What question did this study set out to answer?

This study aims to classify tree-species in subtropical forests using advanced hyperspectral and LiDAR data.

November 17, 2017Remote SensingOpen Access

Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data

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Authors

XSXin ShenNanjing Forestry University LCLin CaoPeoples' Friendship University of Russia

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Implication

Randomized trial investigates tree-species classification accuracy using hyperspectral and LiDAR data in subtropical forests, suggesting improved metrics for management.

Key Points

This study aims to classify tree-species in subtropical forests using advanced hyperspectral and LiDAR data.
Used LiCHy airborne system for data acquisition in southeast China.
Employed point cloud segmentation algorithm for individual tree crown extraction.
Utilized Random Forest with Principal Component Analysis and Gini index for classification.
Achieved overall accuracy of 82.9% for tree delineation with point cloud segmentation.
Classified five tree-species with overall accuracy greater than 85.4%.
Using sunlit crown metrics improved classification accuracy by 2.3%.

Cite This Study

Shen et al. (2017) studied this question.

synapsesocial.com/papers/6a215401df884daff757da39 https://doi.org/10.3390/rs9111180

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Implication

Randomized trial investigates tree-species classification accuracy using hyperspectral and LiDAR data in subtropical forests, suggesting improved metrics for management.

Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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