a rapid extraction of water body features from antarctic coastal oasis using very high resolution satellite remote sensing data

Available online at www.sciencedirect.com ScienceDirect Aquatic Procedia (2015) 125 – 132 INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) A Rapid Extraction of Water Body Features From Antarctic Coastal Oasis Using Very High-Resolution Satellite Remote Sensing Data S D Jawaka* and A J Luisa a Polar Remote Sensing Department, Earth System Science Organization (ESSO), National Centre for Antarctic & Ocean Research (NCAOR), Ministry of Earth Sciences, Govt of India, Headland Sada, Vasco-da-Gama, Goa - 403804, India Abstract Antarctic coastal oases are essential sources of spatially distributed fresh water bodies Mapping water bodies from remote places, such as polar regions, using traditional surveying method is a laborious and logistically expensive task A rapid method for extracting and monitoring water bodies in Antarctic coastal oases has a tremendous application in remote sensing This study discusses the design of a rapid and novel method to extract water body features in Antarctic coastal oasis environment from remotely-sensed images We devised semiautomatic approach for extracting water body features based on a novel set of normalized difference water index (NDWI) by incorporating high-resolution WorldView-2 (WV-2) panchromatic and multispectral image data This study highlights and compares the viability of state-of-the-art spectral processing water body extraction approaches with the newly designed NDWI approach An extensive quantitative evaluation was carried out to test the newly designed NDWI approach for extracting water bodies on Larsemann Hills, eastern Antarctica The results suggest that the modified NDWI approach render intermediate performance with bias error varying from ~1 to ~34 m2 (least amount of misclassified pixels) We also analyzed the distinctive 8-band capability of WV-2 data coupled with semiautomatic extraction methods to compare their reliability in extracting the water body area The results indicate that the use of the modified NDWI approach on 8-band WV-2 data can significantly improve the semiautomatic extraction of water body features, which can ultimately contribute to an enhanced perceptive of the Antarctic coastal oasis in the context of climate change © 2015 Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license © 2015 The Authors Published by Elsevier B.V (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-reviewunder under responsibility of organizing committee of ICWRCOE Peer-review responsibility of organizing committee of ICWRCOE 2015 2015 Keywords: Waterbody extraction; WorldView-2; NDWI * Corresponding author Tel.: +91-832-2525528; fax: +91-832-2520877 E-mail address: shridhar.jawak@ncaor.gov.in 2214-241X © 2015 Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.018 126 S.D Jawak and A.J Luis / Aquatic Procedia (2015) 125 – 132 Introduction Water on the Earth’s surface is an essential part of the hydrological cycle Being able to access the spatial distribution and geographical extent information on water bodies in real time has great significance in limnology and for understanding interactions between regional hydrology and climate change (Papa et al., 2008) Satellite remote sensing has advantages because it can track land surface information in real-time macroscopically, multitemporally, multispectrally, dynamically, and repetitively; hence, it is appropriate for surveying and mapping surface water bodies (Chen et al., 2004) Antarctica’s inclement weather, a few number of fine weather days in summer, and the high cost of ship/ helicopter restricts research trips to Antarctica Therefore, high-resolution satellite remote sensing data and aerial photography are important sources of information for monitoring the short-term and long-term changes that occur at a specific location in Antarctica over time Although high resolution remote sensing data can never replace aerial photographs, which provide images at a resolution as high as 0.2–0.3 m, the WorldView-2 (WV-2) is found to be suitable for semiautomatic feature extraction in the Antarctic, where frequent aerial photography is difficult because of the harsh environment and high costs of logistics (Jawak and Luis, 2012; Jawak and Luis, 2011) Hence, development of automated or semiautomated feature extraction methods using high-resolution remote sensing data is much needed to continuously monitor the geographical features in a cryospheric environment At present, the methods for extracting surface water bodies are prominently based on spectral index or multiband techniques, such as the normalized difference water index (NDWI) (McFeeters, 1996; Lacaux et al., 2007) A novel water extraction index for shoreline delineation by combining the tasseled cap wetness index and the NDWI was proposed by Ouma and Tateishi (2006) Rogers and Kearney (2004) proposed the NDWI for the Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral satellite images (MSI) Lu et al (2011) suggested an integrated