Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat

ABSTRACT

The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat.     

Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres

Reflectance data were obtained over a drought-stressed and a well-watered wheat plot with a hand-held radiometer having bands similar to the MSS bands of the Landsat satellites. Data for 48 clear days were interpolated to yield reflectance values for each day of the growing season, from planting until harvest. With an atmospheric path radiance model and Landsat 2 calibration data, the reflectances were used to simulate Landsat digital counts (not quantized) for the four Landsat bands for each day of the growing season, through a clear ($\text{?100-}\text{km}$ meteorological range) and a turbid ($\text{?10-}\text{km}$ meteorological range) atmosphere. Several ratios and linear combinations of bands were calculated using the simulated data, then assessed for their relative ability to discriminate vegetative growth and plant stress through the two atmospheres. The results showed that water stress was not detected by any of the indices until after growth was retarded, and the sensitivity of the various indices to vegetation depended on plant growth stage and atmospheric path radiance.     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress

This study aimed to determine whether modification of physiological parameters could be detected remotely by monitoring the spectral reflectance of olive leaves in response to different degrees of drought. Three different drought intensities were simulated: (a) a mild drought by feeding abscisic acid to detached branches; (b) a rapid and severe drought by detaching leaves and letting them dry over several hours; (c) a relatively slow drought caused by withholding water to potted olive plants. The three degrees of stress affected gas exchange and chlorophyll fluorescence. When the inhibition of photosynthesis occurred within an hour it was not accompanied by a parallel reduction in chlorophyll concentration in the carotenoid to chlorophyll ratio. Consequently, changes in spectral reflectance in the visible region, e.g. in PRI (photochemical reflectance index) and FRI (fluorescence reflectance indices) were not significantly induced. In contrast, when the inhibition of photosynthesis caused by slow developing drought was prolonged (i.e. more than 24 hours) and led to a decrease in chlorophyll concentration and to a simultaneous increase in carotenoid to chlorophyll ratio, there were significant changes in the visible region of the leaf spectral reflectance and, in turn, in PRI and FRI. We defined 16 new reference wavelengths, from visible to SWIR regions, which are sensitive to both fast‐developing and slow‐developing stresses. These reference wavelengths were used to develop an algorithm, the Relative Reflectance Increment (RRI), that was linearly related to changes in relative water content (RWC, r ² = 0.733). This algorithm showed that the 1455 nm wavelength is highly affected by drought. This wavelength was therefore used to elaborate the water content reflectance index that was inversely related to RWC (r ² = 0.702).     

Expert system for integrated plant protection in pepper (Capsicum annuun L.)

An expert system was developed with the aim of improving decision-making by pepper growers. Knowledge was obtained from the literature and from the experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 11 weeds, 20 insects, 14 diseases, three abiotic factors and control measures. The system is enhanced with 87 photos and drawings that assist the used in the identification process and choosing control measures. The expert system was evaluated with technicians and students. According to the validation results the system was considered very satisfactory with an average rank of 9.15 by technicians and of 8.95 by students with a statistic mode ranking 10 in all the cases. The program can be used as a decision tool for farmers and technicians and for educational purposes.     

Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks

Hyperspectral features in the red-edge region were tested as an index of plant stress responses to soil-oxygen depletion. The aim was to provide the basis for a warning system to identify natural gas leakage by the spectral responses of plants growing in the affected soil.Elevated concentrations of natural gas in the soil atmosphere were used to deplete oxygen concentrations around the roots of grass, wheat (Hordeum vulgare cv Claire) and bean (Vicia faba cv Clipper) growing in a field facility. Visible symptoms due to the natural gas included reduced growth of the plants and chlorosis of the leaves.Spectral responses included increased reflectance in the visible wavelengths and decreased reflectance in the near infra-red. Derivative analysis identified features within the red-edge at 720-730 and 702 nm. Ratios of the magnitude of the derivative at 725 to that at 702 nm were less in areas where gas was present. This ratio enabled identification of stress due to gas leakage up to 7 days before visible symptoms were observed and also at the edges of gassed plots where visible symptoms were not expressed. The technique was able to identify stress responses to long-term leaks in all the crops tested but to short-term leaks only in grass. This study therefore suggests that under appropriate conditions remote sensing could be used to detect pipeline gas leaks from decreases in the ratio of peaks within the red-edge.     

Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model

Leaf area index (LAI) and leaf chlorophyll content (LCC) are major considerations in management decisions, agricultural planning, and policy-making. When a radiative transfer model (RTM) was used to retrieve these biophysical variables from remote-sensing data, the ill-posed problem was unavoidable. In this study, we focused on the use of agronomic prior knowledge (APK), constructing the relationship between LAI and LCC, to restrict and mitigate the ill-posed inversion results. For this purpose, the inversion results obtained using the SAILH+PROSPECT (PROSAIL) canopy reflectance model alone (no agronomic prior knowledge, NAPK) and those linked with APK were compared. The results showed that LAI inversion had high accuracy. The validation results of the root mean square error (RMSE) between measured and estimated LAI were 0.74 and 0.69 for NAPK and APK, respectively. Compared with NAPK, APK improved LCC estimation; the corresponding RMSE values of NAPK and APK were 13.36 µg cm^–2 and 9.35 µg cm^–2, respectively. Our analysis confirms the operational potential of PROSAIL model inversion for the retrieval of biophysical variables by integrating APK.     

Estimation of heat stress in Tanzania by using ISO heat stress indices

The aim of this study was to evaluate the usefulness of the ISO heat stress standards in estimating the heat stress and strain in workplaces in Tanzania. Another aim was to select and to develop simplified methods for measuring physiological parameters in developing countries. The methods were tested in four hot factories and at a construction site. It seems that in tropical working environments the climatic conditions for which the ISO 7933 standard is applicable are too narrow. For instance, the mean skin temperature was incorrectly estimated by ISO 7933. An approximate analysis of the working situation can nevertheless be carried out by assuming the mean skin temperature to be 34.5 degrees C. During the study, heat stress and strain were not as high as expected; deep body temperatures were usually lower than 38 degrees C, sweat rates lower than 400 g/h and heart rates below 100 beats/min for about 72% of the measuring time. This is due to the job rotation of the workers and the long rest periods, because the number of workers is large in the factories, and the weather was not at its hottest during the survey.     

Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices

ABSTRACT

Hyperspectral remote sensing is economical and fast, and it can reveal detailed spectral information of plants. Hence, hyperspectral data are used in this study to analyse the spectral anomaly behaviours of vegetation in porphyry copper mine areas. This analytical method is used to compare the leaf spectra and relative differences among the vegetation indices; then, the correlation coefficients were computed between the soil copper content and vegetation index of Quercus spinosa leaves at both the leaf scale and the canopy scale in the Chundu mine area with different geological backgrounds. Lastly, this study adopts hyperspectral data for the level slicing of vegetation anomalies in the Chundu mine area. The results showed that leaf spectra in the orebody and background area differed greatly, especially in the infrared band (750 nm – 1300 nm); moreover, some indices like the normalized water index (NWI) and normalized difference water index (NDWI) of Quercus spinosa and Lamellosa leaves are sensitive to changes in the geological background. Compared with the canopy, the leaf hyperspectral indices of Quercus spinosa in Chundu can better reflect soil cuprum (Cu) anomaly. In addition, the NWI and NDWI of Quercus spinosa are significantly correlated with the soil Cu content at both the canopy scale and the leaf scale. Consequently, the results of the vegetation anomaly level slicing can adequately reflect the plant anomalies from ore bodies and nearby areas, thereby providing a new ore-finding method for areas with a high degree of vegetation coverage.     

