IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i2p213-d1336116.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/2/213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/2/213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    2. Hideki Murakami & Yukari Matsuse & Koji Mukaigawa & Yushi Tsunoda, 2013. "Product lifecycle and choice of transportation modes: Japan' s evidence of import and export," Discussion Papers 2013-28, Kobe University, Graduate School of Business Administration.
    3. Michele Fratianni & Francesco Marchionne, 2011. "The Limits to Integration," Chapters, in: Miroslav N. Jovanović (ed.), International Handbook on the Economics of Integration, Volume I, chapter 9, Edward Elgar Publishing.
    4. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    5. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Tadashi Kikuchi, 2014. "Vietnamese Inter – regional labor migration: system approach to the modeling 1989, 1999, 2009," EcoMod2014 6998, EcoMod.
    7. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Jean-Paul Azam & Catherine Bonjean, 1995. "La formation du prix du riz : théorie et application au cas d'Antananarivo (Madagascar) ," Revue Économique, Programme National Persée, vol. 46(4), pages 1145-1166.
    9. Nathaniel Geiger & Bryan McLaughlin & John Velez, 2021. "Not all boomers: temporal orientation explains inter- and intra-cultural variability in the link between age and climate engagement," Climatic Change, Springer, vol. 166(1), pages 1-20, May.
    10. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    11. Vittorio Nicolardi, 2009. "The effects of the new 1995 ESA methodologies of estimation on the structural analysis of Italian consumption," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 125-149, March.
    12. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    13. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    14. Raffaella Santolini, 2017. "Electoral Rules And Public Spending Composition: The Case Of Italian Regions," Contemporary Economic Policy, Western Economic Association International, vol. 35(3), pages 551-577, July.
    15. Desbordes, Rodolphe, 2007. "The sensitivity of U.S. multinational enterprises to political and macroeconomic uncertainty: A sectoral analysis," International Business Review, Elsevier, vol. 16(6), pages 732-750, December.
    16. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    17. Zsuzsa Lábiscsák-Erdélyi & Ilona Veres-Balajti & Annamária Somhegyi & Karolina Kósa, 2022. "Self-Esteem Is Independent Factor and Moderator of School-Related Psychosocial Determinants of Life Satisfaction in Adolescents," IJERPH, MDPI, vol. 19(9), pages 1-14, May.
    18. Anjum, Zeba & Burke, Paul J. & Gerlagh, Reyer & Stern, David I., "undated". "Modeling the Emissions-Income Relationship Using Long-Run Growth Rates," Working Papers 249422, Australian National University, Centre for Climate Economics & Policy.
    19. Grzegorz Rybak & Edward Kozłowski & Krzysztof Król & Tomasz Rymarczyk & Agnieszka Sulimierska & Artur Dmowski & Piotr Bednarczuk, 2023. "Algorithms for Optimizing Energy Consumption for Fermentation Processes in Biogas Production," Energies, MDPI, vol. 16(24), pages 1-17, December.
    20. Garcia-Hiernaux, Alfredo & Gonzalez-Perez, Maria T. & Guerrero, David E., 2023. "Eurozone prices: A tale of convergence and divergence," Economic Modelling, Elsevier, vol. 126(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:2:p:213-:d:1336116. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.