Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

References

• Akiyama CM. 2015. Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes. Geocarto Int. 30:1113–1129.
   Web of Science ®Google Scholar
• Baatz M, Schäpe A. 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In Strobl J, Blaschke T, Griesebner G, editors. Angewandte Geographische Informations-verarbeitung XII. Karlsrube: Wichmann Verlag; p. 12–23.
   Google Scholar
• Baltsavias EP. 2004. Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS J Photogramm Remote Sens. 58:129–151.10.1016/j.isprsjprs.2003.09.002
   Web of Science ®Google Scholar
• Bertels L, Vanderstraete T, Van Coillie S, Knaeps E, Sterckx S, Goossens R, Deronde B. 2008. Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata, Tanimbar. Int J Remote Sens. 29:2359–2391.10.1080/01431160701408469
   Web of Science ®Google Scholar
• Bhaskaran S, Paramananda S, Ramnarayan M. 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Appl Geogr. 30:650–665.10.1016/j.apgeog.2010.01.009
   Web of Science ®Google Scholar
• Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens. 65:2–16.10.1016/j.isprsjprs.2009.06.004
   Web of Science ®Google Scholar
• Casal G, Sánchez-Carnero N, Sánchez-Rodríguez E, Freire J. 2011. Remote sensing with SPOT-4 for mapping kelp forests in turbid waters on the south European Atlantic shelf. Estuar Coast Shelf Sci. 91:371–378.10.1016/j.ecss.2010.10.024
   Web of Science ®Google Scholar
• Chen C, Sui X, Zhen G, Guo B, Chen X. 2016. Extraction of cross-sea bridges from GF-2 PMS satellite images using mathematical morphology. IOP Conf Ser: Earth Environ Sci. 46:012041.10.1088/1755-1315/46/1/012041
   Google Scholar
• China Fishery Statistical Yearbooks. 2001–2015. China Agriculture Press.
   Google Scholar
• Cho HJ, Piñeyro B, Gasdia FW. 2016. Water correction for improved benthic vegetation signal using satellite-borne hyperspectral data. Int J Remote Sens. 37:4084–4100.10.1080/01431161.2016.1207262
   Web of Science ®Google Scholar
• Chopin T, Yarish C, Wilkes R, Belyea E, Lu S, Mathieson A. 2000. Developing Porphyra/salmon integrated aquaculture for bioremediation and diversification of the aquaculture industry. J Appl Phycol. 12:99–99.10.1023/A:1008189312167
   Web of Science ®Google Scholar
• Cleve C, Kelly M, Kearns FR, Moritz M. 2008. Classification of the wildland–urban interface: a comparison of pixel- and object-based classifications using high-resolution aerial photography. Comput Environ Urban Sys. 32:317–326.10.1016/j.compenvurbsys.2007.10.001
   Web of Science ®Google Scholar
• Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 37:35–46.10.1016/0034-4257(91)90048-B
   Web of Science ®Google Scholar
• Cushnie JL. 1987. The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies. Int J Remote Sens. 8:15–29.10.1080/01431168708948612
   Web of Science ®Google Scholar
• Duarte CM, Wu J, Xiao X, Bruhn A, Krause-Jensen D. 2017. Can seaweed farming play a role in climate change mitigation and adaptation? Front Mar Sci. 4:100.
   Web of Science ®Google Scholar
• Gao BC. 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Proc Spie. 58:257–266.
   Google Scholar
• Gober P, Brazel A, Grossmanclarke S, Weng Q. 2010. Per-pixel versus object-based classification of urban land cover extraction using high spatial resolution imagery. Geoda Center Working Papers. 115:1145–1161.
   Google Scholar
• Gullström M, Lundén B, Bodin M, Kangwe J, Öhman MC, Mtolera MSP, Björk M. 2006. Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing. Estuar Coast Shelf Sci. 67:399–408.10.1016/j.ecss.2005.11.020
   Web of Science ®Google Scholar
• Han X, Chen X, Feng L. 2015. Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sens Environ. 156:426–437.10.1016/j.rse.2014.10.003
   Web of Science ®Google Scholar
• Hay GJ, Castilla G. 2006. Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT). In Proceedings of 1st International Conference on Object-based Image Analysis (OBIA 2006); Jul 4–5;Austria: Salzburg University, XXXVI (2006) (ISSN 1682-1777).
   Google Scholar
• Hochberg E. 2003. Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra. Remote Sens Environ. 85:174–189.10.1016/S0034-4257(02)00202-X
   Web of Science ®Google Scholar
• Hu C, Feng L, Hardy RF, Hochberg EJ. 2015. Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae. Remote Sens Environ. 167:229–246.10.1016/j.rse.2015.05.022
   Web of Science ®Google Scholar
• Ivits E, Koch B. 2002. Object-oriented remote sensing tools for biodiversity assessment: a European approach. In: Benes T, editors. Geoinformation for European-wide integration: proceedings of the 22nd EARSeL symposium. Rotterdam: Millpress Science; p. 465–473
   Google Scholar
• Jebur MN, Shafri HZM, Pradhan B, Tehrany MS. 2014. Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery. Geocarto Int. 29:792–806.10.1080/10106049.2013.848944
   Web of Science ®Google Scholar
• Jensen JR. 1986. Introductory Digital Image Processing: a Remote Sensing Perspective. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Jensen JR. 2009. Remote sensing of the environment: an earth resource perspective. Pearson Education India.
