基于数字相机图像的西藏当雄高寒草地群落物候模拟

物候现象是环境条件季节和年际变化最直观、敏感的综合指示器, 其发生时间不仅反映了陆地生态系统短期的动态特征, 其微小的变化还会对陆地生态系统产生重要的反馈作用。高寒草地是青藏高原分布广泛、极具代表性的植被类型, 准确地获取高寒草地群落的物候特征, 对于理解和预测气候变化对青藏高原生态系统的影响具有重要意义。该文以西藏当雄高寒草地为研究对象, 探讨了近地面数字相机图像在高寒草地群落季相监测中的作用, 结果如下: 1)通过比较不同绿度指数的差别, 确定了准确表征高寒草地植被群落季相变化的绿度指数——绝对绿度指数(2G_RB); 2)结合土壤含水量数据, 通过线性回归分析得知高寒草地植被群落生长过程与表层(≤10 cm)土壤含水量的变化较为一致(R ² > 0.70); 3)通过对比分析, 发现降水在高寒草地群落季相“变绿”过程中具有“触发”作用。研究表明, 数字相机技术可作为物候监测手段, 实现高寒草地植被群落季相的实时、连续获取, 为更好地揭示气候变化影响下景观尺度季相演变特征, 诊断地方、区域和全球尺度上生态系统对气候变化的快速响应提供了有效的手段。     

城市绿地生态系统多角度高光谱光化学反射植被指数与光能利用率的关系

光能利用率(LUE)是陆地生态系统总初级生产力(GPP)估算的一个重要参数。LUE的准确估算对于在区域甚至全球尺度上使用LUE模型估算GPP是非常重要的。一个基于通量塔的观测视场与通量观测足迹在时空上相匹配的自动多角度遥感平台为LUE在站点尺度上的准确估算提供了一个好方法。该文基于通量塔涡度相关(EC)和自动多角度高光谱连续观测获取的连续30 min的数据, 在站点空间尺度和0.5 h与日时间尺度上, 探讨了城市绿地生态系统秋季光化学反射植被指数(PRI)与LUE之间的关系。研究发现, 反映植被叶面积和色素变化的植被绿度指数在秋季呈现逐渐下降的趋势, 表征了植被冠层的状态与结构变化, 叶片从绿色逐渐变黄凋落, 植被冠层叶片的叶绿素逐渐减少, 裸露的枝干增多; 用空气温度和代表物候过程的绝对绿度指数(2G_RB)做线性回归分析, 得到回归系数(R²)为0.60 (p < 0.001)。说明在城市绿地生态系统中, 空气温度是决定植被物候过程的主要驱动因素, 随着植被物候变化, 叶片的凋落导致的裸露土壤的增多以及随时间变化的色素含量和其比例的变化将影响PRI和LUE的关系; 采用植被生长模型(logistic曲线), 拟合时间与2G_RB, 得到曲率变化最快的点, 确定为秋季植被落叶期的初日, 即第290天。在0.5 h和日时间尺度上, PRI都可以捕捉LUE的变化。但是日尺度上不同物候期, PRI和LUE的关系发生了急剧的变化。在秋季植被正常生长期, PRI和LUE之间的关系最密切(R² = 0.70, p < 0.001)。当土壤温度大于15 ℃、光合有效辐射(PAR)大于300 μmol·m^-2·s^-1以及饱和水汽压差(VPD)大于700 Pa的情况下, PRI能够更好地预测LUE。基于通 量塔尺度上时空尺度相匹配, 利用半经验的核驱动二向反射分布函数模型得到的高光谱PRI和通量观测得到的LUE在不同环境条件下的关系以及考虑到在植被的不同物候期对PRI和LUE的关系的优化, 将会更加准确地估算城市绿地生态系统的LUE。     

内蒙古克氏针茅草原净生态系统碳交换的物候特征及遥感监测

为准确遥感监测草原净生态系统碳交换(NEE)物候,利用内蒙古锡林浩特国家气候观象台2018—2021年的涡度相关系统和气象梯度观测系统观测数据,结合2018年1月1日至2021年12月31日的Sentinel-2卫星数据,分析克氏针茅草原NEE及其物候的变化规律,并探讨了NEE物候的遥感植被指数及阈值。结果表明：2018—2021年,研究区NEE呈季节性变化,4—10月为碳汇,其他月份为碳源,总体呈碳汇。碳吸收开始日期(SCUP)和结束日期(ECUP)的平均儒略日分别为第95天和第259天,碳吸收持续时间平均为165 d。光合有效辐射与日NEE呈极显著负相关,有助于草原生态系统吸收大气中CO₂。最佳阈值10%的红边叶绿素指数能够较好地捕捉SCUP,而最佳阈值75%的归一化植被指数则能较好地反映ECUP。研究结果可为草原生态系统碳源汇的遥感监测提供依据。     

