- W.F. Boer de (1)
- M.A. Cho (3)
- R.O. Chávez Oyanadel (1)
- J.G.P.W. Clevers (1)
- N. Dudeni-Tlhone (1)
- A. Fauzi (2)
- J.G. Ferwerda (2)
- H. Gils (1)
- R.C. Grant (1)
- I.M.A. Heitkonig (3)
- M. Herold (1)
- S. Huber (1)
- S. Huber (2)
- K.I. Itten (2)
- H.J. Knegt de (1)
- M. Kneubuehler (1)
- M. Kneubühler (2)
- N. Knox (1)
- B. Koetz (2)
- E. Kohi (1)
- L. Kumar (1)
- R. Main (1)
- B. Majeke (1)
- R. Mathieu (2)
- F.D. Meer van der (1)
- O. Mutanga (6)
- M.F. Noomen (1)
- M. Ortiz (1)
- H.H.T. Prins (4)
- A. Psomas (2)
- W.H. Putten van der (1)
- A. Ramoelo (2)
- D. Rugege (1)
- M.E. Schaepman (1)
- M. Schlerf (5)
- J.T. Schopfer (1)
- A.K. Skidmore (11)
- A. Skidmore (1)
- R. Slotow (1)
- W. Verhoef (1)
- C. Waal van der (1)
- H.M.A. Werff van der (1)
- N.E. Zimmerman (2)
- N.E. Zimmermann (1)
Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation
Almeida De Carvalho, S. ; Schlerf, M. ; Putten, W.H. van der; Skidmore, A. - \ 2013
International Journal of applied Earth Observation and Geoinformation 24 (2013). - ISSN 0303-2434 - p. 32 - 41.
absorption features - qinling mountains - leaf biochemistry - senecio-jacobaea - golden takin - plant - soil - chemistry - forest - chronosequence
Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season – due to its phenology – and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent. We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs. Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.
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.
Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data
Ramoelo, A. ; Skidmore, A.K. ; Cho, M.A. ; Mathieu, R. ; Heitkonig, I.M.A. ; Dudeni-Tlhone, N. ; Schlerf, M. ; Prins, H.H.T. - \ 2013
ISPRS Journal of Photogrammetry and Remote Sensing 82 (2013). - ISSN 0924-2716 - p. 27 - 40.
kruger-national-park - multiple linear-regression - band-depth analysis - vegetation indexes - south-africa - chlorophyll estimation - imaging spectroscopy - absorption features - biochemical content - mineral-nutrition
Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems.
Savanna grass nitrogen to phosphorous ratio estimation using field spectroscopy and the potential for estimation with imaging spectroscopy
Ramoelo, A. ; Skidmore, A.K. ; Schlerf, M. ; Heitkonig, I.M.A. ; Mathieu, R. ; Cho, M.A. - \ 2013
International Journal of applied Earth Observation and Geoinformation 23 (2013). - ISSN 0303-2434 - p. 334 - 343.
least-squares regression - band-depth analysis - red edge position - n-p ratios - nutrient limitation - reflectance spectra - absorption features - vegetation indexes - mineral-nutrition - continuum removal
Determining the foliar N:P ratio provides a tool for understanding nutrient limitation on plant production and consequently for the feeding patterns of herbivores. In order to understand the nutrient limitation at landscape scale, remote sensing techniques offer that opportunity. The objective of this study is to investigate the utility of field spectroscopy and a potential of hyperspectral mapper (HyMap) spectra to estimate foliar N:P ratio. Field spectral measurements were undertaken, and grass samples were collected for foliar N and P extraction. The foliar N:P ratio prediction models were developed using partial least square regression (PLSR) with original spectra and transformed spectra for field and the resampled field spectra to HyMap. Spectral transformations included the continuum removal (CR), water removal (WR), first difference derivative (FD) and log transformation (Log(1/R)). The results showed that CR and WR spectra in combination with PLSR predicted foliar N:P ratio with higher accuracy as compared to FD and R, using field spectra. For HyMap spectral analysis, addition to CR and WR, FD achieved higher estimation accuracy. The performance of FD, CR and WR spectra were attributed to their ability to minimize sensor and water effects on the fresh leaf spectra, respectively. The study demonstrated a potential to predict foliar N:P ratio using field and HyMap simulated spectra and shortwave infrared (SWIR) found to be highly sensitive to foliar N:P ratio. The study recommends the prediction of foliar N:P ratio at landscape level using airborne hyperspectral data and could be used by the resource managers, park managers, farmers and ecologists to understand the feeding patterns, resource selection and distribution of herbivores (i.e. wild and livestock).
