Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor

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.     

Nanopowders of Na0.5K0.5NbO3 Prepared by the Pechini Method

A Pechini-based chemical synthesis route was used to produce powders of Na_0.5K_0.5NbO₃ (NKN). The thermochemistry of the gel was investigated using thermogravimetric analysis-fourier transform infrared (TGA-FTIR) evolved gas analysis; in addition, powder FTIR was used to analyze the gel residues after different heat treatments. The final decomposition of the organic components occurred at ∼650°C. However, hydrated–carbonated secondary phase(s) were detected by FTIR in powders that had been heated at 700°C, indicating that the NKN nanopowders are susceptible to a reaction with atmospheric moisture and carbon dioxide. The NKN particle sizes were in the range 50–150 nm after decomposition at 700°C.     

Remote sensing of soils in a eucalypt forest environment

The concentration of soil phosphorus and pH correlated significantly with Landsat TM and Compact Airborne Spectrographic Imager (CASI) data, terrain position and aspect. Band ratios of the infrared and visible wavelengths are particularly significant for phosphorus. Exchangeable soil cations (that is calcium and sodium), as well as electrical conductivity, correlated less strongly with CASI and TM, but the correlations were statistically significant. Magnesium, potassium, and pH were significantly correlated with TM data, but not with the CASI data. pH and phosphorus were significantly correlated with terrain position. Three factors may individually, or in combination, be causing these results: illumination differences caused by terrain, direct reflectance of soil, or differences in vegetation (due to the influence of the soil on vegetation). A review of the literature appears to support the latter as the most likely causal factor. The results are important for establishing whether forest soils may be mapped using only remotely-sensed data, digital terrain data, or a combination.     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

Relations between heavy drinking, gender, and substance-free reinforcement.

Behavioral economic theory and laboratory research have suggested that substance abuse may be associated with diminished engagement in enjoyable substance-free activities (substance-free reinforcement). However, college students, in particular men, have reported numerous social benefits from drinking that might mitigate the expected inverse relation between drinking and substance-free reinforcement. In this study, we examined the relations between college student heavy drinking, gender, and several categories of substance-free reinforcement (peer, dating, sexual, school, and family activities). Participants were 246 undergraduate students who were classified as a function of their reported frequency of heavy drinking during a typical week in the past month (120 heavy drinkers, 126 light drinkers). Heavy drinking was associated with significantly higher substance-free peer and sexual reinforcement. This association was gender invariant and remained significant in multiple regression models that controlled for gender, ethnicity, and fraternity or sorority membership. Substance-free reinforcement did not predict frequency of heavy drinking in models that included levels of substance-related reinforcement. The results indicate that college student heavy drinking is not associated with global deficits in substance-free reinforcement and is instead associated with increased peer and sexual activity that occurs outside the context of drinking or drug use. Prevention programs should help students to compensate for the potential loss of social reinforcement associated with reductions in drinking. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

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. ?-SVR, ν-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 (R² = 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.     

Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve,China

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16‐day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) image captured twice a day could potentially overcome these problems. However, its potential for mapping Secchi disk depth of inland water bodies has so far rarely been explored. This study compared two image sources, MODIS and Landsat Thematic Mapper (TM), for mapping the tempo–spatial dynamics of Secchi disk depth in Poyang Lake National Nature Reserve, China. Secchi disk depths recorded at weekly intervals from April to October in 2004 and 2005 were related to 5 Landsat TM and 22 MODIS images respectively. Two multiple regression models including the blue and red bands of Landsat TM and MODIS respectively explained 83% and 88% of the variance of the natural logarithm of Secchi disk depth. The standard errors of the predictions were 0.20 and 0.37 m for Landsat TM and MODIS‐based models. A high correlation (r = 0.94) between the predicted Secchi disk depth derived from the two models was observed. A discussion of advantages and disadvantages of both sensors leads to the conclusion that MODIS offers the possibility to monitor water transparency more regularly and cheaply in relatively big and frequently cloud covered lakes as is with Poyang Lake.     

Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

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.     

Monitoring change in the spatial heterogeneity of vegetation cover in an African savanna

The extent to which a new intensity‐dominant scale approach to characterizing spatial heterogeneity from remote sensing imagery can be used to monitor two‐dimensional changes (i.e. variability and patch size) in the spatial heterogeneity of vegetation cover (estimated from a Landsat Thematic Mapper (TM)‐derived Normalized Difference Vegetation Index (NDVI)) was tested in the Sebungwe region in north‐western Zimbabwe between 1984 and 1992. Intensity of spatial heterogeneity (i.e. the maximum variance obtained when a spatially distributed landscape property is measured with a successively increasing window size) was used to measure variability in vegetation cover. Dominant scale of spatial heterogeneity (i.e. the window size at which the maximum variance in the landscape property is measured) was used to measure the dominant patch dimension of vegetation cover. This approach was validated by testing whether the observed change in the dominant scale and intensity of spatial heterogeneity of vegetation cover between 1984 and 1992 was related to changes in the proportion of arable fields. The results also indicated that there was a significant relationship (p<0.05) between changes in the proportion of agricultural fields and changes in the intensity and the product of intensity and dominant scale of spatial heterogeneity (intensity×dominant scale), suggesting that the new approach captures observable changes in the landscape, and is not an artefact of the data. The results imply that the intensity‐dominant scale approach to quantifying spatial heterogeneity in remote sensing imagery can be used for a comprehensive characterization and monitoring of changes in landscape condition.