研究目的
To evaluate the performance of the enhanced area-based approach (EABA) in improving the estimation of forest biophysical attributes and testing its efficiency when considering co-registration errors.
研究成果
The EABA approach improved the estimation of forest biophysical attributes and mitigated the impact of co-registration errors compared to the traditional ABA method. The implementation of EABA can significantly contribute to improving modern forest inventory applications.
研究不足
The study was conducted in a specific forest type (stone pine forest) with optimal conditions for ITD, which may limit the generalizability of the results to other forest ecosystems. The low density of ALS data and the resolution of the CHM might also affect the performance of the methods.
1：Experimental Design and Method Selection:
The study evaluated the performance of EABA, an edge-correction method based on ALS data that combines ITD and ABA, in a stone pine forest in Central Spain. Regression modeling was used to select optimal predictor variables.
2：Sample Selection and Data Sources:
A network of 35 circular sample plots of 15 m radius was established in the study area. Ground tree-level measurements and ALS data were used.
3：List of Experimental Equipment and Materials:
ALS data were collected using an ALS60 laser scanning system. Ground measurements were taken using submeter precision GNSS equipment and a Vertex IV hypsometer.
4：Experimental Procedures and Operational Workflow:
The ITD approach was implemented to detect tree positions and heights. The EABA method was applied to adjust sample plot edges to the distribution of tree canopies. ALS statistics were computed for both ABA and EABA methods.
5：Data Analysis Methods:
Multiple linear regression was used to estimate forest biophysical attributes. The root mean squared error (RMSE) was used as the criterion for model selection.
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