Declining species richness is a global concern; however, the coarse-scale metrics used at regional or landscape levels might not accurately represent the important habitat characteristics needed to estimate species richness. Currently, there exists a lack of knowledge with regard to the spatial extent necessary to correlate remotely sensed habitat metrics to species richness and animal surveys. We provide a protocol for determining the best scale to use when merging remotely sensed habitat and animal survey data as a step towards improving estimates of vertebrate species richness on broad scales. We test the relative importance of fine-resolution habitat heterogeneity and productivity metrics at multiple spatial scales as predictors of species richness for birds, frogs and mammals using a Bayesian approach and a combination of passive monitoring technologies. Model performance was different for each taxonomic group and dependent on the scale at which habitat heterogeneity and productivity were measured. Optimal scales included a 20-m radius for bats and frogs, an 80-m radius for birds and a 180-m radius for terrestrial mammals. Our results indicate that optimal scales do exist when merging remotely sensed habitat measures with ground-based surveys, but they differ between vertebrate groups. Additionally, the selection of a measurement scale is highly influential to our understanding of the relationships between species richness and habitat characteristics.

The identification and quantification of Antarctic Pygoscelis penguin colonies depends increasingly on recognition of the characteristic optical properties of guano deposits, but almost all knowledge of these properties until now has been compromised by resolution and atmospheric propagation effects. Here we present hyperspectral reflectance data in the range 350–2500 nm, collected in situ from fresh guano deposits in Pygoscelis penguin colonies on Signy Island, South Orkney Islands. The period of data collection included the transition from predominantly white guano to the pink coloration characteristic of a krill-rich diet. The main identifiable features in the spectra are a broad absorption feature centred around 550 nm, responsible for the pink coloration and identified with the pigment astaxanthin, as well as several water absorption features. Variations in these features are responsible for differentiation between spectra. From these results we propose two spectral indices suitable for use with satellite data, one of which responds to the presence of astaxanthin in the guano and the other to water. Our results do not allow us to differentiate between penguin species from their guano, but do suggest that the breeding phenology of Pygoscelis penguins could be determined from a time series of multispectral imagery.

In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R2 was used to draw the two-dimensional distribution of R2 values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R2 higher than 0.88 by using the linear regression model in the study.

In strawberry production, a balanced and accurate irrigation schedule is essential, because of the high sensitivity of strawberry plants to water deficits and waterlogging. The optimal irrigation management strategy can, however, only be obtained by an accurate crop monitoring system. To replace the current visual inspection methods, which are subjective, time consuming and labour-intensive, the performance of the COmpact hyperSpectral Imaging system (COSI) mounted on an RPAS (Remotely Piloted Aircraft System) was evaluated. The study, focusing on different irrigation treatments in strawberry cultivation, unraveled the potential of the COSI system, to monitor field variations, even at small scale. Growth inhibition and differences in plant physiology due to water deficit could be detected. As such, the COSI system has shown potential for steering irrigation management decisions in strawberry cultivation.

Precision farming advances are providing opportunities in both production agriculture and agricultural research. For growers and agronomists, the benefits of identifying where crops are stressed, the location of weeds and estimating yields on a large scale are clear. Researchers, who have different needs, can benefit from a detailed focus on a specific characteristic, such as one disease (e.g. yellow rust). This paper will review how recent advances in technology are beginning to allow the development of specialised tools within research and agriculture and how current precision agriculture tools can be effective at measuring desirable traits.

Mapping weed infestations in an annual crop has implications not only for site-specific herbicide applications but also for planning future management strategies and understanding weed ecology. A controlled laboratory experiment, involving detached leaves, was conducted to investigate the potential to discriminate two crop and five weed species using hyperspectral and multispectral remote sensing. Stepwise discriminant function analyses showed that reflectance in the visible and “red-edge” regions of the spectrum were consistently required for species discrimination. The seven species were correctly identified 90 and 89% of the time using the hyperspectral and multispectral data, respectively, and the classification rules derived from discriminant function analyses. Errant species prediction with the hyperspectral data resulted in a grass being predicted as a grass and a broadleaf as a broadleaf. However, for multispectral data, incorrect classifications were more serious because errant predictions sometimes resulted in a grass being classified as a broadleaf and vice-versa. Further studies using plants at a variety of growth stages, from a variety of environments, and at the canopy level are warranted.

