Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area

References

• Abdikan S, Bilgin G. Sanli FB, Uslu E, Ustuner M. 2015. Enhancing land use classification with fusing dual-polarized TerraSAR-X and multispectral RapidEye data. J Appl Remote Sens. 9:096054.10.1117/1.JRS.9.096054
   Web of Science ®Google Scholar
• Alkan M, Oruc M, Yildirim Y, Seker DZ, Jacobsen K 2013. Monitoring spatial and temporal land use/cover changes; a case study in western Black Sea region of Turkey. J Indian Soc Remote Sens. 41:587–596.10.1007/s12524-012-0227-2
   Web of Science ®Google Scholar
• Amarsaikhan D, Blotevogel HH, van Genderen JL, Ganzorig M, Gantuya R, Nergui B. 2010. Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification. Int J Image Data Fus. 1:83–97.10.1080/19479830903562041
   Google Scholar
• Amarsaikhan D, Saandar M, Ganzorig M, Blotevogel HH, Egshiglen E, Gantuyal R, Nergui B, Enkhjargal D. 2012. Comparison of multisource image fusion methods and land cover classification. Int J Remote Sens. 33:2532–2550.10.1080/01431161.2011.616552
   Web of Science ®Google Scholar
• Atesoglu A. 2015. Multitemporal analysis (1975–2011) of vegetation changes in urban land uses: Case of the city of Bartin, Turkey. J Environ Biol. 36:473–473.
   PubMed Web of Science ®Google Scholar
• Attarchi S, Richard G. 2014. Classifying complex mountainous forests with L-Band SAR and Landsat data integration: a comparison among different machine learning methods in the Hyrcanian Forest. Remote Sens. 6:3624–3647.10.3390/rs6053624
   Web of Science ®Google Scholar
• Ayhan E., Atay G. 2012. Spectral and spatial quality analysis in pan sharpening process. J Indian Soc Remote Sens. 40:379–388.10.1007/s12524-011-0185-0
   Web of Science ®Google Scholar
• Cakir HI, Khorram S. 2008. Pixel level fusion of panchromatic and multispectral images based on correspondence analysis. Photogramm Eng Remote Sens. 74:183–192.10.14358/PERS.74.2.183
   Web of Science ®Google Scholar
• Canty MJ. 2014. Image analysis, classification and change detection in remote sensing: with algorithms for envi/idl and python. Boca Raton (FL): CRC Press.
   Google Scholar
• Celikyay S, Cengiz B. 2006. Ecological planning strategies on Bartin River region. WSEAS Trans Environ Dev. 2:1091–1096.
   Google Scholar
• Chibani Y. 2007. Integration of panchromatic and SAR features into multispectral spot images using the ‘a trous’ wavelet decomposition. Int J Remote Sens. 28:2295–2307.10.1080/01431160600606874
   Web of Science ®Google Scholar
• CNES (Centre National d’Etudes Spatiales). 2016. The orfeo toolbox software guide [Internet]. [cited 2016 Jan 20]. Available from: https://www.orfeo-toolbox.org//packages/OTBSoftwareGuide.pdf
   Google Scholar
• Congalton R, Green K. 1999. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton (FL): CRC/Lewis Press.
   Google Scholar
• Dahiya S, Garg PK, Jat MK. 2013. A comparative study of various pixel-based image fusion techniques as applied to an urban environment. Int J Image Data Fus. 4:197–213.10.1080/19479832.2013.778335
   Google Scholar
• FAO. 2012. Food and Agriculture Organization of the United Nations supporting sustainable forest management through the global forest resources assessment: long-term strategy (2012–2030), Rome. Report of FAO.
   Google Scholar
• Fasbender D, Radoux J, Bogaert P. 2008. Bayesian data fusion for adaptable image pansharpening. IEEE Trans Geosci Remote Sens. 46:1847–1857.10.1109/TGRS.2008.917131
   Web of Science ®Google Scholar
• Franklin SE. 2001. Remote sensing for sustainable forest management. Boca Raton (FL): Lewis Publishers. ISBN 1-56670-394-8.10.1201/9781420032857
   Google Scholar
• General Directorate of Forestry. 2011. Annual Activity Report [Internet]. [cited 2016 Jan 20]. Available from: http://www.ogm.gov.tr/ekutuphane/FaaliyetRaporu/General%20Directorate%20Of%20Forestry%20Annual%20Activity%20Report%202011.pdf
   Google Scholar
• General Directorate of Forestry. 2015. Annual Activity Report [Internet]. [cited 2016 Jan 20]. Available from: http://www.ogm.gov.tr/ekutuphane/FaaliyetRaporu/Orman%20Genel%20M%C3%BCd%C3%BCrl%C3%BC%C4%9F%C3%BC%202014%20Y%C4%B1l%C4%B1%20Faaliyet%20Raporu.pdf. ( In Turkish).