基于MODIS数据模拟华北平原冬小麦农田生态系统的呼吸作用

为探讨华北平原冬小麦农田生态系统的呼吸作用, 利用2013—2017年河南省郑州市农业气象试验站的碳通量观测数据分析了冬小麦生长季的呼吸作用, 基于MODIS遥感数据讨论了该生态系统的环境因子并模拟了冬小麦的呼吸作用。结果表明: 冬小麦呼吸作用(观测值)和增强型植被指数(EVI)、陆地表面水分指数(LSWI)及地表温度(LST)存在较好的相关关系, 可以利用基于MODIS数据的呼吸模型模拟郑州地区冬小麦呼吸作用, 呼吸作用的模拟值白天要大于夜间, 在2013—2017年冬小麦研究阶段(年积日DOY=73—169)模拟的呼吸作用总累积量分别为472.18、477.02、490.43、482.90、487.37 g·m–2。研究表明, 基于MODIS数据模拟郑州地区冬小麦农田生态系统呼吸作用是合理可行的, 可为研究中国区域碳收支评估提供数据基础和技术理论支持。     

水稻冠层高光谱特征变量与叶片叶绿素含量的相关性研究

为了探讨水稻冠层光谱对叶片叶绿素含量的响应规律,以双季早稻为材料,设置不同施氮量处理的田间试验,测定水稻冠层光谱和叶片叶绿素含量,计算基于冠层反射光谱的特征变量,研究水稻冠层高光谱特征变量与叶片叶绿素含量之间的关系。结果表明:施用氮肥对反射光谱有明显的影响,在可见光范围内,不施氮处理的反射率高于施氮处理,尤其在波长550 nm左右的绿峰处显著增加,在近红外区反射率随施氮量的增加而增加;与叶绿素含量相关性较好的光谱位置参数是红边位置和红谷反射率,随着叶绿素含量的增加,红谷反射率降低,红边位置向长波方向移动;比值植被指数R800/R550、R750/R553和R990/R553,以及色素比值指数PSSRa、PSSRb与chla、chlb、chl(a+b)呈极显著正相关,可以作为水稻冠层叶片叶绿素监测的特征变量。     

日光诱导叶绿素荧光遥感及其在陆地生态系统监测中的应用

日光诱导叶绿素荧光(SIF)是近十年来迅速发展的新型植被遥感技术, 可以弥补以“绿度”为基础的植被指数等传统光学遥感观测的不足, 为大尺度植被光合作用监测提供了新方法。随着塔基、无人机、机载和星载SIF观测技术的快速发展以及SIF机理研究的推进, SIF遥感为陆地生态系统生理生化参数和生产力反演、非生物胁迫早期探测、光合物候提取和植被蒸腾作用监测等研究提供了重要技术支撑。该文首先系统阐述了SIF遥感的基本原理、观测技术和反演算法, 进而回顾了SIF遥感在陆地生态系统监测中的应用现状, 最后对天空地一体化SIF观测、SIF机理研究、新兴生态学应用等领域进行展望。     

涡度相关技术及其在陆地生态系统通量研究中的应用

通量观测是定量描述土壤-植被-大气间物质循环和能量交换过程的基础。涡度相关技术作为直接测量植被冠层与大气间能量与物质交换通量的技术手段, 已经逐步发展成为国际通用的通量观测标准方法。随着涡度相关技术在全球碳水循环研究中的广泛应用, 长期连续的通量观测正在为准确评价生态系统碳固持能力、水分和能量平衡状况、生态系统对全球气候变化的反馈作用、区域和全球尺度模型的优化与验证、极端事件对生态系统结构与功能影响等方面的研究提供重要数据支撑和机制理解途径。通过站点尺度通量长期动态观测, 明确了不同气候区和植被类型生态系统碳水通量强度基线及其季节与年际变异特征。通过多站点联网观测, 在区域和全球尺度研究生态系统碳通量空间变异特征, 揭示了区域尺度上温度和降水对生态系统碳通量空间格局的生物地理学控制机制。该文概括地介绍了涡度相关技术的基本原理、假设与系统构成, 总结了涡度通量长期联网观测在陆地生态系统碳水通量研究中的主要应用, 并对通量研究发展前景进行了展望。     

