Extracting water-related features using reflectance data and principal component analysis of Landsat images

References

• Afifi, A., May, S., and Clark, V., 2012. Practical multivariate analysis. Boca Raton: CRC Press.
   Google Scholar
• Alsdorf, D.E., et al., 2000. Interferometric radar measurements of water level changes on the Amazon flood plain. Nature, 404, 174–177. doi:10.1038/35004560
   PubMed Web of Science ®Google Scholar
• Aly, A.A., et al., 2016. Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach. Solid Earth, 7, 713–725. doi:10.5194/se-7-713-2016
   Web of Science ®Google Scholar
• Amari, S. and Wu, S., 1999. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12, 783–789. doi:10.1016/S0893-6080(99)00032-5
   PubMed Web of Science ®Google Scholar
• Ashburn, P., 1979. The vegetative index number and crop identification. In: Proceedings of the technical session, Houston, TX, Vols. 1–2. NASA. Johnson Space Center Proc. of Tech., 843–855.
   Google Scholar
• Atkinson, P.M., 2004. Spatially weighted supervised classification for remote sensing. International Journal of Applied Earth Observation and Geoinformation, 5, 277–291. doi:10.1016/j.jag.2004.07.006
   Google Scholar
• Baret, F. and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161–173. doi:10.1016/0034-4257(91)90009-U
   Web of Science ®Google Scholar
• Barnett, T., et al., 2004. The effects of climate change on water resources in the west: introduction and overview. Climatic Change, 62, 1–11. doi:10.1023/B:CLIM.0000013695.21726.b8
   Web of Science ®Google Scholar
• Bartholy, J., et al., 2009. Analysis of regional climate change modelling experiments for the Carpathian Basin. International Journal of Global Warming, 1, 238–252. doi:10.1504/IJGW.2009.027092
   Web of Science ®Google Scholar
• Basto, M. and Pereira, J.M., 2012. An SPSS R-menu for ordinal factor analysis. Journal of Statistical Software, 46 (4), 1–29. doi:10.18637/jss.v046.i04
   PubMed Web of Science ®Google Scholar
• Bernaards, C.A. and Jennrich, R.I., 2005. Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65, 676–696. doi:10.1177/0013164404272507
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45, 5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Burai, P., et al., 2015. Classification of herbaceous vegetation using airborne hyperspectral imagery. Remote Sensing, 7, 2046–2066. doi:10.3390/rs70202046
   Web of Science ®Google Scholar
• Byrne, G.F., Crapper, P.F., and Mayo, K.K., 1980. Monitoring land-cover change by principal component analysis of multitemporal Landsat data. Remote Sensing of Environment, 10, 175–184. doi:10.1016/0034-4257(80)90021-8
   Web of Science ®Google Scholar
• Chen, X.-L., et al., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133–146. doi:10.1016/j.rse.2005.11.016
   Web of Science ®Google Scholar
• Churches, C.E., et al., 2014. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data. International Journal of Applied Earth Observation and Geoinformation, 30, 203–216. doi:10.1016/j.jag.2014.01.020
   Web of Science ®Google Scholar
• Cingolani, A.M., et al., 2004. Mapping vegetation in a heterogeneous mountain rangeland using Landsat data: an alternative method to define and classify land-cover units. Remote Sensing of Environment, 92, 84–97. doi:10.1016/j.rse.2004.05.008
   Web of Science ®Google Scholar
• Clark Labs, 2012. IDRISI Selva. Worcester: Clark University.
   Google Scholar
• CLC 2000, Corine Land Cover Programme, EEA. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database [Accessed September 2016].
   Google Scholar
• Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46. doi:10.1016/0034-4257(91)90048-B
   Web of Science ®Google Scholar
• Davis, J.C., 1986. Statistics and data analysis in geology. 2nd ed. New York: Wiley.