   Google Scholar
• Ghosh A, Joshi PK. 2013. Assessment of pan-sharpened very high-resolution WorldView-2 images. Int J Remote Sens. 34:8336–8359.10.1080/01431161.2013.838706
   Web of Science ®Google Scholar
• Guneralp I, Filippi AM, Hales BU. 2013. River-flow boundary delineation from digital aerial photography and ancillary images using support vector machines. GISci & Remote Sens. 50:1–25.
   Web of Science ®Google Scholar
• Kavzoglu T, Colkesen I. 2009. A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf. 11:352–359.10.1016/j.jag.2009.06.002
   Web of Science ®Google Scholar
• Klonus S, Ehlers M. 2008. Pansharpening with TerraSAR-X and optical data. Paper presented at: Proceedings: 3rd TerraSAR-X Science Team Meeting, DLR; 25–26 November; Oberpfaffenhofen, Germany.
   Google Scholar
• Larranaga A, Alvarez-Mozos J, Albizua L. 2011. Crop classification in rain-fed and irrigated agricultural areas using Landsat TM and ALOS/PALSAR data. Can J Remote Sens. 37:157–170.10.5589/m11-022
   Web of Science ®Google Scholar
• Lehmann EA, Caccetta P, Lowell K, Mitchell A, Zhou ZS, Held A, Milne T, Tapley I. 2015. SAR and optical remote sensing: assessment of interoperability and complementarity in the context of a large-scale operational forest monitoring system. Remote Sens Env. 156:335–348.
   Web of Science ®Google Scholar
• Lehmann EA, Zhou ZS, Caccetta P, Mitchell A, Milne A, Lowell K, Mcneill S. 2011. Combined analysis of optical and SAR remote sensing data for forest mapping and monitoring. Paper presented at: Int Symp on Digital Earth, Perth, Australia.
   Google Scholar
• Liang S, Li X, Wang J. 2012. Advanced remote sensing – terrestrial information extraction and applications. Amsterdam: Elsevier. ISBN 9780123859549.
   Google Scholar
• van Lier OR, Luther JE, Leckie DG, Bowers WW. 2011. Development of large-area land cover and forest change indicators using multi-sensor Landsat imagery: application to the Humber River Basin. Int J Appl Earth Obs Geoinf. 13:819–82910.1016/j.jag.2011.05.019
   Web of Science ®Google Scholar
• Liesenberg V, Gloaguen R. 2013. Evaluating SAR polarization modes at L-band for forest classication purposes in Eastern Amazon, Brazil. Int J Appl Earth Obs Geoinf. 21:122–135.10.1016/j.jag.2012.08.016
   Web of Science ®Google Scholar
• Lopes, A, Nezry, E, Touzi, R, and Laur, H. 1993. Structure detection and statistical adaptive speckle filtering in SAR images. Int J Remote Sens. 14: 1735–1758. doi:10.1080/01431169308953999.
   Web of Science ®Google Scholar
• Lu D, Li G, Moran E, Batistella M, Freitas CC. 2011b. Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: a case study in an urban–rural landscape in the Brazilian Amazon. ISPRS J Photogramm Remote Sens. 66:798–808.10.1016/j.isprsjprs.2011.08.004
   Web of Science ®Google Scholar
• Lu D, Li G, Moran E, Dutra L, Batistella M. 2011a. A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. GISci Remote Sens. 48:345–370.10.2747/1548-1603.48.3.345
   Web of Science ®Google Scholar
• Mahyari AG, Yazdi M. 2011. Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities. IEEE Trans Geosci Remote Sens. 49:1976–1985.10.1109/TGRS.2010.2103944
   Web of Science ®Google Scholar
• McNairn H, Champagne C, Shang J, Holmstrom D, Gordon Reichert G. 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS J Photogramm Remote Sens. 64:434–449.10.1016/j.isprsjprs.2008.07.006
   Web of Science ®Google Scholar
• Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens. 66:247–259.10.1016/j.isprsjprs.2010.11.001
   Web of Science ®Google Scholar
• Nunez J, Otazu X, Fors O, Prades A, Pala V, Arbiol R. 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens. 37:1204–1211.10.1109/36.763274
   Web of Science ®Google Scholar
• Otukei JR, Blaschke T, Collins M. 2015. Fusion of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda. Int J Appl Earth Obs Geoinf. 38:99–104.10.1016/j.jag.2014.12.012
   Web of Science ®Google Scholar
• PCI Geomatics. PCI geomatics Enterprises. 2014. Help manual, Focus user guide. Chapter 6. Richmond Hill, Ontario, Canada.