Laser-induced fluorescence spectroscopy of dark- and light-adapted bean (Phaseolus vulgaris L.) and wheat (Triticum aestivum L.) plants grown under three irradiance levels and subjected to fluctuating lighting conditions

Fluorescence spectral characteristics associated with growth under different irradiance levels, and during rapidly changing lighting conditions, were measured on healthy bean (Phaseolus vulgaris L.) and wheat (Triticum aestivum L.) plants using a laser-induced fluorescence spectroscopy (LIFS) system. The LIFS system was designed as a prototype of a handheld field remote sensing system and used a tripled Nd:YAG laser to produce ultraviolet (UV) excitation photons at 355 nm. Dark-adapted canopies of the bean and wheat plants grown under 150, 300, or 450 μmol m⁻² s⁻¹ of photosynthetically active radiation (PAR) exhibited LIFS spectra with higher relative fluorescence intensities than emissions from light-adapted plants at all three light levels. Blue/red and blue/far-red leaf fluorescence ratios for both bean and wheat plants increased dramatically as PAR increased, but red/far-red ratios decreased as PAR increased. Light-adapted plants grown under the three light levels were then subjected to several rapidly changing lighting conditions. Plants were exposed sequentially to 150, 300, and 650 μmol m⁻² s⁻¹ PAR from metal halide lamps, followed by a fourth light treatment of 650 μmol m⁻² s⁻¹ PAR from a mixture of metal halide and tungsten-halogen lamps. The tungsten-halogen lamps added significant amounts of near-infrared (NIR) irradiation to the background light environment provided by the metal halide lamps. Results indicated that both bean and wheat canopies generally exhibited stable blue, green, red, and far-red fluorescence emissions when plants were exposed to 150, 300, and 650 μmol m⁻² s⁻¹ PAR from the metal halide lamps. In contrast, when bean and wheat plants were exposed to the NIR-enriched light supplied by the tungsten-halogen lamps, blue and green fluorescence remained stable, but red and far-red fluorescence increased dramatically immediately after exposure to the NIR photons. However, the increased levels of red and far-red fluorescence observed after exposure to NIR light decreased quickly (within 55 s) and returned to “baseline” levels observed at the start of the rapidly changing light experiments. Results indicate that handheld LIFS instruments can be used for remote sensing of plant canopies under a diversity of lighting conditions including full darkness, dawn and dusk lighting environments, and under rapidly changing light environments similar to those encountered on partly cloudy days.     

Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies

Accurate measurement of leaf area index (LAI), an important characteristic of plant canopies directly linked to primary production, is essential for monitoring changes in ecosystem C stocks and other ecosystem level fluxes. Direct measurement of LAI is labor intensive, impractical at large scales and does not capture seasonal or annual variations in canopy biomass. The need to monitor canopy related fluxes across landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index (NDVI), tend to saturate at LAI levels > 4 although tropical and temperate forested ecosystems often exceed that threshold. Using two monospecific shrub thickets as model systems, we evaluated the potential of a variety of algorithms specifically developed to improve accuracy of LAI estimates in canopies where LAI exceeds saturation levels for other indices. We also tested the potential of indices developed to detect variations in canopy chlorophyll to estimate LAI because of the direct relationship between total canopy chlorophyll content and LAI. Indices were evaluated based on data from direct (litterfall) and indirect measurements (LAI-2000) of LAI. Relationships between results of direct and indirect ground-sampling techniques were also evaluated. For these two canopies, the indices that showed the highest potential to accurately differentiate LAI values > 4 were derivative indices based on red-edge spectral reflectance. Algorithms intended to improve accuracy at high LAI values in agricultural systems were insensitive when LAI exceeded 4 and offered little or no improvement over NDVI. Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also saturate when LAI exceeds 4. Comparisons between hyperspectral vegetation indices and a saturated LAI value from indirect measurement may overestimate accuracy and sensitivity of some vegetation indices in high LAI communities. We recommend verification of indirect measurements of LAI with direct destructive sampling or litterfall collection, particularly in canopies with high LAI.     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy).At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation.At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT_canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level (). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT_canopy and was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT_canopy and an overestimation of leaf with relative errors around 19%.Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.     