   Google Scholar
• de Alwis, D.A., et al., 2007. Unsupervised classification of saturated areas using a time series of remotely sensed images. Hydrology and Earth System Sciences, 11, 1609–1620. doi:10.5194/hess-11-1609-2007
   Web of Science ®Google Scholar
• Deering, D.W., et al., 1975. Measuring “forage production” of grazing units from Landsat MSS data. In: Proceedings of the 10th International Symposium on Remote Sensing of Environment, Vol. 2, Ann Arbor: ERIM, 1169–1178.
   Google Scholar
• Demetriades-Shah, T.H., Steven, M.D., and Clark, J.A., 1990. High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 55–64. doi:10.1016/0034-4257(90)90055-Q
   Web of Science ®Google Scholar
• Deng, J.S., et al., 2008. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. International Journal of Remote Sensing, 29, 4823–4838. doi:10.1080/01431160801950162
   Web of Science ®Google Scholar
• Di Baldassarre, G., Schumann, G., and Bates, P.D., 2009. A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. Journal of Hydrology, 367 (3–4), 276–282. doi:10.1016/j.jhydrol.2009.01.020
   Web of Science ®Google Scholar
• Dronova, I., et al., 2015. Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, 193–206. doi:10.1016/j.rse.2014.10.027
   Web of Science ®Google Scholar
• Eklundh, L. and Singh, A., 1993. A comparative analysis of standardised and unstandardised principal components analysis in remote sensing. International Journal of Remote Sensing, 14, 1359–1370. doi:10.1080/01431169308953962
   Web of Science ®Google Scholar
• Estoque, R.C. and Murayama, Y., 2015. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217. doi:10.1016/j.ecolind.2015.03.037
   Web of Science ®Google Scholar
• Fichera, C.R., Modica, G., and Pollino, M., 2012. Land cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing, 45, 1–18. doi:10.5721/EuJRS20124501
   Web of Science ®Google Scholar
• Fisher, A. and Danaher, T., 2013. A water index for SPOT5 HRG satellite imagery, New South Wales, Australia, determined by linear discriminant analysis. Remote Sensing, 5 (11), 5907–5925. doi:10.3390/rs5115907
   Web of Science ®Google Scholar
• Forzieri, G., et al., 2011. Satellite multispectral data for improved floodplain roughness modelling. Journal of Hydrology, 407, 41–57. doi:10.1016/j.jhydrol.2011.07.009
   Web of Science ®Google Scholar
• Frassy, F., et al., 2013. Minimum noise fraction transform for improving the classification of airborne hyperspectral data: two case studies. In: 5th Workshop on hyperstectral image and signal processing: evolution in remote sensing, Gainesville, FL: IEEE, 1–4. doi:10.1109/WHISPERS.2013.8080626.
   Google Scholar
• Gabriel, K.R., 1971. The bi-plot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453–467. doi:10.1093/biomet/58.3.453
   Web of Science ®Google Scholar
• 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. doi:10.1016/S0034-4257(96)00067-3
   Web of Science ®Google Scholar
• Gu, Y., et al., 2008. Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters, 35, L22401. doi: 10.1029/2008GL035772
   Web of Science ®Google Scholar
• Hegedüs, P., et al., 2015. Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary. Open Geosciences, 7, 126–139. doi:10.1515/geo-2015-0017
   Web of Science ®Google Scholar
• Hirosawa, Y., Stuart, E.M., and Kliman, D.H., 1996. Application of standardized principal component analysis to land-cover characterization using multitemporal AVHRR data. Remote Sensing of Environment, 58, 267–281. doi:10.1016/S0034-4257(96)00068-5
   Web of Science ®Google Scholar
• Ho, T.K., 1995. Random decision forests. In: Proceedings of the 3rd international conference on document analysis and recognition, Montreal, QC, 14–16 August 1995. IEEE Computer Society, 278–282.
   Google Scholar
• Jaccard, J. and Wan, C.K., 1996. LISREL approaches to interaction effects in multiple regression. London: Quantitative Applications in the Social Sciences, SAGE Publications.
