PDF(Pubmed)
Abstract：
Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phenological observations into reliable ground truthing for training and validating EO mapping applications remains challenging. This study explored the feasibility of predicting high-resolution phenological phase data for European beech (Fagus sylvatica) using unoccupied aerial vehicle (UAV)-based multispectral indices and machine learning. Employing a comprehensive feature selection process, we identified the most effective sensors, vegetation indices, training data partitions, and machine learning models for phenological phase prediction. The model that performed best and generalized well across various sites utilized Green Chromatic Coordinate (GCC) and Generalized Additive Model (GAM) boosting. The GCC training data, derived from the radiometrically calibrated visual bands of a multispectral sensor, were predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model demonstrated capability in predicting phenological phases on unseen datasets within a root mean squared error threshold of 0.5. This research highlights the potential interoperability among common UAV-mounted sensors, particularly the utility of readily available, low-cost RGB sensors. However, considerable limitations were observed with indices that implement the near-infrared band due to oversaturation. Future work will focus on adapting models to better align with the ICP Forests phenological flushing stages.
摘要：
获取物候事件数据对于研究气候变化对森林动态的影响以及评估与幼叶早发相关的风险至关重要。使用地球观测（EO）数据对森林物候时间进行大规模映射可以通过增加空间分量来增强我们对这些过程的理解。然而,将传统的基于地面的物候观测结果转换为用于培训和验证EO制图应用的可靠地面事实仍然具有挑战性。这项研究探讨了使用基于无人飞行器（UAV）的多光谱指数和机器学习来预测欧洲山毛榉（Fagussylvatica）高分辨率物候阶段数据的可行性。采用全面的特征选择过程，我们确定了最有效的传感器，植被指数，训练数据分区,和用于物候阶段预测的机器学习模型。在各个站点上表现最佳和普遍良好的模型利用了绿色色度坐标（GCC）和广义加性模型（GAM）增强。GCC训练数据，从多光谱传感器的辐射校准视觉波段中得出，使用未校准的RGB传感器数据进行预测。最终的GCC/GAM增强模型证明了在均方根误差阈值为0.5的未见数据集上预测物候阶段的能力。这项研究强调了常见的无人机安装传感器之间的潜在互操作性，特别是现成的实用性，低成本的RGB传感器。然而,由于过饱和，实现近红外波段的指数存在相当大的局限性。未来的工作将侧重于调整模型，以更好地与ICP森林物候冲洗阶段保持一致。
