使用频谱空间特征收集和多层感知来重建森林群的频谱
Fan Wang1,2, Linghan Song1,2, Xiaojie Liu1,2
1College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.
Frontiers in plant science
|December 11, 2023
概括
这项研究引入了一种新的方法,使用LiDAR和多谱数据逆转森林光谱,增强森林管理. 该方法有效地重建了三维光谱分布,改善了森林健康监测.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 三维光谱分布为有效的森林管理提供了对森林生理和生物化学状况的重要空间见解.
- 目前对森林种群的三维光谱研究是有限的,这凸显了对先进方法的需求.
研究的目的:
- 开发和评估一种使用LiDAR和多光谱数据衍生的点云来逆转森林光谱的方法.
- 评估深度学习算法用于语义细分在描述森林种群中的有效性.
- 改进森林精确的三维光谱分布,用于增强的遥感应用.
主要方法:
- 多光谱值与LiDAR点云的融合,然后进行K-means集群以进行数据表征.
- 应用五种深度学习算法用于语义细分,以整体准确性 (oAcc) 和平均交叉比率 (mIoU) 为性能指标.
- 使用语义细分模型重新配置类3D光谱分布,并评估反转结果.
主要成果:
- 在光谱和空间属性之间观察到高相关性 (>0.98),在光谱和空间属性之间观察到中等相关性 (0.43).
- PointMLP表现出最高的性能,oAcc为0.84和mIoU为0.75.
- 该模型实现了准确的局部光谱反转,预测值与真实值密切匹配,NIR值与树冠高度和距树顶距离相关.
结论:
- 将空间融合和语义细分结合起来,有效地逆转了森林种群的三维光谱信息.
- 开发的模型满足了局部光谱逆转的准确性要求,从而提高了森林状况的估计.
- 这些发现为近地遥感和精确森林光谱分布分析提供了基础.
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