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通过利用草原和遥感数据来完善葡萄藤植被状况

Tibor Zsigmond1,2,3, Zsófia Bakacsi4,5, Ágota Horel1,2

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遥感数据,包括标准化差异植被指数 (NDVI),可以准确评估葡萄园植被的健康状况. 整合草原数据与机器学习模型显著改善了葡萄树的NDVI预测.

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线性回归机器学习其他国家植物压力随机森林光谱反射率

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科学领域:

  • 农业科学
  • 遥感技术
  • 生态学

背景情况:

  • 植物指数 (VI) 对于监测植物健康和环境压力至关重要.
  • 标准化差异植被指数 (NDVI) 广泛使用,但其准确性可能受到土壤类型和地形等因素的限制.
  • 综合来自不同来源的数据,如草原和卫星图像,可以增强植被监测能力.

研究的目的:

  • 研究草原和葡萄园生态系统中的特定植被指数 (VI).
  • 评估草原遥感 (RS) 数据在葡萄园中改进NDVI值的潜力.
  • 为了比较不同的机器学习模型在预测葡萄藤VI的性能.

主要方法:

  • 在草原和葡萄园中对NDVI,光化学反射指数 (PRI) 和光合成活性辐射 (PAR) 的实地监测.
  • 获取和分析Sentinel-2 (S2) 光谱数据.
  • 应用机器学习技术,包括线性回归 (LR),随机森林 (RF) 和XGBoost,以完善NDVI测量.

主要成果:

  • 在不同地点观察到VI的显著差异,与土壤化学相关.
  • NDVI显示了总体的树冠活力,而PRI显示了对短期生理变化和压力的更高敏感性.
  • 地基和RS NDVI显示出良好的相关性 (r=0.68).
  • 使用一年中的日期和草原数据训练的射频模型获得了最高的准确性 (r=0.787).
  • 所有机器学习模型都显示,当草原NDVI被纳入训练时,葡萄藤VI的预测得到了改善.

结论:

  • 根据生态系统类型和土壤特性,植被指数有很大差异.
  • 草地遥感数据可以提高葡萄园植被监测的准确性.
  • 机器学习模型,特别是随机森林,对改进NDVI测量和评估葡萄酒健康有希望.