Estimating forest structural attributes by means of ASTER imagery and CART algorithm (Case study: Shastkolateh forest, Gorgan)

Document Type : Research article

Authors

1 Ph. D. Student of Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, I.R. Iran.

2 Associate Prof., Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran.

3 Ph. D. Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran.

4 M. Sc. Forestry, Department of Forestry, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, I.R. Iran.

Abstract

Large-area estimation of forest structural attributes by remotely-sensed data is crucial for cost effective inventory of the stands, and in turn for sustainable forest management. The objective of this research was to investigate the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery for predicting forest structural attributes over Shastkolateh experimental forest in Gorgan. By means of random cluster sampling method, 112 DGPS-established square plots with an area of 0.09 ha were inventoried which were also homogenous by type and aspect. In those plots, the stand volume, basal area and tree stem density were measured. The image data was geometrically and atmospherically corrected. Moreover, information within the data was used to create additional band ratios, principal components, texture indices, and tasseled cap components, which were then added to the original datasets. Classification and Regression Trees (CART) algorithm was applied for modeling the ground inventory data. The models were assessed for their performance by means of root mean square error (RMSE) and Bias using hold-out samples. The results showed the best values of adjusted R-squared to be 76, 73 and 80% for stand volume, basal area and tree stem density, respectively. Whereas the models of standing volume, basal area and stem density retuned  RMSE vauues of 40.22, 38.67, and 58.68, the models were associated with bias values of 17.5 %, 8% and 2.72%, respectively. Results therefore indicate the moderate potential of ASTER imagery for sample plot-based estimation of forest structural attributes.

