| Autor: |
Shamaoma H; Forest Science Postgraduate Programme, Department of Plant and Soil Sciences, University of Pretoria, Private bag X20, Hatfield, Pretoria 0028, South Africa.; Department of Urban and Regional Planning, Copperbelt University, Kitwe 21692, Zambia., Chirwa PW; Forest Science Postgraduate Programme, Department of Plant and Soil Sciences, University of Pretoria, Private bag X20, Hatfield, Pretoria 0028, South Africa., Zekeng JC; Department of Forest Engineering, Advanced Teachers Training School for Technical Education, University of Douala, P.O. Box 1872, Douala, Cameroon.; Oliver R Tambo Africa Research Chair Initiative (ORTARChI), Chair of Environment and Development, Department of Environmental and Plant Sciences, Copperbelt University, Kitwe 21692, Zambia., Ramoelo A; Centre for Environmental Studies (CFES), Department of Geography, Geoinformatics and Meteorology after CFES, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa., Hudak AT; USDA Forest Service, Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 South Main St., Moscow, ID 83843, USA., Handavu F; Department of Geography, Environment and Climate Change, Mukuba University, Kitwe 50100, Zambia., Syampungani S; Forest Science Postgraduate Programme, Department of Plant and Soil Sciences, University of Pretoria, Private bag X20, Hatfield, Pretoria 0028, South Africa.; Oliver R Tambo Africa Research Chair Initiative (ORTARChI), Chair of Environment and Development, Department of Environmental and Plant Sciences, Copperbelt University, Kitwe 21692, Zambia. |
| Abstrakt: |
Accurate maps of tree species distributions are necessary for the sustainable management of forests with desired ecological functions. However, image classification methods to produce species distribution maps for supporting sustainable forest management are still lacking in the Miombo woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS) imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing) to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of Zambia. Object-based image analysis (OBIA) with a random forest algorithm was used on single date, multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the wet Miombo woodlands. It was found that classification accuracy varies both with dates and features used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%, 0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa. After adding spectral indices to multi-date image combination, the accuracy was further improved to 87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance for future studies using multispectral UAS for sustainable management of Miombo tree species. |
