Biomass estimation models and methods for miombo woodlands of Malawi using field and remotely sensed data

Daud Jones Kachamba successfully defended his doctoral thesis, “Biomass estimation models and methods for miombo woodlands of Malawi using field and remotely sensed data”, on 15 December 2016.

The topic for the trial lecture was “Estimating woody biomass for large areas and its uncertainty, by combining remote sensing- and ground data”. We congratulate!

Thesis abstract

Dry tropical forests, such as the miombo woodlands, play an important role in the global
carbon budget as well as in contributing towards the sustainable development of countries such as Malawi. To ensure sustainability of these forests, availability of models and methods for assisting forest managers in quantifying volume and biomass are indispensable. This thesis therefore sought to develop volume and biomass prediction models as well as to test the potential of applying unmanned aerial vehicles (UAVs) in biomass prediction and estimation in miombo woodlands. In Paper 1 and 2, we developed models for predicting tree sectional (twigs, merchantable stem and branches) volume and biomass, total tree volume as well as tree above-and belowground biomass. The performances and evaluations suggested that the models can be used over a wide range of geographical and ecological conditions in Malawi with an appropriate accuracy in predictions. Application of UAVs for biomass prediction and estimation were tested and the results are presented in Papers 3 and 4. In Paper 3, we tested methods to derive digital terrain models (DTMs) while Paper 4 focused on the assessment of the efficiency of UAV-assisted inventories as well as the influence of sample plot sizes and number of sample plots on the precision of biomass estimates. The results, presented in Paper 3, show that among the tested DTMs, the model developed from unsupervised ground filtering based on a grid search approach performed best. Furthermore, the observed prediction errors for the biomass predictions are similar to results from previous studies using airborne laser scanning (ALS) data, thus showing the potential of applying this technology in miombo woodlands. Finally, Paper 4 demonstrated that UAV-assisted inventories produce more precise estimates compared to those based on purely field-based inventories. The results also indicated that large sample plot sizes and sample sizes favour UAV-assisted inventories and that UAV-assisted inventories are more efficient than purely field-based inventories. The developed models and the results from the tested methods presented in the thesis have taken us some steps forward that are expected to support and improve forest management decision-making in general as well as the implementation of a REDD+ MRV system covering the miombo woodlands of Malawi.

Supervisors

Main supervisor: Professor, Dr. Tron Haakon Eid, MINA, NMBU
Co-supervisors:
Professor, Dr. Terje Gobakken, MINA, NMBU
Researcher, Dr. Hans-Ole Ørka, MINA, NMBU
Professor Weston Mwase, Lilongwe University of Agriculture and Natural Resources, Malawi

Evaluation committee

Professor Gherardo Chirici, University of Florence, Italy
Dr. Casey Ryan, University of Edinburgh, United Kingdom
Dr. Ole Martin Bollandsås, MINA, NMBU

Reference

  • D. Kachamba, “Biomass estimation models and methods for miombo woodlands of malawi using field and remotely sensed data,” PhD Thesis, 2016.
    [Bibtex] [Abstract] [Download PDF]

    Dry tropical forests, such as the miombo woodlands, play an important role in the global carbon budget as well as in contributing towards the sustainable development of countries such as Malawi. To ensure sustainability of these forests, availability of models and methods for assisting forest managers in quantifying volume and biomass are indispensable. This thesis therefore sought to develop volume and biomass prediction models as well as to test the potential of applying unmanned aerial vehicles (UAVs) in biomass prediction and estimation in miombo woodlands. In Paper 1 and 2, we developed models for predicting tree sectional (twigs, merchantable stem and branches) volume and biomass, total tree volume as well as tree above-and belowground biomass. The performances and evaluations suggested that the models can be used over a wide range of geographical and ecological conditions in Malawi with an appropriate accuracy in predictions. Application of UAVs for biomass prediction and estimation were tested and the results are presented in Papers 3 and 4. In Paper 3, we tested methods to derive digital terrain models (DTMs) while Paper 4 focused on the assessment of the efficiency of UAV-assisted inventories as well as the influence of sample plot sizes and number of sample plots on the precision of biomass estimates. The results, presented in Paper 3, show that among the tested DTMs, the model developed from unsupervised ground filtering based on a grid search approach performed best. Furthermore, the observed prediction errors for the biomass predictions are similar to results from previous studies using airborne laser scanning (ALS) data, thus showing the potential of applying this technology in miombo woodlands. Finally, Paper 4 demonstrated that UAV-assisted inventories produce more precise estimates compared to those based on purely field-based inventories. The results also indicated that large sample plot sizes and sample sizes favour UAV-assisted inventories and that UAV-assisted inventories are more efficient than purely field-based inventories. The developed models and the results from the tested methods presented in the thesis have taken us some steps forward that are expected to support and improve forest management decision-making in general as well as the implementation of a REDD+ MRV system covering the miombo woodlands of Malawi.

    @PhdThesis{Kachamba2016,
    Title = {Biomass estimation models and methods for miombo woodlands of Malawi using field and remotely sensed data},
    Author = {Daud Kachamba},
    School = {Norwegian University of Life Sciences},
    Year = {2016},
    Abstract = {Dry tropical forests, such as the miombo woodlands, play an important role in the global
    carbon budget as well as in contributing towards the sustainable development of countries
    such as Malawi. To ensure sustainability of these forests, availability of models and methods
    for assisting forest managers in quantifying volume and biomass are indispensable. This
    thesis therefore sought to develop volume and biomass prediction models as well as to test
    the potential of applying unmanned aerial vehicles (UAVs) in biomass prediction and
    estimation in miombo woodlands. In Paper 1 and 2, we developed models for predicting tree
    sectional (twigs, merchantable stem and branches) volume and biomass, total tree volume as
    well as tree above-and belowground biomass. The performances and evaluations suggested
    that the models can be used over a wide range of geographical and ecological conditions in
    Malawi with an appropriate accuracy in predictions. Application of UAVs for biomass
    prediction and estimation were tested and the results are presented in Papers 3 and 4. In Paper
    3, we tested methods to derive digital terrain models (DTMs) while Paper 4 focused on the
    assessment of the efficiency of UAV-assisted inventories as well as the influence of sample
    plot sizes and number of sample plots on the precision of biomass estimates. The results,
    presented in Paper 3, show that among the tested DTMs, the model developed from
    unsupervised ground filtering based on a grid search approach performed best. Furthermore,
    the observed prediction errors for the biomass predictions are similar to results from previous
    studies using airborne laser scanning (ALS) data, thus showing the potential of applying this
    technology in miombo woodlands. Finally, Paper 4 demonstrated that UAV-assisted
    inventories produce more precise estimates compared to those based on purely field-based
    inventories. The results also indicated that large sample plot sizes and sample sizes favour
    UAV-assisted inventories and that UAV-assisted inventories are more efficient than purely
    field-based inventories. The developed models and the results from the tested methods
    presented in the thesis have taken us some steps forward that are expected to support and
    improve forest management decision-making in general as well as the implementation of a
    REDD+ MRV system covering the miombo woodlands of Malawi.},
    Owner = {hanso},
    Timestamp = {2017.09.06},
    Url = {http://statisk.umb.no/ina/forskning/drgrader/2016-Kachamba.pdf}
    }

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