263 research outputs found
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Remote sensing of radiation intercepted by vegetation to estimate aboveground net primary production across western Oregon
Remote sensing of variables necessary to estimate net primary
production of vegetation over large temporal and spatial scales has been a
goal of climate change research. This thesis consists of two studies that
address the reliability of satellite and airborne sensors to quantify a basic
component of all production models, the amount of light intercepted by
vegetation canopies throughout the year.
The studies focus on an empirical model of net primary production:
NPP = [IPAR*f(T)*f(D)*f(V)]*εu, where IPAR is the amount of incident
photosynthetically active radiation intercepted by vegetation during the year,
and environmental limits to production are freezing temperatures (T), drought
(D), and high vapor pressure deficit (D). A relatively constant energy-use efficiency coefficient (En) would allow broad application of this model to a wide
variety of natural vegetation types and climate conditions.
The first study showed that the normalized difference vegetation index
(NDVI), calculated from field spectrometry, provided a good linear estimate of
the fraction of incident PAR intercepted by constructed canopies of bitterbrush
(R2 = 0.83) and manzanita shrubs (A2 = 0.86) at an open canopy ponderosa
pine site. In the second study, Thematic Mapper Simulator NDVI explained
97% of the variation in %IPAR by shrub and forested sites across Oregon.
These studies demonstrated the general ability to estimate %IPAR from
remotely sensed observations.
The second study showed that the fraction of light intercepted by forest
and shrub vegetation, coupled with meterological station measurements of total
annual incident radiation, explained 70% of the variation in primary production.
Additional limitations on the utilization of light should be considered to estimate
production. Constraints on the ability of each species to use intercepted light,
as defined by freezing temperatures, drought, and vapor pressure deficit, were
quantified from hourly meteorological station measurements and physiological
measurements in the field. The environmental limits to light capture by
photosynthesis, however, did not improve the ability to explain variation in
above-ground NPP across the forested and shrub sites. Differences in carbon
allocation patterns among plant life forms appear to be important to fully test
primary production models
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Measuring the Effects of Disturbance & Climate on the CO2 & Energy Exchange of Ponderosa Pine Forests in the Pacific Northwest: Integration of Eddy Flux, Plant and Soil Measurements
The goal is to quantify and understand the influence of climate and disturbance on ecosystem processes and thus net carbon uptake by forests. The objective is to combine tower and ground-based observations to quantify the effects of disturbance on processes controlling carbon storage and CO{sub 2} and energy exchange in varying climatic conditions. Specific objectives are: (1) Investigate the effects of logging and fire on carbon storage and carbon dioxide and energy exchange in chronosequences of ponderosa pine, using consistent methodology; (2) Determine key environmental factors controlling carbon storage and carbon dioxide and energy exchange in these forests through a combination of measurements and process modeling; and (3) Assess spatial variation of the concentrations and transport in complex terrain. The eddy covariance method is used for measurements of CO2, water vapor, and energy exchanges in a chronosequence of ponderosa pine forests (burned in 2002 wildfire, 10 year-old stand, 90 year-old mature stand). The mature stand has been an AmeriFlux site since 2000 (following previous flux sites in young and old stands initiated in 1996). In addition to the eddy covariance measurements, a large suite of biological processes and ecosystem properties are determined for the purpose of developing independent forest carbon budgets and NEP estimates; these include photosynthesis, stand respiration, soil CO{sub 2} fluxes, annual litterfall, foliar chemistry, and bole increment, and soil organic matter among other parameters. The measurements are being integrated and evaluated with two ecosystem models (BIOME-BGC and SPA). Such analyses are needed to assess regional terrestrial ecosystem carbon budgets. The results will contribute scientific understanding of carbon processes, and will provide comprehensive data sets for forest managers and those preparing national carbon inventories to use in assessments of carbon sequestration in relation to interannual climate variation and disturbance. Frameworks and methodologies developed by the PI will contribute to AmeriFlux Network facility functions for data acquisition, exchange and modeling of results in a broad spectrum of carbon cycle research
An Improved Analysis of Forest Carbon Dynamics using Data Assimilation
There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE
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Sensitivity of a subregional scale atmospheric inverse CO 2 modeling framework to boundary conditions
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95175/1/jgrd16614.pd
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Atmospheric inverse modeling to constrain regional‐scale CO 2 budgets at high spatial and temporal resolution
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95480/1/jgrd15697.pd
Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems.
Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes
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Synthesis of Remote Sensing and Field Observations to Model and Understand Disturbance and Climate Effects on the Carbon Balance of Oregon & Northern California
The goal is to quantify and explain the carbon (C) budget for Oregon and N. California. The research compares "bottom -up" and "top-down" methods, and develops prototype analytical systems for regional analysis of the carbon balance that are potentially applicable to other continental regions, and that can be used to explore climate, disturbance and land-use effects on the carbon cycle. Objectives are: 1) Improve, test and apply a bottom up approach that synthesizes a spatially nested hierarchy of observations (multispectral remote sensing, inventories, flux and extensive sites), and the Biome-BGC model to quantify the C balance across the region; 2) Improve, test and apply a top down approach for regional and global C flux modeling that uses a model-data fusion scheme (MODIS products, AmeriFlux, atmospheric CO2 concentration network), and a boundary layer model to estimate net ecosystem production (NEP) across the region and partition it among GPP, R(a) and R(h). 3) Provide critical understanding of the controls on regional C balance (how NEP and carbon stocks are influenced by disturbance from fire and management, land use, and interannual climate variation). The key science questions are, "What are the magnitudes and distributions of C sources and sinks on seasonal to decadal time scales, and what processes are controlling their dynamics? What are regional spatial and temporal variations of C sources and sinks? What are the errors and uncertainties in the data products and results (i.e., in situ observations, remote sensing, models)
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Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network
Terrestrial ecosystem-atmosphere exchange of carbon, water vapor, and energy has been measured for over a decade at many sites globally. To minimize measurement and analysis errors, quality assurance data have been collected over short periods along-side tower instruments at AmeriFlux research sites. Theoretical and empirical error and uncertainty values have been reported for various aspects of the eddy covariance technique but until recently it has not been possible to constrain network level variation based on direct comparison of side-by-side measurements. Paired observations, although rare in practice, offer a possibility to obtain real-world error estimates for flux observations and corresponding uncertainties. In this study, we report the relative instrumental errors from the AmeriFlux quality assurance and quality control (QA/QC) site intercomparisons of 84 site visits (2002–2012). Relative errors, including random and systematic instrumental errors, are presented for meteorological and radiation variables, gas concentrations, and the turbulent fluxes. The lowest relative errors (<2%) were found for the meteorological parameters, while the largest relative errors were found for latent heat and CO₂ fluxes. The mean relative instrumental error for CO₂ flux averaged −8.2% (underestimation by the tower instruments). Sensible and latent heat fluxes exhibited mean errors of −1.7% and −5.2%, respectively. Deviation around the mean was also largest for the turbulent fluxes, approaching 20%. Because the data collected during QA/QC site visits are used to identify and correct errors, our results represent a conservative estimate of instrumental errors in the AmeriFlux database. Overall, the presented results confirm the high quality of the network data and underline its status as a valuable data source for the research community.Keywords: uncertainties, AmeriFlux, random error, instrumental error, Fluxnet, eddy covarianceKeywords: uncertainties, AmeriFlux, random error, instrumental error, Fluxnet, eddy covarianc
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Toward biologically meaningful net carbon exchange estimates for tall, dense canopies: Multi-level eddy covariance observations and canopy coupling regimes in a mature Douglas-fir forest in Oregon
We sought to improve net ecosystem exchange (NEE) estimates for a tall, dense, mature Douglas-Fir forest in the Oregon Coast range characterized by weak flows, systematic wind directional shear, and limited turbulent mixing throughout the diurnal period. We used eddy covariance (EC) observations at two levels and concurrent biological measurements of carbon and water fluxes collected over a period of 6 years (2006-2011) to develop and test a conceptual framework to i) reduce uncertainty by retaining more measurements for the computation of annual NEE sums, and ii) produce defendable and biologically meaningful estimates by accounting for the missing sub-canopy respiration. The framework assumes that a) the scalar exchange between vertical layers can be categorized into discrete canopy coupling regimes, and b) advection leads to a systematic loss of scalar from the observational volume that can indirectly be estimated and accounted for as sub-canopy respiration flux when canopy layers are decoupled.
Periods with a decoupled sub-canopy layer dominated and occupied 65 and 88% of the day- and nighttime periods, respectively. Annual NEE derived from the new framework was estimated as 480 gC m⁻² yr⁻¹, which was reduced by 620 gC m⁻² yr⁻¹ compared to traditional estimates from single-level EC data filtered using a critical friction velocity. The reduced NEE was due to an enhanced ecosystem respiration (RE), while gross ecosystem productivity remained unchanged. Improved RE estimates agreed well with those from independent estimates based on soil, stem, and foliage respiration within 3%. Risks and limitations of the new framework are discussed. We conclude that concurrent above- and sub-canopy EC observations are essential to measure a meaningful carbon balance in tall, dense forests since they do no lend themselves to traditional, standardized processing. The new framework may help to include more tall and dense forests in global carbon cycle synthesis and modeling efforts.KEYWORDS: Douglas-Fir, U-star correction, Eddy covariance, Net ecosystem exchange, Advection, Ecosystem respiration, Canopy flow, Turbulenc
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