下一级植被健康指数预测:使用MODIS时间序列的ConvLSTM研究
Serkan Kartal1, Muzaffer Can Iban2, Aliihsan Sekertekin3
1Department of Computer Engineering, Çukurova University, 01380, Adana, Türkiye.
Environmental science and pollution research international
|February 14, 2024
概括
这项研究使用卫星数据和ConvLSTM模型预测植被健康状况. 一个具有全球规模计算的1层ConvLSTM实现了优越的植被健康指数 (VHI) 预测准确度.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 植被健康指数 (VHI) 对于使用卫星数据监测植被健康至关重要.
- 预测VHI对农业和生态至关重要,但先进的机器学习应用有限.
- 现有的方法往往缺乏未来植被健康预测所需的精度.
研究的目的:
- 使用遥感图像预测植物健康指数 (VHI) 值.
- 评估结合的卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 模型 (ConvLSTM) 对于VHI预测的有效性.
- 将传统的VHI计算与使用NDVI和LST的拟议全球规模方法进行比较.
主要方法:
- 使用MODIS的标准化差异植被指数 (NDVI) 和陆地表面温度 (LST) 数据计算VHI时间序列.
- 采用卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 混合模型 (ConvLSTM) 进行时间序列预测.
- 采用一种全新的全球范围的方法来计算VHI,包括全球最低和最高的NDVI和LST值.
主要成果:
- 一层ConvLSTM结构在VHI预测中通常优于二层和三层模型.
- 实现了较低的平均根平均平方误差 (RMSE) 值:0.025 (1步),0.026 (2步) 和0.026 (3步前).
- 全球规模的VHI计算方法与ConvLSTM结合,比传统方法表现优越.
结论:
- ConvLSTM模型,特别是1层结构,对于从卫星图像中预测VHI是有效的.
- 拟议的全球规模VHI计算提高了预测准确度.
- 这种方法为先进的植被健康监测和预测提供了一个有希望的工具.
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