Prediction of Some Stand Parameters using Pan-Sharpened IKONOS Satellite Image

References

• Akalp T. (1978)—Yield research of Picea orientalis L. forest in Turkey. PhD thesis, Graduate School of Natural and Applied Sciences, University of Istanbul. (In Turkish).
   Google Scholar
• Baskent E.Z., Köse S., Keles S. (2005)—Forest management planning system of Turkey: Constructive criticism towards the sustainable management of forest ecosystems. International Forestry Review, 7: 208–217. doi: http://dx.doi.org/10.1505/ifor.2005.7.3.208.
   Web of Science ®Google Scholar
• Cakir G. (2006)—Establishing spatial data base for forest management planning using remote sensing and geographic information systems. Karadeniz Technical University, Institute of Science. PhD thesis, p. 127.
   Google Scholar
• Chiavetta U., Chirici G., Lamonaca A., Lasserre B., Ottaviano M., Marchetti M. (2008)—Estimation of forest attributes by integration of inventory and remotely sensed data in Alto Molise. Italian Journal of Remote Sensing, 40 (1): 89–106. doi: http://dx.doi.org/10.5721/ItJRS20084018.
   Google Scholar
• Chubey M.S., Franklin S.E., Wulder M.A. (2006)—Object-based analysis of Ikonos-2 imagery for extraction offorest inventory parameters. Photogrammetric Engineering & Remote Sensing, 72: 83–394. doi: http://dx.doi.org/10.14358/PERS.72A383.
   Google Scholar
• Clevers J.G.P.W. (1988)—The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25: 53–70. doi: http://dx.doi.org/10.1016/0034-4257(88)90041-7.
   Web of Science ®Google Scholar
• Corona P., Scotti R., Tarchiani N. (1998)—Relationship between environmental factors and site index in Douglas-fir plantations in central Italy. Forest Ecology and Management, 101: 195–207. doi: http://dx.doi.org/10.1016/S0378-1127(98)00281-3.
   Google Scholar
• Crippen R.E. (1990)—Calculating the vegetation index Faster. Remote Sensing of Environment, 34: 71–73. doi: http://dx.doi.org/10.1016/0034-4257(90)90085-Z.
   Web of Science ®Google Scholar
• Erdas (2002)—Sixth edition. Erdas LLC, Atlanta, Georgia.
   Google Scholar
• Fontes L., Tome M., Thompson F., Yeomans A., Luis J.S., Savill P. (2003)—Modelling the Douglas-fir (Pesudotsuga menziesii (Mirb.) Franco) site index from site factors in Portugal. Forestry, 76: 491–507. doi: http://dx.doi.org/10.1093/forestry/76.5.491.
   Web of Science ®Google Scholar
• Hall R.J., Skakun R.S., Arsenault E.J. (2006)—Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225: 378–390. doi: http://dx.doi.org/10.1016/jforeco.2006.01.014.
   Web of Science ®Google Scholar
• Huete A.R. (1988)—A soil adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295–309. doi: http://dx.doi.org/10.1016/0034-4257(88)90106-X.
   Web of Science ®Google Scholar
• Huete A.R., Justice C., van Leeuwen W. (1999)—MODIS vegetation index (MOD 13) algorithm theoretical basis document (ATBD) Version 3.0, EOS Project Office, NASA Goddard Space Flight Center, Greenbelt, MD, p. 2.
   Google Scholar
• Gebreslasie M.T., Ahmed F.B., Van Aardt J.A.N. (2010)—Predicting forest structural attributes using ancillary data and ASTER satellite data. International Journal of Applied Earth Observation and Geoinformation, 12: 23–26. doi: http://dx.doi.org/10.1016/j.jag.2009.11.006.
   Google Scholar
• Gomez C., Wulder M.A., Montes F., Delgado J.A. (2012)—Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART). Remote Sensing, 4: 135–159. doi: http://dx.doi.org/10.3390/rs4010135.
   Google Scholar
• Jordan C.F. (1969)—Derivation of leaf area index from quality of light on the forest floor. Ecology, 50: 663–666. doi: http://dx.doi.org/10.2307/1936256.
