9 May 2014 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
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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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Siavash Kalbi, Asghar Fallah, Shaban Shataee, "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," Journal of Applied Remote Sensing 8(1), 083632 (9 May 2014). https://doi.org/10.1117/1.JRS.8.083632 . Submission:
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