Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran

Document Type : Research article

Authors

1 M.Sc. Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resources University, Sari, Iran.‎

2 Associate‏ ‏Prof., Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and ‎Natural Resources University, Sari, Iran

3 Associate‏ ‏Prof., Department of Forestry, Faculty of Natural Resources, Gorgan University of ‎Agriculture Sciences and Natural Resources, Gorgan, Iran

4 Ph.D. Student Forestry, Department of Forestry, Faculty of Natural Resources, Sari Agriculture ‎Sciences and Natural Resources University, Sari, Iran.‎

Abstract

   Knowledge on quantitative forest attributes is a prerequisite for forest stand management. The aim of this study was to evaluate high resolution Pleiades data in estimating the standing volume and basal area using non-parametric algorithms in Darabkola forest of Sari, Mazandaran province. A sampling design of 144 plots each with area of 1000 m2 was established using a systematic random sampling method. In each plot, information including as position of plot center, diameter at breast height of all trees within sample plot and height of selected trees were recorded, based on which the standing volume and basal area per ha were derived. The Pleiades data was preprocessed, and the pixel grey values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the standing volume and basal area per ha. Modeling was carried out based on 70% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 30% samples. Support vector machine comparatively retuned the best estimates for stand basal area with root mean square error of 38.75% and relative bias of 3.12, while it predicted the stand volume with root mean square error of 45.13% and relative bias of -3.21 as well. The results of study proved the average spectral and spatial capability of Pleiades data to estimate these two main, where the caveats are concluded to be mainly due to the heterogeneity and the density of forest stands across the study area.

