- D. Bonal (1)
- G. Bönisch (1)
- A. Fauzi (1)
- T.A. Groen (1)
- I.M.A. Heitkonig (1)
- S. Huber (1)
- S. Huber (1)
- K.I. Itten (1)
- M.S. Khan (1)
- M. Kneubühler (2)
- N. Knox (2)
- B. Koetz (1)
- E. Kohi (1)
- M.F. Noomen (1)
- M. Peel (1)
- L. Poorter (1)
- H.H.T. Prins (2)
- A. Psomas (1)
- P.E.L. Putten van der (1)
- P.B. Reich (1)
- M.E. Schaepman (1)
- M. Schlerf (1)
- A.K. Skidmore (4)
- R. Slotow (1)
- P.C. Struik (1)
- M.G. Tjoelker (1)
- W. Verhoef (1)
- C. Waal van der (1)
- H.M.A. Werff van der (1)
- X. Yin (1)
- N.E. Zimmerman (2)
Global variability in leaf respiration in relation to climate, plant functional types and leaf traits
Atkin, O. ; Bloomfield, K. ; Reich, P.B. ; Tjoelker, M.G. ; Asner, G. ; Bonal, D. ; Bönisch, G. ; Poorter, L. - \ 2015
New Phytologist 206 (2015)2. - ISSN 0028-646X - p. 614 - 636.
elevated atmospheric co2 - terrestrial carbon-cycle - tropical rain-forests - dark respiration - thermal-acclimation - temperature sensitivity - vegetation models - photosynthetic capacity - nitrogen concentration - scaling relationships
Leaf dark respiration (R-dark) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of R-dark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in R-dark. Area-based R-dark at the prevailing average daily growth temperature (T) of each siteincreased only twofold from the Arctic to the tropics, despite a 20 degrees C increase in growing T (8-28 degrees C). By contrast, R-dark at a standard T (25 degrees C, R-dark(25)) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher R-dark(25) at a given photosynthetic capacity (V-cmax(25)) or leaf nitrogen concentration ([N]) than species at warmer sites. R-dark(25) values at any given V-cmax(25) or [N] were higher in herbs than in woody plants. The results highlight variation in R-dark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of R-dark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs).
An ecophysiological model analysis of yield differences within a set of contrasting cultivars and an F1 segregating population of potato (Solanum tuberosum L.) grown under diverse environments
Khan, M.S. ; Yin, X. ; Putten, P.E.L. van der; Struik, P.C. - \ 2014
Ecological Modelling 290 (2014). - ISSN 0304-3800 - p. 146 - 154.
quantitative trait loci - dry-matter - nitrogen concentration - breeding applications - physiological traits - horticultural crops - leaf-area - plant - simulation - system
The generic ecophysiological model ‘GECROS’ simulates crop growth and development as affected by genetic characteristics and climatic and edaphic environmental variables. We used this model to analyse differences in tuber yield of potato in five cultivars covering a wide range of maturity types and 100 individuals of a diploid F1 population segregating for maturity type. Six field experiments were conducted, in which contrasting nitrogen availabilities were created to represent six environments. Values of five genotype-specific model-input parameters were estimated and calibrated. Variation among the 100 F1 genotypes was as wide as, or slightly wider than, that among the five contrasting cultivars for any of the five parameters values but not for tuber yield. For the 100 F1 genotypes, the model accounted for 86% of genotypic differences in across-environment average tuber yield and 89% of environmental difference in across-genotype average yield. But the percentage in the genotypic differences in yield for a given experiment accounted for by the model ranged from 2% to 65%. Model analysis identified Nmax (i.e. total crop N uptake) and tuber N concentration as key components affecting tuber yield for all six experiments. Genotypes with higher Nmax and lower tuber N concentration exhibited higher tuber dry matter yield. The development of potato ideotypes for any specific environments should prioritize optimising N-related traits.
Differentiation of plant age in grasses using remote sensing
Knox, N. ; Skidmore, A.K. ; Werff, H.M.A. van der; Groen, T.A. ; Boer, W.F. de; Prins, H.H.T. ; Kohi, E. ; Peel, M. - \ 2013
International Journal of applied Earth Observation and Geoinformation 24 (2013)10. - ISSN 0303-2434 - p. 54 - 62.
difference water index - monitoring vegetation - nitrogen concentration - imaging spectroscopy - hyperspectral data - boreal regions - time-series - green-up - phenology - reflectance
Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland 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.
Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data
Knox, N. ; Skidmore, A.K. ; Prins, H.H.T. ; Heitkonig, I.M.A. ; Slotow, R. ; Waal, C. van der; Boer, W.F. de - \ 2012
ISPRS Journal of Photogrammetry and Remote Sensing 72 (2012). - ISSN 0924-2716 - p. 27 - 35.
south-african savanna - multiple linear-regression - kruger-national-park - mineral-nutrition - leaf biochemistry - hyperspectral reflectance - nitrogen concentration - imaging spectroscopy - grass - quality
Forage quality in grassland-savanna ecosystems support high biomass of both wild ungulates and domestic livestock. Forage quality is however variable in both space and time. In this study findings from ecological and laboratory studies, focused on assessing forage quality, are combined to evaluate the feasibility of a remote sensing approach for predicting the spatial and temporal variations in forage quality. Spatially available ecological findings (ancillary data), and physically linked spectral data (absorption data) are evaluated in this study and combined to create models which predict forage quality (nitrogen, phosphorus and fibre concentrations) of grasses collected in the Kruger National Park, South Africa, and analysed in both dry and wet seasons. Models were developed using best subsets regression modelling. Ancillary data alone, could predict forage components, with a higher goodness of fit and predictive capability, than absorption data (Ancillary: R2 adj ¼ 0:42—0:74 compared with absorption: R2 adj ¼ 0:11—0:51, and lower RMSE values for each nutrient produced by the ancillary models). Plant species and soil classes were found to be ecological variables most frequently included in prediction models of ancillary data. Models in which both ancillary and absorption variables were included, had the highest predictive capabilities ( R2 adj ¼ 0:49—0:74 and lowest RMSE values) compared to models where data sources were derived from only one of the two groups. This research provides an important step in the process of creating biochemical models for mapping forage nutrients in savanna systems that can be generalised seasonally over large areas.
