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Forest stand spectrum reconstruction using spectrum spatial feature gathering and multilayer perceptron.
Fan Wang1,2, Linghan Song1,2, Xiaojie Liu1,2
1College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
Frontiers in Plant Science
|December 11, 2023
Summary
This study introduces a novel method for inverting forest spectra using LiDAR and multispectral data, enhancing forest management. The approach effectively reconstructs three-dimensional spectral distributions, improving forest health monitoring.
Area of Science:
- Forestry
- Remote Sensing
- Geospatial Analysis
Background:
- Three-dimensional spectral distributions offer vital spatial insights into forest physiological and biochemical status for effective forest management.
- Current three-dimensional spectral studies of forest stands are limited, highlighting a need for advanced methodologies.
Purpose of the Study:
- To develop and evaluate a method for inverting forest spectra using point clouds derived from LiDAR and multispectral data.
- To assess the effectiveness of deep learning algorithms for semantic segmentation in characterizing forest stands.
- To improve the precise three-dimensional spectral distribution of forests for enhanced remote sensing applications.
Main Methods:
- Fusion of multispectral values with LiDAR point clouds, followed by K-means clustering for data characterization.
- Application of five deep learning algorithms for semantic segmentation, with overall accuracy (oAcc) and mean intersection ratio (mIoU) as performance metrics.
- Reconfiguration of class 3D spectral distribution using a semantic segmentation model and evaluation of inversion outcomes.
Main Results:
- High correlations ( > 0.98) were observed between spectral and spatial attributes, with a moderate correlation (0.43) between spectral and spatial attributes.
- PointMLP demonstrated the highest performance with an oAcc of 0.84 and mIoU of 0.75.
- The model achieved accurate local spectral inversion, with predicted values closely matching true values and NIR values correlating with canopy height and distance from the tree apex.
Conclusions:
- Combining spatial fusion and semantic segmentation effectively inverts three-dimensional spectral information for forest stands.
- The developed model meets accuracy requirements for local spectral inversion, enhancing forest stand health estimation.
- Findings provide a basis for near-earth remote sensing and precise forest spectral distribution analysis.
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