Düzlerçamı kızılçam ormanında Quickbird uydu verileri kullanılarak gövde hacminin tahmini

Uzaktan algılama verilerinden çıkartılan parametrelerle, mescere özellikleri arasında kuvvetli bir iliski olması durumunda, regresyon ve diğer modelleme teknikleri kullanılarak mescere özellikleri daha az masrafla tahmin edilebilir. Bu çalısmada, aynı yaslı ve tek katlı mescerelerden olusan bir kızılçam ormanında, Quickbird (pan-sharpened) uydu verisinden çıkartılan spektral ve mekansal özellikler ile hektardaki gövde hacmi arasındaki iliskiler arastırılmıstır. Spektral ve mekansal özellikler görüntü segmentasyonu tekniği ile olusturulan poligonlar temel alınarak çıkartılmıstır. Bunlarla mescere özellikleri arasındaki iliskiler basit regresyon modelleriyle ortaya koyulmustur. Regresyon analizlerine göre; hacim ile parlaklık değerleri arasındaki iliskide ulasılan en yüksek korelasyon katsayısı 0,40’dır. Öte yandan hacim ile gölge uzunluğu arasındaki kurulan regresyon modelinde en yüksek korelasyon katsayısı 0,81 olarak hesaplanmıstır. Çalısmanın sonuçları; i) Quickbird görüntüsünden elde edilen parlaklık değerlerinin, gövde hacminin tahmininde kullanılamayacağını, ii) görüntü üzerinden ölçülen gölge uzunluğunun ise gövde hacminin tahmini için güvenilir bir yardımcı değisken olabileceğini göstermistir.

Anahtar Kelimeler:

Antalya, çam, ormanlar, regresyon analizleri, uzaktan algılama, hacim, meşcere özellikleri

Stem volume estimation in the brutian pine forest located in düzlerçamı using Quickbird satellite data

When a strong relationship exists between forest attributes and the features extracted from remote sensing, forest attributes can be estimated in a cost-effective way from RS imagery using regression or other modelling techniques. This study explores the relationship between field measured stem volume and the spectral and spatial features extracted from Quickbird satellite data in a Brutian pine forest that is consisted of even aged stands. These features were extracted based on the polygons that are generated using image segmentation technique. The relationships between the image derived features and forest attributes were determined using simple regression models. According to the regression analyses, the best model for stem volume using brightness values resulted in a correlation coefficient of 0.40. The model for stem volume using shadow length resulted in a correlation coefficient of 0.81. The results showed that i) the brightness values of the Quickbird data used is not an appropriate auxiliary parameter for estimating stem volume, ii) the shadow length measured from the imagery, on the other hand, can be suitable for the estimation of stem volume in Brutian pine forests.

Keywords:

Antalya, pines, forests, regression analysis, remote sensing, volume, stand characteristics,

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Ahern, F.J., Erdle, T., Maclean, D.A., Kneppeck, I.D., A quantitative relationship between forest growth rates and Thematic Mapper reflectance measurements, International Journal of Remote Sensing, 12:3, 387-400.

Akça, A., 2000. Forest Inventory, Institute for Forest Management and Yield Sciences, University of Göttingen, 191 s.

Akca, A., Beisch, TH., Eilermann, F., 1995. Two-Phased Sampling Method, Using Regression Estimators and Small-Scale Aerial IR-Photographs in Volume and Increment Estimation, Proceeding of the Meeting of IUFRO, 6-12 August, Volume II, Tempere, Finland, s. 255-264.

Asan, Ü., 2000. Türkiye İçin Uygun Ulusal Orman Envanteri Modellinin Belirlenmesi, Orman Bakanlığı Bülteni, No: 2, Ankara, s. 18-28.

Asan, Ü., Ercan, M., 2002. Orman Amenajmanında Yeni Açılımlar ve Uygulamalar (Kerpe Örneği), Orman Amenajmanında Kavramsal Açılımlar ve Yeni Hedefler Sempozyumu, 18-19 Nisan, İstanbul, s. 8-22.

Baatz, M., Benz, U., Dehgani, S., Heynan, M., Holtje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M. And Willhauck, G., 200. eCognition Object Oriented Image Analysis User Guide. Munich, Germany, 483,

Baskent, E.Z., Kadıoğulları, A.İ., Spatial and temporal dynamics of land use pattern in Turkey: A case study in İnegöl, Landscape and Urban Planning, 81, 316-327.

Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. And Heynen, M., 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3-4): 239-258.

Birler A.S., 1998. Endüstriyel Plantasyonlar (Orman Ağaçları Tarımı). Çevre ve İnsan, Kıvanç, M., Yücel, E., (Eds.), Anadolu Üniversitesi Yayınları, 1017/560, s. 173-188.