The effects of high soil CO2 concentrations on leaf reflectance of maize plants
Noomen, M.F. ; Skidmore, A.K. - \ 2009
International Journal of Remote Sensing 30 (2009)2. - ISSN 0143-1161 - p. 481 - 497.
red edge position - carbon-dioxide - chlorophyll concentration - nitrogen concentration - spectral reflectance - model simulation - root respiration - wheat genotypes - area index - leaves
Carbon dioxide gas at higher concentrations is known to kill vegetation and can also lead to asphyxiation in humans and animals. The objective of this study is to test whether soil CO2 concentrations ranging from 2% to 50% can be detected using vegetative spectral reflectance. A greenhouse experiment was performed to measure the reflectance of maize plants growing in soil contaminated with high concentrations of CO2. The correlation between leaf chlorophyll and reflectance in both the red edge and the yellow region was studied using different methods. The method that resulted in the strongest correlation between leaf reflectance and chlorophyll was subsequently used to study the effects of CO2 on plant health. The results showed that the method developed by Cho and Skidmore (2006) was the most accurate in predicting leaf chlorophyll (R 2 of 0.72). This index in combination with a new index proposed in this study¿named the yellow edge position or YEP¿showed that an increase in CO2 concentration corresponds to a decrease in leaf chlorophyll. Two first derivative water absorption features at 1400 and 1900 nm indicate that a concentration of 50% CO2 decreased leaf water content. Although upscaling to canopy reflectance is necessary, this experiment shows that leaf reflectance can be used to detect high soil CO2 concentrations, particularly halfway through the growing season.
Estimating foliar biochemistry from hyperspectral data in mixed forest canopy
Huber, S. ; Kneubühler, M. ; Psomas, A. ; Itten, K.I. ; Zimmerman, N.E. - \ 2008
Forest Ecology and Management 256 (2008)3. - ISSN 0378-1127 - p. 491 - 501.
infrared reflectance spectroscopy - imaging spectrometry data - band-depth analysis - absorption features - nitrogen concentration - ecosystem processes - continuum removal - pasture quality - national-park - aviris data
Estimating canopy biochemical composition in mixed forests at the level of tree species represents a critical tool for a better understanding and modeling of ecosystem functioning since many species exhibit differences in functional attributes or decomposition rates. We used airborne hyperspectral data to estimate the foliar concentration of nitrogen, carbon and water in three mixed forest canopies in Switzerland. With multiple linear regression models, continuum-removed and normalized HyMap spectra were related to foliar biochemistry on an individual tree level. The six spectral wavebands used in the regression models were selected using both an enumerative branch-and-bound (B&B) and a forward search algorithm. The models estimated foliar concentrations with adjusted R2 values between 0.47 and 0.63, based on the best-sampled study site. Regression models composed of wavebands selected by the B&B algorithm always performed better than those developed with forward search. When extrapolating nitrogen concentrations from one to another study site, regression models solely based on causal wavebands (known from literature) mostly outperformed models based on all wavebands. The study demonstrates the potential of both the use of causal wavebands and of the B&B algorithm.
Space-based spectro-directional measurements for the improved estimation of ecosystem variables
Kneubühler, M. ; Koetz, B. ; Huber, S. ; Zimmerman, N.E. ; Schaepman, M.E. - \ 2008
Canadian Journal of Remote Sensing 34 (2008)3. - ISSN 1712-7971 - p. 192 - 205.
remote-sensing data - leaf-area index - canopy biophysical variables - radiative-transfer models - nitrogen concentration - surface heterogeneity - absorption features - foliar chemistry - polder data - reflectance
In this paper, four unique information sources of the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA-1) are exploited, namely, the spectral, directional, spatial, and temporal dimensions. Based on the results of three case studies in Switzerland, the use of multi-angular CHRIS-PROBA data for monitoring complex and dynamic vegetation canopies of forests and agricultural crops is demonstrated. We conclude that simultaneous exploitation of the spectrodirectional and temporal behaviours of various vegetation canopies allows for assessing the biochemical and biophysical properties on the one hand and provides additional information on canopy structure via the directional component on the other hand. The study cases focus on various aspects of combining these information dimensions for improved retrieval of vegetation characteristics, namely, (i) the vegetation heterogeneity measurements that use the Minnaert function parameter k, (ii) an improved assessment of foliar water content (CW) and nitrogen concentration (CN) based on multi-angular data, and (iii) continuous leaf area index (LAI) time-profiles lead to more accurate estimates of ecosystem processes and inventorying studies. The first study¿s assessment of canopy structure and heterogeneity from multi-angular data using Minnaert¿s k successfully demonstrates the distinction between closed and medium-density canopies. The second case study shows that the assessment of plant biochemistry from remotely sensed data profits from the information gained from multi-angular datasets. A synergistic approach that integrates multiple sources of information for the estimation of LAI over the season produces promising results for crop growth monitoring in the third case study. CHRIS-PROBA¿s multi-angular observations at the regional scale, while having a comparable spatial resolution of Landsat satellites, can significantly contribute to a better understanding of regional surface anisotropy. This strengthens the link between field observations and canopy scale applications. The results of the three case studies clearly demonstrate the potential and value of spectrodirectional Earth observations at regional scales for ecological monitoring and modeling studies.