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Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

机译:使用数字延时摄像机监测物种特定的下层和过度物候,以支持野生动物栖息地评估

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摘要

Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2?= 0.61, df = 1, F?= 14.3, p?= 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2?= 0.72, df = 1, F?= 23.09, p?= 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.
机译:对栖息地管理至关重要的是,不仅要了解动物粮资源的位置,而且要了解其获取时间。例如,灰熊(Ursus arctos)的饮食随着不同的植被物种进入关键的物候期而季节性变化。在本文中,我们描述了使用由七个基于地面的数码相机系统组成的网络来监视特定物种感兴趣区域内的下层和上层植被的方法。这些摄像机建立在加拿大艾伯塔省西部的海拔梯度上,每天从2009年4月13日至2009年10月27日收集真彩色(RGB)图像。将四阶多项式拟合到RGB派生的索引,然后将其与实地进行比较基于物候阶段的观察。使用线性回归对摄影机和野外数据进行统计相关,结果表明,捕获了野外物候相位数据中观测到的方差的61%(r 2?= 0.61,df = 1,F?= 14.3,p?= 0.0043)在生长季节开始时用相机拍摄,并且生长季节长度变化的72%(r 2≤0.72,df = 1,F 1 = 23.09,p 2 = 0.0009)。基于线性回归模型,预测的和观察到的生长季节开始和生长季节长度之间残差的平均绝对差分别为4天和6天。这项工作通过证明可以使用现成的数码相机技术和基于RGB的植被指数在森林环境中进行物候监测来确定特定的林下和林下物种,从而扩展了以前的研究。

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