An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery Article Swipe
Martina Deur
,
Mateo Gašparović
,
Ivan Balenović
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13101868
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13101868
Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13101868
- https://www.mdpi.com/2072-4292/13/10/1868/pdf?version=1620784966
- OA Status
- gold
- Cited By
- 26
- References
- 96
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3162698582
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3162698582Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs13101868Digital Object Identifier
- Title
-
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite ImageryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-11Full publication date if available
- Authors
-
Martina Deur, Mateo Gašparović, Ivan BalenovićList of authors in order
- Landing page
-
https://doi.org/10.3390/rs13101868Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/13/10/1868/pdf?version=1620784966Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/13/10/1868/pdf?version=1620784966Direct OA link when available
- Concepts
-
Remote sensing, Pixel, Random forest, Computer science, Carpinus betulus, Image resolution, Object based, Deciduous, Satellite imagery, Artificial intelligence, Geography, Pattern recognition (psychology), Forestry, Ecology, Fagus sylvatica, Biology, BeechTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 6, 2023: 6, 2022: 8, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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96Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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