Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground-based time-lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground-based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time-lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground-based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant-community scale. The evaluation revealed an F1 score and root-mean-square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time-lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.

中文翻译：

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利用数字延时相机自动绘制高山生态系统植被分类图

高山生态系统特别容易受到气候变化的影响。规划实际的保护活动需要监测高山植被的分布。然而，传统的野外观测、机载和卫星遥感在高寒地区的覆盖范围、成本和分辨率方面存在困难。地面延时相机已用于观察该地区的融雪和植被物候，并在成本、分辨率和频率方面具有显着优势。然而，它们尚未用于植被分布模式的研究监测。本研究提出了一种从高山地区地面图像绘制地理参考植被分类图的新方法。我们的方法有两个组成部分：植被分类和地理校正。所提出的植被分类方法使用从秋季图像获取的像素时间序列，利用秋季树叶颜色模式。我们证明，使用延时图像和循环神经网络可以提高植被分类的性能。我们还开发了一种新方法，可以将地面图像准确地转换为地理参考数据。我们提出以下方法：（1）获取地面控制点的自动化程序和（2）考虑镜头畸变以进行精确地理校正的相机模型。我们证明了所提出的方法除了达到足够的精度来观察植物群落规模的植被分布外，还优于传统方法。评估结果显示 F1 分数和均方根误差分别为 0.937 和 3。植被分类和地理校正分别为 4 m。我们的研究结果凸显了廉价延时相机在监测高山植被分布方面的潜力。该方法可以为高山生态系统的有效保护规划做出重大贡献。