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.
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.
Dry season mapping of savanna forage quality, using the hyperspectral Carnegie
Knox, N. ; Skidmore, A.K. ; Prins, H.H.T. ; Asner, P. ; Werff, H.M.A. van der; Boer, W.F. de; Waal, C. van der; Knegt, H.J. de; Kohi, E. ; Slotow, R. ; Grant, R.C. - \ 2011
Remote Sensing of Environment 115 (2011)6. - ISSN 0034-4257 - p. 1478 - 1488.
kruger-national-park - african savannas - neural-networks - south-africa - absorption features - leaf biochemistry - mineral-nutrition - grass quality - nitrogen - reflectance
Forage quality within an African savanna depends upon limiting nutrients (nitrogen and phosphorus) and nutrients that constrain the intake rates (non-digestible fibre) of herbivores. These forage quality nutrients are particularly crucial in the dry season when concentrations of limiting nutrients decline and non-digestible fibres increase. Using artificial neural networks we test the ability of a new imaging spectrometer (CAO Alpha sensor), both alone and in combination with ancillary data, to map quantities of grass forage nutrients in the early dry season within an African savanna. Respectively 65%, 57% and 41%, of the variance in fibre, phosphorus and nitrogen concentrations were explained. We found that all grass forage nutrients show response to fire and soil. Principal component analysis, not only reduced image dimensionality, but was a useful method for removing cross-track illumination effects in the CAO imagery. To further improve the mapping of forage nutrients in the dry season we suggest that spectra within the shortwave infrared (SWIR) region, or additional relevant ancillary data, are required.
Impact of multiangular information on empirical models to estimate canopy nitrogen concentration in mixed forest
Huber, S. ; Koetz, B. ; Psomas, A. ; Kneubuehler, M. ; Schopfer, J.T. ; Itten, K.I. ; Zimmermann, N.E. - \ 2010
Journal of Applied Remote Sensing 4 (2010)1. - ISSN 1931-3195
hyperspectral brdf data - imaging spectroscopy - bidirectional reflectance - spectral measurements - absorption features - ecosystem processes - chlorophyll content - carbon - leaf - photosynthesis
Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (C-N) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing C-N on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the C-N estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy C-N with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring.
Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation
Cho, M.A. ; Aardt, J. van; Main, R. ; Majeke, B. - \ 2010
International Journal of Remote Sensing 31 (2010)12. - ISSN 0143-1161 - p. 3143 - 3159.
multiple linear-regression - red edge position - chlorophyll content - vegetation indexes - reflectance indexes - steady-state - precision agriculture - spectral reflectance - absorption features - leaf reflectance
Remote sensing is viewed as a cost-effective alternative to intensive field surveys in assessing site factors that affect growth of Eucalyptus grandis over broad areas. The objective of this study was to assess the utility of hyperspectral remote sensing to discriminate between site qualities in E. grandis plantation in KwaZulu-Natal, South Africa. The relationships between physiology-based hyperspectral indicators and site quality, as defined by total available water (TAW), were assessed for E. grandis plantations through one-way analysis of variance (ANOVA). Canopy reflectance spectra for 68 trees (25 good, 25 medium and 18 poor sites) were collected on clear-sky days using an Analytical Spectral Device (ASD) spectroradiometer (350-2500 nm) from a raised platform. Foliar macronutrient concentrations for N, P, K, S, Ca, Mg and Na and their corresponding spectral features were also evaluated. The spectral signals for leaf water - normalized difference water index (NDWI), water band index (WBI) and moisture stress index (MSI) - exhibited significant differences (p 0.05) between sites. The magnitudes of these indices showed distinct gradients from the poor to the good sites. Similar results were observed for chlorophyll indices. These results show that differences in site quality based on TAW could be detected via imaging spectroscopy of canopy water or chlorophyll content. Among the macronutrients, only K and Ca exhibited significant differences between sites. However, a Tukey post-hoc test showed differences between the good and medium or medium and poor sites, a trend not consistent with the TAW gradient. The study also revealed the capability of continuum-removed spectral features to provide information on the physiological state of vegetation. The normalized band depth index (NBDI), derived from continuum-removed spectra in the region of the red-edge, showed the highest potential to differentiate between sites in this study. The study thus demonstrated the capability of hyperspectral remote sensing of vegetation canopies in identifying the site factors that affect growth of E. grandis in KwaZulu Natal, South Africa.
