Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey

Sinan Bulut, Alkan Günlü and Sedat Keleş
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Alkan Günlü: Department of Forest Engineering, Faculty of Forestry, Çankiri Karatekin University, Çankiri, Turkey
Sedat Keleş: Department of Forest Engineering, Faculty of Forestry, Çankiri Karatekin University, Çankiri, Turkey

Journal of Forest Science, 2019, vol. 65, issue 1, 18-26

Abstract: The objective of this study is to estimate stand development stages (SDS) and stand crown closures (SCC) of forest using different classification methods (maximum likelihood, support vector machine: linear, polynomial, radial and sigmoid kernel functions and artificial neural network) based on satellite imagery of different resolution (Landsat 7 ETM+ and IKONOS). The results showed that SDS and SCC were estimated with Landsat 7 ETM+ image using the artificial neural network with a 0.83 and 0.78 kappa statistic value, and 92.57 and 89.77% overall accuracy assessments, respectively. On the other hand, SDS and SCC were predicted with IKONOS image using support vector machine (polynomial) method with a 0.94 and 0.88 kappa statistic value, and 95.95 and 91.17% overall accuracy assessments, respectively. Our results demonstrated that IKONOS satellite image and support vector machine (polynomial) method produced a better estimation of SDS and SCC as compared to Landsat 7 ETM+ and other supervised classification methods used in this study.

Keywords: supervised classification; stand attributes; support vector machine; artificial neural network; Landsat 7 ETM+; IKONOS (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:65:y:2019:i:1:id:127-2018-jfs

DOI: 10.17221/127/2018-JFS

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