Near-Earth Remote Sensing Images Used to Determine the Phenological Characteristics of the Canopy of Populus tomentosa B301 under Three Methods of Irrigation

Due to global warming, changes in plant phenology such as an early leaf spreading period in spring, a late abscission period in autumn, and growing season extension are commonly seen. Here, near-earth remote sensing images were used to monitor the canopy phenology of Populus tomentosa B301 in planted forests under full drip irrigation, full furrow irrigation, and no irrigation (rain fed). Experiments were conducted to collect phenological data across a growing season. Continuous canopy images were used to calculate different vegetation indices; the key phenological period was determined via the double logistic model and the curvature method. The effects of irrigation methods and precipitation in the rainy season on tree growth changes and key phenological periods were analyzed. The results showed that: (1) The green chromatic coordinate (GCC) conformed to the vegetation index of the tree species canopy phenological study. (2) During the phenological period throughout the year, the GCC reaching peak time (MOE) of the canopy phenology of Populus tomentosa B301 was the same in the three methods, while the time of shedding at the end of the growing season without irrigation (preset point 1) was 8 days longer than with full drip irrigation (preset point 3), and 7 days faster than with full furrow irrigation (preset point 5). (3) In the preliminary rainy season, different irrigation volumes induced different growth changes and phenological periods of the trees, resulting in different data of vegetation indicators under different growth conditions. (4) During the rainy season, the precipitation had different effects on cultivating P. tomentosa B301 using the three methods, that is, high precipitation could increase the growth rate of the fully irrigated area, otherwise the growth rate of this tree species was increased in full drip irrigation areas. Precipitation was lower and irregular, and the growth rate of this species was faster than the other two irrigation methods in the non-irrigated area, which was more adaptable to external environmental changes. The internal growth mechanism of the phenological changes in different areas of the planted forests was influenced by the different cultivation methods. Moreover, the collected phenological data provide a basis for the study of plant phenology with large data sets and deepens our understanding of the phenology of planted forests in response to climate change.

Analysis of canopy phenology in man-made forests using near-earth remote sensing

BACKGROUND

Forest canopies are highly sensitive to their growth, health, and climate change. The study aims to obtain time sequence images in mix foresters using a near-earth remote sensing method to track the seasonal variation in the color index and select the optimal color index. Three different regions of interest (RIOs) were defined and six color indexes (GRVI, HUE, GGR, RCC, GCC, and GEI) were calculated to analyze the microenvironment difference. The key phenological phase was identified using the double logistic model and the derivative method, and the phenology forecast of color indexes was performed based on the long short-term memory (LSTM) model.

RESULTS

The results showed that the same color index in different RIOs and different color indexes in the same RIO present a slight difference in the days of growth and the days corresponding to the peak value, exhibiting different phenological phases; the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the LSTM model was 0.0016, 0.0405, 0.0334, and 12.55%, respectively, indicating that this model has a good forecast effect.

CONCLUSIONS

In different areas of the same forest, differences in the micro-ecological environment in the canopies were prevalent, with their internal growth mechanism being affected by different cultivation ways and the external environment. Besides, the optimal color index also varies with species in phenological response, that is, different color indexes are used for different forests. With the data of color indexes as the training set and forecast set, the feasibility of the LSTM model in phenology forecast is verified.

Crown Structure Explains the Discrepancy in Leaf Phenology Metrics Derived from Ground- and UAV-Based Observations in a Japanese Cool Temperate Deciduous Forest

Unmanned aerial vehicles (UAV) provide a new platform for monitoring crown-level leaf phenology due to the ability to cover a vast area while offering branch-level image resolution. However, below-crown vegetation, e.g., understory vegetation, subcanopy trees, and the branches of neighboring trees, along with the multi-layered structure of the target crown may significantly reduce the accuracy of UAV-based estimates of crown leaf phenology. To test this hypothesis, we compared UAV-derived crown leaf phenology results against those based on ground observations at the individual tree scale for 19 deciduous broad-leaved species (55 individuals in total) characterized by different crown structures. The mean crown-level green chromatic coordinate derived from UAV images poorly explained inter- and intra-species variations in spring leaf phenology, most probably due to the consistently early leaf emergence in the below-crown vegetation. The start dates for leaf expansion and end dates for leaf falling could be estimated with an accuracy of <1-week when the influence of below-crown vegetation was removed from the UAV images through visual interpretation. However, a large discrepancy between the phenological metrics derived from UAV images and ground observations was still found for the end date of leaf expansion (EOE) and start date of leaf falling (SOF). Bayesian modeling revealed that the discrepancy for EOE increased as crown length and volume increased. The crown structure was not found to contribute to the discrepancy in SOF value. Our study provides evidence that crown structure is a pivotal factor to consider when using UAV photography to reliably estimate crown leaf phenology at the individual tree-scale.

