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Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Author

Listed:
  • Yam Bahadur KC

    (School of Forestry, Beijing Forestry University, Haidian District, Beijing 100107, China
    Institute of Forestry, Tribhuvan University, Hetauda 44107, Nepal)

  • Qijing Liu

    (School of Forestry, Beijing Forestry University, Haidian District, Beijing 100107, China)

  • Pradip Saud

    (College of Forestry, Agricultural, and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USA)

  • Damodar Gaire

    (Institute of Forestry, Tribhuvan University, Hetauda 44107, Nepal)

  • Hari Adhikari

    (Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland
    Forest Nepal, Amar Marg 88, C3534, Butwal 32907, Nepal)

Abstract

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R 2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

Suggested Citation

  • Yam Bahadur KC & Qijing Liu & Pradip Saud & Damodar Gaire & Hari Adhikari, 2024. "Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data," Land, MDPI, vol. 13(2), pages 1-18, February.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:2:p:213-:d:1336116
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
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