Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Authors

Bo Xie, Chinese Academy of Sciences
Chunxiang Cao, Chinese Academy of Sciences
Min Xu, Chinese Academy of Sciences
Barjeece Bashir, Chinese Academy of Sciences
Ramesh P. Singh, Chapman UniversityFollow
Zhibin Huang, Chinese Academy of Sciences
Xiaojuan Lin, Chinese Academy of Sciences

Document Type

Article

Publication Date

1-22-2020

Abstract

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m³/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10⁻⁴), leading to the best performances of RF (R² = 0.618, root mean square error, RMSE = 43.641 m³/ha, mean absolute error, MAE = 33.016 m³/ha), followed by KNN (R² = 0.617, RMSE = 43.693 m³/ha, MAE = 32.534 m³/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

Comments

This article was originally published in Remote Sensing, volume 12, in 2020. https://doi.org/10.3390/rs1203036

Recommended Citation

Xie, B.; Cao, C.; Xu, M.; Bashir, B.; Singh, R. P.; Huang, Z.; Lin, X. Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data. Remote Sens. 2020, 12, 360. https://doi.org/10.3390/rs12030360

Peer Reviewed

1

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