* Corresponding Author
Comparison of Various Spectral Indices for Optimum Extraction of Tropical Wetlands Using Landsat 8 OLI
https://doi.org/10.22146/ijg.49914 Syamani D. Ali(1*), Hartono Hartono(2), Projo Danoedoro(3)
(1) Faculty of Forestry, Universitas Lambung Mangkurat
(2) Faculty of Geography, Universitas Gadjah Mada
(3) Faculty of Geography, Universitas Gadjah Mada
(*) Corresponding Author
Abstract
This research specifically aims to investigate the most accurate spectral indices in extracting wetlands geospatial information taking South Kalimantan, Indonesia, as an example of wetlands in tropical areas. Ten spectral indices were selected for testing their ability to extract wetlands, those are NDVI, NDWI, MNDWI, MNDWIs2, NDMI, WRI, NDPI, TCWT, AWEInsh, andAWEIsh. Tests were performed on Landsat 8 OLI path/row 117/062 and 117/063. The threshold method which was used to separate the wetland features from the spectral indices imagery is Otsu method. The results of this research showed that generally MNDWIs2 was the most optimal spectral indices in wetlands extraction. Especially tropical wetlands that rich with green vegetation cover. However, MNDWIs2 is very sensitive to dense vegetation, this feature has the potential to be detected as wetlands. Furthermore, to improve the accuracy and prevent detection of the dryland vegetation as wetlands, the threshold value should be determined carefully.
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Amani, M., Salehi, B., Mahdavi, S. and Brisco, B.. (2018). Spectral analysis of wetlands using multi-source optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 119-136.
Ashraf, M. and Nawaz, R..(2015). A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi-Temporal Landsat Dataset. Journal of Geographic Information System, 7, 1-19.
Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., Nelson, A..(2014). Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems.PLoS ONE 9 (2), e88741. doi:10.1371/journal.pone.0088741
Chavez, P.S..(1988). An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data. Remote Sensing of Environment, 24, 459–479.
Chavez, P.S..(1996). Image-based Atmospheric Corrections—Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62, 1025–1036.
Chen, D., Huang, J., and Jackson, T.J..(2005). Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-wave Infrared Bands. Remote Sensing of Environment, 98, 225-236.
Chen, Y., Guerschmana, J.P., Cheng, Z., and Guo, L..(2019). Remote sensing for vegetation monitoring in carbon capture storage regions: A review. Applied Energy, 240, 312-326.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Boehner, J..(2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4.. Geoscientific Model Development, 8, 1991-2007, doi:10.5194/gmd-8-1991-2015.
Das, R.J. and Pal, S..(2016). Identification of Water Bodies from Multispectral Landsat Imageries of Barind Tract of West Bengal. International Journal of Innovative Research and Review, 4 (1), 26-37.
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X..(2016). Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sensing, 8, 354-372, doi:10.3390/rs8040354.
Feyisa, L.G., Meilby, H., Fensholt, R., and Proud, S.R..(2014). Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery. Remote Sensing of Environment, 140 (2014), 23–35.
Gao, B.C..(1996). NDWI A – Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58, 257-266.
Hong, G., Xing-fa, G., Young, X., Tau, Y., Hai-liang, G., Xiang-qin, W., and Qi-yue, L..(2014). Evaluation of Four Dark Object Atmospheric Correction Methods Based on XY-3 CCD Data [Abstract]. Spectroscopy and Spectral Analysis, 34 (8), 2203-2207.
Islam, Md.A., Thenkabail, P.S., Kulawardhana, R.W., Alankara, R., Gunasinghe, S., Edussriya, C., and Gunawardana, A..(2008). Semi‐automated Methods for Mapping Wetlands using Landsat ETM+ and SRTM Data. International Journal of Remote Sensing, 29 (24), 7077-7106, doi: 10.1080/01431160802235878.
Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., and Hunt, E.R..(2004). Vegetation Water Content Mapping Using Landsat Data Derived Normalized Difference Water Index for Corn and Soybeans. Remote Sensing of Environment, 92, 475-482.
Ji, L., Zhang, L., and Wylie, B..(2009). Analysis of Dynamic Thresholds for the Normalized Difference Water Index, Photogrammetric Engineering and Remote Sensing, 75, (11), 1307-1317.
Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., and Xiao, T.. (2014). An Automated Method for Extracting Rivers and Lakes from Landsat Imagery. Remote Sensing, 6, 5067-5089.
Kwak, Y. and Iwami, Y..(2014). Nationwide Flood Inundation Mapping in Bangladesh by Using Modified Land Surface Water Index. ASPRS 2014 Annual Conference, Louisville, Kentucky, March 23-28, 2014.
Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione, J.A., Lafaye, M..(2007). Classification of Ponds from High-spatial Resolution Remote Sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment, 106, 66–74.
Li, B., Ti, C., Zhao, Y., and Yan, X..(2015). Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies. Remote Sensing, 8, 38-55, doi:10.3390/rs8010038.
Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B., and Zhang, X..(2013). A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI. Remote Sensing, 5, 5530-5549.
Li, W., Nie., J., Hu, H., Zhang, B., Wu, W. and Wang, L.. (2009). Dynamic change estimation of water resources based on remotely sensed imageries. Proceedings of SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74950Q.
Matthews, G.V.T..(2013). The Ramsar Convention on Wetlands: its History and Development. Ramsar Convention Bureau, Gland, Switzerland, p. 41.
McFeeters, S.K..(1996). The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17 (7), 1425-1432.
Otsu, N..(1979). A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–69.
Osgouei, P. E., Kaya, S., Sertel, E. and Alganci, U.. (2019). Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote sensing, 11 (3), 345.
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D. W..(1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351 I, 309-317.
Schneider, C.A., Rasband, W.S., and Eliceiri, K.W..(2012). NIH Image to ImageJ: 25 Years of Image Analysis. Nature Methods, 9(7), 671-675, PMID 22930834.
Schindelin, J., Rueden, C.T., and Hiner, M.C. et al..(2015). The ImageJ Ecosystem: An open Platform for Biomedical Image Analysis. Molecular Reproduction and Development, PMID 26153368.
Shen, L. and Li, C..(2010). Water Body Extraction from Landsat ETM+ Imagery Using Adaboost Algorithm. In Proceedings of 18th International Conference on Geoinformatics, 18–20 June, Beijing, China, 1–4.
Stehman, S.V. and Czaplewski, R.L..(1997). Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles. Remote Sensing of Environment, 1998 (64), 331-344.
United States Environmental Protection Agency (EPA).(2004). Wetlands Overview, EPA 843-F-04-011a. Office of Water, December 2004.
Wilson, E.H. and Sader, S.A..(2002). Detection of Forest Harvest Type using Multiple Dates of Landsat TM Imagery. Remote Sensing Environment, 80, 385–396.
World Wildlife Fund (WWF).(2004). Global Lakes and Wetlands Database: Lakes and Wetlands Grid (Level 3). Washington, D.C., http://www.worldwildlife.org/ publications/global-lakes-and-wetlands-database-lakes-and-wetlands-grid-level-3.
Yang, L., Tian, S., Yu, L., Ye, F., Qian, J., and Qian, Y..(2015). Deep Learning for Extracting Water Body from Landsat Imagery. International Journal of Innovative Computing, Information and Control, 11 (6), 1913–1929.
Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., et al..(2002). Observation of Flooding and Rice Transplanting of Paddy Rice Fields at the Site to Landscape Scales in China using VEGETATION Sensor Data. International Journal of Remote Sensing, 23, 3009–3022, doi:10.1080/01431160110107734.
Xie, H., Luo, X., Xu, X., Pan, H., and Tong, X..(2016). Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery. Remote Sensing, 8 (7), 584-599.
Xu, H..(2006). Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27 (14), 3025–3033, doi: 10.1080/01431160600589179.
Zhai, K., Wu, X., Qin, Y., and Du, P..(2015). Comparison of Surface Water Extraction Performances of Different Classic Water Indices using OLI and TM Imageries in Different Situations. Geo-spatial Information Science, 18 (1), 32-42, doi: 10.1080/ 10095020.2015.1017911.
Zhang, Z., He, G., and Wang, X..(2010). A Practical DOS Model-Based Atmospheric Correction Algorithm. International Journal of Remote Sensing, 31 (11), 2837-2852.
DOI: https://doi.org/10.22146/ijg.49914
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