Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Tree growth variation in the tropical forest: understanding effects of temperature, rainfall and CO2
Schippers, P. ; Sterck, F.J. ; Vlam, M. ; Zuidema, P.A. - \ 2015
Global Change Biology 21 (2015)7. - ISSN 1354-1013 - p. 2749 - 2761.
water-use efficiency - global vegetation models - woody-tissue respiration - leaf-area index - elevated co2 - thermal-acclimation - carbon sink - scaling relationships - stomatal conductance - primary productivity
Tropical forest responses to climatic variability have important consequences for global carbon cycling, but are poorly understood. As empirical, correlative studies cannot disentangle the interactive effects of climatic variables on tree growth, we used a tree growth model (IBTREE) to unravel the climate effects on different physiological pathways and in turn on stem growth variation. We parameterized the model for canopy trees of Toona ciliata (Meliaceae) from a Thai monsoon forest and compared predicted and measured variation from a tree-ring study over a 30-year period. We used historical climatic variation of minimum and maximum day temperature, precipitation and carbon dioxide (CO2) in different combinations to estimate the contribution of each climate factor in explaining the inter-annual variation in stem growth. Running the model with only variation in maximum temperature and rainfall yielded stem growth patterns that explained almost 70% of the observed inter-annual variation in stem growth. Our results show that maximum temperature had a strong negative effect on the stem growth by increasing respiration, reducing stomatal conductance and thus mitigating a higher transpiration demand, and – to a lesser extent – by directly reducing photosynthesis. Although stem growth was rather weakly sensitive to rain, stem growth variation responded strongly and positively to rainfall variation owing to the strong inter-annual fluctuations in rainfall. Minimum temperature and atmospheric CO2 concentration did not significantly contribute to explaining the inter-annual variation in stem growth. Our innovative approach – combining a simulation model with historical data on tree-ring growth and climate – allowed disentangling the effects of strongly correlated climate variables on growth through different physiological pathways. Similar studies on different species and in different forest types are needed to further improve our understanding of the sensitivity of tropical tree growth to climatic variability and change.
Estimation of Aerodynamic Roughness Length over Oasis in the Heihe River Basin by Utilizing Remote Sensing and Ground Data
Chen, Q. ; Jia, L. ; Hutjes, R.W.A. ; Menenti, M. - \ 2015
Remote Sensing 7 (2015)4. - ISSN 2072-4292 - p. 3690 - 3709.
laser altimeter measurements - time-series analysis - leaf-area index - surface-roughness - vegetation - parameters - canopy - forest - lidar - displacement
Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed.
Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements
Calders, K. ; Schenkels, T. ; Bartholomeus, H.M. ; Armston, J. ; Verbesselt, J. ; Herold, M. - \ 2015
Agricultural and Forest Meteorology 203 (2015). - ISSN 0168-1923 - p. 158 - 168.
leaf-area index - pulsed-laser systems - canopy gap fraction - temperate forest - deciduous forest - climate-change - part i - profiles - environments - photography
Vegetation phenology studies the timing of recurring seasonal dynamics and can be monitored through estimates of plant area index (PAI). Shifts in spring phenology are a key indicator for the effect of climate change, in particular the start of the growing season of forests. Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, is an active remote sensing technique and measures the forest structure with high spatial detail and accuracy. TLS provides information about the 3D distribution of canopy constituents and vertical plant profiles can be derived from these data. Vertical plant profiles describe the plant area per unit volume as a function of height, and can be used to used to monitor seasonal dynamics through PAI. Here, we present a TLS time series based on 48 measurement days of four sampling locations in a deciduous forest in the Netherlands. Vertical plant profiles are derived for each measurement and allow us to quantify not only total canopy integrated PAI, but also monitor PAI at specific horizontal layers. Sigmoidal models show a good fit to the derived total canopy integrated PAI time series (CV(RMSE) 0.99). The start of season (SOS) based on these models occurs between March 29 and April 3, 2014, depending on the species composition. The SOS derived from the TLS data corresponds well with field observations and occurs 7–12 days earlier compared to the SOS estimate from the MODIS NDVI time series. This is mainly caused by the lower relative standard deviation for TLS measurements in leaf-off conditions (0.72% compared to 2.87% for the MODIS NDVI data), which allows us to significantly detect small changes in phenology earlier. TLS allows us to monitor PAI at specific horizontal layers and we defined an understorey, intermediate and upper canopy layer. Even though our study area had only a sparse understorey, small differences are observed in the SOS between the different layers. We expect that these phenological differences will be more pronounced in multi-layered forests and TLS shows the potential to study seasonal dynamics not only as a function of time, but also as a function of canopy height.
