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Tree species classification for clarification of forest inventory data using Sentinel-2 images
Author(s): Anna Y. Denisova; Ludmila M. Kavelenova; Evgeniy S. Korchikov; Nataly V. Prokhorova; Daria A. Terentyeva; Victor A. Fedoseev
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Paper Abstract

Many ecological applications (e.g., human activity management in protected areas, conservation status assignment) require actual forest inventory data. However, frequent field research of forest areas is very expensive. Therefore, in practice, forest inventory data are slowly updated, approximately one time per decade. From the other hand, modern remote sensing systems combine high-quality imagery with short revisiting time and can be used for the forest inventory data clarification. Our paper presents an investigation of tree species classification based on seasonal Sentinel-2 data (2018) and the latest forest inventory information (2013–2014). The main advantages of Sentinel-2 satellites are a short revisiting time and a large field of view that is important in large area analysis. Our classification model was based on support vector machines method combined with specific spatial processing methods. We used the known inventory data for training and validation the classifier. Misclassified regions were further analyzed in ground surveys to produce the inventory data clarification. The paper addresses the optimal image dates selection, image preprocessing and classification procedure evaluation issues. The study was carried out for the territory of the Krasnosamarskoe forestry in Samara region, Russia. The experiments have shown that the proper Sentinel-2 data selection and classification procedure configuration allow reaching the classification accuracy of about 0.82 for the control sample. The ground survey confirmed that classification errors are mainly caused by the dominant tree species changes. Thus, we concluded that Sentinel-2 data can be effectively used for the forest inventory data clarification.

Paper Details

Date Published: 27 June 2019
PDF: 10 pages
Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 1117408 (27 June 2019); doi: 10.1117/12.2531805
Show Author Affiliations
Anna Y. Denisova, Samara National Research Univ. (Russian Federation)
Ludmila M. Kavelenova, Samara National Research Univ. (Russian Federation)
Evgeniy S. Korchikov, Samara National Research Univ. (Russian Federation)
Nataly V. Prokhorova, Samara National Research Univ. (Russian Federation)
Daria A. Terentyeva, Samara National Research Univ. (Russian Federation)
Victor A. Fedoseev, Samara National Research Univ. (Russian Federation)
Image Processing Systems Institute (Russian Federation)


Published in SPIE Proceedings Vol. 11174:
Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Kyriacos Themistocleous; Giorgos Papadavid; Silas Michaelides; Vincent Ambrosia; Diofantos G. Hadjimitsis, Editor(s)

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