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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    Comparison of remote sensing and plant trait-based modelling to predict ecosystem services in subalpine grasslands
    Homolova, L. ; Schaepman, M.E. ; Lamarque, P. ; Clevers, J.G.P.W. ; Bello, F. de; Thuiller, W. ; Lavorel, S. - \ 2014
    Ecosphere 5 (2014)8. - ISSN 2150-8925
    land-use change - leaf chlorophyll content - imaging spectroscopy - water-content - aviris data - spectral reflectance - hyperspectral data - species richness - area index - vegetation
    There is a growing demand for spatially explicit assessment of multiple ecosystem services (ES) and remote sensing (RS) can provide valuable data to meet this challenge. In this study, located in the Central French Alps, we used high spatial and spectral resolution RS images to assess multiple ES based on underpinning ecosystem properties (EP) of subalpine grasslands. We estimated five EP (green biomass, litter mass, crude protein content, species diversity and soil carbon content) from RS data using empirical RS methods and maps of ES were calculated as simple linear combinations of EP. Additionally, the RS-based results were compared with results of a plant trait-based statistical modelling approach that predicted EP and ES from land use, abiotic and plant trait data (modelling approach). The comparison between the RS and the modelling approaches showed that RS-based results provided better insight into the fine-grained spatial distribution of EP and thereby ES, whereas the modelling approach reflected the land use signal that underpinned trait-based models of EP. The spatial agreement between the two approaches at a 20-m resolution varied between 16 and 22% for individual EP, but for the total ecosystem service supply it was only 7%. Furthermore, the modelling approach identified the alpine grazed meadows land use class as areas with high values of multiple ES (hot spots) and mown-grazed permanent meadows as areas with low values and only few ES (cold spots). Whereas the RS-based hot spots were a small subset of those predicted by the modelling approach, cold spots were rather scattered, small patches with limited overlap with the modelling results. Despite limitations associated with timing of assessment campaigns and field data requirements, RS offers valuable data for spatially continuous mapping of EP and can thus supply RS-based proxies of ES. Although the RS approach was applied to a limited area and for one type of ecosystem, we believe that the broader availability of high fidelity airborne and satellite RS data will promote RS-based assessment of ES to larger areas and other ecosystems.
    Minimizing measurement uncertainties of coniferous needle-leaf optical properties, part I: methodological review
    Yanez Rausell, L. ; Schaepman, M.E. ; Clevers, J.G.P.W. ; Malenovsky, Z. - \ 2014
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (2014)2. - ISSN 1939-1404 - p. 399 - 405.
    revised measurement methodology - chlorophyll content estimation - radiative-transfer model - reflectance spectra - hyperspectral data - bifacial leaf - boreal forest - leaves - light - absorption
    Optical properties (OPs) of non-flat narrow plant leaves, i.e., coniferous needles, are extensively used by the remote sensing community, in particular for calibration and validation of radiative transfer models at leaf and canopy level. Optical measurements of such small living elements are, however, a technical challenge and only few studies attempted so far to investigate and quantify related measurement errors. In this paper we review current methods and developments measuring optical properties of narrow leaves. We discuss measurement shortcomings and knowledge gaps related to a particular case of non-flat nonbifacial coniferous needle leaves, e.g., needles of Norway spruce (Picea abies (L.) Karst.).
    Trait estimation in herbaceous plant assemblages from in situ canopy spectra
    Roelofsen, H.D. ; Bodegom, P.M. van; Kooistra, L. ; Witte, J.M. - \ 2013
    Remote Sensing 5 (2013)12. - ISSN 2072-4292 - p. 6323 - 6345.
