This study investigates the utility of an off-the-shelf, consumer-grade unmanned aerial vehicle (UAV) for invasive species mapping in a lacustrine fringe environment. Specifically, this work sought to determine whether such a UAV would be capable of creating accurate maps of the extent of patches of an invasive plant, yellow flag iris (Iris pseudacorus L.), more efficiently than could be accomplished by a traditional field survey, which is often considered in the literature to provide the most accurate maps. The study was conducted at two lakes in the central interior of British Columbia. The UAV used in this study was a DJI Phantom 3 Professional that can acquire images using the built-in 12.4 MP digital camera. This UAV was selected because it is representative of the type of aerial platform that is easily accessible to invasive plant managers in terms of cost, ease of use, and lack of legal restrictions. Three methods of mapping the yellow flag iris were compared: (1) field survey, (2) manual interpretation of the raw UAV-acquired imagery and the orthoimage created from these data, and (3) pixel-based classification of the orthoimage created from the UAV imagery using a random forest classifier. The results revealed that, at both lakes considered, manual interpretation of the UAV-acquired imagery produced the most accurate maps of yellow flag iris infestation, with a false-positive and false-negative classification rate of less than 1%.

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.

This study investigates the combination of image processing and supervised classification to identify invasive yellow flag iris (YFI; Iris pseudacorus) plants in images collected by an un-calibrated, visible-light camera carried aloft by an unmanned aerial vehicle. Specifically, the image-processing steps of colour thresholding, template matching, and/or de-speckling prior to training a supervised random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within an image. The impacts of performing feature selection prior to training the random forest classifier are also explored. This analysis demonstrates the importance of image processing when preparing images for classification and reveals that applying the image-processing steps of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI plants using spectral and textural features provided the best results.

Radio frequency identification (RFID) provides a simple and inexpensive approach for examining the movements of tagged animals, which can provide information on species behavior and ecology, such as habitat/resource use and social interactions. In addition, tracking animal movements is appealing to naturalists, citizen scientists, and the general public and thus represents a tool for public engagement in science and science education. Although a useful tool, the large amount of data collected usingRFIDmay quickly become overwhelming. Here, we present an R package (feedr) we have developed for loading, transforming, and visualizing time‐stamped, georeferenced data, such asRFIDdata collected from static logger stations. Using our package, data can be transformed from rawRFIDdata to visits, presence (regular detections by a logger over time), movements between loggers, displacements, and activity patterns. In addition, we provide several conversion functions to allow users to format data for use in functions from other complementary R packages. Data can also be visualized through static or interactive maps or as animations over time. To increase accessibility, data can be transformed and visualized either through R directly, or through the companion site:http://animalnexus.ca, an online, user‐friendly, R‐based Shiny Web application. This system can be used by professional and citizen scientists alike to view and study animal movements. We have designed this package to be flexible and to be able to handle data collected from other stationary sources (e.g., hair traps, static very high frequency (VHF) telemetry loggers, observations of marked individuals in colonies or staging sites), and we hope this framework will become a meeting point for science, education, and community awareness of the movements of animals. We aim to inspire citizen engagement while simultaneously enabling robust scientific analysis.

Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.