Investigation of Classification Tree Analysis Algorithm Using Landsat 8 and Sentinel 2 Satellite Images and Visual Interpretation of Google Earth Images in Separation and Classification of Plant Ecological Units

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

One of the most important methods of extracting information from satellite data is various image classification techniques. The current research was conducted to separate and classify plant ecological units by classification tree analysis algorithm on satellite images and also visual interpretation of Google Earth images in one of the semi-steppe rangelands of Chaharmahal and Bakhtiari province. After applying the classification algorithm in Idrisi TerrSet software, the software generated the error matrix, and then based on the values inside this matrix, the extracted statistics were evaluated. The results of visual interpretation showed that finally, seven types of plant ecological units that were different in terms of structural features were identified and expressed as descriptive statistics, including Astragalus verus, Bromus tomentellus, Scariola orientalis, Astragalus verus-Bromus tomentellus, Astragalus verus -Stipa hohenikeriana, Bromus tomentellus-Stipa hohenikeriana, and Stipa hohenikeriana. The results also showed that the overall accuracy and kappa coefficients of 0.92% and 0.89 for Landsat 8 images and 0.94% and 0.92 for sentinel 2 images were achieved. Based on the obtained results, it was found that satellite images and aerial images have a suitable separation capability to prepare a map of plant ecological units. Since the visual interpretation of Google Earth images is a time-consuming and expensive method, it can be concluded that satellite images, especially Centile 2, can be used as a tool due to their high resolution and high-resolution images, Practical and usable, provide accurate information and details of the earth's surface phenomena and be used as a suitable source for preparing a map of plant ecological units.

Language:
Persian
Published:
Iranian Journal of Range and Desert Research, Volume:29 Issue: 4, 2023
Pages:
608 to 626
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