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

Sami Dawood Madundo; Ernest William Mauya; Nandera Juma Lolila; Hadija Ahmed Mchelu

doi:doi:10.11648/j.ijnrem.20220701.13

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

Sami Dawood Madundo, Ernest William Mauya, Nandera Juma Lolila, Hadija Ahmed Mchelu

Published in International Journal of Natural Resource Ecology and Management (Volume 7, Issue 1)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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

Sami Dawood Madundo

Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania
Ernest William Mauya

Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania
Nandera Juma Lolila

Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture, Morogoro, Tanzania
Hadija Ahmed Mchelu

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

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Plain Text BibTeX RIS

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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