Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series
Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series
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DOI：
10.1016/j.rse.2023.113790
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发表时间：
2023-11
影响因子：
13.5
通讯作者：
Yilun Zhao;C. Diao;Carol K. Augspurger;Zi-Ling Yang
中科院分区：
文献类型：
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作者：
Yilun Zhao;C. Diao;Carol K. Augspurger;Zi-Ling Yang
森林砍伐、城市化和农业扩张日益加剧了森林破碎化。通过树冠尺度叶物候学的视角监测森林碎片对于了解树种对气候变化的物候响应和识别易受环境干扰的碎片物种至关重要。尽管物候监测遥感技术取得了进展，但在支离破碎的森林中检测树冠尺度的叶子物候仍然具有挑战性。对生态系统功能和气候变化响应至关重要的关键春季物候事件的同步跟踪也被忽视。为了应对这些挑战，我们开发了一种新颖的树冠规模遥感物候监测框架，以描述落叶森林碎片中单棵树木的所有关键春季物候事件，并以伊利诺伊州尚佩恩的 Trelease Woods 为案例研究。该新颖框架包括四个部分：1）利用混合深度学习融合模型从多尺度卫星时间序列生成高时空分辨率融合图像； 2) 使用直方图匹配，用融合数据校准 PlanetScope 图像时间序列； 3）使用基于贝克逻辑的方法对树冠尺度物候轨迹进行建模； 4）使用几种物候度量提取方法（即基于阈值和曲线特征的方法）检测树冠尺度物候事件的多样性。结合 2017 年至 2020 年对 12 种阔叶树种的 123 棵单株树进行的每周现场物候观测，该框架有效地在树冠尺度上为关键春季物候事件（即发芽、发芽、叶子扩展和叶子成熟事件）连接了基于卫星和实地的物候测量，特别是对于大型个体（大多数事件的 RMSE <1 周）。考虑到景观破碎化，使用多尺度卫星融合数据校准 PlanetScope 图像对于监测森林碎片的树木物候至关重要。与基于曲线特征的方法相比，基于阈值的表观提取方法在检测单棵树的春季叶片物候动态方面表现出增强的能力。在物候事件中，使用校准的 PlanetScope 时间序列（RMSE 为 3 至 5 天，R 平方高于 0.8）高精度检索全叶和早期叶展开事件。通过密集的卫星和野外物候学工作，这一新颖的框架处于在破碎森林环境中具有生物学意义的野外物候事件背景下解释树冠尺度遥感物候指标的前沿。
Forest fragmentation has been increasingly exacerbated by deforestation, urbanization, and agricultural expansion. Monitoring the forest fragments via the lens of tree-crown scale leaf phenology is critical to understand tree species phenological responses to climate change and identify the fragment species vulnerable to environmental disturbance. Despite advances in remote sensing for phenology monitoring, detecting tree-crown scale leaf phenology in fragmented forests remains challenging. Simultaneous tracking of key spring phenological events that are crucial to ecosystem functions and climate change responses is also neglected. To address these challenges, we develop a novel tree-crown scale remote sensing phenological monitoring framework to characterize all the critical spring phenological events of individual trees of deciduous forest fragments, with Trelease Woods in Champaign, Illinois as a case study. The novel framework comprises four components: 1) generate high spatiotemporal resolution fusion imagery from multi-scale satellite time series with a hybrid deep learning fusion model; 2) calibrate PlanetScope imagery time series with fusion data using histogram matching; 3) model tree-crown scale phenology trajectory with a Beck logistic-based method; 4) detect a diversity of tree-crown scale phenological events using several phenological metric extraction methods (i.e., threshold- and curve feature-based methods). Combined with weekly in-situ phenological observations of 123 individual trees across 12 broadleaf species from 2017 to 2020, the framework effectively bridges the satellite- and field-based phenological measures for the key spring phenological events (i.e., budswell, budburst, leaf expansion, and leaf maturity events) at the tree-crown scale, particularly for large individuals (RMSE <1 week for most events). Calibration of PlanetScope imagery using multi-scale satellite fusion data in consideration of landscape fragmentation is critical for monitoring tree phenology of forest fragments. Compared to curve feature-based methods, threshold-based phenometric extraction methods demonstrate enhanced capability in detecting spring leaf phenological dynamics of individual trees. Among the phenological events, full leaf out and early leaf expansion events are retrieved with high accuracy using calibrated PlanetScope time series (RMSE from 3 to 5 days and R-squared higher than 0.8). With both intensive satellite and field phenological efforts, this novel framework is at the forefront of interpreting tree-crown scale remotely sensed phenological metrics in the context of biologically meaningful field phenological events in fragmented forest setting.