涡度相关技术及其在陆地生态系统通量研究中的应用

通量观测是定量描述土壤-植被-大气间物质循环和能量交换过程的基础。涡度相关技术作为直接测量植被冠层与大气间能量与物质交换通量的技术手段,已经逐步发展成为国际通用的通量观测标准方法。随着涡度相关技术在全球碳水循环研究中的广泛应用,长期连续的通量观测正在为准确评价生态系统碳固持能力、水分和能量平衡状况、生态系统对全球气候变化的反馈作用、区域和全球尺度模型的优化与验证、极端事件对生态系统结构与功能影响等方面的研究提供重要数据支撑和机制理解途径。通过站点尺度通量长期动态观测,明确了不同气候区和植被类型生态系统碳水通量强度基线及其季节与年际变异特征。通过多站点联网观测,在区域和全球尺度研究生态系统碳通量空间变异特征,揭示了区域尺度上温度和降水对生态系统碳通量空间格局的生物地理学控制机制。该文概括地介绍了涡度相关技术的基本原理、假设与系统构成,总结了涡度通量长期联网观测在陆地生态系统碳水通量研究中的主要应用,并对通量研究发展前景进行了展望。     

华北平原冬小麦冠层导度的环境响应及模拟

通过引入叶面积指数，将叶片水平的气孔导度组合模型扩展到冠层水平，建立了冠层导度环境响应组合模型，组合模型所需参数较少，且均可在冠层水平直接测量，便于应用；模型由潜在气孔导度（PSC）和相对气孔开度（RDO）组成，二者分别由环境变量的日际（inter-day）和日间（intra-day）的值决定。分析表明，冠层导度在日际尺度和日间尺度上对环境变量具有多尺度响应特性，在日际尺度上，温度是影响冠层导度的主要因子，在日间尺度，光是影响气孔开闭的主要因素。利用以温度和光合有效辐射为输入变量构建的组合模型，模拟了华北平原冬小麦农田生态系统的冠层导度，并用Penman-Monteith方程估算的表面导度进行验证。结果显示，在不同天气情况下，二者的日变化均具有较好的一致性；将组合模型与电学类比模型结合，进一步估算了拔节．灌浆期的冠层潜热通量，利用涡度相关系统观测的潜热通量数据进行验证，结果表明对冬小麦冠层潜热通量模拟精度较高，直线回归斜率为0．7054，R^2=0．7894。     

帽儿山地区森林冠层叶面积指数的地面观测与遥感反演

叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effective LAI,LAIe).本文以东北林业大学帽儿山实验林场为研究区,利用LAI2000观测森林冠层LAIe,并结合TRAC观测的叶片聚集度系数估算了森林冠层LAI,并通过分析基于Landsat5-TM数据计算的不同植被指数与LAIe之间的关系,建立了该区森林LAI遥感估算模型.结果表明:研究区阔叶林的LAI和LAIe基本相当,而针叶林的LAI比LAIe大27%;减化比值植被指数(reduced simple ratio,RSR)与该区LAIe的相关性最好(R2=0.763,n=23),最适合该区LAI的遥感提取.当海拔<400 m时,LAI随海拔高度的上升而快速增大;当海拔在400～750 m时,LAI随海拔高度的上升缓慢增大;当海拔>750 m时,LAI呈下降趋势.研究区森林冠层LAI与森林地上生物量存在显著的正相关关系.     

Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data

Recent studies have shown that the greenness index derived from digital camera imagery has high spatial and temporal resolution. These findings indicate that it can not only provide a reasonable characterization of canopy seasonal variation but also make it possible to optimize ecological models. To examine this possibility, we evaluated the application of digital camera imagery for monitoring winter wheat phenology and modeling gross primary production (GPP).By combining the data for the green cover fraction and for GPP, we first compared 2 different indices (the ratio greenness index (green-to-red ratio, G/R) and the relative greenness index (green to sum value, G%)) extracted from digital images obtained repeatedly over time and confirmed that G/R was best suited for tracking canopy status. Second, the key phenological stages were estimated using a time series of G/R values. The mean difference between the observed phenological dates and the dates determined from field data was 3.3 days in 2011 and 4 days in 2012, suggesting that digital camera imagery can provide high-quality ground phenological data.Furthermore, we attempted to use the data (greenness index and meteorological data in 2011) to optimize a light use efficiency (LUE) model and to use the optimal parameters to simulate the daily GPP in 2012. A high correlation (R² = 0.90) was found between the values of LUE-based GPP and eddy covariance (EC) tower-based GPP, showing that the greenness index and meteorological data can be used to predict the daily GPP. This finding provides a new method for interpolating GPP data and an approach to the estimation of the temporal and spatial distributions of photosynthetic productivity.In this study, we expanded the potential use of the greenness index derived from digital camera imagery by combining it with the LUE model in an analysis of well-managed cropland. The successful application of digital camera imagery will improve our knowledge of ecosystem processes at the temporal and spatial levels.     

