DEVELOPMENT OF METHODOLOGY FOR PLANT PHENOLOGY MONITORING BY GROUND-BASED OBSERVATION USING DIGITAL CAMERA

DEVELOPMENT OF METHODOLOGY FOR PLANT PHENOLOGY MONITORING BY GROUND-BASED OBSERVATION USING DIGITAL CAMERA
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DOI:
10.5194/isprs-annals-iv-3-w1-65-2019
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发表时间:
2019-03
期刊:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
影响因子:
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通讯作者:
M. Yamashita;Y. Shinomiya;M. Yoshimura
M. Yamashita;Y. Shinomiya;M. Yoshimura
中科院分区:
其他
文献类型:
--
作者:
M. Yamashita;Y. Shinomiya;M. Yoshimura

文献摘要

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抽象。在地面监测物候时,更重要的是继续进行长期观测,并查明各种物候事件的时间,如长叶、开花和秋季衰老。在这项研究中,开发的方法,植物物候监测使用数码相机,我们研究了多个图像指数,这是来自多个时间可见光图像,响应于几个目标物种的植物的叶和花的颜色的变化,并试图检测各种物候事件通过跟踪时间序列变化的坐标的特征空间中的两个指数。结果发现,从每一个影像指标可以了解不同物种的物候事件的特徴。此外,它被确定为与两个指数的组合的效用将是有效的,以检测物候事件的时间在两个指数的特征空间。在实际的物候监测中,采用单一指标来了解季节特征,采用两种指标相结合的方法,通过在特征空间中追踪时间序列的变化来检测物候事件的发生时间,将是有效的。
Abstract. When monitoring phenology at ground level, it would be more important to continue observations in long terms and to detect the timing of various phenological events such as leafing, flowering and autumn senescence. In this study, to develop the methodology for plant phenology monitoring by using digital camera, we examined how multiple image indices, which are derived from multi-temporal visible images, respond to the changes of colors of leaves and flowers for several target species of plants, and tried to detect various phenology events by tracing time series changes of the coordinate in the feature spaces of two indices. As a result, we found out that it was possible to understand the characteristics of the phenological events for different species from each image index. Also, it was identified that the utility of combination with two indices would be effective to detect the timing of phenology events in the feature space of two indices. In the actual phenology monitoring, it would be effective to use a single index for understanding the seasonal characteristics and to use the combination of two indices for detection of the timing of phenology events by tracing the time series changes in the feature space.