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Sedat KELEŞ,Alkan GÜNLÜ,Emin Zeki BAŞKENT 한국산림과학회 2012 Forest Science And Technology Vol.8 No.4
In this paper, we developed empirical relationships between crown closure and Landsat Thematic Mapper (TM) and IKONOS spectral response in a forest ecosystem in Turkey. The linear relationships between the crown closure and satellite images were examined and for that the normalized difference vegetation index (NDVI) was generated for correlation analysis. Satellite images were georeferenced with respect to corresponding forest stand type maps for the same area. The digital numbers (DNs) in all bands were positively correlated with the crown closure in Landsat-7ETM image. Regarding the Landsat ETM+, the DN of NIR band (band 4) had the highest correlation coefficient value with the crown closure (r=0.715), and the DN of blue band (band 1) had the lowest correlation value with the crown closure (r=0.373) and explains 51.1% and 13.9% of data variability, respectively, in Landsat-7 ETM image. In IKONOS image, the DNs in three bands (red, green and NIR) were positively correlated with the crown closure at the level of r=0.385, r=0.359 and r=0.829, respectively. The NIR band of the Landsat-7-ETMþimage showed the highest correlation with the NDVI (r=0.886) and the red band had the lowest correlation with the NDVI (r=0.419),while the IKONOS image showed that the NIR band had the highest correlation with the NDVI (r=0.781) and the green band resulted in the lowest correlation with the NDVI (r=0.367). Results also indicated that the relationships between the crown closure and the NDVIs of Landsat-7 ETM and IKONOS were positively correlated with r=0.794and r=0.871, respectively.
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Alkan Gunlu,Sedat Kele¸,Emin Zeki Ba¸sken,Ali Ihsan Kadıogulları 한국산림과학회 2016 Forest Science And Technology Vol.12 No.1
The objective of this study was to estimate crown closure using different satellite images, Landsat TM and QuickBird. As reference data, forest cover type map were used in the study, and two different methods were used to determine the crown closure. First, to classify crown closures from satellite images, a supervised classification method was used. Second, the linear mixture modeling (LMM) fuzzy classification method was used to determine the stand crown closure. Landsat TM and QuickBird data were used in this frame. The training dataset was derived from a fine spatial resolution land cover classification of QuickBird imagery. This classification was combined to estimate the percentage crown closure in Landsat TM spatial resolution. The research also focused on classifying and mapping the crown closure classes with the spatial analysis functions of geographical information systems (GIS). The results showed that the QuickBird image had the highest classification accuracies compared to the spatial analyses results of Landsat TM and fuzzy classification.
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