   Google Scholar
• Ji L, Wang J, Geng X, Gong P. 2016. Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series. arXiv:1612.07801.
   Google Scholar
• Jobin B, Labrecque S, Grenier M, Falardeau G. 2008. Object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the grasshopper sparrow. Environ Manage. 41:20–31.10.1007/s00267-007-9031-0
   PubMed Web of Science ®Google Scholar
• Johansen K, Arroyo LA, Phinn S, Witte C, Hay GJ, Blaschke T. 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Photogramm Eng Remote Sens. 76:123–136.10.14358/PERS.76.2.123
   Web of Science ®Google Scholar
• Laliberte AS, Rango A, Havstad KM, Paris JF, Beck RF, McNeely R, Gonzalez AL. 2004. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sens Environ. 93:198–210.10.1016/j.rse.2004.07.011
   Web of Science ®Google Scholar
• Li X, Zhao S, Rui Y, Tang W. 2007. An object-based classification approach for high-spatial resolution imagery. Proceedings of SPIE, the International Society for Optical Engineering Society of Photo-optical Instrumentation Engineers; Nanjing, China. 6752, 675230, 0277-786X/07.doi: 10.1117/12.761260.
   Google Scholar
• Li J, Liu Y, Cheng JJ, Mos M, Daroch M. 2015. Biological potential of microalgae in China for biorefinery-based production of biofuels and high value compounds. New Biotech. 32:588–596.10.1016/j.nbt.2015.02.001
   PubMed Web of Science ®Google Scholar
• Liang Z, Bing Z, Zhengchao C, Lanfen Z, Qingxi T. 2009. The application of hyperspectral remote sensing to coast environment investigation. Acta Oceanol Sin (Chinese version). 28:1–13.
   Web of Science ®Google Scholar
• Liu D, Xia F. 2010. Assessing object-based classification: advantages and limitations. Remote Sens Lett. 1:187–194.10.1080/01431161003743173
   Web of Science ®Google Scholar
• Liu Y, Bian L, Meng Y, Wang H, Zhang S, Yang Y, Shao X, Wang B. 2012. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS J Photogramm Remote Sens. 68:144–156.10.1016/j.isprsjprs.2012.01.007
   Web of Science ®Google Scholar
• Liu C, Hong L, Chu S, Chen J. 2014. A SVM ensemble approach combining pixel-based and object-based features for the classification of high resolution remotely sensed imagery. Proceedings of the International Workshop on earth Observation and Remote sensing Applications; IEEE; p. 140–144.
   Google Scholar
• Lyons MB, Phinn SR, Roelfsema CM. 2012. Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland. ISPRS J Photogramm Remote Sens. 71:34–46.10.1016/j.isprsjprs.2012.05.002
   Web of Science ®Google Scholar
• Malinverni ES, Tassetti AN, Mancini A, Zingaretti P, Frontoni E, Bernardini A. 2011. Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery. Int J Geogr Inf S. 25:1025–1043.10.1080/13658816.2011.566569
   Web of Science ®Google Scholar
• Marquez GPB, Santiañez WJE, Trono GC, Montaño MNE, Araki H, Takeuchi H, Hasegawa T. 2014. Seaweed biomass of the Philippines: sustainable feedstock for biogas production. Renew Sust Energ Rev. 38:1056–1068.10.1016/j.rser.2014.07.056
   Web of Science ®Google Scholar
• Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ. 115:1145–1161.10.1016/j.rse.2010.12.017
   Web of Science ®Google Scholar
• Navulur K. 2007. Multispectral image analysis using the object-oriented paradigm. Boca Raton, FL: CRC Press, Taylor and France Group; 165p.
   Google Scholar
• Pasqualini V, Pergent-Martini C, Pergent G, Agreil M, Skoufas G, Sourbes L, Tsirika A. 2005. Use of SPOT 5 for mapping seagrasses: an application to Posidonia oceanica. Remote Sens Environ. 94:39–45.10.1016/j.rse.2004.09.010
   Web of Science ®Google Scholar
• Petropoulos GP, Kalaitzidis C, Prasad Vadrevu K. 2012. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput Geosci. 41:99–107.10.1016/j.cageo.2011.08.019
   Web of Science ®Google Scholar
• Philippsen A, Wild P, Rowe A. 2014. Energy input, carbon intensity and cost for ethanol produced from farmed seaweed. Renew Sust Energ Rev. 38:609–623.10.1016/j.rser.2014.06.010
   Web of Science ®Google Scholar
• Pu R, Landry S. 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens Environ. 124:516–533.10.1016/j.rse.2012.06.011
   Web of Science ®Google Scholar
• Qian Y, Zhou W, Yan J, Li W, Han L. 2014. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sens. 7:153–168.10.3390/rs70100153
   Web of Science ®Google Scholar
• Radoux J, Defourny P. 2007. A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery. Remote Sens Environ. 110:468–475.10.1016/j.rse.2007.02.031
   Web of Science ®Google Scholar
• Sagawa T, Mikami A, Aoki MN, Komatsu T. 2012. Mapping seaweed forests with IKONOS image based on bottom surface reflectance. In: Frouin RJ, Ebuchi N, Pan D, Saino T, edtiors. Proceedings of the Remote Sensing of the Marine Environment II; Spie-Int Soc Optical Engineering.