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.
Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data
Mutanga, O. ; Kumar, L. - \ 2007
International Journal of Remote Sensing 28 (2007)21. - ISSN 0143-1161 - p. 4897 - 4911.
kruger-national-park - neural-networks - reflectance spectroscopy - absorption features - feature-selection - south-africa - aviris data - nitrogen - classification - quality
We tested the utility of imaging spectroscopy and neural networks to map phosphorus concentration in savanna grass using airborne HyMAP image data. We also sought to ascertain the key wavelengths for phosphorus prediction using hyperspectral remote sensing. The remote sensing of foliar phosphorus has received very little attention as compared to nitrogen, yet it plays an equally important role in explaining the distribution and feeding patterns of herbivores. Band depths from two continuum-removed absorption features as well as the red edge position (REP) were input into a backpropagation neural network. Following a series of experiments to ascertain the optimum wavelengths, the best trained neural network was used to predict and ultimately to map grass phosphorus concentration in the Kruger National Park. The results indicate that the best trained neural network could predict phosphorus distribution with a coefficient of determination of 0.63 and a root mean square error (RMSE) of 0.07 (28% of the mean observed phosphorus concentration) on an independent test data set. Our results also show that the absorption feature located in the shortwave infrared (R 2015-2199) contains more information on phosphorus distribution, a region that has hardly been explored before in most spectroscopic experiments for phosphorus as compared to the visible bands. Overall, the study demonstrates the potential of imaging spectroscopy in mapping grass phosphorus concentration in savanna rangelands.
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.
Red edge shift and biochemical content in grass canopies
Mutanga, O. ; Skidmore, A.K. - \ 2007
ISPRS Journal of Photogrammetry and Remote Sensing 62 (2007)2. - ISSN 0924-2716 - p. 34 - 42.
kruger-national-park - vegetation indexes - absorption features - chlorophyll content - leaf reflectance - south-africa - nitrogen - spectroscopy - regression - quality
The concentration of foliar nitrogen in tropical grass is one of the factors that explain the distribution of wildlife. Therefore, the remote sensing of foliar nitrogen contributes to a better understanding of wildlife feeding patterns. This study evaluated changes in the red edge position of the 680 nm continuum removed chlorophyll feature in the reflectance spectra of samples of Cenchus ciliaris grass grown in a greenhouse under three levels of nitrogen supply. Canopy spectral measurements from each treatment were recorded under controlled laboratory conditions over a four-week period using a GER 3700 spectroradiometer. Results indicate that the mean wavelength positions of the three fertilization treatments were statistically different. An increase in nitrogen supply yielded a shift in the red edge position to longer wavelengths. The red edge position, amplitude, slope at 713 nm and slope at 725 nm were significantly correlated to measured nitrogen concentration (bootstrapped r=0.89, -0.28, 0.63 and 0.75, respectively) even at canopy level. Based on these results, the red edge position is strongly correlated with biochemical concentration in plants compared to the other methods tested. The study provides conclusive evidence that confirms the strength of a red edge-nitrogen relationship that remains underused in remote sensing. This method is promising for estimating nutrient content in grasslands. (C) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna
Mutanga, O. ; Rugege, D. - \ 2006
International Journal of Remote Sensing 27 (2006)16. - ISSN 0143-1161 - p. 3499 - 3514.
