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...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。展开更多
Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greennes...Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.展开更多
延时摄影因可靠、高效和低成本的优势,在冰川监测中应用广泛,特别是对于获取冰川表面连续变化信息而言。本文基于2020年3月—2021年9月物候相机拍摄的梅里雪山明永冰川末端照片及多期无人机影像,利用地面摄影测量技术和互相关算法,提取...延时摄影因可靠、高效和低成本的优势,在冰川监测中应用广泛,特别是对于获取冰川表面连续变化信息而言。本文基于2020年3月—2021年9月物候相机拍摄的梅里雪山明永冰川末端照片及多期无人机影像,利用地面摄影测量技术和互相关算法,提取了日尺度冰川表面运动速度。结果表明:通过物候图像获取的冰川表面运动速度分辨率高,从海拔2 880~3 150 m a. s. l.,冰川总位移介于(129.38±7.76)~(669.95±247.88) m,年均表面运动速度达(79.14±4.74)~(412.86±152.75) m·a-1,呈从中间向两侧减缓的空间分布特征。冰川表面运动速度随季节变化,夏季流速[(0.13±0.06)~(1.99±0.37) m·d-1]快于冬季流速[(0.07±0.06)~(1.35±0.37) m·d-1]。与冬季流速相比,夏季流速受降水和气温升高的影响不稳定。根据流速分离结果,明永冰川末端底部全年处于融化或压融状态,底部滑动对冰川表面运动速度的贡献介于76%~93%。冬季底部滑动占表面流速高达82%,夏季底部滑动对冰川运动起绝对主导作用。本文采用的技术为进一步研究季风海洋型冰川的运动机制提供了参考方案。展开更多
文摘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。
基金National Natural Science Foundation of China(41601478)National Key Research and Development Program of China(2018YFB0505301,2016YFC0500103)
文摘Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.
文摘延时摄影因可靠、高效和低成本的优势,在冰川监测中应用广泛,特别是对于获取冰川表面连续变化信息而言。本文基于2020年3月—2021年9月物候相机拍摄的梅里雪山明永冰川末端照片及多期无人机影像,利用地面摄影测量技术和互相关算法,提取了日尺度冰川表面运动速度。结果表明:通过物候图像获取的冰川表面运动速度分辨率高,从海拔2 880~3 150 m a. s. l.,冰川总位移介于(129.38±7.76)~(669.95±247.88) m,年均表面运动速度达(79.14±4.74)~(412.86±152.75) m·a-1,呈从中间向两侧减缓的空间分布特征。冰川表面运动速度随季节变化,夏季流速[(0.13±0.06)~(1.99±0.37) m·d-1]快于冬季流速[(0.07±0.06)~(1.35±0.37) m·d-1]。与冬季流速相比,夏季流速受降水和气温升高的影响不稳定。根据流速分离结果,明永冰川末端底部全年处于融化或压融状态,底部滑动对冰川表面运动速度的贡献介于76%~93%。冬季底部滑动占表面流速高达82%,夏季底部滑动对冰川运动起绝对主导作用。本文采用的技术为进一步研究季风海洋型冰川的运动机制提供了参考方案。