Comparison and validation of heat stress indices in experimental studies

The present study aimed at (1) deriving the best methodology for using heat stress indices in fluctuating ambient conditions, (2) assessing various ways for improving the prediction ability of the Required Sweat Rate index (SW_req). The data base included the results from five experimental series involving 32 volunteers; these series involved environmental variations at constant metabolic rate, work-rest cycles in constant ambient conditions and alternate periods of work in heat and rest in neutral conditions. Observed and predicted variations in sweat rate were compared. During exposures to rapid changes in climatic conditions, the best prediction of the body sweat loss was provided by a model involving the simulation of the response of skin temperature and sweat rate to a step change in ambient conditions. The model used exponential weighting algorithms with time constants of 3 and 10 minutes respectively for these two variables. The study showed also that the prediction accuracy of SW_req index can be significantly improved by adopting. a new expression for the calculation of the evaporative efficiency of sweating, by predicting the mean T_sk value as a function of the parameters of the work situation and by applying the exponential weighting to the predicted values of sweat rate. Among the various heat stress indices tested in the study, the Required Sweat Rate Index gave the best approximation of the body sweat loss.     

Methodology to map the spread of an invasive plant (Lantana camara L.) in forest ecosystems using Indian remote sensing satellite data

The primary objective of this paper is to evaluate the utility of different Indian remote sensing sensors for detection, mapping and patch size estimation of Lantana camara L. (Kurri). The latter, of the family Verbinaceae, is one of the most aggressive invasive plant species and has colonized large areas of forest land in the Himalayan foothills (Shiwalik range). The State Forest Departments of India are planning to develop a suitable strategy to halt its invasion. The first step in this direction is to have accurate information on the location and spread of the plant in spatial format. The test site is part of the forest of the Rajaji National Park, Uttarakhand. Indian Remote Sensing-Linear Imaging Self-Scanning Sensor (IRS-LISS) III (multi-spectral, 23.5 m), IRS-LISS IV (multi-spectral, 5.8 m), Cartosat-1 (Panchromatic, 2.5 m) and a merged image of LISS IV and Cartosat-1 using Brovey fusion techniques were used to map Lantana camara L. Further improvement was obtained using texture analysis. The study demonstrates the potentiality of LISS IV and Cartosat-1 data for detection and mapping of Lantana camara L. The results show the feasibility of developing a semi-automated procedure to map and analyse the distribution of Lantana in forest areas.     

Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery

Exotic plant invasion is a major environmental and ecological concern and is a particular issue for Mediterranean-type ecosystems. Early detection of invasive plants is crucial for effective weed management. Several studies have explored hyperspectral imagery for mapping invasive plants with promising results. However, only a few extensive or comparative studies about image processing techniques for invasive plant detection have been reported, and even fewer studies have involved very high spatial and spectral resolution imagery. The primary goal of this study was to investigate the utility of very high spatial (0.5 m) and spectral (4 nm) resolution imagery and several classification approaches for detecting tamarisk (Tamarix spp.) infestations, the most problematic invasive plant species in the riparian habitats of southern California.Hierarchical clustering was a particularly effective and efficient statistical method for identifying wavebands and spectral transforms having the greatest discriminatory power. Products resulting from the classification of airborne hyperspectral image data varied by scene, input data type, classifier, and minimum patch size. Overall accuracy of image classification accuracy of products co-varied with commission error rates, such that products having strong agreement with reference data also had a high number of false detections. Integrating the findings from qualitative map analysis, areal proportion statistics, and object-based accuracy assessment indicates that the parallelepiped classifier with several narrow wavebands selected through hierarchical clustering yielded the most accurate and reliable tamarisk classification products.     

Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data

A systematic comparison of two types of method for estimating the nitrogen concentration of rape is presented: the traditional statistical method based on linear regression and the emerging computationally powerful technique based on artificial neural networks (ANN). Five optimum bands were selected using stepwise regression. Comparison between the two methods was based primarily on analysis of the statistic parameters. The rms. error for the back-propagation network (BPN) was significantly lower than that for the stepwise regression method, and the T-value was higher for BPN. In particular, for the first-difference of inverse-log spectra (log 1/R)′, T-values performed with a 127.71% success rate using BPN. The results show that the neural network is more robust to training and estimating rape nitrogen concentrations using canopy hyperspectral reflectance data.