ground-based radiometry - kruger-national-park - spectral reflectance - absorption features - vegetation indexes - south-africa - quality - environment - derivation - imagery
Modelling herbaceous biomass is critical for an improved understanding of wildlife feeding patterns and distribution as well as for the development of early warning systems for fire management. Most savannas in South Africa are characterized by complex stand structure and abundant vegetation species. This has prohibited accurate estimation of biomass in such environments. We investigated the possibility of improving biomass predictions in tropical savannas using cokriging. Individual bands and ratios computed from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery were correlated with field measurements of biomass covering the Kruger National Park, South Africa. The band that yielded the highest correlation with biomass was then used for further analysis using cokriging. Three variogram models were developed: one for the herbaceous biomass, one for the best MODIS band and a cross variogram between all pairs of variables involved in the estimation. The variogram models were then used in cokriging to predict biomass distribution over the whole study area. Results indicate that a combination of remotely sensed data with field biomass measurements through cokriging improves the estimation accuracy compared to ordinary kriging and stepwise linear regression. Given the high temporal resolution of the freely available MODIS imagery, the result is critical for the improved monitoring and management of wildlife habitats.
Continuum removed band depth analysis for detecting the effects of natural gas, methane and ethane on maize reflectance
Noomen, M.F. ; Skidmore, A.K. ; Meer, F.D. van der; Prins, H.H.T. - \ 2006
Remote Sensing of Environment 105 (2006)3. - ISSN 0034-4257 - p. 262 - 270.
leaf water-content - spectral reflectance - absorption features - red-edge - pasture quality - plant stress - vegetation - leaves - resolution - responses
It is known that natural gas in the soil affects vegetation health, which may be detected through analysis of reflectance spectra. Since natural gas is invisible, changes in the vegetation could potentially indicate gas leakage. Although it is known that gas in soil affects plant reflectance, the relationship between natural gas and the development and reflectance properties of plants has not been studied. The objective of this study was to test whether natural gas and its two main components, methane and ethane, affect vegetation reflectance in the chlorophyll and water absorption regions. An experiment was carried out in which maize (Zea mays) plants were grown in pots that were flushed with 10 l of gas per day for 39 ±4 days. Leaf reflectance was measured once a week with a spectrophotometer. The reflectance was analysed using continuum removal of the blue (400-550 nm), red (550-750 nm) and two water absorption features (1370-1570 nm and 1870-2170 nm), after which the band depths and normalized band depths were analyzed for each treatment. The band depth analysis showed that ethane caused an initial increase of 10% in reflectance between 560 and 590 nm, followed by a decrease during the course of the experiment. Normalized band depth analysis showed that ethane caused a reflectance shift of 1 to 5 nm towards longer wavelengths compared to the control reflectance in the visible region. All gases caused an increase in reflectance in the water absorption bands. The physiological reflectance index, PRI, which has previously linked water stress to photosynthetic activity, suggested that the hydrocarbon gases (particularly ethane) decreased the photosynthetic activity of the plants. The combination of reduced band depths in the chlorophyll and water absorption regions and the increased PRI suggests that ethane gas in the soil hampered a normal water uptake by maize plants in an early stage of their growth. Although further research is necessary to upscale the results from the laboratory to the field, the increased reflectance in the 560-590 nm region caused by ethane together with the increased PRI are promising indicators for gas leakage.
Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species
Ferwerda, J.G. ; Skidmore, A.K. ; Mutanga, O. - \ 2005
International Journal of Remote Sensing 26 (2005)18. - ISSN 0143-1161 - p. 4083 - 4095.
band vegetation indexes - absorption features - spectroscopic determination - precision agriculture - linear-regression - canopy - reflectance - prediction - wheat - performance
The main focus of recent studies relating vegetation leaf chemistry with remotely sensed data is the prediction of chlorophyll and nitrogen content using indices based on a combination of bands from the red and infrared wavelengths. The use of high spectral resolution data offers the opportunity to select the optimal wavebands for predicting plant chemical properties. In order to test the optimal band combinations for predicting nitrogen content, normalized ratio indices were calculated for all wavebands between 350 and 2200 nm for five different species. The correlation between these indices and the nitrogen content of the samples was calculated and compared between species. The results show a strong correlation between individual normalized ratio indices and the nitrogen content for different species. The spectral regions that are most effective for predicting nitrogen content are, for each individual species, different from the normalized difference vegetation index (NDVI) spectral region. By combining the areas of maximum correlation it was possible to determine the optimal spectral regions for predicting leaf nitrogen content across species. In a cross species situation, normalized ratio indices using the combination of reflectance at 1770 nm and at 693 nm may give the best relation to nitrogen content for individual species
Discriminating sodium concentration in a mixed grass species environment of the Kruger National Park using field spectrometry
Mutanga, O. ; Skidmore, A.K. ; Prins, H.H.T. - \ 2004
International Journal of Remote Sensing 25 (2004)20. - ISSN 0143-1161 - p. 4191 - 4201.
infrared reflectance spectroscopy - absorption features - stimulates growth - panicum-coloratum - nitrogen - leaves - photosynthesis - quality - forest - africa
Sodium has been found to be a scarce element needed and sought by mammals. To date, most geophagical studies have mainly concentrated on sodium in the soil with limited attention being given to the plant component. Mapping foliar sodium distribution is important to understand wildlife feeding patterns and distribution. In this study, we established whether remote sensing can be used to discriminate different levels of sodium concentration in grass. A GER 3700 spectrometer was used to measure spectral reflectance of grass in the field. Since savannah rangelands are characterized by mixed grass species, we first established the variation of foliar sodium concentration in different grass species and tested for possible effects of species-sodium interaction on spectral reflectance. Our results showed statistically significant differences between the mean reflectance for the low and medium sodium classes. No significant differences were observed between reflectance in the high sodium class and the lower classes. However, there was a significant interaction between sodium classes and species in influencing reflectance. We concluded that, in combination with knowledge of grass species distribution, hyperspectral remote sensing may be useful in classifying foliar sodium concentration in savannah rangelands. This may help to understand the distribution of mammals in some African savannahs where mineral nutrient availability is limiting.
Integrating Imaging spectrometry and Neural Networks to map tropical grass quality in the Kruger National Park, South Africa
Mutanga, O. ; Skidmore, A.K. - \ 2004
Remote Sensing of Environment 90 (2004)1. - ISSN 0034-4257 - p. 104 - 115.
land-cover classification - chlorophyll content - red edge - absorption features - feature-selection - vegetation types - nitrogen - leaf - savanna - spectrometry
A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly the most limiting nutrient for grazers. Therefore, the remote sensing of foliar nitrogen concentration in savanna rangelands is important for an improved understanding of the distribution and feeding patterns of wildlife. Continuum removal was applied on two absorption features located in the visible (R550-757) and the SWIR (R2015-2199) from an atmospherically corrected HYMAP MKI image. A feature selection algorithm was used to select wavelength variables from the absorption features. Selected band depths from the absorption features as well as the red edge position (REP) were input into a backpropagation neural network. The best-trained neural network was used to map nitrogen concentration over the whole study area. Results indicate that the new integrated approach could explain 60% of the variation in savanna grass nitrogen concentration on an independent test data set, with a root mean square error (rmse) of 0.13 (+/- 8.30% of the mean observed nitrogen concentration). This result is better compared to the result obtained using multiple linear regression, which yielded an R-2 of 38%, with a RMSE of 0.16 (+/- 10.30% of the mean observed nitrogen concentration) on an independent test data set. The study demonstrates the potential of airborne hyperspectral data and neural networks to estimate and ultimately to map nitrogen concentration in the mixed species environments of Southern Africa. (C) 2004 Elsevier Inc. All rights reserved.