In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL).

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. In many cases, the supervised classifications accentuated noise or features in the mosaic that were artifacts of color balancing and “feathering” areas of flightline overlap. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus).

The ability to estimate foliar nitrogen in semi-arid landscapes can yield information on nutritional status and improve our limited understanding of controls on canopy photosynthesis. We examined two spectroscopic methods for estimating sagebrush dried leaf and live shrub nitrogen content: first derivative reflectance (FDR) and continuum removal. Both methods used partial least squares (PLS) regression to select wavebands most significantly correlated with nitrogen concentrations in the samples. Sagebrush dried leaf spectra produced PLS models (R ² = 0.76–0.86) that could predict nitrogen concentrations within the data set more accurately than PLS models generated from live shrub spectra (R ² = 0.41–0.63). Inclusion of wavelengths associated with leaf water in the FDR transformations appeared to improve regression results. These findings are encouraging and warrant further exploration into sagebrush reflectance spectra to characterize nitrogen concentrations.

Eliminating all stop words from the feature space is a standard practice of preprocessing in text mining, regardless of the domain which it is applied to. However, this may result in loss of important information, which adversely affects the accuracy of the text mining algorithm. Therefore, this paper proposes a novel methodology for selecting the optimal set of domain specific stop words for improved text mining accuracy. First, the presented methodology retains all the stop words in the text preprocessing phase. Then, an evolutionary technique is used to extract the optimal set of stop words that result in the best classification accuracy. The presented methodology was implemented on a corpus of open source news articles related to critical infrastructure hazards. The first step of mining geo-dependencies among critical infrastructures from text is text classification. In order to achieve this, article content was classified into two classes: 1) text content with geo-location information, and 2) text content without geo-location information. Classification accuracy presented methodology was compared to accuracies of four other test cases. Experimental results with 10-fold cross validation showed that the presented method yielded an increase of 1.76% or higher in True Positive (TP) rate and a 2.27% or higher increase in the True Negative (TN) rate compared to the other techniques.

Balsam woolly adelgid is an invasive pest of firs in the United States. Aerial surveys are conducted for detection of adelgid infestations but other remotely sensed data may also be useful. Our objective was to determine if high spectral resolution, branch-level data can be used to distinguish infested from noninfested trees. Stepwise discriminant analysis yielded a three-variable model (the red-green index and two narrow-bands (one at 670 nm and the other at 1912 nm)) that classified infested versus non-infested trees with 94% accuracy compared with the 83% accuracy obtained with a single-variable model. The response of trees in narrow spectral bands was integrated across wavebands to simulate measurements from the multispectral SPOT5-HRVIR sensor. Stepwise discriminant analysis again yielded a three-variable model (simple ratio, the SPOT5-HRVIR band in the SWIR region and NDVI) with similar accuracy (93%) at discriminating infested from non-infested trees compared with the 83% accuracy obtained with a single-variable model.