Assessment of automatic extraction of surface water dynamism using multi-temporal satellite data

The present paper aims to determine the best spectral index for extraction of surface water bodies and considerable variation in water surface area during the period between 1990 and 2018. Five spectral indices are tested using Landsat series (1990, 2000, 2009 and 2018) and their performance in delineating surface water are assessed. The results of each algorithm have been matched and verified by Pearson correlation and root mean square error (RMSE). The results indicated that a modified normalized difference water index (MNDWI) was created for 1990, 2000, 2009 and 2018 by RMSE with accurate spatial information on waterbodies of 23.54, 33.14, 22.87 and 17.28 respectively. The estimated area of surface water bodies is increased by 2207.28 ha (1990–2018) derived through MNDWI. Hence, the process could be very useful for accurately mapping and monitoring surface water.

[1]  Przemysław Tymków,et al.  Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle , 2019, Water.

[2]  Y. Ouma,et al.  A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data , 2006 .

[3]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[4]  Wang Zongmin,et al.  Water Body Extraction Methods Study Based on RS and GIS , 2011 .

[5]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[6]  Brigitte Poulin,et al.  Desiccation and cracking behaviour of clay layer from slurry state 1 under wetting-drying cycles 2 3 , 2011 .

[7]  A. Pandey Environmental Impacts of Canal Irrigation in India , 2013 .

[8]  J. Ryu,et al.  Waterline extraction from Landsat TM data in a tidal flat: a case study in Gomso Bay, Korea , 2002 .

[9]  A Study on Moyna Basin Water-Logged Areas (India) Using Remote Sensing and GIS Methods and Their Contemporary Economic Significance , 2014 .

[10]  Zhou Chun-guo,et al.  Water surface change detection and analysis of bottomland submersion-emersion of wetlands in Poyang Lake Reserve using ENVISAT ASAR data. , 2010 .

[11]  Zhiqiang Du,et al.  Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China , 2012 .

[12]  B. Wylie,et al.  Analysis of Dynamic Thresholds for the Normalized Difference Water Index , 2009 .

[13]  Yang Siquan,et al.  Review on Disaster Reduction Application Potentiality of Synthetic Aperture Radar , 2013 .

[14]  C. Karan,et al.  Changing Pattern of Land Utilization: Using Remote Sensing and GIS Methods in Moyna Block, Purba Medinipur District, West Bengal , 2015 .

[15]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[16]  Pierre Grussenmeyer,et al.  Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery , 2018, Remote Sensing of Environment.

[17]  Yunliang Li,et al.  Seasonal–spatial variation and remote sensing of phytoplankton absorption in Lake Taihu, a large eutrophic and shallow lake in China , 2010 .

[18]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[19]  A. Negm,et al.  Performances Evaluation of Surface Water Areas Extraction Techniques Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL) , 2016 .

[20]  Ali Selamat,et al.  Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery , 2014, Remote. Sens..

[21]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[22]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[23]  Robert K. Colwell,et al.  Ecological assessment. , 1972, Science.

[24]  Li Shen,et al.  Water body extraction from Landsat ETM+ imagery using adaboost algorithm , 2010, 2010 18th International Conference on Geoinformatics.

[25]  Xi Chen,et al.  Global Monitoring of Inland Water Dynamics: State-of-the-Art, Challenges, and Opportunities , 2016, Computational Sustainability.

[26]  A. Fisher,et al.  Comparing Landsat water index methods for automated water classification in eastern Australia , 2016 .

[27]  Gulcan Sarp,et al.  Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey , 2017 .

[28]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[29]  Jie Cao,et al.  A Decision Tree Model for Meteorological Disasters Grade Evaluation of Flood , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[30]  Gulcan Sarp,et al.  Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul , 2014 .