Modeling Stand Volume using Landsat TM Data for Fir Stands (Abies bornmuelleriana Matth.) Located in Buyukduz Planning Unit, TURKEY

Year 2012, Volume: 12 Issue: 3, 65 - 71, 01.09.2012

Alkan Günlü İlker Ercanlı Muammer Şenyurt Ayşe TEZCAN Yayla

Abstract

Keywords

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Modeling Stand Volume using Landsat TM Data for Fir Stands (Abies bornmuelleriana Matth.) Located in Buyukduz Planning Unit, TURKEY

Abstract

Remotely sensed data in the form of satellite images have been used for decades to estimate forest parameters in support of forest management planning (Leyk et al., 2002). Since satellite data can be repeatedly acquired with reliable data quality, methods about modeling some stand attributes with dataoriginated satellite images is appropriate for obtaining information on land cover on forest areas (Wulderand and Seemann, 2003). Based on 97 sample plots, it is aiming to model relationships between stand volume and band values based on Landsat TM data for fir stands (Abies bornmuelleriana Matth.) located in Buyukduz Planning Unit, TURKEY. Multiple linear regression models were used to predict stand volumes with band values, including TM 1 - TM 5 and TM 7, originated from Landsat TM satellite image. The regression models, including different independent variables alternatives and band values, were compared with some information criteria, e.g. the adjusted coefficient of determination (R2), with Reduced Akaike’s Information Criterion (AIC), Sawa’s Bayesian Information Criteria (BIC), Schwarz Bayesian Criteria (SBC), the root mean square error (RMSE) and Mallow’s Cp, which criteria are measures of goodness of fit for regression models. These statistical analyses were performed by PROC REG and PROC RSQUARE procedures of the SAS/ETS V9 software (SAS Institute Inc, 2004). The best results for predictive performance were obtained by multiple linear regression model including TM 2 and TM 4 as independent variables. This model, statistically significant at 95% level with model parameters, explained 54.09% of the observed stand volume variability with 634.29 of AIC, 637.02 of BIC, 640.36 of SBC, 28.69 of RMSE and -0.315 of Cp. The results showed that the Landsat TM data are beneficial to estimate forest stand volume. Thus, forest managers could use remote sensing data, e.g. Landsat TM data, for predicting stand volume and for generating maps necessary for developing forest management plans

Keywords

Landsat TM satellite image, Multiple regression analysis, Stand volume, Abies

References

• Akaike H., 1973. Information theory and an extension of the maximum likelihood principle. In B.N. Petrov and F. Csaki (Eds.), Second international symposium on information theory, 267-281. Budapest: Academiai Kiado.
• Akaike H., 1987. Factor analysis and AIC. Psychometrika, 52, 317-332.
• Anonymous., 2010. Forest Management planning.
• Beal, D.J., 2007. Information criteria methods in SAS for multiple linear regression models, SAS Note, Paper SA05, 10 s.
• Bozdogan H., 1987. Model selection and Akaike’s information criterion (AIC): the general theory Psychometrika, 52, 345-370. analytical extensions.
• Bozdogan H., 2000. Akaike’s information criterion informational Mathematical Psychology, 44, 62-91. in of Journal
• Chapman R.A., Heitzman E., Shelton M.G. 2006. Long-term changes in forest structure and species composition of an upland oak forest in Arkansas. Forest Ecology and Management, 236, 85–92.
• Cohen W.B., Goward S.N. 2004. Landsat's role in ecological applications of remote sensing. BioScience, 54, 535–545.
• 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.
• Dodge A.G., Bryant E.S. 1976. Forest type mapping with satellite data. J. For. 74, 526–531.
• Erdas, 2002. Sixth edition. Erdas LLC, Atlanta, Georgia.
• Fontes L., Margarida T., Thompson F., Yeomans A., Luis J.S., Savill P. 2003. Modelling the Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) site index from site factors in Portugal. Forestry, 76, 491-507.
• 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.
• Huiyan G., Dai L., Wu G., Xu D., Wang S., Wang H. 2006. Estimation of forest volumes by integrating Landsat TM imagery and forest inventory data. Science in China Series E. Technological Sciences, 49, 54–62.
• Leyk S., Köhl M., Oncét P. 2002. Application of Future TerraSAR Data for Improvement of Forest Resource Assessments, ForestSAT Symposium, Heriot Watt University, Edinburgh, 5–9 August 2002. Available online at: http://www.forestry.gov. uk/pdf/leyk.pdf/$FILE/leyk.pdf February 2006). (accessed 1
• Makela H., Pekkarınen A. 2004. Estimation of forest stands volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, 196, 245–255.
• Malingreau J.P., Cunha R., Justice C. 1992. Proceedings of the World Forest Watch Conference, Say Jose Dos Campos, European Commission.
• Mallinis G., Koutsias N., Makras A., Karteris M. 2003. Forest parameters estimation in a European remotely sensed data. Forest Science, 50(4), 450– 460. landscape using
• Mallows C.L., 1973. Some comments on Cp. Technometrics, 15, 661-675.
• Mohammadi J., Joibary S.S., Yaghmaee F., Mahiny A.S. 2010. Modelling forest stand volume and tree density using Landsat ETM data. International Journal of Remote Sensing, 31, 2959–2975.
• SAS Institute Inc., 2004. SAS/STAT 9.1 User's Guide: statistics, Version 9.1, SAS Institute Inc., Cary, NC., 816 s.
• Sawa T., 1978. Information criteria for discriminating among alternative regression models. Econometrica, 46, 1273-1282.
• Schwarz G., 1978. Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
• Sironen S., Kangas A., Maltamo M., Kangas J. 2001. Estimating individual tree growth with k- nearest neighbor and k-most similar neighbour methods. Silva Fennica, 35, 453-467.
• Sivanpillai R., Smith C.T., Srinivasan R., Messina M.G., Ben Wu X. 2006. Estimation of managed loblolly pine stand age and density with Landsat ETM+ data. Forest Ecology and Management, 223, 247–254.
• 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.
• Wulder M.A., Seemann D. 2003. Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Canadian Journal of Remote Sensing, 29(5), 536– 543.
• 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.