Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

References

• Baatz M, Benz U, Dehghani S, Heynen M, Holtje A, Hofmann P, Lingenfelder I, Mimler M, Sohlbach M, Weber M, Willhauck G (2004) eCognition professional: user guide definiens-imaging. Definiens, Munich
   Google Scholar
• BoydDSDansonFMSatellite remote sensing of forest resources: three decades of research developmentProg Phys Geogr20052912610.1191/0309133305pp432ra
   Web of Science ®Google Scholar
• BreimanLFriedmanJHOlshenRAStoneCJClassification and regression trees1984MontereyWadsworth and Brooks
   Google Scholar
• CastillaGHayGJBlaschkeTLangSHayGJImage objects and geographic objectsObject-based image analysis2008BerlinSpringer91110
   Google Scholar
• CoburnCARobertsACBA multiscale texture analysis procedure for improved forest stand classificationInt J Remote Sens2004254287430810.1080/0143116042000192367
   Web of Science ®Google Scholar
• CohenJA coefficient of agreement for nominal scalesEduc Psychol Meas196020374610.1177/001316446002000104
   Web of Science ®Google Scholar
• CongaltonRGA review of assessing the accuracy of classifications of remotely sensed dataRemote Sens Environ199137354610.1016/0034-4257(91)90048-B
   Web of Science ®Google Scholar
• FranklinSERemote sensing for sustainable forest management2001FloridaCRC
   Google Scholar
• FranklinSEHallRJMoskalLMMaudieAJLavigneMBIncorporating texture into classification of forest species composition from airborne multispectral imagesInt J Remote Sens200021617910.1080/014311600210993
   Web of Science ®Google Scholar
• FranklinSEWulderMAGeryloGRTexture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British ColumbiaInt J Remote Sens20012226272632
   Web of Science ®Google Scholar
• GergelSEStangeYCoopsNCJohansenKKirbyKRWhat is the value of a good map? An example using high spatial resolution imagery to aid riparian restorationEcosystems20071068870210.1007/s10021-007-9040-0
   Web of Science ®Google Scholar
• HájekFObject-oriented classification of IKONOS satellite data for the identification of tree species compositionJ For Sci200652181187
   Google Scholar
• HaralickRMShanmugamKDinsteinITextural features for image classificationIEEE Trans Syst Man Cybern1973SMC-361062110.1109/TSMC.1973.4309314
   Web of Science ®Google Scholar
• Ito T (2004) Forestry GIS. In: GIS Association of Japan (eds) The encyclopedia of geographical information science. Asakura, Tokyo, pp 138–139 (in Japanese)
   Google Scholar
• KatohMClassifying tree species in a northern mixed forest using high-resolution IKONOS dataJ For Res2004971410.1007/s10310-003-0045-z
   Web of Science ®Google Scholar
• KayitakireFHamelCDefournyPRetrieving forest structure variables based on image texture analysis and IKONOS-2 imageryRemote Sens Environ200610239040110.1016/j.rse.2006.02.022
   Web of Science ®Google Scholar
• KellyMMeentemeyerRKLandscape dynamics of the spread of Sudden Oak DeathPhotogramm Eng Remote Sens20026810011009
   Web of Science ®Google Scholar
• LozanoFJSuárez-SeoaneSKellyMLuisEA multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean regionRemote Sens Environ200811270871910.1016/j.rse.2007.06.006
   Web of Science ®Google Scholar
• LuDWengQA survey of image classification methods and techniques for improving classification performanceInt J Remote Sens20072882387010.1080/01431160600746456
   Web of Science ®Google Scholar
• MurakamiTSeasonal variation in classification accuracy of forest-cover types examined by a single band or band combinationsJ For Res2004921121510.1007/s10310-004-0075-1
   Web of Science ®Google Scholar
• MurakamiTHow is short-wave infrared (SWIR) useful to discrimination and classification of forest types in warm temperate region?J For Plan2006128185
   Google Scholar
• MurataISarukiSKubotaKInoueSTashiroNEnokiTUtsumiYInoueSEffects of sika deer (Cervus nippon) and dwarf bamboo (Sasamorpha borealis) on seedling emergence and survival in cool-temperate mixed forests in the Kyushu MountainsJ For Res20091429630110.1007/s10310-009-0131-y
   Web of Science ®Google Scholar
• OzdemirINortonDAOzkanUYMertASenturkOEstimation of tree size diversity using object oriented texture analysis and aster imagerySensors200884709472410.3390/s8084709
   PubMed Web of Science ®Google Scholar
• RaoPVNSesha SaiMVRSreenivasKRaoMVKRaoBRMDwivediRSVenkataratnamLTextural analysis of IRS-1D panchromatic data for land cover classificationInt J Remote Sens2002233327334510.1080/01431160110104665
   Web of Science ®Google Scholar
• SalajanuDOlsonCEThe significance of spatial resolution: identifying forest cover from satellite dataJ For2001993238
   Web of Science ®Google Scholar
• TuominenSPekkarinenAPerformance of different spectral and textural aerial photograph features in multi-source forest inventoryRemote Sens Environ20059425626810.1016/j.rse.2004.10.001
   Web of Science ®Google Scholar
• WulderMOptical remote-sensing techniques for the assessment of forest inventory and biophysical parametersProg Phys Geogr199822449476
   Web of Science ®Google Scholar
• WunderleALFranklinSEGuoXAge class estimation of western red cedar using SPOT-5 pan-sharpened imagery in British Columbia, CanadaGeocarto Int200924476310.1080/10106040801950559
   Google Scholar
• YuQGongPClintonNBigingGKellyMSchirokauerDObject-based detailed vegetation classification with airborne high spatial resolution remote sensing imageryPhotogramm Eng Remote Sens200672799811
   Web of Science ®Google Scholar
• ZhangCFranklinSEWulderMAGeostatistical and texture analysis of airborne-acquired images used in forest classificationInt J Remote Sens20042585986510.1080/01431160310001618059
   Web of Science ®Google Scholar