Two perspectives on the coupled carbon, water and energy exchange in the planetary boundary layer
Combe, M. ; Vilà-Guerau De Arellano, J. ; Ouwersloot, H.G. ; Jacobs, C.M.J. ; Peters, W. - \ 2015
Biogeosciences 12 (2015). - ISSN 1726-4170 - p. 103 - 123.
ensemble kalman filter - land-surface model - leaf-area index - soil-moisture - use efficiency - climate-change - crop model - stomatal conductance - data assimilation - plant geography
Understanding the interactions between the land surface and the atmosphere is key to modelling boundary-layer meteorology and cloud formation, as well as carbon cycling and crop yield. In this study we explore these interactions in the exchange of water, heat and CO2 in a cropland–atmosphere system at the diurnal and local scale. To that end, we couple an atmospheric mixed-layer model (MXL) to two land-surface schemes developed from two different perspectives: while one land-surface scheme (A-gs) simulates vegetation from an atmospheric point of view, the other (GECROS) simulates vegetation from a carbon-storage point of view. We calculate surface fluxes of heat, moisture and carbon, as well as the resulting atmospheric state and boundary-layer dynamics, over a maize field in the Netherlands, on a day for which we have a rich set of observations available. Particular emphasis is placed on understanding the role of upper-atmosphere conditions like subsidence in comparison to the role of surface forcings like soil moisture. We show that the atmospheric-oriented model (MXL-A-gs) outperforms the carbon storage-oriented model (MXL-GECROS) on this diurnal scale. We find this performance is partly due to the difference of scales at which the models were made to run. Most importantly, this performance strongly depends on the sensitivity of the modelled stomatal conductance to water stress, which is implemented differently in each model. This sensitivity also influences the magnitude of the surface fluxes of CO2, water and heat (surface control) and subsequently impacts the boundary-layer growth and entrainment fluxes (upper atmosphere control), which alter the atmospheric state. These findings suggest that observed CO2 mole fractions in the boundary layer can reflect strong influences of both the surface and upper-atmosphere conditions, and the interpretation of CO2 mole fraction variations depends on the assumed land-surface coupling. We illustrate this with a sensitivity analysis where high subsidence and soil moisture depletion, typical for periods of drought, have competing and opposite effects on the boundary-layer height h. The resulting net decrease in h induces a change of 12 ppm in the late-afternoon CO2 mole fraction. Also, the effect of such high subsidence and soil moisture depletion on the surface Bowen ratio are of the same magnitude. Thus, correctly including such two-way land-surface interactions on the diurnal scale can potentially improve our understanding and interpretation of observed variations in atmospheric CO2, as well as improve crop yield forecasts by better describing the water loss and carbon gain.
Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability
Weiss, M. ; Miller, P.A. ; Hurk, B.J.J.M. van den; Noije, T. van; Stefanescu, S. ; Haarsma, R. ; Ulft, L.H. van; Hazeleger, W. ; Sager, P. Le; Smith, B. ; Schurgers, G. - \ 2014
Journal of Climate 27 (2014)22. - ISSN 0894-8755 - p. 8563 - 8577.
leaf-area index - ensemble forecasts - data assimilation - soil-moisture - model - prediction - system - impact - skill - oscillation
In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere-land-ocean-sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.
Forest summer albedo is sensitive to species and thinning: how should we account for this in Earth system models?
