Recognition possibility of trees canopy die back using high resolution satellite image of Quick bird (Case study: Shastkolate forest, Gorgan) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 3, Volume 19, Issue 4 - Serial Number 46, December 2012, Pages 477-466 PDF (318.48 K) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2011.107504 | ||
| Authors | ||
| Sahar Barazmand1; Sha'ban Shataee Joybari* 2; Omid Abdi1 | ||
| 1M.Sc. graduated of forestry, Faculty of forestry and wood and paper technology, Gorgan University of Agricultural Sciences and Natural Resources | ||
| 2Associate Prof., Faculty of forestry and wood and paper technology, Gorgan University of Agricultural Sciences and Natural Resources | ||
| Abstract | ||
| The purpose of this research is the possibility to recognize of trees canopy die back using Quickbird satellite images and mapping of its distribution in district one from Shastkolate forest, North-west of Iran. After geometric quality and radiometric evaluation of data, geometric correction of panchromatic image was carried out with 45 ground control points and RMSE of 0.65 at X axis direction and 0.78 at Y axis direction. Moreover, the multi-spectral images were registered with georeferenced panchromatic image with 60 ground control points and RMSE of 0.19 at X axis direction and 0.25 at Y axis direction. Using ratioing, principal component analysis and creation of suitable vegetation indices, some artificial bands were created and used as suitable bands for image analysis. In order to prepare the training area and to evaluate classification accuracy, sample ground truth were provided by recording the died back trees using DGPS on a 500 m×100 m systematic network and 360 sample plots with 1000 m² area. After selection of training area and suitable bands collection, data were classified with supervised method by using maximum likelihood and density slicing. Results of the classification accuracy evaluation on 4 main bands and also 7 selective bands by maximum likelihood algorithm and vegetation indexes by density slicing algorithm showed that overall accuracy amount and Kappa coefficient for 2 forest classes and died back for 4 main bands and 7 best selected bands by maximum likelihood algorithm were 77%, 0.56, 83% and 0.685, respectively. In addition, overall accuracy amount and Kappa coefficient for density slicing of NDVI and TNDVI vegetation indexes were 51%, 0.16, 56% and 0.19, respectively. Results showed that recognition of died back trees using Quickbird satellite image was not completely possible due to reflection of shrub and under storey plants, adjacent trees crowns and low ratio of reflection of dried branches in compare to rest green crown, which are registered as digital value of pixels. | ||
| Keywords | ||
| crown die back; Quick bird; Classification; DGPS; Shastkolate forest; Gorgan | ||
| References | ||
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