Stephen Klosterman

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.

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.

Near-Surface Sensor-Derived Phenology

“Near-surface” remote sensing provides a novel approach to phenological monitoring. Optical sensors mounted in relatively close proximity (typically 50 m or less) to the land surface can be used to quantify, at high temporal frequency, changes in the spectral properties of the surface associated with vegetation development and senescence. The scale of these measurements—intermediate between individual organisms and satellite pixels—is unique and advantageous for a variety of applications. In this chapter, we review and discuss a variety of approaches to near-surface remote sensing of phenology, including methods based on broad- and narrow-band radiometric sensors, and using commercially available digital cameras as inexpensive imaging sensors.

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Mesic Temperate Deciduous Forest Phenology

Deciduous forests in temperate climates are characterized by significant seasonal changes in ecological and biogeochemical processes that are directly linked to forest phenology. The timing of spring leaf emergence and autumn leaf senescence is heavily determined by weather and climate, and these phenological events influence the seasonal cycles of water, energy, and carbon fluxes. In addition to its role in ecological interactions and in regulating ecosystem processes, deciduous forest phenology has also been shown to be a robust indicator of the biological impacts of climate change on forest ecosystems. With an emphasis on spring leaf emergence and autumn leaf senescence, this chapter highlights the phenology of canopy trees in mesic temperate deciduous forests by describing the climate of these forests, environmental drivers of phenology, feedback of phenology on lower atmospheric processes, impacts of climate change on phenology, and future research directions.

MODELING AND SIMULATION OF ARSENATE FATE AND TRANSPORT IN A DISTRIBUTION SYSTEM SIMULATOR

Multi-species water quality models can be used to predict the fate and transport of contaminants such as arsenic in water distribution networks. In recent work, water quality models have been used to simulate hypothetical contamination events, estimate potential human health effects, and characterize the ability of sensors to detect contamination. Little work has been done to calibrate water quality models and validate them against experimental data generated in Distribution System Simulators (DSSs). In this paper, results are reported from bench scale and pilot scale experiments performed with a DSS at U. S. EPA’s Test and Evaluation Facility in Cincinnati, Ohio. The parameters for a reversible adsorption model were estimated from bench scale data generated over two days. The model was used with the EPANET-MSX software package to simulate the pilot scale experiment in the DSS. Model results match the pilot scale data very well for the first two days after the arsenate injection, however pilot scale data after this time deviates from model predictions. This deviation may be due to limitations in the time scale or sample size of the bench scale experiment. Additional modeling, simulation, and experimental work is planned to develop a fate and transport model that can be used in practical settings to design decontamination strategies following intentional arsenic contamination of water distribution systems.

Comparing Single- and Multi-Species Water Quality Modeling Approaches for Assessing Contamination Exposure in Drinking Water Distribution Systems

New software such as EPANET-MSX enables water quality models that account for multiple reactive species in the distribution system. This allows for a more complete analysis of network water quality, including processes such as adsorption and biological inactivation by a disinfectant. For each of these reaction processes, three models are presented: single-specie conservative, single-specie reactions modeled by wall demand or bulk decay, and multi-species. The implications of model selection on the resulting exposure to contaminants that undergo these reaction processes are investigated for a hypothetical intentional contamination event and a simple single pipe system.

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.

Adsorption Model for Arsenate Transport in Corroded Iron Pipes with Application to a Simulated Intrusion in a Water Distribution Network

AbstractAdsorption to pipe wall materials significantly affects the fate and transport of certain contaminants in water distribution systems. For example, arsenate has a strong affinity for iron oxide, a substance common in water distribution pipes. In this paper a mathematical model for arsenate adsorption to iron oxide pipe wall materials is developed. The effects of two common assumptions on modeled arsenate transport are explored: a theoretical smooth pipe mass transfer coefficient and an assumption of rapid equilibrium of adsorption at the pipe wall surface. The effects of these assumptions are explored in a single pipe sensitivity analysis and found to yield significantly different results than parameters estimated from experimental data. In simulations of a hypothetical arsenate contamination event in a model water distribution system, the two assumptions result in different predictions of system contamination and contaminant exposure to consumers. These results indicate that even though water qual...

Modeling fate and transport of arsenic in a chlorinated distribution system

Experimental and modeling studies were conducted to understand the fate and transport properties of arsenic in drinking water distribution systems. Pilot scale experiments were performed in a distribution system simulator by injecting arsenic and measuring both adsorption onto iron pipe material and the oxidation of arsenite by hypochlorite in tap water to form arsenate. A mathematical model describing these processes was developed and simulated using EPANET-MSX, a hydraulic and multi-species water quality software for pipe networks. Model parameters were derived from the pilot-scale experiments. The model was applied to both the distribution system simulator and EPANET example network #3, a real-world model of a drinking water system serving approximately 78,000 customers. The model can be applied to systems-level studies of arsenic fate and transport in drinking water resulting from natural occurrences, accidental spills, or intentional introduction into water.