Keywords

References

- Anonymus, 1996. Forest Management Plan of Darabkola Forest. Published by Forests, Range and Watershed Management Organization, Tehran, 82p (In Persian).
- Baret, F. and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 55: 161-173.
- Bell, G.E., Howell, B.M., Johnson, G.V., Solie, J.B., Raun, W.R. and Stone, M.L., 2004. A comparison of measurements obtained using optical sensing with turf growth, chlorophyll content, and tissue nitrogen. Horticultural Science, 39(5):1130-1132.
- Breiman, L., 2001. Random forests. Machine Learning, 45(1): 5-32.
- Finely, A.O., McRobert, R.E. and Ek, A.R., 2006. Applying an efficient k-nearest neighbor search to forest attribute imputation. Forest Science, 52: 130-135.
- Gonzales, R.C. and woods, R.E., 2002. Digital Image Processing. Prentice Hal, 750p.
- Gu, H., Dai, L., Wu, G., Xu, D., Wang, S. and Wang, H., 2006. Estimation of forest volumes by integration Landsat TM imagery and forest inventory data. Science in China series E. Technological science, 49: 54-62.
- Günlü, A., Ercanl, I., Sönmez, T. and Zeki Başkent, E., 2014. Prediction of some stand parameters using pan-sharpened Ikonos satellite Image. European Journal of Remote Sensing, 47: 329-342.
- Holmstrom, H. and Fransson, J.E.S., 2003. Combining remotely sensed optical and radar data in K-NN estimation of forest variables. Forest Ecology and Management, 49(3):409-418.
- Horning, N., Robinson, J.A., Sterling, E.J., Turner, W. and Spector, S., 2010. Retrospective analysis of Two Northern California Wild-land Fires Via Landsat Five Satellite Imagery and Normalized Difference Vegetation Index (NDVI). Remote Sensing for Ecology and Conservation. A Handbook of Techniques. Oxford University Press, Oxford, pp. 4-371.
- Huiyan, G., Limin, D., Gang, X., Shunzhong, W. and Hui, W., 2006. Estimation of forest volumes by integration Landsat TM imagery and forest inventory data. Science in China series E. Technological science, 49: 54-62.
- Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S. and Zhu, Y.H., 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128:109-120.
- Kajisa, T., Murakami, T., Mizoue, N., Kitahara, F. and Yoshida, S., 2008. Estimation of stand volumes using the k-nearest neighbors method in Kyushu, Japan. Journal of Forest Research, 13(4): 249-254.
- Kalbi, S., 2011. Capability of SPOT and ASTER data in estimation of some forest structure attributes (Case study: Darabkola's Forests). M.Sc. thesis, Department of Forestry, Sari  Agricultural Sciences & Natural Resources University, 107p (In Persian).
- Kerr, J.T. and Ostrovsky, M., 2003. From space to species: Ecological applications for remote sensing. Trends in Ecology and Evolution, 18: 299–305.
- Khorami, K.R., 2004. Investigation of the potential of Landsat 7 ETM+ data in volume estimating of beech forest stands (Case study: Sangedeh area in north of Iran). M.Sc. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 80p (In Persian).
- Kilpelainen, P. and Tokola, T., 1999. Gain to be achieved from stand delineation in Landsat TM image-based estimation of stand volume. Forest Ecology and Management, 124(2-3): 105-111.
-  Kutzer, C., 2008. Potential of the K-NN method for estimation and monitoring off-reserve forest resources in Ghana. Ph.D. thesis, Department of Forestry and Environmental Sciences, Germany University of Albert-Ludwigs, 145p.
- Leboeuf, A., Fournier, R.A., Luther, J.E., Beaudoin, A. and Guindon, L., 2012. Forest attribute estimation of northeastern Canadian forests using QuickBird imagery and a shadow fraction method. Forest Ecology and Management, 266: 66-74.
- Lu, D., Mausel, P., Brondizio, E. and Moran, E., 2004. Relationships between forest stand parameters and Landsat TM spectral response in the Brazilian Amazon Basin. Forest Ecology and Management, 198:149-167.
- Makela, H. and 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.
- Mohamadi, J., 2007. Estimation of some characteristics of a forest to explore the possibility of creating spatial prediction models using satellite spectral data (Case study, oak Loveh). M.Sc. thesis, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 71p (In Persian).
- Mohamadi, J., 2013. Improve estimates of the quantitative characteristics of forest structure using a combination of data commonly called lidar and aerial digital images (Case Study: broadleaf forests Shastkalate Gorgan). Ph.D. thesis, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 241p (In Persian).
- Mosadegh, A., 2004. Silviculture. University of Tehran Press, 481p (In Persian).
- Noorian, N., 2013. Comparison of various medium (Aster and TM) and high (Quickbird) spatial resolution in estimation of a forest quantitative variables. M.Sc. thesis, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 120p (In Persian).
- Packalen, P., Maltamo, M., 2007. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment, 109(3): 328-341.
- Reese, H., Nilsson, M., Sandstorm, P. and Olsson, H., 2002. Applications using estimates of forest parameters derived from satellite and forest inventory data. Computers and Electronics in Agriculture, 37(1): 37-55.
- Richardson, A.J. and Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogramnetric Engineering and Remote Sensing, 43(12): 1541-1552.
- Roujean, J.L. and Breon, F.M., 1995. Estimating PAR Absorbed by vegetation from Bidirectional reflectance measurement. Remote Sensing of Environment, 51: 375-384.
- Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973. Monitoring vegetation system in the Great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC., 10-14 Dec. 1973: 309-317.
- Shataee, Sh., 2011. Non-parametric forest attributes estimation using LIDAR and TM data. The Asian Conference on Remote Sensing, Taiwan, 3-7 Oct. 2011: 887-894.
- Shataee, Sh., Kalbi, S. and Fallah. A., 2012. Forest attributes imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33(19): 6254-6280 (In Persian).
- Tokola, T. and Heikikkilä, J., 1997. Improving Satellite image based forest inventory by using a priori site quality information. Silva Fennica, 31: 67-78.
- Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150.
Iranian Journal of Forest and Poplar Research
Volume 23, Issue 3 - Serial Number 3
September 2015
Pages 465-477

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