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Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data

Received: 25 January 2022     Accepted: 16 February 2022     Published: 25 February 2022
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Abstract

Accurate information on above-ground biomass (AGB) is important for sustainable forest management as well as for global initiatives aimed at combating climate change in the Tropics. In this study, AGB was estimated using a combination of field and Sentinel-2 earth observation data. The study was conducted at Magamba Nature Reserve in Lushoto district, Tanzania. Field plot-based AGB values were regressed against eighteen Sentinel-2 remote sensing variables (bands and vegetation indices) using Random Forest (RF) models based on centroid and weighted approaches. Results showed that the weighted model had the highest fit and precision (pseudo-R2 = 0.21, rRMSE = 68.23%). A prediction map was produced with a mean AGB of 223.47 Mg ha-1 which was close to that of the field (225.19 Mg ha-1). Furthermore, the standard deviation of the AGB obtained from the map (i.e 174.04 Mg ha-1) was relatively lower as compared to the one obtained from the field-based measurements (i.e 97.42 Mg ha-1). This study demonstrated that Sentinel-2 imagery and RF-based regression techniques have potential to effectively support large scale estimation of forest AGB in the tropical rainforests.

Published in International Journal of Natural Resource Ecology and Management (Volume 7, Issue 1)
DOI 10.11648/j.ijnrem.20220701.13
Page(s) 15-21
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Above-ground Biomass, Earth Observation Data, Modelling, Random Forest, Sentinel-2

References
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Cite This Article
  • APA Style

    Sami Dawood Madundo, Ernest William Mauya, Nandera Juma Lolila, Hadija Ahmed Mchelu. (2022). Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data. International Journal of Natural Resource Ecology and Management, 7(1), 15-21. https://doi.org/10.11648/j.ijnrem.20220701.13

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    ACS Style

    Sami Dawood Madundo; Ernest William Mauya; Nandera Juma Lolila; Hadija Ahmed Mchelu. Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data. Int. J. Nat. Resour. Ecol. Manag. 2022, 7(1), 15-21. doi: 10.11648/j.ijnrem.20220701.13

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    AMA Style

    Sami Dawood Madundo, Ernest William Mauya, Nandera Juma Lolila, Hadija Ahmed Mchelu. Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data. Int J Nat Resour Ecol Manag. 2022;7(1):15-21. doi: 10.11648/j.ijnrem.20220701.13

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  • @article{10.11648/j.ijnrem.20220701.13,
      author = {Sami Dawood Madundo and Ernest William Mauya and Nandera Juma Lolila and Hadija Ahmed Mchelu},
      title = {Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data},
      journal = {International Journal of Natural Resource Ecology and Management},
      volume = {7},
      number = {1},
      pages = {15-21},
      doi = {10.11648/j.ijnrem.20220701.13},
      url = {https://doi.org/10.11648/j.ijnrem.20220701.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnrem.20220701.13},
      abstract = {Accurate information on above-ground biomass (AGB) is important for sustainable forest management as well as for global initiatives aimed at combating climate change in the Tropics. In this study, AGB was estimated using a combination of field and Sentinel-2 earth observation data. The study was conducted at Magamba Nature Reserve in Lushoto district, Tanzania. Field plot-based AGB values were regressed against eighteen Sentinel-2 remote sensing variables (bands and vegetation indices) using Random Forest (RF) models based on centroid and weighted approaches. Results showed that the weighted model had the highest fit and precision (pseudo-R2 = 0.21, rRMSE = 68.23%). A prediction map was produced with a mean AGB of 223.47 Mg ha-1 which was close to that of the field (225.19 Mg ha-1). Furthermore, the standard deviation of the AGB obtained from the map (i.e 174.04 Mg ha-1) was relatively lower as compared to the one obtained from the field-based measurements (i.e 97.42 Mg ha-1). This study demonstrated that Sentinel-2 imagery and RF-based regression techniques have potential to effectively support large scale estimation of forest AGB in the tropical rainforests.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Modelling and Mapping Forest Above-Ground Biomass Using Earth Observation Data
    AU  - Sami Dawood Madundo
    AU  - Ernest William Mauya
    AU  - Nandera Juma Lolila
    AU  - Hadija Ahmed Mchelu
    Y1  - 2022/02/25
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijnrem.20220701.13
    DO  - 10.11648/j.ijnrem.20220701.13
    T2  - International Journal of Natural Resource Ecology and Management
    JF  - International Journal of Natural Resource Ecology and Management
    JO  - International Journal of Natural Resource Ecology and Management
    SP  - 15
    EP  - 21
    PB  - Science Publishing Group
    SN  - 2575-3061
    UR  - https://doi.org/10.11648/j.ijnrem.20220701.13
    AB  - Accurate information on above-ground biomass (AGB) is important for sustainable forest management as well as for global initiatives aimed at combating climate change in the Tropics. In this study, AGB was estimated using a combination of field and Sentinel-2 earth observation data. The study was conducted at Magamba Nature Reserve in Lushoto district, Tanzania. Field plot-based AGB values were regressed against eighteen Sentinel-2 remote sensing variables (bands and vegetation indices) using Random Forest (RF) models based on centroid and weighted approaches. Results showed that the weighted model had the highest fit and precision (pseudo-R2 = 0.21, rRMSE = 68.23%). A prediction map was produced with a mean AGB of 223.47 Mg ha-1 which was close to that of the field (225.19 Mg ha-1). Furthermore, the standard deviation of the AGB obtained from the map (i.e 174.04 Mg ha-1) was relatively lower as compared to the one obtained from the field-based measurements (i.e 97.42 Mg ha-1). This study demonstrated that Sentinel-2 imagery and RF-based regression techniques have potential to effectively support large scale estimation of forest AGB in the tropical rainforests.
    VL  - 7
    IS  - 1
    ER  - 

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Author Information
  • Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania

  • Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania

  • Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania

  • Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania

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