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

Geospatial technologies are increasingly important tools used to assess the spatial distributions and predict the spread of invasive species. The objective of our research was to quantify and map four dominant invasive plant species, including saltcedar, Russian olive, Canada thistle, and musk thistle, along the flood plain of the North Platte River corridor within a 1-mile (1.6-km) buffer. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from visible to near infrared), we evaluated an image processing technique known as spectral angle mapping for mapping the invasive species distribution. A minimum noise fraction algorithm was used to remove the inherent noise and redundancy within the dataset during the classification. The classification algorithm applied on the AISA image revealed five categories of invasive species distribution including (1) saltcedar; (2) Russian olive; and a mix of (3) Canada and musk thistle, (4) Canada/musk thistle and reed canary grass, or (5) Canada/musk thistle, saltcedar, and reed canary grass. Validation procedures confirmed an overall map accuracy of 74%. Saltcedar and Russian olive classes showed producer and user accuracies of greater than 90%, whereas the mixed categories revealed accuracy values of between 35 and 74%. The immediate benefit of this research has been to provide information on the spatial distribution of invasive species to land managers for implementation of management programs. In addition, these data can be used to establish a baseline of the species distributions for future monitoring and control efforts.

Perennial pepperweed is an aggressive, exotic weed invading wetland and riparian areas in California, including the San Francisco Bay/Sacramento–San Joaquin Delta Estuary. Effective management will require detailed and accurate maps of its distribution. Remote sensing technologies offer the capability to map weed species over broad areas and with rapid return intervals. As a first step in assessing the potential to map perennial pepperweed with hyperspectral remote sensing data, this study determined its spectral uniqueness relative to co-occurring species. Spectral measurements were conducted during summer drought conditions in the Sacramento–San Joaquin Delta region. Reflectance spectra of perennial pepperweed and seven co-occurring species were collected with a portable spectrometer. Nineteen physiological indexes were calculated from the reflectance data. Physiological indexes are sensitive to narrow spectral features and encapsulate reflectance information in ecologically relevant ways. Classification trees generated from these indexes were able to discriminate both flowering and fruiting perennial pepperweed from co-occurring species with high levels of cross-validated accuracy when using the original spectrometer data and also when this data set was resampled to simulate the spectral resolution of two widely used airborne hyperspectral imagers. Perennial pepperweed's characteristic white flowers are the major component of the spectral uniqueness of this species. Phenological state influenced reflectance spectra more strongly than variation in intraseasonal maturity. Field spectrometer spectra were qualitatively and quantitatively similar to perennial pepperweed spectra extracted from airborne image data. These results suggest that hyperspectral remote sensing will be a powerful tool for the mapping and monitoring of perennial pepperweed. Future work will extend these analyses to image data encompassing the San Francisco Bay/Sacramento–San Joaquin Delta region.

