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评估草原物种分布在景观尺度上的变化,使用超光谱遥感

Obumneke Ohiaeri¹, Carlos Portillo-Quintero², Haydee Laza¹

¹Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409-2122, USA.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

超光谱遥感量化了俄克拉荷马州牧场的土地覆盖变化. 草生植被减少,裸露的土壤扩大,显示了半干旱草原组成的微妙变化.

科学领域:

地球和环境科学地球和环境科学
遥感遥感遥感遥感
生态生态学生态生态学

背景情况:

超光谱遥感在复杂生态系统中推进了土地覆盖的表征.
量化土地覆盖类别的部分丰度对于生态监测至关重要.

研究的目的:

将线性光谱分离 (LSU) 算法应用于NEON高光谱图像,以量化土地覆盖变化.
评估将LSU与无人机数据整合在一起,以检测半干旱草原组成的变化.

主要方法:

线性光谱分离 (LSU) 算法应用于从2018年到2022年的NEON高光谱图像.
用于高分辨率参考数据采集和模型验证的无人机图像.
草本植物,混合草本植物和裸土的部分丰度量化.

主要成果:

草木覆盖面从16.02公减少到11.56公;赤裸的土壤从3.37公扩大到6.39公.
混合面积的覆盖面保持相对稳定 (12.38公到13.82公).
基于斜坡的趋势图显示了局部植被损失和再生模式,尽管平均丰度的统计变化并不显著.

结论:

与无人机数据集成的线性光谱分离有效地检测了半干旱草原组成的微妙,生态意义上的变化.
该研究强调了超光谱遥感对于监测牧场动态的有用性.
结果表明,在实验范围内的实地植被动态.

关键词:

国家生态观测站网络 (NEON)分数的丰富度地图.线性光谱没有混合.机器学习是机器学习.远程传感是一种遥感技术.植被组成植被组成

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