   Google Scholar
• Pekkarinen A, Reithmaier L, Strobl P. 2009. Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data. ISPRS J Photogramm Remote Sens 64:171–183.10.1016/j.isprsjprs.2008.09.004
   Web of Science ®Google Scholar
• Pereira LO, da Costa Corina, Freitas CC, Sant′Anna SJS, Dengsheng LuD, Moran EF. 2013. Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon. GISci Remote Sens. 50:301–321.
   Web of Science ®Google Scholar
• Pohl C, van Genderen J. 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens. 19:823–854.10.1080/014311698215748
   Web of Science ®Google Scholar
• Pohl C, van Genderen J. 2014. Remote sensing image fusion: an update in the context of Digital Earth. Int J Digit Earth. 7:158–172.10.1080/17538947.2013.869266
   Web of Science ®Google Scholar
• Pohl C, van Genderen J. 2015. Structuring contemporary remote sensing image fusion. Int J Image Data Fusion. 6:3–21.10.1080/19479832.2014.998727
   Web of Science ®Google Scholar
• Reiche J, Verbesselt J, Hoekman D, Herold M. 2015. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sens Env. 156:276–293.
   Web of Science ®Google Scholar
• Sarp G. 2014. Spectral and spatial quality analysis of pan-sharpening algorithms: a case study in Istanbul. Eur J Remote Sens. 47:19–28.10.5721/EuJRS
   Web of Science ®Google Scholar
• Setiawan Y, Yoshino K, Prasetyo LB. 2014. Characterizing the dynamics change of vegetation cover on tropical forestlands using 250m multi-temporal MODIS EVI. Int J Appl Earth Obs and Geoinf. 26:132–144.10.1016/j.jag.2013.06.008
   Web of Science ®Google Scholar
• Singh SK, Srivastava PK, Gupta M, Thakur JK, Mukherjee S. 2014. Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci. 71:2245–2255.10.1007/s12665-013-2628-0
   Web of Science ®Google Scholar
• Song XP, Huang C, Feng M, Sexton JO, Channan S, Townshend JR. 2014. Integrating global land cover products for improved forest cover characterization: an application in North America. Int J Digit Earth. 7:709–724.10.1080/17538947.2013.856959
   Web of Science ®Google Scholar
• Taylor S, Kumar L, Reid N. 2010. Mapping lantana camara: accuracy comparison of various fusion techniques. Photogramm. Eng. Remote Sens. 76:691–700.10.14358/PERS.76.6.691
   Web of Science ®Google Scholar
• Tso B, Mather PM. 2009. Classification methods for remotely sensed data. Boca Raton (FL): CRC Press. ISBN 978-1-4200-9072-7.
   Google Scholar
• Tunay M, Atesoglu A. 2004. Determining of non-natural changes in Bartin Province using remote sensing technique and GIS (in Turkish). Paper presented at: Proceedings: 3rd. GIS Days in Turkey; Istanbul, October 6–9.
   Google Scholar
• Vapnik VN. 1998. Statistical learning theory. New York (NY): Wiley. 1.
   Google Scholar
• Verburg PH, Neumann K, Nol L. 2011. Challenges in using land use and land cover data for global change studies. Global Change Biol. 17:974–989.10.1111/gcb.2010.17.issue-2
   Web of Science ®Google Scholar
• Wang Z, Bovik AC. 2002. A universal image quality index. IEEE Signal Proc Let. 9:81–84.10.1109/97.995823
   Web of Science ®Google Scholar
• Witharana C, Civco DL, Meyer TH. 2013. Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows. Appl Geogr. 37:63–87.10.1016/j.apgeog.2012.10.008
   Web of Science ®Google Scholar