   Web of Science ®Google Scholar
• Kayitakire F., Hamel C., Defourny P. (2006)—Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102: 390–401. doi: http://dx.doi.org/10.1016/j.rse.2006.02.022.
   Web of Science ®Google Scholar
• Leckie D.G., Gillis M.D. (1995)—Forest inventory in Canada with emphasis on map production. The Forestry Chronicle, 71: 74–88. doi: http://dx.doi.org/10.5558/tfc71074-1.
   Web of Science ®Google Scholar
• Leboeuf A., Beaudoin A., Fournier R.A., Guindon L., Luther J.E., Lambert M.C. A. (2007)—A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery. Remote Sensing of Environment, 110: 488–500. doi: http://dx.doi.org/10.1016/j.rse.2006.05.025.
   Web of Science ®Google Scholar
• Mohammadi J., Joibary S.S., Yaghmaee F., Mahiny A.S. (2010)—Modeling forest stand volume and tree density using Landsat ETM data. International Journal of Remote Sensing, 31: 2959–2975. doi: http://dx.doi.org/10.1080/01431160903140811.
   Web of Science ®Google Scholar
• Mora B., Wulder M.A., White J.C. (2010)—Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment. Remote Sensing of Environment, 114: 2474–2484. doi: http://dx.doi.org/10.1016/j.rse.2010.05.022.
   Web of Science ®Google Scholar
• Özdemir I. (2008)—Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened QuickBird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 29: 5643–5655. doi: http://dx.doi.org/10.1080/01431160802082155.
   Web of Science ®Google Scholar
• Özdemir İ., Karnieli A. (2011)—Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel. International Journal of Applied Earth Observation and Geoinformation, 13 (5): 701–710. doi: http://dx.doi.org/10.1016/jjag.2011.05.006.
   Web of Science ®Google Scholar
• Pasher J., King D.J. (2010)—Multivariate forest structure modeling and mapping using high resolution airborne imagery and topographic information, Remote Sensing of Environment, 114: 1718–1732. doi: http://dx.doi.org/10.1016/j.rse.2010.03.005.
   Google Scholar
• Peuhkurinen J., Maltamo M., Vesa L., Packalén P. (2008)—Estimation of forest stand characteristics using spectral histograms derived from an IKONOS Satellite Image, Photogrammetric Engineering & Remote Sensing, 74: 1335–1341. doi: http://dx.doi.org/10.14358/PERS.74.11.1335.
   Google Scholar
• Poulain M., Peña M., Schmidt A., Schmidt H., Schulte A. (2010)—Relationships between forest variables and remote sensing data in a Nothofagus pumilio forest. Geocarto International, 25: 25–43. doi: http://dx.doi.org/10.1080/10106040902803558.
   Web of Science ®Google Scholar
• Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C. (1973)—Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, MD.
   Google Scholar
• Sivanpillai R., Smith C.T., Srinivasan R., Messina M.G., Wu X.B. (2006)—Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223: 247–254. doi: http://dx.doi.org/10.1016/j.foreco.2005.11.013.
   Web of Science ®Google Scholar
• Song C., Dickinson M.B., Su L., Zhang S., Yaussey D. (2010)—Estimating average tree crown size using spatial information from IKONOS and QuickBird images: Across- sensor and across-site comparisons. Remote Sensing of Environment, 114: 1099–1107. doi: http://dx.doi.org/10.1016/j.rse.2009.12.022.
   Web of Science ®Google Scholar
• SPSS (2007)—SPSS Base 15.0 User's Guide. Institute Inc.
   Google Scholar
• Trotter C.M., Dymond J.R., Goulding C.J. (1997)—Estimation of timber volume in a coniferous plantation forest using Landsat TM. International Journal Remote Sensing, 18: 2209–2223. doi: http://dx.doi.org/10.1080/014311697217846.
   Web of Science ®Google Scholar
• White H. (1980)—A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48 (4): 817–838. doi: http://dx.doi.org/10.2307/1912934.
   Web of Science ®Google Scholar
• Zimble D.A., Evans D.L., Carison G.C., Parker R.C., Grado S.C., Gerard P.D. (2003)—Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sensing of Environment, 87 (2–3): 171–182. doi: http://dx.doi.org/10.1016/S00344257(03)00139-1.
   Web of Science ®Google Scholar