeeters , NDWI Xu , and AWEI non-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 NDWI McFeeters and NDWI Xu showed consistent lake area (r 2  = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km 2 and mean absolute error (MAE) values of 25.7 and 29.04 km 2 , 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