Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
Environ Monit Assess. 2011 Sep;180(1-4):1-13. doi: 10.1007/s10661-010-1768-x. Epub 2010 Nov 18.
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.
对生境管理至关重要的是,不仅要了解动物食物资源的位置,还要了解其可利用性的时间。例如,灰熊(Ursus arctos)的饮食会随着不同植被物种进入关键物候阶段而季节性变化。在本文中,我们描述了使用一个由七个地面数字相机系统组成的网络来监测特定物种感兴趣区域的林下和林上植被。这些相机在加拿大阿尔伯塔省西部的海拔梯度上建立,从 2009 年 4 月 13 日到 10 月 27 日每天收集真彩色(RGB)图像。四阶多项式拟合到 RGB 衍生指数,然后与物候阶段的现场观测进行比较。使用线性回归对相机和现场数据进行统计学关联,结果表明,在野外物候阶段数据中观察到的 61%(r (2) = 0.61,df = 1,F = 14.3,p = 0.0043)的方差可以由相机捕捉到生长季节的开始,而 72%(r (2) = 0.72,df = 1,F = 23.09,p = 0.0009)的方差可以捕捉到生长季节的长度。基于线性回归模型,预测和观察到的生长季节开始和生长季节长度之间的残差的平均绝对差异分别为 4 天和 6 天。这项工作通过演示在森林环境中使用现成的数字相机技术和基于 RGB 的植被指数,可以针对特定的林下和林上物种进行物候监测,扩展了之前的研究。