Capability of SPOT5-HRG data for forest density mapping (Case study: Deilaman forests in Guilan province)

In order to investigate the capability of SPOT5-HRG data for forest density mapping in Caspian forests, the data of this sensor with 5 and 10 spatial resolutions dated 2002 were analyzed. The study area with 10000 ha is located in south western of Amlash city in Guilan province. In addition to original bands, some synthetic bands using ratio, fusion and transformation methods were created and used. In order to accuracy assessment of classification results, a ground truth map covering 26% of total area was prepared based on seven aerial photos (1:40000) dated 2001. The aerial photos were orthorectified and mosaiced. A total of 2520 circle sample plots with one ha area were selected on the digital orthophotomosaic. Canopy closure percent of each plot was interpreted using a 45 dot grid. Satellite data were classified by supervised classification methods including minimum distance to mean (MD) and maximum likelihood (ML). The highest overall accuracy and kappa coefficient equal to 74% and 0.33 were obtained by maximum likelihood classifier with four classes (1-10%, 10-50%, 50-100% and non-forest). Third density class (50-100%) represented highest producer and user accuracy, 95% and 82%, respectively. Lower producer and user accuracy were related to first density class 11% and 32%, respectively. It could be concluded that due to low kappa coefficient (0.33), even if reaching to pretty good overall accuracy (74%), the result of classification was not desirable. To obtain a better result, it is suggested to test other classification methods like object-based. Using higher spectral resolution data are also offered.