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Decadal forest cover change analysis of the tropical forest of Tadoba-Andhari, India.
- Source :
- Signal, Image & Video Processing; Mar2024, Vol. 18 Issue 2, p1705-1714, 10p
- Publication Year :
- 2024
-
Abstract
- Deforestation is a major concern for preserving the biodiversity of the entire globe. During the last few years, machine learning and deep learning methods have been employed for mapping deforestation. There is still scope for ample improvement in these methods as they are prone to errors and can give inaccurate results because of over or under-segmentation. This paper uses deep convolutional neural network-based semantic segmentation to process multispectral satellite images to monitor forest cover changes in Tadoba-Andhari National Park during the period 2000–2022. The proposed approach uses the U-Net architecture with extended inputs which gives more accuracy as compared to U-Net with only image input. Landsat images along with vegetation indices have been used as training data. The proposed method requires less time to train the model and is also cost-efficient in terms of computing requirements. The performance of the proposed method was compared with state-of-the-art methods where the proposed method outperformed the other models with an F1-score of 0.90 and an accuracy of 84.83%. When compared with U-Net trained with Landsat images only, it was observed that the U-Net model trained with extended input was able to achieve better results. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18631703
- Volume :
- 18
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Signal, Image & Video Processing
- Publication Type :
- Academic Journal
- Accession number :
- 175542553
- Full Text :
- https://doi.org/10.1007/s11760-023-02872-w