预测和绘制Populus alba var.中的叶子水含量. 皮拉米达体使用超光谱图像
Zhao-Kui Li1, Hong-Li Li1, Xue-Wei Gong2
1School of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China.
Plant methods
|December 19, 2024
概括
超光谱成像非侵入性地估计干旱压力树的叶子水含量 (LWC). 该方法准确地绘制了水分分布图,有助于森林干旱监测和死亡风险评估.
科学领域:
- 植物生理学 植物生理学
- 遥感是一种远程传感.
- 林业林业 林业 林业 林业
背景情况:
- 叶子含水量 (LWC) 对于评估树木干旱压力和森林衰退风险至关重要.
- 目前的LWC测量方法是破坏性的和低效的.
- 超光谱成像为LWC评估提供了一个非侵入性的替代方案.
研究的目的:
- 开发和验证高光谱成像模型,用于估计Populus alba var.中的LWC. 一个金字塔.Pyramidalis.
- 绘制叶子湿度分布的地图,并确定保留水量较低的区域.
- 为在水资源有限的地区提供大规模干旱压力监测工具.
主要方法:
- 收集了来自Populus alba var.的叶子样本. 皮拉米达里斯被脱水到不同的LWC水平.
- 同时获得的超光谱图像和LWC测量.
- 开发了窄带光谱指数和多变量模型 (例如,FDRL-UVE-PLSR) 用于LWC估计.
- 使用校准和验证数据集验证模型性能.
主要成果:
- 两个窄带指数 (R627和R437-R444) 显示出强烈的LWC相关性.
- 多变量模型证明了LWC的强大的预测准确性.
- 该FDRL-UVE-PLSR模型实现了高R2 (0.9925校准,0.9853验证) 和低RMSE.
- 可视化了叶子的湿度分布,显示了叶子边缘较低的水含量.
结论:
- 超光谱成像有效地估计LWC,并非侵入性地绘制湿度分布图.
- 开发的光谱模型为干旱监测提供了准确的LWC预测.
- 这种方法增强了对植物水状况的理解,并促进了干旱地区森林健康评估.
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