Keywords

References

- Anonymous, 2007. Forest management plan. Faculty of Natural Resources, Gorgan University of Agricultural Sciences & Natural Resources, 478p (In Persian).
- Azizi, Z., Najafi, A., Fatehi, P. and Pirbavaghar, M. 2009. Forest stand volume estimation using satellite IRS-P6 (LISS-IV) data (Case study: Lirehsar, Tonekabon). Iranian Journal of Forest and Poplar Research, 18(1): 143-151 (In Persian).
- Chiao, K.M. 1996. Comparisons of three remotely sensed data on forest crown closure and volume estimations, International Archives of photogrammetric and remote sensing, 31(B7): 123-130.
- Cohen, W.B. and Spies, T.A. 1992. Estimation structural attributes of Douglas fir/western hemlock Forests stand from Landsat and SPOt imagery. Remote Sensing of Environment, 41: 1-17.
- Daghestani, M. 2008. Surveying the relationship of beech stand structural attributes and its spectral signature on satellite data. Ph. D. thesis, Department of Forestry, Islamic Azad University, Science and Research Branch, Tehran, 92p (In Persian).
- Darvishsefat, A.A., Fatehi, P., Khalilpour, A. and Farzaneh, A. 2004. Comparison of Spot 5 and Landsat 7 for Forest Area Mapping, XX th ISPRS Congress, Istanbul, Turkey, 4p.
- Franco-Lopez, H., Ek, A.R. and Bauer, M.E. 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment, 77(3): 251-274.
- Gebreslasie, M.T., Ahmed, F.B., Jan, A.N. and Adrdt, V. 2010. Predicting forest structural attributes using ancillary data and ASTER satellite data. International Journal of Applied Earth Observation and Geo Information, 125: 523-526.
- Hall, R.J. and Skakun, R.S. 2007. Mapping inventory attributes across coniferous, deciduous and mixed wood stand types in the northwest territories from high spatial resolution Quickbird satellite imagery. Natural Resources Canada, Environment Through Remote Sensing, 6p.
- Hall, R.J., Skakun, R.S., Arsenault, E.J. and 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: 375-390.
- Heikki, A. 2003. High forest-forest estimation from high resolution remote sensing data. Technical Research Center of Finland Information Technology Inform motion System, 8p.
- Huang, X. and Jensen, J. 1997. A machine- learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric Engineering and Remote Sensing. 63(10): 1185-1194.
- Hyyppa, J., Hyyppa, H. Inkinen, M., Engdahi, M., Linko, S. and Zhu, Y.H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of stand attributes. Forest Ecology and management, 128: 109-120.
- Khorrami, K.R. 2004. Investigation of the potential of Landsat7 ETM+ data in volume estimating of beech forest stand (Case Study: Sangdeh area in north of Iran). M. Sc. thesis, University of Tehran, Faculty of Natural Resources, 80p (In Persian).
- Kalbi, S. 2011. Estimation of forest structural attributes using ASTER and SPOT-HRG data (Case study: Darabkola forest). M. Sc- thesis, Department of Forestry, Sari University of Agricultural Sciences & Natural Resources, 107p (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.
- Lu, D., Mausel, P., Brondizio E. and Moran, E. 2004. Relationships between forest stand parameters and Landsat TM specteral in the Brazilian Amazon basin. Forest Ecology and Management, 198: 149-167.
- Makela, H. and Pekkarine, A. 2004. Estimation of forest stands volumes by Landsat TM imagery and stand- Level field- inventory data. Forest Ecology and Management, 196: 245-255.
- May, A.M.B., Pinder, J.E. and Kroh, G.C. 1997. A comparison of Landsat Thematic Mapper and Spot multi-spectral imagery for the classification of shrub and meadow vegetation in northern California-U.S.A. International Journal of Remote Sensing, 18: 3719-3728.
- McRobert, R., Tomppo, E., Finley, A. and Heikkinen, J. 2007. Estimating aerial means and variances of forestattributes using the k-Nearest Neighbors technique and satellite imagery. Remote sensing of environment, 111: 466-480.
- Mohammadi, J. 2007. Modeling of forest structural attributes using satellite data in the Loveh Forests in Golestan. M. Sc. thesis, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 68p (In Persian).
- Mohammadi, J., Shataee, Sh., Yaghmaee, F. and Mahiny, A.S. 2010. Modeling forest stand volume and tree density using Landsat ETM+ data. International Journal of Remote Sensing, 31: 2959-2975.
- Muukkonen, P. and Heiskanen, J. 2005. Estimation biomass for boreal forests using ASTER satellite data combined with stand wise forest inventory data. Remote Sensing of Environment, 99: 434-447.
- Naseri, F. 2003. Forest classification and estimation of forest structural attributes using satellite data in the dry land forests. Ph. D. thesis, University of Tehran, 155p (In Persian).
- Pax-Lenney, M., Woodcock, C.E., Macomber, S.A., Gopal, S. and Song, C. 2001. Forest mapping with a generalized classifier and Landsat TM data. Remote Sensing of Environment, 77:241-250.
- Ripple, W.J., Wang, S., Isaacson, D.L. and Paine, D.P. 1991. A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume. International Journal of Remote Sensing, 12(9):1971-1977.
- Shataee, Sh. 2005. Remote sensing. Educational Pamphlet, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 78p (In Persian).
- Shataee, Sh., Kalbi, S., Fallah, A. and Pelz, D.R. 2012. Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33: 6254-6280.
- Tokola, T. and Heikikkila, J. 1997. Improving satellite image based forest inventory by using a priori site quality information. Siva Fennica, 31:67-78.
- Wolter, T.P., Townsend, P.A. and Sturtevant, B.R. 2009. Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sensing, 113: 2019-2036.
- Woodcock, C.E., Collins, J.B., Jakabhazy, V.D., Li, X., Macomber, S.A. and Wu, Y. 1997. Inversion of the Li-Strahler canopy reflectance model for mapping forest structure. IEEE Transactions on Geoscience and Remote Sensing, 35:405-414.
- Yarbrough, L.D., Easson, G. and Kuszmaul, J.S. 2005. Using at-sensor radiance and reflectance Tasseled Cap transforms applied to change detection for the ASTER sensor. IEEE Third International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Beau Rivage, Biloxi, Mississippi, USA, 5p.
- Yazdani, S. 2011. Estimation of forest structural attributes using Quickbird data. M. Sc. thesis, Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, 156p (In Persian).
 
Iranian Journal of Forest and Poplar Research
Volume 22, Issue 3 - Serial Number 3
October 2014
Pages 434-446

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