Otto, J. ; Berveiller, D. ; Bréon, F.M. ; Delpierre, N. ; Geppert, G. ; Granier, A. ; Jans, W.W.P. ; Knohl, A. ; Schelhaas, M.J. ; Moors, E.J. - \ 2014
Biogeosciences 11 (2014). - ISSN 1726-4170 - p. 2411 - 2427.
leaf-area index - boreal forest - reflectance models - canopy reflectance - carbon-cycle - climate - radiation - stands - simulations - variability
Although forest management is one of the instruments proposed to mitigate climate change, the relationship between forest management and canopy albedo has been ignored so far by climate models. Here we develop an approach that could be implemented in Earth system models. A stand-level forest gap model is combined with a canopy radiation transfer model and satellite-derived model parameters to quantify the effects of forest thinning on summertime canopy albedo. This approach reveals which parameter has the largest affect on summer canopy albedo: we examined the effects of three forest species (pine, beech, oak) and four thinning strategies with a constant forest floor albedo (light to intense thinning regimes) and five different solar zenith angles at five different sites (40° N 9° E–60° N 9° E). During stand establishment, summertime canopy albedo is driven by tree species. In the later stages of stand development, the effect of tree species on summertime canopy albedo decreases in favour of an increasing influence of forest thinning. These trends continue until the end of the rotation, where thinning explains up to 50% of the variance in near-infrared albedo and up to 70% of the variance in visible canopy albedo. The absolute summertime canopy albedo of all species ranges from 0.03 to 0.06 (visible) and 0.20 to 0.28 (near-infrared); thus the albedo needs to be parameterised at species level. In addition, Earth system models need to account for forest management in such a way that structural changes in the canopy are described by changes in leaf area index and crown volume (maximum change of 0.02 visible and 0.05 near-infrared albedo) and that the expression of albedo depends on the solar zenith angle (maximum change of 0.02 visible and 0.05 near-infrared albedo). Earth system models taking into account these parameters would not only be able to examine the spatial effects of forest management but also the total effects of forest management on climate.
How light competition between plants affects their response to climate change
Loon, M.P. van; Schieving, F. ; Rietkerk, M. ; Dekker, S.C. ; Sterck, F.J. ; Anten, N.P.R. - \ 2014
New Phytologist 203 (2014)4. - ISSN 0028-646X - p. 1253 - 1265.
leaf-area index - co2 enrichment face - canopy carbon gain - elevated co2 - atmospheric co2 - stomatal conductance - terrestrial ecosystems - nitrogen availability - global change - gas-exchange
How plants respond to climate change is of major concern, as plants will strongly impact future ecosystem functioning, food production and climate. Here, we investigated how vegetation structure and functioning may be influenced by predicted increases in annual temperatures and atmospheric CO2 concentration, and modeled the extent to which local plant–plant interactions may modify these effects. A canopy model was developed, which calculates photosynthesis as a function of light, nitrogen, temperature, CO2 and water availability, and considers different degrees of light competition between neighboring plants through canopy mixing; soybean (Glycine max) was used as a reference system. The model predicts increased net photosynthesis and reduced stomatal conductance and transpiration under atmospheric CO2 increase. When CO2 elevation is combined with warming, photosynthesis is increased more, but transpiration is reduced less. Intriguingly, when competition is considered, the optimal response shifts to producing larger leaf areas, but with lower stomatal conductance and associated vegetation transpiration than when competition is not considered. Furthermore, only when competition is considered are the predicted effects of elevated CO2 on leaf area index (LAI) well within the range of observed effects obtained by Free air CO2 enrichment (FACE) experiments. Together, our results illustrate how competition between plants may modify vegetation responses to climate change.
Pan-Arctic modelling of net ecosystem exchange of CO2
Shaver, G.R. ; Rastetter, E.B. ; Salmon, V. ; Street, L.E. ; Weg, M.J. van de; Rocha, A. ; Wijk, M.T. van; Williams, M. - \ 2013
Philosophical Transactions of the Royal Society. Series B, Biological Sciences 368 (2013)1624. - ISSN 0962-8436
leaf-area index - primary productivity - vascular plants - carbon balance - climate-change - temperature - vegetation - sensitivity - nitrogen - alaska
Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic.
Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity
Yin, X. - \ 2013
Annals of Botany 112 (2013)3. - ISSN 0305-7364 - p. 465 - 475.
open-top chambers - leaf-area index - carbon-dioxide enrichment - climate-change impacts - open-air conditions - c-3 plants - photosynthetic capacity - stomatal conductance - winter-wheat - maintenance respiration
Background - Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2]. Analysis - A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon–nitrogen interactions during crop growth. Conclusions - The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity.
Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery
Fauzi, A. ; Skidmore, A.K. ; Gils, H. ; Schlerf, M. ; Heitkonig, I.M.A. - \ 2013
Marine Pollution Bulletin 76 (2013)1-2. - ISSN 0025-326X - p. 42 - 51.
leaf-area index - species discrimination - absorption features - chlorophyll content - squares regression - vegetation indexes - avicennia-marina - canopy nitrogen - reflectance - forest
Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.