    least-squares regression - hyperspectral data - economics spectrum - vegetation indexes - indicator values - nitrogen-content - national-park - chlorophyll - reflectance - model
    Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities of plant traits in a herbaceous setting and how different trait-aggregation methods influence estimation accuracies. Canopy reflectance of 40 herbaceous plant assemblages was measured in situ and biomass was analysed for N, P and C concentration, chlorophyll, lignin, phenol, tannin and specific water concentration, expressed on a mass basis (mg·g-1). Using Specific Leaf Area (SLA) and Leaf Area Index (LAI), traits were aggregated to two additional expressions: mass per leaf surface (mg·m-2) and mass per canopy surface (mg·m-2). All traits were related to reflectance using partial least squares regression. Accuracy of trait estimation varied between traits but was mainly influenced by the trait expression. Chlorophyll and traits expressed on canopy surface were least accurately estimated. Results are attributed to damping or enhancement of the trait signal upon conversion from mass based trait values to leaf and canopy surface expressions. A priori determination of the most appropriate trait expression is viable by considering plant growing strategies
    Measurement methods and variability assessment of the Norway spruce total leaf area: Implications for remote sensing
    Homolova, L. ; Lukes, P. ; Malenovsky, Z. ; Lhotakova, Z. ; Kaplan, V. ; Hanus, J. - \ 2013
    Trees-Structure and Function 27 (2013)1. - ISSN 0931-1890 - p. 111 - 121.
    chlorophyll-a - light-interception - hyperspectral data - picea-abies - imaging spectroscopy - conifer needles - surface-area - gas-exchange - canopy - biochemistry
    Estimation of total leaf area (LAT) is important to express biochemical properties in plant ecology and remote sensing studies. A measurement of LAT is easy in broadleaf species, but it remains challenging in coniferous canopies. We proposed a new geometrical model to estimate Norway spruce LAT and compared its accuracy with other five published methods. Further, we assessed variability of the total to projected leaf area conversion factor (CF) within a crown and examined its implications for remotely sensed estimates of leaf chlorophyll content (Cab). We measured morphological and biochemical properties of three most recent needle age classes in three vertical canopy layers of a 30 and 100-year-old spruce stands. Newly introduced geometrical model and the parallelepiped model predicted spruce LAT with an error >5 % of the average needle LAT, whereas two models based on an elliptic approximation of a needle shape underestimated LAT by up to 60 %. The total to projected leaf area conversion factor varied from 2. 5 for shaded to 3. 9 for sun exposed needles and remained invariant with needle age class and forest stand age. Erroneous estimation of an average crown CF by 0. 2 introduced an error of 2-3 µg cm-2 into the crown averaged Cab content. In our study, this error represents 10-15 % of observed crown averaged Cab range (33-53 µg cm-2). Our results demonstrate the importance of accurate LAT estimates for validation of remotely sensed estimates of Cab content in Norway spruce canopies.
    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.
    Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer
    Malenovsky, Z. ; Homolova, L. ; Zurita-Milla, R. ; Lukes, P. ; Kaplan, V. ; Hanus, J. ; Gastellu-Etchegorry, J.P. ; Schaepman, M.E. - \ 2013
    Remote Sensing of Environment 131 (2013). - ISSN 0034-4257 - p. 85 - 102.
    canopy reflectance models - optical-properties model - area index - hyperspectral data - forest canopies - precision agriculture - vegetation canopies - red - band - absorption
    We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (Cab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new Cab sensitive index located between 650 and 720 nm and termed ANCB650–720. The performance of ANCB650–720 was validated against ground-measured Cab of ten spruce crowns and compared with Cab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710 nm (ND925&710), simple reflectance ratio between 750 and 710 nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable Cab spatial patterns, the ground validation revealed that the ANCB650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R2 = 0.72 for both methods). ANCB650–720 estimated Cab with an RMSE = 2.27 µg cm- 2 (relative RRMSE = 4.35%) and ANN with an RMSE = 2.18 µg cm- 2 (RRMSE = 4.18%), while SR750/710 with an RMSE = 4.16 µg cm- 2 (RRMSE = 7.97%), ND925&710 with an RMSE = 9.07 µg cm- 2 (RRMSE = 17.38%) and TCARI/OSAVI with an RMSE = 12.30 µg cm- 2 (RRMSE = 23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for Cab values below 30 µg cm- 2, which are slightly overestimated (RMSE = 2.42 µg cm- 2). The computationally efficient ANCB650–720 retrieval provides accurate high spatial resolution airborne Cab maps, considerable as a suitable reference data for validating satellite-based Cab products.
