Leafy spurge is a designated noxious weed. Accurate mapping and monitoring of this species are needed to understand leafy spurge's extent and spread. Current methods are based on ground crews who survey patches. Development of an affordable technique to map and monitor leafy spurge would contribute to the control of this species. High spatial, temporal, and spectral resolution imagery was used to classify the amount of leafy spurge present with ground and aerial-based imagery. A proof of concept study was performed in 2008 using ground-based images of an area infested with leafy spurge. This proof of concept project guided the development of the methods to be used for the 2009 aerial portion of the study. Thirty-five randomly selected reference points were selected in a range area in southwest Montana. These reference points were ground surveyed to record the density of leafy spurge in a 0.5-m radius area around the reference point. Images were captured approximately 108-m from the study area and classified using random forest classification. Multiple images were collected throughout the summer in order to determine at which time period leafy spurge is most easily detected. A classification using multiple image dates was also performed to determine if a time series of images improves classification. Single date accuracies were highest late in the summer with the highest single date classification achieving 83% accuracy. The multiple date classification significantly increased overall accuracy. Several aerial images were acquired in southwest Montana over the 2009 summer. Fifty randomly selected 2-m x 2-m reference areas were surveyed for percent cover of leafy spurge as well as several other variables. Aerial images were collected at flight elevations between 300-m to 460-m. Classifications were performed using random forest classifier, and both single date and multiple date classifications were performed. Leafy spurge was most accurately detected early and late in the growing season, and significant classification accuracy increases were observed with the multiple date classification. Single date accuracies achieved 90% accuracy in early June, while multiple date classifications achieved over 96% accuracy.

Mixture Tuned Matched Filtering (MTMF) techniques were applied to hyperspectral data to locate leafy spurge in remote areas near Swan Valley, Idaho. Leafy spurge infestations are typically small in Swan Valley, and of three hyperspectral flight lines acquired for this study, only one had a quality training area. Data was processed in mosaic form to maximize the applicability of existing training data. Additionally, an index was developed that utilized the bivariate relationship between output matched filter and infeasibility scores to reduce false positive classifications further and increase overall accuracy. Leafy spurge was successfully mapped to a maximum overall accuracy between 74% and 94% with image-derived endmembers and a variety of classification and accuracy assessment strategies. This study also assessed the ability of MTMF to differentiate sub-pixel abundances, with a maximum accuracy of 83%.

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future.