water body extraction method with HJ-1A/B satellite imagery by using normalized difference vegetation index (NDVI) and NDWI These modified indices have been frequently used to map surface water bodies using Landsat and MODIS images (Li and Zhou, 2009; Soti et al., 2009) However, because of the complications in cryospheric environments, diverse ground targets may have the similar spectrum characteristics Therefore, only one category of index method can extract water bodies only under certain conditions (Jawak and Luis, 2013a; Jawak and Luis, 2013b) Additionally, these spectral indices were developed for traditional visible near-IR (VNIR) systems Hence, the WV-2 offers a prospect to adapt a modification of NDWI using eight spectral bands (Jawak et al., 2013) Jawak and Mathew (2011) proposed an object oriented method for semiautomatic extraction of roads and water bodies using QuickBird imagery Matched Filtering (MF), Mixture Tuned Matched Filtering (MTMF), Spectral Angle Mapper (SAM), MF/SAM ratio, and Principal Component Analysis (PCA), have been implemented for improved lake feature extraction in cryospheric environment (Jawak and Luis, 2014a; Jawak and Luis, 2014b) Target extraction methods, such as Constrained Energy Minimization (CEM), Adaptive Coherence Estimator (ACE), Target-Constrained Interference-Minimized Filter (TCIMF), Mixture Tuned TCIMF (MT-TCIMF), and Orthogonal Subspace Projection (OSP) methods have been used to improve semiautomatic target detection (Jawak and Luis, 2014) In this work, we performed semiautomatic extraction of water bodies in the Larsemann Hills of Antarctica by employing 14 types of pixel-wise methods Our experiment is focused on two objectives: (a) designing a modified NDWI approach to extract water body features, and (b) comparing the performance of supervised feature extraction algorithms with the newly developed modified NDWI approach using visual analysis and statistical accuracy Study area and data The Larsemann Hills are located on the Ingrid Christensen Coast, Princess Elizabeth Land, eastern Antarctica The Larsemann Hills is roughly situated in the middle of the Amery Ice Shelf and the Vestfold Hills The region hosts a number of water bodies, ranging from small, shallow ponds ( ACE > mNDWI3 > MTMF > TCIMF > mNDWI2 > CEM > mNDWI1 > PCA > mNDWI4 > OSP > MF/SAM > SAM > MF This order suggests that the methods can be ranked as: target extraction approach > modified NDWI approach > spectral processing approach The average RMSE values for the modified NDWI methods ranged from ~226 to ~240 m2, (bias ~1 to ~34 m2) and for the spectral matching methods and the target extraction methods, RMSE values varied from ~226 to 252 m2 (bias ~7 to ~37 m2) and from ~202 to ~240 m2, (bias ~8 to ~32 m2) respectively (Table 2) The smallest variation in RMSE observed in the modified NDWI approach suggests that it is a more stable and consistent approach than the other two approaches In this study, we tested PAN-sharpening algorithms (GS, HCS, EF, and BT) for fusing an MS image with PAN An evaluation of the performance of each PAN-sharpening algorithm for water body extraction was undertaken, since the performance of these algorithms is spectrally and spatially dependent From Table 2, where average RMSE is depicted, the performance hierarchy of PAN-sharpening algorithms for 14 extraction methods, was HCS > GS > 130 S.D Jawak and A.J Luis / Aquatic Procedia (2015) 125 – 132 EF > BT, which suggests that the HCS algorithm outperformed the others, given its small average RMSE The HCSsharpened water body features that were extracted using the 14 different methods are portrayed in Figure Table Statistical evaluation (Bias and RMSE, m2) of 14 method used for water body extraction across PAN-sharpening algorithms The lowest values in each row (italics) and column (bold) are highlighted The row-wise average RMSE, column-wise average RMSE, and local RMSE averages are bolded and underlined Method PAN-sharpening methods RMSE Bias Modified NDWI GS BT HCS EF Average Average mNDWI1 199.47 224.12 275.49 245.81 236.22 –34.14 mNDWI2 242.98 231.38 222.65 234.26 232.82 –27.33 mNDWI3 201.71 269.73 175.24 260.14 226.70 0.25 mNDWI4 259.04 253.82 207.75 240.92 240.38 –6.361 Average RMSE 225.8 244.76 220.28 245.28 234.03 –16.90 MF 255.65 257.77 233.92 260.95 252.07 –7.95 MTMF 214.47 208.31 239.4 245.58 226.94 –25.50 SAM 226.83 271.35 258.61 250.91 251.92 –30.60 MF/SAM 242.45 249.18 223.64 274.21 247.37 –37.85 PCA 255.37 215.81 221.39 268.29 240.21 –15.55 Average RMSE 238.95 240.48 235.39 259.99 243.7 –23.49 CEM 223.87 283.23 259.99 173.01 235.03 –8.55 ACE 223.25 253.82 201.57 211.76 222.6 –32.97 OSP 254.15 218.09 217.15 278.4 241.95 –21.77 TCIMF 167.3 287.38 244.44 216.71 228.96 –14.40 MT-TCIMF 213.95 211.45 184.6 199.41 202.35 –29.18 Average RMSE 216.51 250.79 221.55 215.86 226.18 –21.38 Total average (RMSE) m2 227.18 245.39 226.13 240.03 Spectral Matching Target Extraction Figure The overall performance trend for all 14 water body extraction methods, in terms of RMSE S.D Jawak and A.J Luis / Aquatic Procedia (2015) 