   Google Scholar
• Jin, S., Li, D., and Wang, J., 2005. A comparison of support vector machine with maximum likelihood classification algorithms on texture features. In: Proceedings. IEEE international geoscience and remote sensing symposium, IGARSS ‘05, Vol. 5, 3717–3720. doi:10.1109/IGARSS.2005.1526659
   Google Scholar
• Jolliffe, I.T., 2002. Principal component analysis. 2nd ed. New York: Springer.
   Google Scholar
• Jöreskug, K. and Sörbom, D., 1996. LISREL 8: user’s reference guide. Lincolnwood: Scientific Software International.
   Google Scholar
• Kassambara, A. and Mundt, F., 2017. Factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.4. https://CRAN.R-project.org/package=factoextra [Accessed September 2017].
   Google Scholar
• Keuchel, J., et al., 2003. Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sensing of Environment, 86, 530–541. doi:10.1016/S0034-4257(03)00130-5
   Web of Science ®Google Scholar
• Khan, N.M., et al., 2005. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77, 96–109. doi:10.1016/j.agwat.2004.09.038
   Web of Science ®Google Scholar
• Kit, O., Lüdeke, M., and Reckien, D., 2012. Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography, 32, 660–667. doi:10.1016/j.apgeog.2011.07.016
   Web of Science ®Google Scholar
• Ko, B.C., Kim, H.H., and Nam, J.Y., 2015. Classification of potential water bodies using landsat 8 OLI and a combination of two boosted random forest classifiers. Sensors, 15, 13763–13777. doi: 10.3390/s150613763
   PubMed Web of Science ®Google Scholar
• Kohler, U. and Luniak, M., 2005. Data inspection using bi-plots. The Stata Journal, 5, 208–223.
   Web of Science ®Google Scholar
• Koutsias, N., Mallinis, G., and Karteris, M., 2009. A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 37–46. doi:10.1016/j.isprsjprs.2008.06.004
   Web of Science ®Google Scholar
• Kumar, D., 2015. Remote sensing based vegetation indices analysis to improve water resources management in urban environment. Aquatic Procedia, 4, 1374–1980. doi:10.1016/j.aqpro.2015.02.178
   Google Scholar
• Kuti, L., Kerék, B., and Vatai, J., 2006. Problem and prognosis of excess water inundation based on agrogeological factors. Carpathian Journal of Earth and Environmental Sciences, 1, 5–18.
   Web of Science ®Google Scholar
• Lê, S., Josse, J., and Husson, F., 2008. FactoMineR: an R package for multivariate analysis. Journal of Statistical Software, 25 (1), 1–18. doi:10.18637/jss.v025.i01
   Web of Science ®Google Scholar
• Ladányi, Z., et al., 2015. Multi-indicator sensitivity analysis of climate change effects on landscapes in the Kiskunság National Park, Hungary. Ecological Indicators, 58, 8–20. doi:10.1016/j.ecolind.2015.05.024
   Web of Science ®Google Scholar
• Li, X., et al., 2013. Comparative study of water-body information extraction methods based on electronic sensing image. Lecture Notes in Electrical Engineering, 178, 331–336. doi:10.1007/978-3-642-31528-2_52
   Google Scholar
• Liu, J., Pattey, E., and Jégo, G., 2012. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347–358. doi:10.1016/j.rse.2012.04.002
   Web of Science ®Google Scholar
• Louppe, G., et al., 2013. Understanding variable importances in forests of randomized trees. In: Advances in neural information processing systems. NIPS proceedings, 431–439. Neural Information Processing Systems Conference, Lake Tahoe. Neural Information Processing Systems Foundation, Inc.
