Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 891–895, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-891-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 891–895, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-891-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

BEECH TREE DENSITY ESTIMATION USING SENTINEL-2 DATA (CASE STUDY: KHYROUD FOREST)

G. Ronoud, A. A. Darvish Sefat, and P. Fatehi G. Ronoud et al.
  • Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran

Keywords: Tree Density, Sentinel-2 Data, Machine Learning Algorithms, Fagus Orientalis Stands

Abstract. Obtaining information about forest attributes is essential for planning, monitoring, and management of forests. Due to the time and cost consuming of Tree Density (TD) using field measurements especially in the vast and remote areas, remote sensing techniques have gained more attention in scientific community. Khyroud forest, a part of Hyrcanian forest of Iran, with a high species biodiversity and growing volume stock plays an important role in carbon storage. The aim of this study was to assess the capability of Sentinel-2 data for estimating the tree density in the Khyroud forest. 65 square sample plots with an area of 2025 m2 were measured. In each sample plot, trees with diameter at the breast height (DBH) higher than 7.5-cm were recorded. The quality of Sentinel-2 data in terms of geometric correction and cloud effect were investigated. Different processing approaches such as vegetation indices and Tasseled Cap transformation on spectral bands in combination with an empirical approach were implemented. Also, some of biophysical variables were computed. To assess the model performance, the data were randomly divided into parts, 70% of sample plots were used for modelling and 30% for validation. The results showed that the SVR algorithm (linear kernel) with a relative RMSE of 23.09% and a R2 of 0.526 gained the highest performance for tree density estimation.