植被物候能反映植被生长状况及其对气候变化的响应,在景观或更小尺度上自动化观测分析植被物候的演化是对大尺度遥感分析和单株植物人工观测的有效补充。基于物候相机观测网络(PhenoCam)中3种典型植被类型(森林、草地和农作物)站点数据,首先在群落尺度的感兴趣区(region of interesting, ROI)和像素2个尺度上计算植被指数,然后利用多种曲线拟合植被生长轨迹,提取关键物候参数,最后对相机物候参数进行了不确定性分析和卫星遥感物候的比较验证。结果表明:自定义ROI区域可以精确划定植被聚集区域,减少天空、地面等非植被要素的干扰;多方法的生长曲线拟合实验表明双逻辑斯蒂拟合法比较适用于单生长期植被,样条法较适用于多生长期植被;单生长期植被可直接采用多种物候参数提取方法(Klosterman, Gu, TRS, Derivatives)从生长曲线上提取关键物候参数,而多生长期植被可先用样条法拟合生长轨迹,然后采用变化点方法提取关键物候参数;生长曲线拟合与物候参数提取组合方法的不确定性分析发现,Klosterman方法具有较好的鲁棒性,各组合方法模拟实验的均方根误差均小于0.005;相机物候参数与MODIS EVI提取的遥感物候参数对比验证表明,二者在森林、农作物上的物候参数比较一致;像素级返青期参数的探索性分析发现,在像素尺度上能够识别群落内物种及个体间的物候差异,未来经过更深入的不确定性分析后,可尝试作为自动化分析群落尺度生物多样性的方法。
英文摘要:
Vegetation phenology is an effective index that reflects the growth condition of vegetation and their response to climate change. Analyzing vegetation phenology at landscape or smaller scale will be a powerful supplement to remote- sensing based and artificial observed phenological studies. In this study, we applied the time series digital images from typical observation stations in camera-based phenology network (PhenoCam) to fit the seasonal growth curves at region of interest (ROI) and pixel scales. Key vegetation phenological parameters on the growth curve were extracted using multiple methods. First, we realized the custom definition of ROI by arbitrarily drawing polygons on the image and growth curve fitting based on the greenness index in the ROI. The results indicate that double logistic method is adapted for modeling the vegetation growing process at middle-high latitude that have a single growth peak. The spline method shows a good performance for fitting growth curves of vegetation that has multiple growth seasons. Second, based on the fitted curve, we used four models (Klosterman, Gu, TRS, and Derivatives) to derive the key phenological parameters for vegetation having single growth season. MODIS EVI-based phenological parameters were extracted as a comparison with camera based phenological parameters. We found that their capabilities in detecting phenological parameters in forest and cropland were consistent. For the vegetation with more than one growth season, the growth curve was fitted with spline method, then the change point detection method was applied to determine growth seasons on the curve and extract the key phenological parameters. The uncertainty of the fitting methods and phenology methods were estimated, with all of the RMSE less than 0.005. Klosterman method is identified as the most robust. Vegetation greenup date was extracted for each pixel in the ROI that can be useful for distinguishing the phonological diversity between different species. Pixel level vegetation phenology analysis could be used to identify biodiversity at landscape scale in the future.