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Journal of Applied Remote Sensing

Estimation of forest attributes in the Hyrcanian forests, comparison of advanced space-borne thermal emission and reflection radiometer and satellite poure I’observation de la terre-high resolution grounding data by multiple linear, and classification and regression tree regression models
Author(s): Siavash Kalbi; Asghar Fallah; Shaban Shataee
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Paper Abstract

Estimation of structural forest attributes, such as volume, basal area, and tree density using a combination of remote sensing and field data, is currently considered a favored option compared to only using field survey data. In a comparative study, multiple linear regression (MLR) and classification and regression tree (CART) models were used to estimate volume, basal area, and tree density using advanced space-borne thermal emission and reflection radiometer (ASTER) and satellite poure I’observation de la terre (SPOT)-high resolution grounding (HRG) imagery in the Darabkola forests, located at the Hyrcanian region of Iran. Results showed that the CART model using SPOT-HRG data achieved the best overall performance for all three forest structural attributes, with adjusted R 2 =0.746 and RMSE=67.9  m 3  ha −1 for volume, adjusted R 2 =0.771 and RMSE=3.94  m 2  ha −1 for basal area, and adjusted R 2 =0.871 and RMSE=34.71  nha −1 for tree density. In general, the CART model, using both ASTER and SPOT-HRG data, produced better estimates of forest attributes compared to the MLR model. In addition, results showed that forest attribute estimations using SPOT-HRG were better than those obtained from ASTER data.

Paper Details

Date Published: 9 May 2014
PDF: 18 pages
J. Appl. Remote Sens. 8(1) 083632 doi: 10.1117/1.JRS.8.083632
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Siavash Kalbi, Sari Agriculture and Natural Resource Univ. (Iran, Islamic Republic of)
Asghar Fallah, Sari Agriculture and Natural Resource Univ. (Iran, Islamic Republic of)
Shaban Shataee, Gorgan Agriculture and Natural Resource (Iran, Islamic Republic of)


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