Review of optical-based remote sensing for plant trait mapping
Homolova, L. ; Malenovsky, Z. ; Clevers, J.G.P.W. ; Garcia-Santos, G. ; Schaepman, M.E. - \ 2013
Ecological Complexity 15 (2013). - ISSN 1476-945X - p. 1 - 16.
leaf-area index - light-use efficiency - photochemical reflectance index - gross primary production - dry-matter content - terrestrial chlorophyll fluorescence - multiple linear-regression - forest canopy reflectance - fuel moisture-content - nitrogen concentratio
Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates. The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists. We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.
A Bayesian object-based approach for estimating vegetation biophysical and biochemical variables from APEX at-sensor radiance data
Laurent, V.C.E. ; Verhoef, W. ; Damm, A. ; Schaepman, M.E. ; Clevers, J.G.P.W. - \ 2013
Remote Sensing of Environment 139 (2013). - ISSN 0034-4257 - p. 6 - 17.
radiative-transfer models - leaf-area index - sun-induced fluorescence - remote-sensing data - reflectance data - global products - brdf model - inversion - canopy - lai
Vegetation variables such as leaf area index (LAI) and leaf chlorophyll content (Cab) are important inputs for vegetation growth models. LAI and Cab can be estimated from remote sensing data using either empirical or physically-based approaches. The latter are more generally applicable because they can easily be adapted to different sensors, acquisition geometries, and vegetation types. They estimate vegetation variables through inversion of radiative transfer models. Such inversions are ill-posed but can be regularized by coupling models, by using a priori information, and spatial and/or temporal constraints. Striving to improve the accuracy of LAI and Cab estimates from single remote sensing images, this contribution proposes a Bayesian object-based approach to invert at-sensor radiance data, combining the strengths of regularization by model coupling, as well as using a priori data and object-level spatial constraints. The approach was applied to a study area consisting of homogeneous agricultural fields, which were used as objects for applying the spatial constraints. LAI and Cab were estimated from at-sensor radiance data of the Airborne Prism EXperiment (APEX) imaging spectrometer by inverting the coupled SLC-MODTRAN4 canopy-atmosphere model. The estimation was implemented in two steps. In the first step, up to six variables were estimated for each object using a Bayesian optimization algorithm. In the second step, a look-up-table (WT) was built for each object with only LAI and Cab as free variables, constraining the values of all other variables to the values obtained in the first step. The results indicated that the Bayesian object-based approach estimated LAI more accurately (R-2 = 0.45 and RMSE = 1.0) than a LUT with a Bayesian cost function (LUT-BCF) approach (R-2 = 022 and RMSE = 2.1), and Cab with a smaller absolute bias (-9 versus -23 mu g/cm(2)). The results of this study are an important contribution to further improve the regularization of ill-posed RT model inversions. The proposed approach allows reducing uncertainties of estimated vegetation variables, which is essential to support various environmental applications. The definition of objects and a priori data in cases where less extensive ground data are available, as well as the definition of the observation covariance matrix, are critical issues which require further research. (C) 2013 Elsevier Inc All rights reserved.
The contribution of nitrogen deposition to the photosynthetic capacity of forests.
Fleischer, K. ; Rebel, T. ; Molen, M.K. van der; Erisman, J.W. ; Wassen, M.J. ; Loon, E.E. ; Montagnani, L. ; Gough, C.M. ; Herbst, M. - \ 2013
Global Biogeochemical Cycles 27 (2013)1. - ISSN 0886-6236 - p. 187 - 199.