Because of interest in monitoring crop response to weed interference, greenhouse experiments were conducted to evaluate interference of purple and yellow nutsedge on the growth, development, and spectral response of cotton and soybean. Cotton fresh weight was reduced 9 to 42% compared with the control when grown with yellow and purple nutsedge. Fresh weight of soybean was reduced 27 to 60% when it emerged simultaneously with yellow nutsedge and 45 to 63% when it emerged 7 d after yellow nutsedge. Soybean fresh weight was reduced 30 to 35% when it emerged simultaneously with purple nutsedge and 44 to 72% when it emerged 7 d after purple nutsedge. Reflectance data were analyzed using wavelet transformation techniques with the HAAR mother wavelet. Nine extracted features from the cotton and soybean leaf reflectance measurements were used to classify single-leaf cotton and soybean reflectance measurements to predict whether cotton or soybean was growing in the presence or absence of purple and yellow nutsedge. After training the system, the ability to separate leaf reflectance measurements of crops growing weed free from those growing in the presence of purple and yellow nutsedge was tested using cross-validation with the nearest mean classifier. Cross-validation accuracy results for cotton were 62 to 70%. Cross-validation accuracy for soybean and yellow nutsedge was similar, regardless of emergence, and ranged from 60 to 71%. Features extracted from the soybean reflectance measurements were not as effective in classifying soybean leaf reflectance measurements based on the presence or absence of purple nutsedge. A decrease in accuracy was observed for both simultaneous and delayed soybean emergence in purple nutsedge fresh weight categories from more than 2,560 g to more than 3,420 g. Overall, the system correctly classified soybean emerging simultaneously with purple nutsedge 58 to 74% and soybean emerging 7 d after purple nutsedge 53 to 67%. These results indicate the potential of differentiating crops under stress using spectral reflectance, although refinements to the system must be made before it is field ready.

Hyperspectral cathodoluminescence mapping is used to examine a carbonado diamond. The hyperspectral dataset is examined using a data clustering algorithm to interpret the range of spectral shapes present within the dataset, which are related to defects within the structure of the diamond. The cathodoluminescence response from this particular carbonado diamond can be attributed to a small number of defect types: N-V0, N2V, N3V, a 3.188 eV line, which is attributed to radiation damage, and two broad luminescence bands. Both the N2V and 3.188 eV defects require high-temperature annealing, which has implications for interpreting the thermal history of the diamond. In addition, bright halos observed within the diamond cathodoluminescence, from alpha decay radiation damage, can be attributed to the decay of 238U.

The measurement of near-infrared (NIR) cathodoluminescence (CL) with sufficient sensitivity to allow full spectral mapping has been investigated through the application of optimized grating spectrometers that allow the ultraviolet (UV), visible, and NIR CL spectra to be measured simultaneously. Two optical spectrometers have been integrated into an electron microprobe, allowing simultaneous collection of hyperspectral CL (UV-NIR), characteristic X-rays, and electron signals. Combined hyperspectral CL spectra collected from two natural apatite (Ca5[PO4]3[OH,F]) samples from Wilberforce (Ontario, Canada) and Durango (Mexico) were qualitatively analyzed to identify the emission centers and then deconvoluted pixel-by-pixel using least-squares fitting to produce a series of ion-resolved CL intensity maps. Preliminary investigation of apatite has shown strong NIR emissions associated primarily with the rare-earth element Nd. Details of growth and alteration were revealed in the NIR that were not discernable with electron-induced X-ray mapping. Intense emission centers from Nd3+ and Sm3+ were observed in the spectra from both apatites, along with minor emissions from other 3+ rare-earth elements. Quantitative electron probe microanalysis was performed on points within the mapped area of the Durango apatite to produce a calibration line relating cathodoluminescent intensity of the fitted peak centered at 1,073 nm (1.156 eV) to the Nd concentration.

A method for the analysis of cathodoluminescence spectra is described that enables quantitative trace-element-level distributions to be mapped within minerals and materials. Cathodoluminescence intensities for a number of rare earth elements are determined by Gaussian peak fitting, and these intensities show positive correlation with independently measured concentrations down to parts per million levels. The ability to quantify cathodoluminescence spectra provides a powerful tool to determine both trace element abundances and charge state, while major elemental levels can be determined using more traditional X-ray spectrometry. To illustrate the approach, a scheelite from Kalgoorlie, Western Australia, is hyperspectrally mapped and the cathodoluminescence is calibrated against microanalyses collected using a laser ablation inductively coupled plasma mass spectrometer. Trace element maps show micron scale zoning for the rare earth elements Sm3+, Dy3+, Er3+, and Eu3+/Eu2+. The distribution of Eu2+/Eu3+ suggests that both valences of Eu have been preserved in the scheelite since its crystallization 1.63 billion years ago.