Comparing phenocam color indices with phenological observations of black spruce in the boreal forest

Bud phenology identifies the growing period of trees and determines the pattern of mass and energy exchanges between forest and atmosphere over time and space. Canopy color metrics derived from phenocams have been widely used to investigate tree phenology. However, it remains unclear which color-based index better tracks the seasonal variations of tree phenology in evergreen forest ecosystems. Herein, we compared four color metrics (red chromatic coordinate (RCC), green chromatic coordinate (GCC), vegetation contrast index (VCI) and excess green index (ExG)) derived from phenocam images with bud phenological phases recorded in black spruce [Picea mariana (Mill.) B.S·P] during 2017–2020 at a boreal forest site in Quebec, Canada. Canopy redness (RCC) and greenness (GCC, ExG, and VCI) showed a bimodal and bell-shaped seasonal pattern, respectively. The phases of bud burst and bud set lasted from end-May to end-June and from mid-July to end-September, respectively. The neural network model indicated that GCC had the best predictive ability in capturing the sequential phases of bud phenology. Bud phenological phases predicted by GCC showed the highest correlation with actual bud phenological phases among four indices, with R² above 0.9 and RMSE lower than 0.5. Overall, color indices performed better when representing bud burst than bud set. Our findings improve the efficiency and confidence of the phenocam greenness index to characterize the growing season of evergreen forests.     

Functional differences between summer and winter season rain assessed with MODIS‐derived phenology in a semi‐arid region

Questions: We asked several linked questions about phenology and precipitation relationships at local, landscape, and regional spatial scales within individual seasons, between seasons, and between year temporal scales. (1) How do winter and summer phenological patterns vary in response to total seasonal rainfall? (2) How are phenological rates affected by the previous season rainfall? (3) How does phenological variability differ at landscape and regional spatial scales and at season and inter‐annual temporal scales? Location: Southern Arizona, USA. Methods: We compared satellite‐derived phenological variation between 38 distinct 625‐km² landscapes distributed in the northern Sonoran Desert region from 2000 to 2007. Regression analyses were used to identify relationships between landscape phenology dynamics in response to precipitation variability across multiple spatial and temporal scales. Results: While both summer and winter seasons show increases of peak greenness and peak growth with more precipitation, the timing of peak growth was advanced with more precipitation in winter, while the timing of peak greenness was advanced with more precipitation in summer. Surprisingly, summer maximum growth was negatively affected by winter precipitation. The spatial variations between summer and winter phenology were similar in magnitude and response. Larger‐scale spatial and temporal variation showed strong differences in precipitation patterns; however the magnitudes of phenological spatial variability in these two seasons were similar. Conclusions: Vegetation patterns were clearly coupled to precipitation variability, with distinct responses at alternative spatial and temporal scales. Disaggregating vegetation into phenological variation, spanning value, timing, and integrated components revealed substantial complexity in precipitation‐phenological relationships.     

Using digital cameras for comparative phenological monitoring in an evergreen broad-leaved forest and a seasonal rain forest

Digital cameras have been used in phenological observations for their high accuracy and low labor cost. Most studies successfully use greenness indices derived from digital images for timing the events related to leaf development. However, when timing the leaf senescence events, wide discrepancies between actual and estimated dates are common. In this study, images of three species (two from an evergreen broad-leaved forest and one from a seasonal rain forest) were used to estimate three phenological events of leaf development and senescence. Other than the greenness index, a redness index was also employed. Different annual patterns in color indices developed among the species. The redness index was more accurate when estimating leaf senescence, while the greenness index was more accurate for estimating leaf development events in Acer heptalobum and Machilus bombycina. The absolute differences in estimations of phenological events ranged from − 3 to 1 day, which is more accurate than estimates based on the greenness index only (− 2 to 27 days). With the introduction of the redness index, this technique has been much improved and is possible to be applied to more species. Furthermore, variations of color indices during periods of phenological events were highly related to the climatic factors with a time lag of around 10 days. Because of the ease of use and efficiency (i.e., automatic daily data output), digital cameras are expected to be used in ecosystem process modeling, networks of phenology assessment and validation of the remote sensing results from satellites.     

