The use of aesthetic factors in the selection of suitable ornamental trees and shrubs for urban green space with emphasis on native species (case study: Langroud city)
Subject Areas :
Fatemeh Hosseini Koumleh
1
(Department of Horticultural Science, Rasht Branch, Islamic Azad University, Rasht, Iran)
Alireza Eslami
2
(Rasht, Islamic Azad University)
Behzad Kaviani
3
(Department of Horticultural Science, Rasht Branch, Islamic Azad University, Rasht, Iran)
Keywords: Classification, Multispectral Images, Phenology Random forest algorithm, RGB Images,
Abstract :
Accurate information about the tree composition of a forest is required for many forest monitoring and conservation purposes. In recent years, the use of modern remote sensing methods and techniques based on unmanned vehicles have been used to regularly update information in the forest. In this research, different data sources including multi-spectral images (Storm drone) and real color images (Phantom drone) with very high spatial resolution in the forest plains of Noor City located in Mazandaran province were used to identify tree species. Also, imaging was performed in a growing season to prepare a time series of UAV-RGB images and investigating the effect of tree crown phonological changes on the classification accuracy level. To classify and identify forest species, calculating indices based on true color images such as NRB and NGB, multispectral indices such as CIgreen and NDVI, raw bands, and random forest classification method were used. Based on single-time images, the images of the end of April with an overall accuracy of 75% provided the highest overall accuracy. The results related to time series images also identified trees with 86% accuracy. Also, species identification based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image with imaging at the right time using a drone equipped with an RGB sensor, compared to taking a time series and using drones equipped with multispectral sensors, has acceptable and less expensive results for tree recognition in the study area.
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