基于火灾后卫星图像的植被变化检测和恢复评估,使用深度学习
1Information Science and Technology, College of Engineering Guindy, Anna University, 12 Sardar Patel Road, Chennai, 600 025, India. shanmurajendran2@gmail.com.
Scientific reports
|June 1, 2024
概括
野火对植被的影响是使用新型人工智能来评估的. 深度嵌入式集群 (DEC) 和自适应生成对抗神经网络 (AdaptiGAN) 模型准确地检测植被变化和火灾后的恢复.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 野火大大改变了生态系统和植被.
- 地球观测数据对于监测火灾后的植被变化至关重要.
- 现有的方法在评估植被恢复时可能缺乏准确性.
研究的目的:
- 引入一种用于评估火灾后植被影响的新方法.
- 使用人工智能精确检测和分类野火后的植被变化.
- 绘制火灾影响地区植被恢复的地图.
主要方法:
- 利用深层嵌入式集群 (DEC),一种无监督的方法,用于检测植被变化.
- 员工增强植被指数 (EVI) 趋势分析以量化绿化和色分数.
- 应用自适应生成对抗神经网络 (AdaptiGAN) 用于植被恢复映射.
主要成果:
- 在分类植被变化方面取得了96.17%的准确性.
- 量化绿化分数 (0.122.4平方公里) 和色分数 (0.118.1平方公里).
- 在植被恢复评估中,AdaptiGAN表现出0.075的低训练误差.
结论:
- 拟议的方法在火灾后的植被分析方面取得了重大进展.
- 由人工智能驱动的方法为生态系统恢复提供了准确和详细的见解.
- 这项研究强调了先进机器学习在生态监测中的潜力.
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