Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods Article Swipe
Martina Deur
,
Mateo Gašparović
,
Ivan Balenović
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12233926
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12233926
Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs12233926
- https://www.mdpi.com/2072-4292/12/23/3926/pdf?version=1606730434
- OA Status
- gold
- Cited By
- 67
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3110459943
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3110459943Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs12233926Digital Object Identifier
- Title
-
Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning MethodsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-30Full publication date if available
- Authors
-
Martina Deur, Mateo Gašparović, Ivan BalenovićList of authors in order
- Landing page
-
https://doi.org/10.3390/rs12233926Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/12/23/3926/pdf?version=1606730434Direct 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/2072-4292/12/23/3926/pdf?version=1606730434Direct OA link when available
- Concepts
-
Random forest, Support vector machine, Artificial intelligence, Multispectral image, Computer science, Remote sensing, Deciduous, Satellite imagery, Pattern recognition (psychology), Contextual image classification, Carpinus betulus, Forestry, Geography, Image (mathematics), Ecology, Beech, Biology, Fagus sylvaticaTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
67Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 15, 2023: 14, 2022: 14, 2021: 11Per-year citation counts (last 5 years)
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74Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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