Turfgrass Disease Diagnosis: Past, Present, and Future

Turfgrass is a multibillion-dollar industry severely affected by plant pathogens including fungi, bacteria, viruses, and nematodes. Many of the diseases in turfgrass have similar signs and symptoms, making it difficult to diagnose the specific problem pathogen. Incorrect diagnosis leads to the delay of treatment and excessive use of chemicals. To effectively control these diseases, it is important to have rapid and accurate detection systems in the early stages of infection that harbor relatively low pathogen populations. There are many methods for diagnosing pathogens on turfgrass. Traditional methods include symptoms, morphology, and microscopy identification. These have been followed by nucleic acid detection and onsite detection techniques. Many of these methods allow for rapid diagnosis, some even within the field without much expertise. There are several methods that have great potential, such as high-throughput sequencing and remote sensing. Utilization of these techniques for disease diagnosis allows for faster and accurate disease diagnosis and a reduction in damage and cost of control. Understanding of each of these techniques can allow researchers to select which method is best suited for their pathogen of interest. The objective of this article is to provide an overview of the turfgrass diagnostics efforts used and highlight prospects for disease detection.

"Green pointillism": detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras

Phenological cameras have been used over a decade for identifying plant phenological markers (budburst, leaf senescence) and more generally the greenness dynamics of forest canopies. The analysis is usually carried out over the full camera field of view, with no particular analysis of the variability of phenological markers among trees. Here we show that images produced by phenological cameras can be used to quantify the within-population variability of budburst (WPVbb) in temperate deciduous forests. Using seven site-years of image analyses, we report a strong correlation (r² = 0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation. We show that WPVbb varies strongly (by a factor of 4) from year to year in a given population and that those variations are linked with temperature conditions during the budburst period, with colder springs associated to a higher differentiation of budburst (higher WPVbb) among trees. Deploying our approach at the continental scale, i.e., throughout phenological cameras networks, would improve the understanding of the spatial (across populations) and temporal (across years) variations of WPVbb, which have strong implications on forest functioning, tree fitness and phenological modelling.

Grazing alters the phenology of alpine steppe by changing the surface physical environment on the northeast Qinghai-Tibet Plateau, China

The response of vegetation phenology to environmental changes is very complex. We used time-lapse digital cameras to monitor the phenology of an alpine steppe in four winter pastures with different grazing intensities during 2015-2017. The results showed that the beginning of the growing season (BGS) and the growing season length (GSL) of the alpine steppe separately presented advances or prolonged trends with the increase in grazing intensity. There was no regularity in the end of the growing season (EGS) under the change in grazing intensity gradient, but the EGS of the no grazing (NG) plot occurred 24 days ahead of the other plots disturbed by grazing. Different winter grazing intensities obviously had different influences on the surface litter, soil temperature (ST), and soil moisture (SM) during spring but not during autumn. The ST under different grazing intensities played a decisive role in controlling the BGS of alpine steppe, followed by surface litter and SM. The EGS showed a significant correlation with the surface litter in autumn but did not show correlations with ST and SM. These results could further help us understand the phenological mechanisms of the soil surface and guide the scientific management of grazing to adapt to climate change.

Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment

Near-surface remote sensing is an effective tool for in situ monitoring of canopy phenology, but the uncertainties involved in sensor-types and their deployments are rarely explored. We comprehensively compared three types of sensor (i.e., digital camera, spectroradiometer, and routine radiometer) at different inclination- and azimuth-angles in monitoring canopy phenology of a temperate deciduous forest in Northeast China for three years. The results showed that the greater contribution of understory advanced the middle of spring (MOS) for large inclination-angle of camera and spectroradiometer. The length of growing season estimated by camera from the east direction extended 11 d than that from the north direction in 2015 due to the spatial heterogeneity, but there was no significant difference in 2016 and 2018.The difference infield of view of sensors caused the MOS and the middle of fall, estimated by camera, to lag a week behind those by spectroradiometer and routine radiometer. Overall, the effect of azimuth-angle was greater than that of inclination-angle or sensor-type. Our assessments of the sensor types and their deployments are critical for the long-term accurate monitoring of phenology at the site scale and the regional/global-integration of canopy phenology data.

Differentiated seasonal vegetation cover dynamics of degraded grasslands in Inner Mongolia recorded by continuous photography technique

Influence of climate change on the grassland phenology has attracted more and more attentions of ecologists. Although dozens of studies have been conducted, there have been few records examining the phenology differences of grasslands with different plant species compositions. Using continuous photography and image processing methods, this study examined seasonal vegetation cover dynamics of grasslands along a degradation gradient to clarify the influence of vegetation composition on the dynamics of vegetation cover during growing season. Our results revealed that phenological patterns of grasslands differentiated with their degradation status. Abandoned farmland (AF) and severely degraded grassland (SD) with most annuals and least climax species had the earliest start of growing season, while AF and extremely degraded grassland (ED) dominated by grasses had the earliest end of growing season. The start and end of growing season were strongly related to the relative cover of climax species and grasses. The results presented in this study support the possibility of using digital photography to capture the role of plant species composition on vegetation phenology in grasslands.

Linking Phenological Indices from Digital Cameras in Idaho and Montana to MODIS NDVI

Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial based sensors. This study links greenness indices derived from digital images in a network of rangeland and forested sites in Montana and Idaho to 16-day normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Multiple digital cameras were placed along a transect at each site to increase the observational footprint and correlation with the coarser MODIS NDVI. Digital camera phenology indices were averaged across cameras on a site to derive phenological curves. The phenology curves, as well as green-up dates, and maximum growth dates, were highly correlated to the satellite derived MODIS composite NDVI 16-day data at homogeneous rangeland vegetation sites. Forested and mixed canopy sites had lower correlation and variable significance. This result suggests the use of MODIS NDVI in forested sites to evaluate understory phenology may not be suitable. This study demonstrates that data from digital camera networks with multiple cameras per site can be used to reliably estimate measures of vegetation phenology in rangelands and that those data are highly correlated to MODIS 16-day NDVI.

Observing Spring and Fall Phenology in a Deciduous Forest with Aerial Drone Imagery

Plant phenology is a sensitive indicator of the effects of global change on terrestrial ecosystems and controls the timing of key ecosystem functions including photosynthesis and transpiration. Aerial drone imagery and photogrammetric techniques promise to advance the study of phenology by enabling the creation of distortion-free orthomosaics of plant canopies at the landscape scale, but with branch-level image resolution. The main goal of this study is to determine the leaf life cycle events corresponding to phenological metrics derived from automated analyses based on color indices calculated from drone imagery. For an oak-dominated, temperate deciduous forest in the northeastern USA, we find that plant area index (PAI) correlates with a canopy greenness index during spring green-up, and a canopy redness index during autumn senescence. Additionally, greenness and redness metrics are significantly correlated with the timing of budburst and leaf expansion on individual trees in spring. However, we note that the specific color index for individual trees must be carefully chosen if new foliage in spring appears red, rather than green-which we observed for some oak trees. In autumn, both decreasing greenness and increasing redness correlate with leaf senescence. Maximum redness indicates the beginning of leaf fall, and the progression of leaf fall correlates with decreasing redness. We also find that cooler air temperature microclimates near a forest edge bordering a wetland advance the onset of senescence. These results demonstrate the use of drones for characterizing the organismic-level variability of phenology in a forested landscape and advance our understanding of which phenophase transitions correspond to color-based metrics derived from digital image analysis.

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.

Automated processing of webcam images for phenological classification

Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material.

Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol

The increasingly important effect of climate change and extremes on alpine phenology highlights the need to establish accurate monitoring methods to track inter-annual variation (IAV) and long-term trends in plant phenology. We evaluated four different indices of phenological development (two for plant productivity, i.e., green biomass and leaf area index; two for plant greenness, i.e., greenness from visual inspection and from digital images) from a 5-year monitoring of ecosystem phenology, here defined as the seasonal development of the grassland canopy, in a subalpine grassland site (NW Alps). Our aim was to establish an effective observation strategy that enables the detection of shifts in grassland phenology in response to climate trends and meteorological extremes. The seasonal development of the vegetation at this site appears strongly controlled by snowmelt mostly in its first stages and to a lesser extent in the overall development trajectory. All indices were able to detect an anomalous beginning of the growing season in 2011 due to an exceptionally early snowmelt, whereas only some of them revealed a later beginning of the growing season in 2013 due to a late snowmelt. A method is developed to derive the number of samples that maximise the trade-off between sampling effort and accuracy in IAV detection in the context of long-term phenology monitoring programmes. Results show that spring phenology requires a smaller number of samples than autumn phenology to track a given target of IAV. Additionally, productivity indices (leaf area index and green biomass) have a higher sampling requirement than greenness derived from visual estimation and from the analysis of digital images. Of the latter two, the analysis of digital images stands out as the more effective, rapid and objective method to detect IAV in vegetation development.

Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis

The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.

Determining past leaf-out times of New England's deciduous forests from herbarium specimens

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PREMISE OF THE STUDY

There is great interest in studying leaf-out times of temperate forests because of the importance of leaf-out in controlling ecosystem processes, especially in the face of a changing climate. Remote sensing and modeling, combined with weather records and field observations, are increasing our knowledge of factors affecting variation in leaf-out times. Herbarium specimens represent a potential new source of information to determine whether the variation in leaf-out times observed in recent decades is comparable to longer time frames over past centuries.•

METHODS

Here we introduce the use of herbarium specimens as a method for studying long-term changes in leaf-out times of deciduous trees. We collected historical leaf-out data for the years 1834-2008 from common deciduous trees in New England using 1599 dated herbarium specimens with young leaves.•

KEY RESULTS

We found that leaf-out dates are strongly affected by spring temperature, with trees leafing out 2.70 d earlier for each degree C increase in mean April temperature. For each degree C increase in local temperature, trees leafed out 2.06 d earlier. Additionally, the mean response of leaf-out dates across all species and sites over time was 0.4 d earlier per decade. Our results are of comparable magnitude to results from studies using remote sensing and direct field observations.•

CONCLUSIONS

Across New England, mean leaf-out dates varied geographically in close correspondence with those observed in studies using satellite data. This study demonstrates that herbarium specimens can be a valuable source of data on past leaf-out times of deciduous trees.

Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Photographic assessment of temperate forest understory phenology in relation to springtime meteorological drivers

Phenology shows sensitive responses to seasonal changes in atmospheric conditions. Forest understory phenology, in particular, is a crucial component of the forest ecosystem that interacts with meteorological factors, and ecosystem functions such as carbon exchange and nutrient cycling. Quantifying understory phenology is challenging due to the multiplicity of species and heterogeneous spatial distribution. The use of digital photography for assessing forest understory phenology was systematically tested in this study within a temperate forest during spring 2007. Five phenology metrics (phenometrics) were extracted from digital photos using three band algebra and two greenness percentage (image binarization) methods. Phenometrics were compared with a comprehensive suite of concurrent meteorological variables. Results show that greenness percentage cover approaches were relatively robust in capturing forest understory green-up. Derived spring phenology of understory plants responded to accumulated air temperature as anticipated, and with day-to-day changes strongly affected by estimated moisture availability. This study suggests that visible-light photographic assessment is useful for efficient forest understory phenology monitoring and allows more comprehensive data collection in support of ecosystem/land surface models.

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

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.

Near-surface remote sensing of spatial and temporal variation in canopy phenology

There is a need to document how plant phenology is responding to global change factors, particularly warming trends. "Near-surface" remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras ("webcams") can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for "regions of interest" within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface-atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux.