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Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Author

Listed:
  • Nizar Ali

    (Department of Forestry and Wildlife Management, University of Haripur, Haripur, Pakistan)

  • Muhammad Saad

    (Department of Forestry and Wildlife Management, University of Haripur, Haripur, Pakistan)

  • Anwar Ali

    (Pakistan Forest Institute Peshawar, Peshawar, Pakistan)

  • Naveed Ahmad

    (Department of Forestry and Range Management, University of Arid Agriculture Rawalpindi, Pakistan)

  • Ishfaq Ahmad Khan

    (Department of Forest Science & Biodiversity, Faculty of Forestry and Environment, University Putra Malaysia, UPM Serdang, Malaysia)

  • Habib Ullah

    (School of Forestry, North-East Forestry University, Herbin, China)

  • Areeba Binte Imran

    (Department of Forestry and Range Management, University of Arid Agriculture Rawalpindi, Pakistan)

Abstract

The presented study estimated the aboveground biomass (AGB) of Pinus roxburghii (chir pine) natural forests and plantations, and created biomass maps using a relationship (regression model) between AGB and Sentinel-2 spectral indices. The mean AGB and BGB (belowground biomass) of natural forests were 79.54 Mg.ha-1 and 20.68 Mg.ha-1, respectively, whereas the mean AGB and BGB of plantations were 94.48 Mg.ha-1 and 24.56 Mg.ha-1, respectively. Correlation showed that mean diameter at breast height (DBH) and mean height have weak relationships with AGB, and BGB has shown correlation coefficients (R2 = 0.46) and (R2 = 0.56) for polynomial models. Regression models between AGB (Mg.ha-1) of Pinus roxburghii natural forest and Sentinel-2 spectral indices showed a strong relationship with Ratio Vegetation Index (RVI) with R2 = 0.72 followed by Normalized Difference Vegetation Index (NDVI) and Atmospherically Resistant Vegetation Index (ARVI) with R2 = 0.70. In contrast, the lower performance of spectral indices has been shown in regression with plantation AGB. Correlation coefficients (R2) were 0.41, 0.41, and 0.40 for RVI, NDVI, and ARVI, respectively. All indices showed that the distribution of AGB data was not the best fit with the linear regression model. Therefore, non-linear exponential and power models were considered the best fit for NDVI, RVI, and ARVI. A biomass map was developed from RVI for both natural forests and plantation because RVI has the highest R2 and lowest P-value.

Suggested Citation

  • Nizar Ali & Muhammad Saad & Anwar Ali & Naveed Ahmad & Ishfaq Ahmad Khan & Habib Ullah & Areeba Binte Imran, 2023. "Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 69(7), pages 287-304.
    • Handle: RePEc:caa:jnljfs:v:69:y:2023:i:7:id:125-2022-jfs
    DOI: 10.17221/125/2022-JFS
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    References listed on IDEAS

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    1. R. A. Houghton & Brett Byers & Alexander A. Nassikas, 2015. "A role for tropical forests in stabilizing atmospheric CO2," Nature Climate Change, Nature, vol. 5(12), pages 1022-1023, December.
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