   Google Scholar
• Lu, D., et al., 2014. Methods to extract impervious surface areas from satellite images. International Journal of Digital Earth, 7, 93–112. doi:10.1080/17538947.2013.866173
   Web of Science ®Google Scholar
• Lu, D. and Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823–870. doi:10.1080/01431160600746456
   Web of Science ®Google Scholar
• Marshall, M. and Thenkabail, P., 2015. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 205–218. doi:10.1016/j.isprsjprs.2015.08.001
   Web of Science ®Google Scholar
• 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, 1425–1432. doi:10.1080/01431169608948714
   Web of Science ®Google Scholar
• Meglen, R.R., 1992. Examining large databases: a chemometric approach using principal component analysis. Marine Chemistry, 39, 217–237. doi:10.1016/0304-4203(92)90103-H
   Web of Science ®Google Scholar
• Melesse, A.M. and Shih, S.F., 2002. Spatially distributed storm runoff depth estimation using Landsat images and GIS. Computers and Electronics in Agriculture, 37, 173–183. doi:10.1016/S0168-1699(02)00111-4
   Web of Science ®Google Scholar
• Mika, J., 2013. Meteorological extremes and their changes: phenomenology and empirical aproaches. Climatic Change, 121, 15–26. doi:10.1007/s10584-013-0914
   Web of Science ®Google Scholar
• Mills, R.T., et al., 2013. Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis. Procedia Computer Science, 18, 2396–2405. doi:10.1016/j.procs.2013.05.411
   Google Scholar
• Mukherjee, S., et al., 2006. Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Advances in Computational Mathematics, 25, 161–193. doi:10.1007/s10444-004-7634-z
   Web of Science ®Google Scholar
• Munyati, C., 2004. Use of principal component analysis (PCA) of remote sensing images in wetland change detection on the Kafue Flats, Zambia. Geocarto International, 19, 11–22. doi:10.1080/10106040408542313
   Google Scholar
• Narasimhan, B. and Srinivasan, R., 2005. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133, 69–88. doi:10.1016/j.agrformet.2005.07.012
   Web of Science ®Google Scholar
• Nyarko, B.K., et al., 2015. Floodplain wetland mapping in the White Volta River Basin of Ghana. GIScience & Remote Sensing, 52, 374–395. doi:10.1080/15481603.2015.1026555
   Web of Science ®Google Scholar
• Otukei, J.R. and Blaschke, T., 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12 (S1), S27–S31. doi:10.1016/j.jag.2009.11.002
   Web of Science ®Google Scholar
• Ozesmi, S. and Bauer, M.E., 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10, 381–402. doi:10.1023/A:1020908432489
   Google Scholar
• Pal, M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, 217–222. doi:10.1080/01431160412331269698
   Web of Science ®Google Scholar
• Pásztor, L., et al., 2015. Spatial risk assessment of hydrological extremities: inland excess water hazard, Szabolcs-Szatmár-Bereg County, Hungary. Journal of Maps, 11, 636–644. doi:10.1080/17445647.2014.954647
   Web of Science ®Google Scholar
• Pásztor, L., et al., 2016. Integrated spatial assessment of wind erosion risk in Hungary. Natural Hazards and Earth System Sciences, 16, 2421–2432. doi:10.5194/nhess-16-2421-2016
   Web of Science ®Google Scholar
• Perry Jr., C. and Lautenschlager, L.F., 1984. Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14, 169–182. doi:10.1016/0034-4257(84)90013-0
   Web of Science ®Google Scholar
• R Core Team, 2016. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/ [Accessed 10 May 2015].
   Google Scholar
• Revelle, W., 2015. psych: procedures for personality and psychological research. Evanston: Northwestern University. http://CRAN.R-project.org/package=psych [Accessed September 2017].
   Google Scholar
• Richards, J.A., 1984. Thematic mapping from multitemporal image data using the principal components transformation. Remote Sensing of Environment, 16, 35–46. doi:10.1016/0034-4257(84)90025-7
   Web of Science ®Google Scholar
• Richardson, A.J. and Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogramnetric Engineering and Remote Sensing, 43, 1541–1552.
   Web of Science ®Google Scholar
• Rodarmel, C. and Shan, J., 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62, 115–122.