    Estimation of grassland biomass and nitrogen using MERIS data
    Ullah, G. ; Si, Y. ; Schlerf, M. ; Skidmore, A.K. ; Shafique, M. ; Iqbal, I.A. - \ 2012
    International Journal of applied Earth Observation and Geoinformation 19 (2012)1. - ISSN 0303-2434 - p. 196 - 204.
    terrestrial chlorophyll index - band-depth analysis - red edge position - vegetation indexes - aboveground biomass - hyperspectral data - branta-leucopsis - broad-band - canopy - quality
    This study aimed to investigate the potential of MERIS in estimating the quantity and quality of a grassland using various vegetation indices (NDVI, SAVI, TSAVI, REIP, MTCI and band depth analysis parameters) at a regional scale. Green biomass was best predicted by NBDI (normalised band depth index) and yielded a calibration R2 of 0.73 and a Root Mean Square Error (RMSE) of 136.2 g m-2 (using an independent validation dataset, n = 30) compared to a much higher RMSE obtained from soil adjusted vegetation index SAVI (444.6 g m-2). Nitrogen density was also best predicted by NBDI and yielded a calibration R2 of 0.51 and a RMSE of 4.2 g m-2 compared to a relatively higher RMSE obtained from MERIS terrestrial chlorophyll index MTCI (6.6 g m-2). For the estimation of nitrogen concentration (%), band depth analysis parameters showed poor R2 of 0.21 and the results of MTCI and REIP were statistically non-significant (P > 0.05). It is concluded that band depth analysis parameters consistently showed higher accuracy than vegetation indices, suggesting that band depth analysis parameters could be used to monitor grassland condition over time at regional scale.
    An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis
    Ullah, S. ; Skidmore, A.K. ; Naeem, M. ; Schlerf, M. - \ 2012
    Science of the Total Environment 437 (2012). - ISSN 0048-9697 - p. 145 - 152.
    remote-sensing imagery - hyperspectral data - mu-m - spatial heterogeneity - reflectance data - transform - vegetation - leaves - classification - compression
    Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0µm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R 2=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R 2=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.
    Identifying plant species using mid-wave infrared (2.5-6µm) and thermal infrared (8-14µm) emissivity spectra
    Ullah, S. ; Schlerf, M. ; Skidmore, A.K. ; Hecker, C. - \ 2012
    Remote Sensing of Environment 118 (2012)4. - ISSN 0034-4257 - p. 95 - 102.
    salt-marsh vegetation - hyperspectral data - biomass estimation - reflectance - discrimination - indexes - imagery - leaves - classification - spectroscopy
    Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5µm-6µm) and the thermal infrared (TIR; 8µm-14µm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5µm-14µm) results in reliable species discrimination.
    Penalized regression techniques for prediction: a case study for predicting tree mortality using remotely sensed vegetation indices
    Lazaridis, D.C. ; Verbesselt, J. ; Robinson, A.P. - \ 2011
    Canadian Journal of Forest Research 41 (2011)1. - ISSN 0045-5067 - p. 24 - 34.
    nonorthogonal problems - hyperspectral data - ridge regression - cross-validation - lasso - infestation - shrinkage - selection - forests - imagery
    Constructing models can be complicated when the available fitting data are highly correlated and of high dimension. However, the complications depend on whether the goal is prediction instead of estimation. We focus on predicting tree mortality (measured as the number of dead trees) from change metrics derived from moderate-resolution imaging spectroradiometer satellite images. The high dimensionality and multicollinearity inherent in such data are of particular concern. Standard regression techniques perform poorly for such data, so we examine shrinkage regression techniques such as ridge regression, the LASSO, and partial least squares, which yield more robust predictions. We also suggest efficient strategies that can be used to select optimal models such as 0.632+ bootstrap and generalized cross validation. The techniques are compared using simulations. The techniques are then used to predict insect-induced tree mortality severity for a Pinus radiata D. Don plantation in southern New South Wales, Australia, and their prediction performances are compared. We find that shrinkage regression techniques outperform the standard methods, with ridge regression and the LASSO performing particularly well.
    Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data
    Somers, B. ; Verbesselt, J. ; Ampe, E.M. ; Sims, N. ; Verstraeten, W.W. ; Coppin, P. - \ 2010
    International Journal of applied Earth Observation and Geoinformation 12 (2010)4. - ISSN 0303-2434 - p. 270 - 277.
    forest health surveillance - mountain pine-beetle - hyperspectral data - endmember variability - indexes - vegetation - imagery - damage - tree - classification
    Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R2no age correction = 0.55 vs R2age correction = 0.73 for Landsat) and hyperspectral (R2no age correction = 0.12 vs R2age correction = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.
    Merging the Minnaert- k parameter with spectral unmixing to map forest heterogeneity with CHRIS/PROBA data
    Verrelst, J. ; Schaepman, M.E. ; Clevers, J.G.P.W. - \ 2010
    IEEE Transactions on Geoscience and Remote Sensing 48 (2010)11. - ISSN 0196-2892 - p. 4014 - 4022.
    vegetation indexes - hyperspectral data - rpv model - canopy - reflectance - surface - cover - land - misr - classification
    The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set.
    Classification of sugar beet and volunteer potato reflection spectra with a neural network and statistical discriminant analysis to select discriminative wavelengths
    Nieuwenhuizen, A.T. ; Hofstee, J.W. ; Zande, J.C. van de; Meuleman, J. ; Henten, E.J. van - \ 2010
    Computers and Electronics in Agriculture 73 (2010)2. - ISSN 0168-1699 - p. 146 - 153.
    hyperspectral data - winter-wheat - crop - identification - indexes - corn
    The objectives of this study were to determine the reflectance properties of volunteer potato and sugar beet and to assess the potential of separating sugar beet and volunteer potato at different fields and in different years, using spectral reflectance characteristics. With the ImspectorMobile, vegetation reflection spectra were successfully repeatedly gathered in two fields, on seven days in 2 years that resulted in 11 datasets. Both in the visible and in the near-infrared reflection region, combinations of wavelengths were responsible for discrimination between sugar beet and volunteer potato plants. Two feature selection methods, discriminant analysis (DA) and neural network (NN), succeeded in selecting sets of discriminative wavebands, both for the range of 450–900 and 900–1650 nm. First, 10 optimal wavebands were selected for each of the 11 available datasets individually. Second, by calculating the discriminative power of each selected waveband, 10 fixed wavebands were selected for all 11 datasets analyses. Third, 3 fixed wavebands were determined for all 11 datasets. These three wavebands were chosen because these had been selected by both DA and NN and were for sensor 1: 450, 765, and 855 nm and for sensor 2: 900, 1440, and 1530 nm. With the resulting three sets of wavebands, classifications were performed with a DA, a neural network with 1 hidden neuron (NN1) and a neural network with two hidden neurons (NN2). The maximum classification performance was obtained with the near-infrared sensor coupled to the NN2 method with an optimal adapted set of 10 wavebands, where the percentages were 100 ± 0.1 and 1 ± 1.3% for true negative (TN) classified volunteer potato plants and false negative (FN) classified sugar beet plants respectively. In general the NN2 method gave the best classification results, followed by DA and finally the NN1 method. When the optimal adapted waveband sets were generalized to a set of 10 fixed wavebands, the classification results were still at a reasonable level of a performance at 87% TN and 1% FN for the NN2 classification method. However, when a further reduction and generalization was made to 3 fixed wavebands, the classification results were poor with a minimum performance of 69% TN and 3% FN for the NN2 classification method. So, these results indicate that for the best classification results it is required that the sensor and classification system adapt to the specific field situation, to optimally discriminate between volunteer potato and sugar beet pixel spectra
    Nitrogen prediction in grasses: effect of bandwidth and plant material state on absorption feature selection
    Knox, N. ; Skidmore, A.K. ; Schlerf, M. ; Boer, W.F. de; Wieren, S.E. van; Waal, C. van der; Prins, H.H.T. ; Slotow, R. - \ 2010
    International Journal of Remote Sensing 31 (2010)3. - ISSN 0143-1161 - p. 691 - 704.
