Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R² = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

中文翻译：

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利用Landsat和MODIS产品的数据融合对韩国济州岛亚高山森林进行物候分析

气候变化给高山生态系统带来了不成比例的风险。有效监测森林对气候变化的物候响应对预测和管理对高山种群的威胁至关重要。遥感可用于监测动态景观中的森林群落，以响应物种一级的气候变化。使用遥感图像的时空融合技术是检测随时间变化的逐渐物候变化以及对气候变化的季节性响应的有效方法。时空自适应反射融合模型（STARFM）是Landsat和MODIS图像广泛使用的数据融合算法。这项研究旨在通过融合Landsat和MODIS影像的时空数据，在物种-群落水平上识别森林物候特征和变化。R ²＝ 0.795。我们发现归一化植被指数（NDVI）值随时间增加，这表明由于气候变化导致温度升高已经影响了研究区域生长季节的开始。从这项研究中，我们发现温度升高会影响这些地区的物候，森林管理策略（如使用遥感技术监测物候）应评估气候变化的影响。