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Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes
AbstractApproaches to deriving forest information from laser scanner data have generally made use of two methods: the area-based and individual tree-based approaches. In this paper, these two methods were evaluated and compared for their abilities to predict forest attributes at the plot level using the same datasets. Airborne laser scanner data were collected over the Evo forest area, southern Finland, with an averaging point density of 2.6 points/m2. Mean height, mean diameter and volume were predicted from laser-derived features for plots (area-based method) or tree height, diameter at breast height and volume for individual trees (individual tree-based method) using random forests technique. To evaluate and compare the two forest inventory methods, the root-mean-squared error (RMSE) and correlation coefficient (R) between the predicted and observed plot-level values were computed. The results indicated that both area-based method (with an RMSE of 6.42% for mean height, 10.32% for mean diameter and 20.90% for volume) and individual tree-based method (with an RMSE of 5.69% for mean height, 10.77% for mean diameter and 18.55% for volume) produced promising and compatible results. Increase in point density is expected to increase the accuracy of the individual tree-based technique more than that of the area-based technique.
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Yu, X.; Hyyppä, J.; Holopainen, M.; Vastaranta, M. Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes. Remote Sens. 2010, 2, 1481-1495.View more citation formats
Yu X, Hyyppä J, Holopainen M, Vastaranta M. Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes. Remote Sensing. 2010; 2(6):1481-1495.Chicago/Turabian Style
Yu, Xiaowei; Hyyppä, Juha; Holopainen, Markus; Vastaranta, Mikko. 2010. "Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes." Remote Sens. 2, no. 6: 1481-1495.
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