Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data
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Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data from GeoEye-1 Amin Mahdavi Saeidi & Sasan Babaie Kafaky & Asadollah Mataji
Received: 26 May 2020 / Accepted: 14 September 2020 / Published online: 23 September 2020 # Springer Nature Switzerland AG 2020
Abstract 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, objectbased, 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 A. Mahdavi Saeidi : S. Babaie Kafaky (*) : A. Mataji Department of Forestry, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran e-mail: [email protected]
A. Mahdavi Saeidi e-mail: [email protected] A. Mataji e-mail: [email protected]
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. Keywords Pixel base . Object base . Model base . Segmentation . Stand development stages . Temperate deciduous natural forests
Introduction Nowadays, remote sensing, as a set of technologies which collect, store, and analyze spatial information in a wide range of geometrics, has made it possible to study and monitor ecosystems at the lowest possible cost and time and the highest speed (Kavzoglu et al. 2015; Chen et al. 2012; Desclée et al. 2006; Morales 2012; Al-Doski et al. 2013; Bulut et al. 2019). Remote sensing technology can provide access to information and patterns to be obtained, which normally require extensive and long-term presence in the natural realm (Amiri 2013; Morales 2012; Rafieyan et al. 2011; Mollaeia et al. 2019; Nourian et al. 2016; Motlagh et al. 2018). Also, Earth observational satellites provide a wide and varied range of information with the spatial, temporal, spectral, and radiometric resolution. These data and
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