Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data from GeoEye-1

Nowadays, taking advantage of multispectral sensors with the high spatial and spectral resolution and using a variety of plant indices and remote sensing have provided the possibility of more accurate analysis and classification of satellite data in the identification of natural phenomena. Nowadays, obtaining information about the structure of forests through remote sensing data to manage renewable resources is of interest to managers and researchers. This study produced the development maps of natural forests in northern Iran by taking advantage of GeoEye-1 data, training samples, and various algorithms through the pixel-based, object-based, and model-based methods. The classification’s ultimate accuracy was calculated by each of the above methods with the overall accuracy parameters and kappa coefficient. By examining the accuracy of map classification resulting from different methods, the maximum accuracy (78%) in object-based method was estimated based on the segmentation of NDVI and the maximum likelihood algorithm. Meanwhile, some other classification methods showed much less accuracy. The results showed that the algorithms following the structural patterns for pixel distribution classification provided a higher accuracy. Also, these results showed the high potential of high-resolution data from GeoEye-1 in the production of forest development maps and the effect of choosing the appropriate algorithm in the production of higher accuracy maps.