Articles containing the keyword 'protected areas'
Category : Research article
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Munna,
Department of Soil and Geological Sciences, Sokoine University of Agriculture, P.O. Box 3008, Morogoro, Tanzania
https://orcid.org/0000-0001-8858-0457
E-mail:
amabmunna81@gmail.com
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Amuri,
Department of Soil and Geological Sciences, Sokoine University of Agriculture, P.O. Box 3008, Morogoro, Tanzania
https://orcid.org/0000-0003-3092-3458
E-mail:
namuri@sua.ac.tz
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Hieronimo,
Department of Agricultural Engineering, Sokoine University of Agriculture, P.O. Box 3003 Morogoro, Tanzania
https://orcid.org/0000-0002-4450-5073
E-mail:
phmusigula@gmail.com
- Woiso, Department of Biosciences, Sokoine University of Agriculture, P.O. Box 3038 Morogoro, Tanzania E-mail: dino@sua.ac.tz
Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.
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Hardenbol,
University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland
https://orcid.org/0000-0002-0615-505X
E-mail:
alwin.hardenbol@uef.fi
- Kuzmin, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: anton.kuzmin@uef.fi
- Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
- Korpelainen, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pasi.korpelainen@uef.fi
- Kumpula, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.kumpula@uef.fi
- Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
- Kouki, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jari.kouki@uef.fi
