研究目的
To improve the single tree parameter estimation of low density LiDAR data by using the information provided by high density LiDAR data acquired over the same forest at different times.
研究成果
The method significantly improves the accuracy of individual tree crown parameter estimation on low density LiDAR data by using information from high density data, while preserving temporal differences relevant for forest dynamics analysis. Future developments include considering the time passed between acquisitions to weight the inferred information differently.
研究不足
The method assumes that large changes such as cut trees have been already identified. It is tested only on coniferous forests and may require adjustments for other forest types.
1：Experimental Design and Method Selection:
The method involves pre-processing, crown parameter estimation on high density data, and crown parameter estimation on low density data. It uses an Iterative Closest Point algorithm for co-registration and a 3-D ellipsoid model for tree crown shape characterization.
2：Sample Selection and Data Sources:
The method was tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps.
3：List of Experimental Equipment and Materials:
LiDAR data with different point densities (high density >10 pts/m2 and low density ≤5 pts/m2).
4：2). Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The point clouds are co-registered, normalized, and then used for crown parameter estimation. High density data are used to characterize individual tree crown shapes, which then guide the parameter estimation on low density data.
5：Data Analysis Methods:
The method uses a Differential Evolution algorithm for fitting the 3-D ellipsoid model to the data and calculates the Root Mean Square Error (RMSE) for parameter estimation accuracy.
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