Estimation of Crown Closure and Tree Density Using Landsat TM Satellite Images in Mixed Forest Stands


Kahriman A., Gunlu A., KARAHALİL U.

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, vol.42, no.3, pp.559-567, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 3
  • Publication Date: 2014
  • Doi Number: 10.1007/s12524-013-0355-3
  • Title of Journal : JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
  • Page Numbers: pp.559-567

Abstract

The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R2 = 0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R2 = 0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R2 = 0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.

The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R-2 = 0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R-2 = 0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R-2 = 0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.