    multiple linear-regression - leaf biochemistry - canopy chemistry - reflectance spectroscopy - heterogeneous grassland - hyperspectral data - forest ecosystems - oregon transect - vegetation lai - aviris data
    We analysed stability and predictive capabilities of known nitrogen absorption features between plant material prepared for NIRS (dried) and RS (fresh) studies. Grass spectra were taken of the plant canopy, and again after the grass sample was dried and ground. Models were derived using stepwise multiple linear regression (sMLR). Regression values (adj.r2) produced using the dried material were greater than those produced using canopy material. For dried material only wavebands from the SWIR region were selected. Wavebands selected by sMLR on canopy material were located in both the VNIR and SWIR regions. Using wavebands selected for dried material models produced low adj.r2 values when applied to canopy plant material; differences in adj.r2 values are smaller when wavebands selected in canopy material models are applied to dried material. Widening of nitrogen features produced higher adj.r2 values for both dried and canopy material. This work shows that obtaining models with high predictive capabilities for nitrogen concentration is possible, but waveband selection should not be limited to features identified by NIRS studies. To accommodate for variability in absorption features, and instrument errors, absorption features should be widened.
    Earth system science related imaging spectroscopy - An assessment
    Schaepman, M.E. ; Ustin, S.L. ; Plaza, A.J. ; Painter, T.H. ; Verrelst, J. ; Liang, S. - \ 2009
    Remote Sensing of Environment 113 (2009)Suppl.1. - ISSN 0034-4257 - p. S123 - S137.
    radiative-transfer model - leaf-area index - forest reflectance model - remote-sensing applications - deciduous broadleaf forest - spectral mixture analysis - dynamic vegetation model - thematic mapper data - hyperspectral data - canopy reflectance
    The science of spectroscopy has existed for more than three centuries, and imaging spectroscopy for the Earth system for three decades. We first discuss the historical background of spectroscopy, followed by imaging spectroscopy, introducing a common definition for the latter. The relevance of imaging spectroscopy is then assessed using a comprehensive review of the cited literature. Instruments, technological advancements and (pre-)processing approaches are discussed to set the scene for application related advancements. We demonstrate these efforts using four examples that represent progress due to imaging spectroscopy, namely (i) bridging scaling gaps from molecules to ecosystems using coupled radiative transfer models (ii) assessing surface heterogeneity including clumping, (iii) physical based (inversion) modeling, and iv) assessing interaction of light with the Earth surface. Recent advances of imaging spectroscopy contributions to the Earth system sciences are discussed. We conclude by summarizing the achievements of thirty years of imaging spectroscopy and strongly recommend this community to increase its efforts to convince relevant stakeholders of the urgency to acquire the highest quality imaging spectrometer data for Earth observation from operational satellites capable of collecting consistent data for climatically-relevant periods of time.
    Using spectral information from the NIR water absorption features for the retrieval of canopy water content
    Clevers, J.G.P.W. ; Kooistra, L. ; Schaepman, M.E. - \ 2008
    International Journal of applied Earth Observation and Geoinformation 10 (2008)3. - ISSN 0303-2434 - p. 388 - 397.
    leaf optical-properties - fuel moisture-content - imaging spectrometry data - vegetation liquid water - reflectance data - ecosystem processes - hyperspectral data - aviris data - red edge - part 1
    Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.
    Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis
    Blackburn, G.A. ; Ferwerda, J.G. - \ 2008
    Remote Sensing of Environment 112 (2008)4. - ISSN 0034-4257 - p. 1614 - 1632.
    hyperspectral data - vegetation - calibration - prospect - pigment - spectroscopy - indexes - models - red
    The dynamics of foliar chlorophyll concentrations have considerable significance for plant¿environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.