125 – 132 Figure A sample of extracted water bodies from a HCS-sharpened image using the 14 extraction methods The GS-sharpened image performed best for the mNDWI1 method (RMSE = 199.47 m2), while HCS showed the best performance for mNDWI2 (RMSE = 222.65 m2), mNDWI3 (RMSE = 175.24 m2), and mNDWI4 (RMSE = 207.75 m2) In general, HCS and GS are the optimal sharpening algorithms for the modified NDWI methods, while EF is superior for target extraction methods Discussion A robust, accurate, and user-friendly method can extremely reduce the laborious manual digitization A semiautomatic water body feature extraction can be applied in an operational environment only if it provides better performance in terms of following quality measures, which are the advantages of our modified NDWI method x Accuracy: Extraction results should be correct and the geometric errors shall be minimized The result must be better or at least comparable to that from the manual digitizing The RMSE values of modified NDWI shows that our approach could extract water bodies accurately with respect to the manually digitized reference x Visual comparison: The semiautomatic method should provide the extraction results which can be compared visually against the manual reference data Visual comparison also shows that all the 36 water bodies were detected in the NDWI images, and the boundaries of the extracted water bodies match the actual boundaries of the water bodies in the images or reference digitized data closely x Water body size: An ideal water body extraction method should extract all sizes of water body features, including small ponds to large glacial lakes NDWI performed better than the other two approaches for extracting even small sized water body features x Error: The variation in error should be least Our accuracy assessment shows that modified NDWI performed better than the other two methods for extracting water bodies x Geometric errors: Semiautomatic method should produce the results with actual representation of shapes of water bodies, maintaining the integrity of shape Visual comparison shows that all the three approaches worked better for maintaining the integrity of shape Conclusions The spectrum characteristics of water bodies from WV-2 images were analyzed by using semiautomatic extraction capability involving a combination of advanced image processing methods The use of popular PANsharpening algorithms coupled with a modification of NDWI provided an effective tool to support semiautomatic extraction of Antarctic water body features The use of modified NDWI combinations derived by using the duplet set of Blue and NIR bands offered a precise means for extracting water body areas The magnitude of the spectral and spatial distortions induced by PAN-sharpening, influenced consequent water body extraction processing, and significantly influenced the final accuracy of the analysis Inclusion of the distinctive new spectral WV-2 bands offered a contextual foundation for surface water mapping by using feature extraction methods and scene characterization Different band combinations of NDWI provide a broad vision to resolve minor variations in spectrum properties of various water bodies and consequently the performance of the practiced extraction procedures This would have not been possible if we used other satellite data that contained only the single infrared, 131 132 S.D Jawak and A.J Luis / Aquatic Procedia (2015) 125 – 132 red, and blue bands The different band combinations used in this study also facilitated a deep understanding of the role of specific spectral bands used in varied combinations to produce the best water body extraction from PANsharpened images Acknowledgements We thank the Australian Antarctic Data Centre for providing us with the supplementary GIS data layers for the study area We acknowledge Mr Parag Khopkar and Mr Tejas Godbole of University of Pune, for their assistance in initial data processing We acknowledge Dr S Rajan, Director, NCAOR, for encouragement and motivation of this research This is NCAOR contribution No 35/2014 References Bakker, H.W et al., 2009 Principles of Remote Sensing, The International Institute for GeoInformation Science and Earth Observation (ITC), Enschede Chen, Q.L., Zhang, Y.Z., Ekroos, A., Hallikainen, M., 2004 The role of remote sensing technology in the EU water framework directive (WFD) Environmental Science and Policy 7, 267–276 Jawak, S.D., Luis, A.J., 2014a A semiautomatic extraction of Antarctic lake features using WorldView-2 imagery Photogrammetric Engineering & Remote Sensing 80(10), 33-46 Jawak, S.D., 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the 36 extracted... Hyderabad, India, paper# 263, 247-257 Jawak, S.D., Luis, A. J., 2011 Applications of WorldView-2 satellite data for extraction of polar spatial information and DEM of Larsemann Hills, East Antarctica,... changes that occur at a specific location in Antarctica over time Although high resolution remote sensing data can never replace aerial photographs, which provide images at a resolution as high as 0.2–0.3