   Google Scholar
• Rouse, Jr., J.W., et al., 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. In: NASA/GSFC type III final report, Greenbelt, MD, 371. Texas A&M University, Remote Sensing Center.
   Google Scholar
• Sawaya, K.E., et al., 2003. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88, 144–156. doi:10.1016/j.rse.2003.04.006
   Web of Science ®Google Scholar
• Schölkopf, B., et al., 1997. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45, 2758–2765. doi:10.1109/78.650102
   Web of Science ®Google Scholar
• Schowengerdt, R.A., 2007. Remote sensing. San Diego: Academic Press – Elsevier.
   Google Scholar
• Singh, A., 1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10 (6), 989–1003. doi:10.1080/01431168908903939
   Web of Science ®Google Scholar
• Sonka, M., Hlavac, V., and Boyle, R., 2014. Image processing, analysis, and machine vision. 4h ed. Stamford: Cengage Learning.
   Google Scholar
• Srivastava, P.K., et al., 2012. Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250–1265. doi:10.1016/j.asr.2012.06.032
   Web of Science ®Google Scholar
• Szabó, S., et al., 2014. Testing algorithms for the identification of asbestos roofing based on hyperspectral data. Environmental Engineering and Management Journal, 143, 2875–2880.
   Web of Science ®Google Scholar
• Szabó, S., Gácsi, Z., and Balázs, B., 2016. Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Acta Geographica Debrecina Landscape and Environment, 10 (3–4), 194–202. doi:10.21120/LE/10/3-4/13
   Google Scholar
• Thiam, A.K., 1997. Geographic information systems and remote sensing methods for assessing and monitoring land degradation in the Sahel: the case of Southern Mauritania. Dissertation. Clark University, Worcester Massachusetts.
   Google Scholar
• van den Berg, R.A., et al., 2006. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics, 7, 142. doi:10.1186/1471-2164-7-142
   PubMed Web of Science ®Google Scholar
• van Leeuwen, B., et al., 2012. Identification of inland excess water floodings using an artificial neural network. Carpathian Journal of Earth and Environmental Sciences, 7, 173–180.
   Web of Science ®Google Scholar
• Vapnik, V., 1995. The nature of statistical learning theory. New York: Springer-Verlag.
   Google Scholar
• Vörösmarty, C.J., et al., 2000. Global water resources: vulnerability from climate change and population growth. Science, 289, 284–288. doi:10.1126/science.289.5477.284
   PubMed Web of Science ®Google Scholar
• Wallace, J., Behn, G., and Furby, S., 2009. Vegetation condition assessment and monitoring from sequences of satellite imagery. Ecological Management and Restoration, 7, S31–S36. doi:10.1111/j.1442-8903.2006.00289.x
   Google Scholar
• Water Quality Report, 2000. Chronology of the flood of spring of 2000. Vol. 7. Szolnok: Environmental Agency of the Middle-Tisza Region, 1–8 (in Hungarian).
   Google Scholar
• Weng, Q., 2012. Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sensing of Environment, 117, 34–49. doi:10.1016/j.rse.2011.02.030
   Web of Science ®Google Scholar
• Williams, G.J., 2011. Data mining with Rattle and R: the art of excavating data for knowledge discovery, use R! New York: Springer.
   Google Scholar
• Wright, C. and Gallant, A., 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107, 582–605. doi:10.1016/j.rse.2006.10.019
   Web of Science ®Google Scholar
• Xu, H., 2005. A study on information extraction of water body with the Modified Normalized Difference Water Index (MNDWI). Journal of Remote Sensing, 9, 589–595.
   Google Scholar
• Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033. doi:10.1080/01431160600589179
   Web of Science ®Google Scholar
• Zlinszky, A., et al., 2015. Mapping Natura 2000 habitat conservation status in a pannonic salt steppe with airborne laser scanning. Remote Sensing, 7, 2991–3019. doi:10.3390/rs70302991
   Web of Science ®Google Scholar