    Comparison of two canopy reflectance models inversion for mapping forest crown closure using imaging spectroscopy
    Zeng, Y. ; Schaepman, M.E. ; Huang, H.A. ; Bruin, S. de; Clevers, J.G.P.W. - \ 2008
    Canadian Journal of Remote Sensing 34 (2008)3. - ISSN 1712-7971 - p. 235 - 244.
    radiative-transfer model - high-resolution imagery - thematic mapper data - vegetation canopy - bidirectional reflectance - biophysical variables - coniferous forests - hyperspectral data - boreal forests - etm+ data
    We compare the inversion of two canopy reflectance models to estimate forest crown closure (CC) using an EO-1 Hyperion image: the Kuusk¿Nilson forest reflectance and transmittance (FRT) model, and the Li¿Strahler geometric¿optical model. For predicting CC on a per-pixel basis, the FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. The inversion of the Li¿Strahler model mainly depends on the relationship between the scene component ¿sunlit background¿ and forest structural parameters. We complement prediction deficiencies of the inverted Li¿Strahler model CC using a spatial interpolation algorithm (regression kriging) in infeasible regions. Field-measured CCs of 40 sample sites are used to validate the inversion quality of both models. The results indicate that the Li¿Strahler model inversion (R2 = 0.67, RMSE = 0.043) performs better than the FRT model inversion (R2 = 0.53, RMSE = 0.072) for CC retrieval. Estimated CC using the Li¿Strahler model inversion combined with spatial interpolation yield a final, continuous CC map for the Longmenhe forest nature reserve in China, which is used as a study area for this work. The advantages and disadvantages of these two models inversion combined with imaging spectrometer data for mapping forest CC are discussed
    Can nutrient status of four woody plant species be predicted using field spectrometry?
    Ferwerda, J.G. ; Skidmore, A.K. - \ 2007
    ISPRS Journal of Photogrammetry and Remote Sensing 62 (2007)6. - ISSN 0924-2716 - p. 406 - 414.
    reflectance spectroscopy - absorption features - vegetation indexes - hyperspectral data - leaf - nitrogen - variability - regression - quality - corn
    This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.
    Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties
    Koetz, B. ; Schaepman, M.E. ; Morsdorf, F. ; Bowyer, P. ; Itten, K.I. ; Allgower, B. - \ 2004
    Remote Sensing of Environment 92 (2004)3. - ISSN 0034-4257 - p. 332 - 344.
    leaf-area index - airborne imaging spectrometry - remote-sensing data - chlorophyll content estimation - equivalent water thickness - reflectance model - vegetation water - bidirectional reflectance - hyperspectral data - danger assessment
    Imaging spectrometer data were acquired over conifer stands to retrieve spatially distributed information on canopy structure and foliage water content, which may be used to assess fire risk and to manage the impact of forest fires. The study relied on a comprehensive field campaign using stratified systematic unaligned sampling ranging from full spectroradiometric characterization of the canopy to conventional measurements of biochemical and biophysical variables. Airborne imaging spectrometer data (DAIS7915 and ROSIS) were acquired parallel to the ground measurements, describing the canopy reflectance of the observed forest. Coniferous canopies are highly heterogeneous and thus the transfer of incident radiation within the canopy is dominated by its structure. We demonstrated the viability of radiative transfer representation and compared the performance of two hybrid canopy reflectance models, GeoSAIL and FLIGHT, within this heterogeneous medium. Despite the different nature and canopy representation of these models, they yielded similar results. Subsequently, the inversion of a hyperspectral GeoSAIL version demonstrated the feasibility of estimating structure and foliage water content of a coniferous canopy based on radiative transfer modeling. Estimates of the canopy variables showed reasonably accurate results and were validated through ground measurements. (C) 2004 Elsevier Inc. All rights reserved.
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