Tree species diversity monitoring using GeoEye satellite image in Gardeshi forest District

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AKBARI H.; KALBI S.

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Journal: Journal of Wood and Forest Science and Technology Year:2019 | Volume:26 | Issue:2 Page(s): 51-62

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Information Journal Paper

Title

Tree species diversity monitoring using GeoEye satellite image in Gardeshi forest District

Author(s)

AKBARI H. | KALBI S. | Issue Writer Certificate

Keywords

Shannon winner indexQ1

Simpson and Gardeshi DistrictQ1

Tree diversityQ1

Abstract

Background and Objectives: Understanding the link between conservation of biodiversity and ecosystem processes is one of the main issues in ecological research. Forests are one of the valuable natural resources of the planet, which plays an important role in the ecological balance and the lives of human societies. Tree species diversity is a key parameter to describe forest ecosystems in close-to-nature forest management. Modeling and mapping of Tree diversity are a useful tool for conservation and management of forests. Hyrcanian forest, In terms of Tree diversity, the richest forests in Iran during recent years has been subject to Extreme changes. Ecologists in recent decades have been paying more attention to estimating Tree diversity through quick and non-destructive methods. Integrating remote-sensing data with ground data can be a suitable method for this purpose. The aim of this study is determination of GeoEye satellite image capability for tree species diversity monitoring in Gardeshi forest District. Materials and Methods: For this purpose, using field sampling, 150 samples with 30 * 30 dimension were taken. Then the Shannon-Weiner, Simpson and Simpson index were calculated in each plots. Preprocessing and processing include principle component analyze, vegetation index and texture analyze carry out in the satellite image. 70% of training samples were used for modeling. For modeling, classification and regression tree methods, Random forest, different variants of the nearest neighbor and different kernels of machine support vector were used. The best bands were selected for modeling. Models were evaluated using 30% of the samples. Then the best models were specified for each diversity index. Results: The results showed that among the indices, the Near-Infrared band and derived texture analyses bands extracted from Near Infrared band were selected as the best band for modeling. The results showed RBF kernel of SVM with a 58% determined coefficient and a root mean square error of 46% and Relative bias of 1. 9 % for the Simpson Model was the best. Also, for the Shannon Wiener Diversity index the highest results with the determined coefficient of 54. 4 percent and relative bias of 0. 06 percent, was related to sigmoid kernel. Conclusion: The results showed that GeoEye-1 satellite data lacked satisfactory results in estimating Tree diversity in circular forest forests. The models used by the RBF kernel method and the Sigmoid kernel method had the best result in the carrier vector machine method

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APA: Copy

AKBARI, H., & KALBI, S.. (2019). Tree species diversity monitoring using GeoEye satellite image in Gardeshi forest District. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, 26(2 ), 51-62. SID. https://sid.ir/paper/368839/en

Vancouver: Copy

AKBARI H., KALBI S.. Tree species diversity monitoring using GeoEye satellite image in Gardeshi forest District. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY[Internet]. 2019;26(2 ):51-62. Available from: https://sid.ir/paper/368839/en

IEEE: Copy

H. AKBARI, and S. KALBI, “Tree species diversity monitoring using GeoEye satellite image in Gardeshi forest District,” JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, vol. 26, no. 2 , pp. 51–62, 2019, [Online]. Available: https://sid.ir/paper/368839/en

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