net primary productivity - terrestrial carbon sink - leaf-area index - ecosystem respiration - boreal forests - temperate - co2 - sequestration - biosphere - trends
[1] Global terrestrial carbon (C) sequestration has increased over the last few decades. The drivers of carbon sequestration, the geographical spread and magnitude of this sink are however hotly debated. Photosynthesis determines the total C uptake of terrestrial ecosystems and is a major flux of the global C balance. We contribute to the discussion on enhanced C sequestration by analyzing the influence of nitrogen (N) deposition on photosynthetic capacity (Amax) of forest canopies. Eddy covariance measurements of net exchange of carbon provide estimates of gross primary production, from which Amax is derived with a novel approach. Canopy Amax is combined with modeled N deposition, environmental variables and stand characteristics to study the relative effects on Amax for a unique global data set of 80 forest FLUXNET sites. Canopy Amax relates positively to N deposition for evergreen needleleaf forests below an observed critical load of¿~¿8¿kg¿N ha–1¿yr–1, with a slope of 2.0¿±¿0.4 (S.E.) µmol CO2 m–2¿s–1 per 1¿kg¿N ha–1¿yr–1. Above this threshold canopy Amax levels off, exhibiting a saturating response in line with the N saturation hypothesis. Climate effects on canopy Amax cannot be separated from the effect of N deposition due to considerable covariation. For deciduous broadleaf forests and forests in the temperate (-continental) climate zones, the analysis shows the N deposition effect to be either small or absent. Leaf area index and foliar N concentration are positively but weakly related to Amax. We conclude that flux tower measurements of C fluxes provide valuable data to study physiological processes at the canopy scale. Future efforts need to be directed toward standardizing measures N cycling and pools within C monitoring networks to gain a better understanding of C and N interactions, and to disentangle the role of climate and N deposition in forest ecosystems.
Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression
Axelsson, C. ; Skidmore, A.K. ; Schlerf, M. ; Fauzi, A. ; Verhoef, W. - \ 2013
International Journal of Remote Sensing 34 (2013)5. - ISSN 0143-1161 - p. 1724 - 1743.
infrared reflectance spectroscopy - remote-sensing data - band-depth analysis - leaf-area index - nitrogen concentration - continuum removal - absorption features - deciduous forests - canopy nitrogen - pasture quality
Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to (1) assess the accuracy of foliar chemistry retrieval, (2) compare the performance of models based on support vector regression (SVR), i.e. e-SVR, ¿-SVR, and least squares SVR (LS-SVR), to models based on partial least squares regression (PLSR), and (3) investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level (R 2 = 0.67, root mean square error (RMSE) = 0.17, normalized RMSE (nRMSE) = 15%), whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.
Bias in lidar-based canopy gap fraction estimates
Vaccari, S. ; Leeuwen, M. van; Calders, K. ; Coops, N.C. ; Herold, M. - \ 2013
Remote Sensing Letters 4 (2013)4. - ISSN 2150-704X - p. 391 - 399.
leaf-area index - digital hemispherical photography - forests - instrument - vegetation
Leaf area index and canopy gap fraction (GF) provide important information to forest managers regarding the ecological functioning and productivity of forest resources. Traditional measurements such as those obtained from hemispherical photography (HP) measure solar irradiation, penetrating the forest canopy, but do not provide information regarding the three-dimensional canopy structure. Terrestrial laser scanning (TLS) is an active sensor technology able to describe structural forest attributes by measuring interceptions of emitted laser pulses with the canopy and is able to record the spatial distribution of the foliage in three dimensions. However, due to the beam area of the laser, interceptions are detected more frequently than using conventional HP, and GF is typically underestimated. This study investigates the effects of laser beam area on the retrieval of GF by using morphological image processing to describe estimation bias as a function of canopy perimeters. The results show that, using canopy perimeter, improvements in correlation between HP and TLS can be achieved with an increase in the coefficient of determination R 2 up to 28% (from an original R 2 of 0.66 to an adjusted R 2 of 0.85).
Modelling the spectral response of the desert tree Prosopis tamarugo to water stress
Chávez Oyanadel, R.O. ; Clevers, J.G.P.W. ; Herold, M. ; Ortiz, M. ; Acevedo, E. - \ 2013
International Journal of applied Earth Observation and Geoinformation 21 (2013). - ISSN 0303-2434 - p. 53 - 65.
leaf-area index - radiative-transfer models - atacama desert - red-edge - bidirectional reflectance - absorption features - canopy reflectance - optical-properties - radiance data - plant stress
In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm2, while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm2. The red-edge chlorophyll index (CIred-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.
