RELATIONSHIPS BETWEEN EASTERN BEECH FORESTS STAND PARAMETERS AND LANDSAT ETM SPECTRAL RESPONSES IN TURKEY

Öz

This paper explores relationships between forest stand parameters and Landsat Enhancement Thematic Mapper (ETM), atmospheric correction applied, spectral responses thorough analyses of study area in Mugada, Bartin and its vicinity where natural beech (Fagus orientalis L.) stands. ETM bands and many vegetation indices were examined thorough integration of spectral responses and field vegetation inventory data. Pearson’s correlation coefficients were used to interpret relationships between forest stand parameters and TM data. Besides, regression analysis method for the development of multi linear regression models was used. This study concludes that vegetation indices such as KT2 (greenness of the tasselled transform), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR), Soil Adjusted Vegetation Index (SAVI) and PC1 (the first component in a principal components analysis) were significantly correlated with forest stand parameters. Some vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and KT3 (Wetness of the tasselled transform), were not significantly with selected forest stand parameters. To estimate the stand parameters by making use of the relations between stand parameters and remote sensing data multiple linear regression models were formed using stepwise regression analysis method. As the resulting product, thematic maps were produced concerning basal area, tree height and volume.

Anahtar Kelimeler

• Atmospheric correction, Fagus orientalis L., forest stand parameters, spectral response, vegetation indices

RELATIONSHIPS BETWEEN EASTERN BEECH FORESTS STAND PARAMETERS AND LANDSAT ETM SPECTRAL RESPONSES IN TURKEY

Öz

Anahtar Kelimeler

• -

Kaynakça

1. o Astola, H., Bounsaythip, C., Ahola, J., Häme, T., Parmes, E., Sirro, L., Veikkanen, B., 2004. HighForest – Forest parameter estimation from high resolution remote sensing data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, Vol. XXXV, Part B, pp. 335-341. o Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indexes for LAI and APAR assessment. Remote Sensing of Environment, 35 (2–3), 161–173. o Chaves, S., 1996. Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering & Remote Sensing, 62, 1025–1036. o Choudhury, B.J., 1994. Synergism of multispectral satellite observation for estimating regional land surface evaporation. Remote Sensing of Environment, 49, 264-274. o Crist, E.P., Kauth, R.J., 1986. The tasseled cap demystified. Photogrammetric Engineering and Remote Sensing, 52 (1), 81-86. o Eklundh, L., Harrie, L., Kuusk, A., 2001. Investigating relationships between Landsat ETM sensor data and leaf area index in a boreal conifer forest. Remote Sensing of Environment, 78, 239–251. o Fazakas, Z., Nilsson, M., Olsson, H., 1999. Regional forest biomass and wood volume estimation using satellite data and ancillary data. Agricultural Forest meteorology, 98-99, 417-425. o Freitas, S.R., Mello, M.C.S., Cruz, C.B.M., 2005. Relationship between forest structure and vegetation indices in Atlantic Rainforest. Forest Ecology and Management, 218, 353-362. o Gong, P., Pu, R., Miller, J.R., 1995. Coniferous forest leaf area index estimation along the Oregon transects using compact airborne spectrographic imager data. Photogrammetric Engineering & Remote Sensing, 61, 1107–1117. o Goodenoughl, D.G., Deguisel, J., Robson, M.A., 1990. Multiple expert systems for using digital terrain models. In. Proceedings of IGARS’90, Washington, pp. 96. o Hall, R., J., Skakun, R., S., Arsenault, E., J., Case, B., S., 2006. Modelling forest stand structure attributes using Landsat ETM data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225, 378-390. o Holmgren, J., Nilsson, M., Olsson, H., 2003. Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Science, 49, 419−428. o Holström, H., Fransson, J.E., 2003. Combining remotely sensed optical and radar data in kNNestimation of forest variables. Forest Science, 49, 409−418. o İ nan, M., 2004. Remote sensıng data for determınıng forest resources. Ph. D. Thesis. İstanbul University. o Kachhwala, T.S., 1985. Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing. In. Proceedings of the International 6th Asian Conference on Remote Sensing, Hyderabad, pp. 77-83. o Kalıpsız, A., 1993. Forest mensuration. Forest Faculty, No: 3793, Istanbul University. o Konukçu, M., 2001. Forests and Forestry. T. R. Ministry of Development, No: 2630. o Lefsky, M.A., Hudak, A.T., Cohen, W,B., Acker, S.A., 2005. Patterns of covariance between forests stand and canopy structure in the Pacific Northwest. Remote Sensing of Environment, 95, 517-531. o Liang, S., Fang, H., Chen, M., 2001. Atmospheric correction of Landsat ETM+ land surface imagery— part 1: methods. IEEE Transactions on Geosciences and Remote Sensing, 39, 2490-2498. o Lillesand, T.M., Kiefer, R.W., 2004. Remote sensing and image interpretation. New York: John Wiley & Sons. o Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon basin. Forest Ecology and Management, 198, 149-1 o Makela, H., Pekkarinen, A., 2004. Estimation of forest stands volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, 196, 245–255. o McRoberts, R.E., Tomppo, E.O., 2007. Remote sensing support for National Forest Inventories. Remote Sensing of Environment, 110 (4), 412-419. o Özhan, S., 1991. Land-use technique. M. Sc. Thesis. Istanbul University. o PCI Guide, 2005. Geomatica focus user guide. Canada: PCI Geomatica. o Quaidrari, H., Vermote, E.F., 1999. Operational atmospheric correction of Landsat TM data. Remote Sensing of Environment, 70, 4-15. o Reese, H., Nilsson, M., Sandstrom. P., Olsson, H., 2002. Applications using estimates of forest parameters derived from satellite and forest inventory data. Computers and Electronics in Agriculture, 37, 37-55. o Richter, R., 1996. A spatially adaptive fast atmospheric correction algorithm. International Journal Remote Sensing, 17, 1201-1214. o Richter, R., 1998. Correction of satellite imagery over mountainous terrain applied. Optics, 37, 400440 o Richter, R., 2008. Atmospheric/Topographic correction for satellite imagery. ATCOR-2/3 user guide. Wessling: DLR IB 565-01/08. o Roy, P.S., Ravan, S.A., 1996. Biomass estimation using satellite remote-sensing data–an investigation on possible approaches for natural forest. Journal of Biosciences, 21, 535–561. o Rubner K (1960) Die pflanzengeographischen grundlagen des waldbaues. Berlin: Neumann Verlag. o Sarıkaya, Ö.V., 2006. Water quality analysis in the Golden Horn (Haliç) with the help of Ikonos imagery. M. Sc. Thesis. Istanbul University. o Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230-244. o Zhang, M., Carder, K., Muller-Karger, F.E., Lee, Z., Goldgof, D.B., 1999. Noise reduction and atmospheric correction for coastal applications of Landsat Thematic Mapper imagery. Remote Sensing of Environment, 70, 167-180. o Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., Ryu, S., 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93, 402-411.