Investigating Water Body Changes Using Remote Sensing Water Indices and Google Earth Engine: Case Study of Poldokhtar Wetlands, Lorestan Province

Document Type : Research Article

Authors

1 .Sc Student of Geological remote sensing, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

2 Assistant Professor, Ecology Department, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

Abstract

Wetlands are the most important natural ecosystems on the earth, and assessing changes in them is one of the essential necessities in the natural resource management of this valuable natural ecosystem. The aim of this study is to investigate water body changes using remote sensing water indices and Google Earth Engine (GEE) in the study area of Poldokhtar wetlands, Lorestan province. Remote sensing water indices includes AWEInsh, AWEIsh, NDWI, mNDWI, NDWI plus VI, mNDWI plus VI and LSWI plus VI that were used TM, ETM+ and OLI Landsat satellite images, and Google Earth Engine were applied Landsat Water Product data. The results demonstrated temporo-spatial distribution of water body changes in the study area and they were compared to real data indicating AWEIsh and AWEInsh with overall accuracy of 99.39 and 99.19 and Kappa coefficient of 0.94 and 0.91 were the best water indices among all in enhancing water bodies. Furthermore, GEE results showed overall accuracy of 87 and kappa Coefficient of 0.86. These results indicate that water indices and GEE are useful tools in detection of increasing and decreasing trends in water bodies that can assist planner and policy-makers in protecting and managing natural resources in the study area.

Keywords


[1]. Emadi, M., et al., An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems. Environmental monitoring and assessment, 2010. 164(1-4): p. 501-511.
[2].  Wang, C., et al., Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin. Remote Sensing, 2018. 10(10): p. 1635.
[3].  Xie, H., et al., Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction. International Journal of Remote Sensing, 2016. 37(8): p. 1826-1844.
[4].  Sarp, G. and M. Ozcelik, Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science, 2017. 11(3): p. 381-391.
[5].  Syphard, A.D. and M.W. Garcia, Human-and beaver-induced wetland changes in the Chickahominy River watershed from 1953 to 1994. Wetlands, 2001. 21(3): p. 342-353.
[6].  Winter, T.C., et al., Water source to four US wetlands: implications for wetland management. Wetlands, 2001. 21(4): p. 462-473.
[7].  Augusteijn, M. and C. Warrender, Wetland classification using optical and radar data and neural network classification. International Journal of remote sensing, 1998. 19(8): p. 1545-1560.
[8].  Ozemi, S. and M. Bauer, Satellite Remote Sensing of Wetlands, Wetlands Ecology and Management. 2002.
[9].  Zhang, Y., I.O. Odeh, and E. Ramadan, Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data. International Journal of Remote Sensing, 2013. 34(1): p. 168-189.
[10].            Qi, H. and M. Altinakar, Simulation-based decision support system for flood damage assessment under uncertainty using remote sensing and census block information. Natural hazards, 2011. 59(2): p. 1125-1143.
[11].            Barton, I.J. and J.M. Bathols, Monitoring floods with AVHRR. Remote sensing of Environment, 1989. 30(1): p. 89-94.
[12].            Evora, N.D., D. Tapsoba, and D. De Seve, Combining artificial neural network models, geostatistics, and passive microwave data for snow water equivalent retrieval and mapping. IEEE Transactions on Geoscience and Remote Sensing, 2008. 46(7): p. 1925-1939.
[13].            Zou, Z., et al., Continued decrease of open surface water body area in Oklahoma during 1984–2015. Science of the Total Environment, 2017. 595: p. 451-460.
[14].            Henits, L., C. Jürgens, and L. Mucsi, Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data. International Journal of Remote Sensing, 2016. 37(15): p. 3439-3454.
 
[15].            Li, N., C. Yan, and J. Xie, Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land. Resources, Conservation and Recycling, 2015. 94: p. 129-135.
[16].            Rundquist, D.C., et al., THE RELATIONSHIP BETWEEN SUMMER‐SEASON RAINFALL EVENTS AND LAKE‐SURFACE AREA 1. JAWRA Journal of the American Water Resources Association, 1987. 23(3): p. 493-508.
[17].            Gao, B.-C., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 1996. 58(3): p. 257-266.
[18].            Xu, H., Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 2006. 27(14): p. 3025-3033.
[19].            Chen, B., et al., Mapping forest and their spatial–temporal changes from 2007 to 2015 in tropical hainan island by integrating ALOS/ALOS-2 L-band SAR and landsat optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018. 11(3): p. 852-867.
[20].            Wulder, M.A., et al., Current status of Landsat program, science, and applications. Remote sensing of environment, 2019. 225: p. 127-147.
[21].            Zhu, Z., et al., Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 2019. 224: p. 382-385.
[22].            Huang, H., et al., Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 2017. 202: p. 166-176.
[23].            Liu, X., et al., High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote sensing of environment, 2018. 209: p. 227-239.
[24].            Xiong, J., et al., Nominal 30-m cropland extent map of continental Africa by integrating
pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sensing, 2017. 9(10): p. 1065.
[25].            Wang, Y., et al., Long-Term Dynamic of Poyang Lake Surface Water: A Mapping Work Based on the Google Earth Engine Cloud Platform. Remote Sensing, 2019. 11(3): p. 313.
[26].            Xia, H., et al., Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing, 2019. 11(15): p. 1824.
[27].            McFeeters, S.K., The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, 1996. 17(7): p. 1425-1432.
[28].            Masocha, M., et al., Surface water bodies mapping in Zimbabwe using landsat 8 OLI multispectral imagery: A comparison of multiple water indices. Physics and Chemistry of the Earth, Parts A/B/C, 2018. 106: p. 63-67.
[29].            Feyisa, G.L., et al., Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 2014. 140: p. 23-35.
[30].            Menarguez, M.A., Global Water Body Mapping from 1984 to 2014 Using High Resolution Multispectral Satellite Imagery, 2015, University of Oklahoma.
[31].            Gorelick, N., et al., Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017. 202: p. 18-27.
[32].            Google, P.E.J. and Tags, Google, Data availability (time) Mar 16, 1984 - Oct 18, 2018, Provider. landsat-derived, jrc, google, surface, water, geophysical,ImageID;JRC/GSW1_0/ GlobalSurfaceWater.
[33].            Banko, G., A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory. 1998.
[34].            Guide, E.U.s., ENVI on-line software user’s manual. ITT Visual Information Solutions, 2008.
Volume 7, Issue 1
April 2020
Pages 131-146
  • Receive Date: 06 December 2019
  • Revise Date: 09 March 2020
  • Accept Date: 09 March 2020
  • First Publish Date: 20 March 2020