A note on upscaling coniferous needle spectra to shoot spectral albedo
Rautiainen, M. ; Mottus, M. ; Yanez Rausell, L. ; Homolova, L. ; Malenovsky, Z. ; Schaepman, M.E. - \ 2012
Remote Sensing of Environment 117 (2012). - ISSN 0034-4257 - p. 469 - 474.
leaf-area index - scots pine - optical-properties - silhouette area - radiation - canopies - light - stands - model - simulations
Mutual shading of needles in coniferous shoots and small-scale variations in needle area density both within and between shoots violate conventional assumptions used in the definition of the elementary volume in radiative transfer models. In this paper, we test the hypothesis if it is possible to scale needle spectral albedo up to shoot spectral albedo using only one structural parameter: the spherically averaged shoot silhouette to total area ratio (STAR). To test the hypothesis, we measured both structural and spectral properties of ten Scots pine (Pinus sylvestris) shoots and their needles. Our results indicate that it is possible to upscale from needle to shoot spectral albedo using STAR. The upscaling model performed best in the VIS and SWIR regions, and for shoots with high STAR values. As STAR is linearly related to photon recollision probability, it is also possible to apply the upscaling model as integral part of radiative transfer models.
Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data
Verrelst, J. ; Romijn, J.E. ; Kooistra, L. - \ 2012
Remote Sensing 4 (2012)9. - ISSN 2072-4292 - p. 2866 - 2889.
leaf-area index - radiative-transfer model - hyperspectral brdf data - chris-proba data - flow resistance - climate-change - rhine basin - sugar-beet - forest - cover
River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (-36°) and forward (+36°) scatter direction. The -36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.
Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity
Street, L.E. ; Shaver, G.R. ; Rastetter, E.B. ; Wijk, M.T. van; Kaye, B.A. ; Williams, M. - \ 2012
Global Change Biology 18 (2012)9. - ISSN 1354-1013 - p. 2838 - 2852.
leaf-area index - economics spectrum - air-temperature - carbon-exchange - c-3 plants - co2 flux - photosynthesis - vegetation - leaves - tundra
Arctic vegetation is characterized by high spatial variability in plant functional type (PFT) composition and gross primary productivity (P). Despite this variability, the two main drivers of P in sub-Arctic tundra are leaf area index (LT) and total foliar nitrogen (NT). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT. Our objective was to test the LT–NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan-Arctic. Including PFT-specific parameters in models of LT–NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT–NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass (NM), and we show for the first time that LT–NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area (LM). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT–NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales
Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment
Curnel, Y. ; Wit, A.J.W. de; Duveiller, G. ; Defourny, P. - \ 2011
Agricultural and Forest Meteorology 151 (2011)12. - ISSN 0168-1923 - p. 1843 - 1855.
ensemble kalman filter - leaf-area index - sensing data assimilation - radiative-transfer models - band vegetation indexes - crop model - simulation experiments - precision agriculture - chlorophyll content - reflectance data
An Observing System Simulation Experiment (OSSE) has been defined to assess the potentialities of assimilating winter wheat leaf area index (LAI) estimations derived from remote sensing into the crop growth model WOFOST. Two assimilation strategies are considered: one based on Ensemble Kalman Filter (EnKF) and the second on recalibration/re-initialisation of uncertain model parameters and initial state conditions. The main objective of the OSS Experiment is to estimate the requisites for the remotely sensed LAI, in terms of accuracy and sampling frequency, to reach target of either 25 or 50% reduction of errors on the final estimation of grain yields. Our results demonstrate that EnKF is not suitable for assimilating LAI in WOFOST as the average error on final grain yields estimation globally increases. These poor results can be explained by the possible differences of phenological development existing between assimilated and modelled LAI values (difference called “phenological shift” in our study) which is not corrected by the EnKF-based assimilation strategy. On the contrary, a recalibration-based assimilation approach globally improves the estimation of final grain yields in a significant way. On average, such improvement can reach up to approximately 65% when observations are available all along the growing season. Improvements on the order of 20% can be already be attained early in the season, which is of great interest in a crop yield forecasting perspective. If the first objective (25%) of error reduction on final grain yields can be reached in a quite high number of assimilated LAI observations availabilities and uncertainty levels, the field of possibilities is significantly restricted for the second objective (50%) and implies to have LAI observations available all along the growing season, at least on a weekly basis and with an uncertainty level equal or ideally lower than 10%. These requirements are not currently met from neither a technological nor an operational point of view but the results presented here can provide guidelines for future missions dedicated to crop growth monitoring. --------------------------------------------------------------------------------
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