Automatically drawing vegetation classification maps using digital time‐lapse cameras in alpine ecosystems

IF 3.9 2区 环境科学与生态学 Q1 ECOLOGY Remote Sensing in Ecology and Conservation Pub Date : 2023-08-10 DOI:10.1002/rse2.364
Ryotaro Okamoto, R. Ide, H. Oguma
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引用次数: 0

Abstract

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.
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自动绘制植被分类地图使用数字时移相机在高山生态系统
高山生态系统特别容易受到气候变化的影响。需要监测高山植被的分布,以规划实际的保护活动。然而,在高山地区,传统的实地观测、机载和卫星遥感在覆盖范围、成本和分辨率方面都很困难。地面延时相机已被用于观测该地区的融雪和植被酚学,并在成本、分辨率和频率方面具有显著优势。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的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:植被分类和地理分区。所提出的植被分类方法使用从秋季图像中获取的像素时间序列,利用落叶的颜色模式。我们证明,使用延时图像和递归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,将基于地面的图像准确地转换为地理参考数据。我们提出了以下方法:(1)获取地面控制点的自动化程序;(2)考虑镜头畸变的相机模型,以实现精确的地理定位。我们证明,除了达到足够的精度来观察植物群落尺度上的植被分布外,所提出的方法优于传统方法。评估显示F1分数和均方根误差分别为0.937和3.4 m分别在植被分类和地理分区中。我们的研究结果突出了廉价的延时相机监测高山植被分布的潜力。所提出的方法可以为高山生态系统的有效保护规划做出重大贡献。
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来源期刊
Remote Sensing in Ecology and Conservation
Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
9.80
自引率
5.50%
发文量
69
审稿时长
18 weeks
期刊介绍: emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.
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