Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 363-367, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-363-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 363-367, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-363-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

H. T. T. Nguyen1, T. M. Doan1, and V. Radeloff2 H. T. T. Nguyen et al.
  • 1Department of Forest resource & Environment management (Frem), Faculty of Agriculture and Forestry, Tay Nguyen University, Le Duan Str. 567, Buon Ma Thuot City, Daklak Province, Vietnam
  • 2Department of Forest and Wildlife Ecology 120 Russell Laboratories, 1630 Linden Drive Madison WI 53706-1598 USA

Keywords: Classification; Landsat 8 OLI; Land use Land cover; Random Forest; Decision Tree

Abstract. This study used the Random Forest classifier (RF) running in R environment to map Land use/Land cover (LULC) of Dak Lak province in Vietnam based on the Landsat 8 OLI. The values of two RF parameters of ntree (number of tree) and mtry (the number of variables used to split at each node) were tested and compared. In current study the best results indicate the number of suitable decision trees involved in the classification process is 300 (ntree), and the suitable number of variables used to split at each node is 4 variables (mtry). These parameters were used to classify 7 bands multi-spectral resolution from 1–7 of Landsat 8 into ten classes of LULC including natural broad-leaved evergreen, semi-evergreen, dipterocarp deciduous forest, plantation forest, rubber, coffee land, crop land, barren land, residential area and water surface. The overall accuracy of 90.32 % with Kappa coefficient of 0.8434 was found in this case.