De Wulf, R.R., Goosens, R.E., De Roover, B.P., Borry, F.C., 1990. Extraction of forest stand parameters from panchromatic and multispectral SPOT-1 data, International Journal of Remote Sensing, 11:9, 1571-1588.

Donoghue, D.N.M., Watt, P.J., 2006. Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests. International Journal of Remote Sensing, 27-11: 2161-2175.

Erdin, K., 1986. Fotoyorumlama ve Uzaktan Algılama. Đ.Ü.Orman Fak., Yayın No: 3404-381, İstanbul, 183 s.

Freitas SR, Mello MCS, Cruz CBM., 2005. Relationships between forest structure and vegetation indices in Atlantic Rainforest. Forest Ecology and Management 218 (1-3): 353-362.

Gering, LR., May, DM., 1995., The relationship of Diameter at Breast Height and Crown Diameter for Four Species Groups in Hardin County Tennessee. Southern Journal of Applied Forestry 19-4, 177-181.

Hagan, G.F., Smith, JL., 1986. Predicting tree ground line diameter from crown measurement made on 35-mm aerial photography. Photogrammetric Engineering and Remote Sensing, 52:5, 687-690.

Hall R.J., Skakun, R.S., Arsenault, E.J., Case, B.S., 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.

Köhl, M., 1993. Forest Inventory, Pancel, L. (ed), Tropical Forestry Handbook, Springer Verlag, 243332, Heidelberg, s. 273-276.

Leboeuf, A., Beaudoin, A., Fournier, R.A., Goindon, L., Luther, J.E., Lambert, M.C., 2007. A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery Remote Sensing of Environment, 110 (4), 488-500.

McDonald AJ, Gemmell FM, Lewis PE., 1998. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing of Environment 66 (3): 250-272.

Myeong S, Nowak DJ, Duggin MJ., 2006. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment 101 (2): 277-282. Nilson, T.,Kuusk, A., Lang M., Lükk., 2003. Forest Reflectance Modeling: Theoretical Aspects and Applications, Ambio, 33: 8, 534-540.

Özdemir, Đ., 2004. Orman Envanterinde Uydu Verilerinden Yararlanma Olanakları, SDÜ, Orman Fakültesi Dergisi, Seri A, Sayı 1, Isparta, 84-96.

Paine, D.P., McCadden, R.J., 1988, Simplified forest inventory using large-scale 70-mm photography and tariff tables. Photogrammetric Engineering and Remote Sensing, v. 54, no. 10, p. 1423-1427.

Puhr, C.B., Donoghue, D.N.M., 2000. Remote sensing of upland conifer plantations using Landsat TM data: A Case Study from Galloway, South-West Scotland. International Journal of Remote Sensing, 21, 633-646.

Radeloff, V.C., Mladenoff, D.J., Boyce, M.S., 1999. Detecting jack pine budworm defoliation using spectral mixture analysis: Separating effects from determinants, Remote Sensing of Environment, 69 (2), 156-169.

Shiver, B.D., Borders, B.E., 1996. Sampling Techniques for Forest Resource Inventory, John Willey&Sons, New York, s. 190-195.

Sivanpillai, R., Smith, CT., Srinivasan, R., Messina, MG., Ben Wu, X., 2006. Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management 223 (1-3): 247-254.

Song, C., 2007. Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery. International Journal of Remote Sensing, 28: 3305-3322.

Sunar, F., Özkan, C., Forest fire analysis with remote sensing data, International Journal of Remote Sensing. 22 (12): 2265-2277.

Tomppo, E. 1993. Multi-Source National Forest Inventory of Finland. Proceedings of Ilvessalo Symposium on National Forest Inventories. Finland 17-21 August, 1992. IUFRO S4.02. The Finnish Forest Research Institute. Research Papers 444. pp. 52-60.

Trotter, C.M., Dymond, J.R., Goulding, C.J., 1997. Estimation of timber volume in a coniferous plantation forest using Landsat TM. International Journal of Remote Sensing, 18-10: 2209 –2223.

Yesil, A., Asan, Ü., Coskun, G., Örmeci, C., Kaya, S.,1999. Statically Modeling and Stand Type Forest Mapping Selected Area Around Đstanbul Using Landsat-TM and SPOT Data, Proceedings of the International Symposium On Remote Sensing & Integrated Technologies, İstanbul, 151-162.

Zheng DL, Rademacher J, Chen JQ, Crow T, Bresee M, le Moine J, Ryu SR., 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment 93 (3): 402-411.