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Improvement of forest canopy density mapping of sparse forests using RS/GIS-based classification approach

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Abstract

In low-density forests, canopy cover mapping is an important factor, and applying accurate and performance methods to improve canopy cover mapping is necessary. This study used high-resolution QuickBird and WorldView-2 images to map canopy cover density in two sparse forests using an indirect (RS/GIS-based) method in conjunction with direct remote sensing methods. In addition, concentric plots with areas of 1000, 1200, 2500, 5000, 7500, and 10,000 m2 were studied to determine the optimal plot area to identify tree canopy density. As a result of using the direct method, the best results were obtained in the Dashte Barm forest area with a plot size of 7500 m2 (overall accuracy = 56.57%, Kappa coefficient = 0.32) and in the Ilam forest area with a plot size of 5000 m2 (overall accuracy = 45.71%, Kappa coefficient = 0.263). Additionally, the best canopy cover density map was produced using the indirect method (RS/GIS-based) in the Dashte Barm and Ilam forest areas with plot sizes of 10,000 m2 (overall accuracy = 82.69% and Kappa coefficient = 0.744) and then with 1000 m2 (overall accuracy = 74.27%, Kappa coefficient = 0.69). From these results, it can be concluded that the indirect method significantly improved the results over the direct method. In addition, the results showed that plots with different areas could be used to map the canopy cover density based on the conditions of the canopy cover density in forest stands.

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Authors and Affiliations

  1. Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Gorgan, 386, Iran

    Mohammad Hassan Naseri & Shaban Shataee Jouibary

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  1. Mohammad Hassan Naseri
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  2. Shaban Shataee Jouibary
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Correspondence to Mohammad Hassan Naseri.

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Naseri, M.H., Shataee Jouibary, S. Improvement of forest canopy density mapping of sparse forests using RS/GIS-based classification approach. Arab J Geosci 16, 525 (2023). https://doi.org/10.1007/s12517-023-11633-5

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