Use of digital webcam images to track spring green-up in a deciduous broadleaf forest

Understanding relationships between canopy structure and the seasonal dynamics of photosynthetic uptake of CO₂ by forest canopies requires improved knowledge of canopy phenology at eddy covariance flux tower sites. We investigated whether digital webcam images could be used to monitor the trajectory of spring green-up in a deciduous northern hardwood forest. A standard, commercially available webcam was mounted at the top of the eddy covariance tower at the Bartlett AmeriFlux site. Images were collected each day around midday. Red, green, and blue color channel brightness data for a 640 × 100-pixel region-of-interest were extracted from each image. We evaluated the green-up signal extracted from webcam images against changes in the fraction of incident photosynthetically active radiation that is absorbed by the canopy (f _APAR), a broadband normalized difference vegetation index (NDVI), and the light-saturated rate of canopy photosynthesis (A _max), inferred from eddy flux measurements. The relative brightness of the green channel (green %) was relatively stable through the winter months. A steady rising trend in green % began around day 120 and continued through day 160, at which point a stable plateau was reached. The relative brightness of the blue channel (blue %) also responded to spring green-up, although there was more day-to-day variation in the signal because blue % was more sensitive to changes in the quality (spectral distribution) of incident radiation. Seasonal changes in blue % were most similar to those in f _APAR and broadband NDVI, whereas changes in green % proceeded more slowly, and were drawn out over a longer period of time. Changes in A _max lagged green-up by at least a week. We conclude that webcams offer an inexpensive means by which phenological changes in the canopy state can be quantified. A network of cameras could offer a novel opportunity to implement a regional or national phenology monitoring program.     

Usability of time-lapse digital camera images to detect characteristics of tree phenology in a tropical rainforest

We evaluated the usability of the red (R), green (G), and blue (B) digital numbers (DN_RGB) extracted from daily phenological images of a tropical rainforest in Malaysian Borneo. We examined temporal patterns in the proportions of DN_R, DN_G, and DN_B as percentages of total DN (denoted as %R, %G and %B), in the hue, saturation, and lightness values in the HSL color model, and in a green excess index (GEI) of the whole canopy and of individual trees for 2 years. We also examined temporal patterns in the proportions of the red, green, and blue reflectance of the whole canopy surface as percentages of total reflectance (denoted as %ref_R, %ref_G, and %ref_B), and vegetation indices (the normalized-difference vegetation index, enhanced vegetation index, and green–red vegetation index) of the whole canopy by using daily measurements from quantum sensors. The temporal patterns of %RGB and saturation of individual trees revealed the characteristics of tree phenology caused by flowering, coloring, and leaf flushing. In contrast, those of the whole canopy did not, nor did those of %ref_R, %ref_G, or %ref_B, or the vegetation indices. The temporal patterns of GEI, however, could detect differences among individual trees caused by leaf flushing and coloring. Our results show the importance of installing multiple time-lapse digital cameras in tropical rainforests to accurately evaluate the sensitivity of tree phenology to meteorological and climatic changes. However, more work needs to be done to adequately describe whole-canopy changes.     

表征亚热带常绿林光合作用季节变化特征的多种植被指数

利用遥感方法可以在区域尺度反演地表植被的光合生理状况和生产力变化,但亚热带常绿林冠层结构季节变化较小,传统的光谱植被指数对植被光合作用难以准确捕捉。利用2014—2015年中国科学院广东省鼎湖山森林生态试验站多角度自动光谱观测系统的光谱反射数据,分别反演传统冠层结构型植被指数(NDVI)、光合生理生化型植被指数(CCI)和叶绿素荧光型植被指数(NDFI_(685)和NDFI_(760)),并利用不同类型植被指数的组合,构建多元线性回归模型。结果表明:亚热带常绿针阔混交林三种类型植被指数均与GPP的动态变化有显著的相关性,其中,NDVI是表征GPP较优的植被指数(R~2=0.60,P0.01),其次为CCI(R~2=0.55,P0.01),而NDFI能够作为辅助指数,有效提高NDVI(R~2=0.68,P0.001)和CCI(R~2=0.67,P0.001)表征GPP的程度。多个植被指数参与构建的多元回归模型能够有效提高亚热带地区常绿林GPP季节动态变化的拟合精度,提升遥感精确评估亚热带森林生产力的能力。     

Refining the role of phenology in regulating gross ecosystem productivity across European peatlands

The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.     

Measuring phenological variability from satellite imagery

Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.