Earticle

초록

영어

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approximately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

목차

Abstract
Introduction
Materials and Methods
Study area
Used data
Research method
Result and Discussion
Distribution characteristics of deep learning training data by category
Evaluation of deep learning model consistency based on training conditions
Comparison of forest degradation detection consistency and analysis of misclassification characteristics across different deep learning models
Conclusion
Acknowledgements

키워드

저자

• Woo-Dam Sim [ Division of Forest Sciences, Department of Forest Management, College of Forest and Environmental Sciences, Kangwon National University ]
• Jung-Soo Lee [ Division of Forest Sciences, Department of Forest Management, College of Forest and Environmental Sciences, Kangwon National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of Forest and Environmental Science [산림과학연구]

• 간기

  계간

• pISSN

  2288-9744

• eISSN

  2288-9752

• 수록기간

  1981~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 526 DDC 634