   Google Scholar
• Shang ZT. 2008. Analysis of the general situations of laver culture in Jiangsu province and its climatic suitability. J Anhui Agri Sci (Chinese version). 36:5315–5319.
   Google Scholar
• Shi W, Wang M. 2009. Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008. J Geophys Res. 114:207–211.
   Web of Science ®Google Scholar
• Singh RP, Sridhar VN, Dadhwal VK, Navalgund RR, Singh KP. 2002. Comparative evaluation of Indian remote sensing multi-spectral sensors data for crop classification. Geocarto Int. 17:7–12.10.1080/10106040208542230
   Google Scholar
• Szekielda AKH, Marmorino GO, Bowles JH, Gillis DB. 2010. High spatial resolution spectrometry of rafting macroalgae (Sargassum). J Appl Remote Sens. 4:1400–1415.
   Web of Science ®Google Scholar
• Tassan S. 1996. Modified Lyzenga’s method for macroalgae detection in water with non-uniform composition. Int J Remote Sens. 17:1601–1607.10.1080/01431169608948725
   Web of Science ®Google Scholar
• Turner DP, Cohen WB, Kennedy RE, Fassnacht KS, Briggs JM. 1999. Relationships between leaf area index and Landsat TM spectral vegetation Indices across three temperate zone sites. Remote Sens Environ. 70:52–68.10.1016/S0034-4257(99)00057-7
   Web of Science ®Google Scholar
• Vahtmäe E, Kutser T, Martin G, Kotta J. 2006. Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters – a Baltic Sea case study. Remote Sens Environ. 101:342–351.10.1016/j.rse.2006.01.009
   Web of Science ®Google Scholar
• Vieira MA, Formaggio AR, Rennó CD, Atzberger C, Aguiar DA, Mello MP. 2012. Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas. Remote Sens Environ. 123:553–562.10.1016/j.rse.2012.04.011
   Web of Science ®Google Scholar
• Walter V. 2004. Object-based classification of remote sensing data for change detection. ISPRS J Photogramm Remote Sens. 58:225–238.10.1016/j.isprsjprs.2003.09.007
   Web of Science ®Google Scholar
• Wang L, Sousa WP, Gong P. 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens. 25:5655–5668.10.1080/014311602331291215
   Web of Science ®Google Scholar
• Wang A, Chen J, Jing C, Ye G, Wu J, Huang Z, Zhou C. 2015. Monitoring the invasion of Spartina alterniflora from 1993 to 2014 with Landsat TM and SPOT 6 Satellite Data in Yueqing Bay, China. PLoS One. 10:e0135538.10.1371/journal.pone.0135538
   PubMed Web of Science ®Google Scholar
• Wang H, Wang C, Wu H. 2016. Using GF-2 imagery and the conditional random field model for urban forest cover mapping. Remote Sens Lett. 7:378–387.10.1080/2150704X.2016.1142682
   Web of Science ®Google Scholar
• Xiao X, Agusti Susana, Lin Fang, Li Ke, Pan Yaoru, Yan Yu, Zheng Yuhan, Jiaping Wu, Duarte Carlos M. 2017. Nutrient removal from Chinese coastal waters by large-scale seaweed aquaculture. Sci Rep. 7.doi:10.1038/srep46613.
   Google Scholar
• Xing Q, Hu C, Tang D, Tian L, Tang S, Wang X, Lou M, Gao X. 2015. World’s largest Macroalgal Blooms altered Phytoplankton biomass in summer in the Yellow Sea: satellite observations. Remote Sens. 7:12297–12313.10.3390/rs70912297
   Web of Science ®Google Scholar
• Zhang F, Yang G, Tian Y. 2016. Study on the extraction method of cannabis plot based on China-made GF-2 remote sensing image-taking Subu town in Anhui Province as an example. J Anhui Agri Univ (Chinese version). 43:582–586.
   Google Scholar
• Zhou W, Troy A. 2008. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. Int J Remote Sens. 29:3119–3135.10.1080/01431160701469065
   Web of Science ®Google Scholar
• Zhou W, Huang G, Troy A, Cadenasso ML. 2009. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: a comparison study. Remote Sens Environ. 113:1769–1777.10.1016/j.rse.2009.04.007
   Web of Science ®Google Scholar
• Ziaei Z, Pradhan B, Mansor SB. 2014. A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images. Geocarto Int. 29:554–569.10.1080/10106049.2013.819039
   Web of Science ®Google Scholar