DOI: 10.5061/dryad.3ffbg79j2
title: Phenology-based classification of invasive annual grasses to the species level

description: The ability to detect and map invasive plants to the species level, both
 at high resolution and over large extents, is essential for their targeted
 management. Yet development of such remote sensing methodology is
 challenged by the spectral and structural similarities among many invasive
 and native plant species. We developed a multi-temporal classification
 approach that uses unmanned aerial vehicle (UAV) imagery to map two
 invasive annual grasses to the species level, and to distinguish these
 from key functional types of native vegetation, based upon differences in
 plant phenology. For a case study area in the western Great Basin, USA, we
 intentionally over-sampled with frequent (n=9) UAV flights over the
 growing season. Using this information we compared the importance of
 spectral variation at a given point in time (i.e., with and without
 near-infrared wavelengths), with spectral variation across multiple time
 periods. We found that differences in species phenology allowed for
 accurate classification of nine cover types, including the two annual
 grass species of interest, using just three dates of imagery that captured
 species-specific differences in the timing of active growth, seed head
 production, and senescence. Availability of near-infrared imagery proved
 less important than true-color RGB imagery collected at appropriate time
 periods. Thus, multi-temporal information provides a substitute for more
 extensive spectral information obtained from a single point in time. The
 substitution of temporal for spectral information is particularly well
 suited to UAV remote sensing, where RGB image collection is inexpensive,
 and the timing can be flexible. The datasets arising from our
 multi-temporal classification approach provide high-resolution information
 for modeling patterns of invasive plant spread, for quantifying plant
 invasion risk, and for early detection of novel plant invasions when patch
 sizes are still small. Widespread application and up-scaling of our
 approach requires 42 advances in our ability to model the variability in
 phenology that occurs across years and over fine spatial scales, even
 within a single species.
description: UAV imagery was collected across the growing seasons
 of medusahead and cheatgrass between the spring thaw and the summer dry
 down from May 15th to July 20th, 2017, as frequently as every week (8
 total flights – May 15, May 19, June 1, June 7, June 15, June, July 20).
 The 5-band spectral data were collected with a Micasense RedEdge sensor
 mounted on a Tarot quadcopter UAV platform flown at an altitude of
 approximately 30m. In addition to the 5-band Micasense RedEdge sensor we
 also collected data using a 3-band true color Sony A5100 24.3-megapixel
 camera with the Blue, Green, and Red bands centered at 475, 551, and 627
 nm. Flight times were typically 45 minutes centered around solar noon and
 were based on pre-programmed flight plans using Universal Ground Control
 Software (UgCS) version 3.0.1302 software (SPH Engineering, Riga, Latvia)
 with a single battery swap and a single pass flight pattern. Photographs
 were captured ensuring 80% frontal overlap and 70% side overlap.
 The resulting data have 2-cm spatial resolution
 and spectral resolution of Blue (475 nm center, 20 nm bandwidth), Green
 (560 nm center, 20 nm bandwidth), Red (668 nm center, 10 nm
 bandwidth), Red Edge (717 nm center, 10 nm
 bandwidth), and NIR (840 nm center, 40 nm bandwidth). Eight ground control
 point tiles were deployed for the duration of the study and used to
 co-align data from different flight dates and geo-reference the image
 stack using a Trimble R10 GPS receiver\ with RTX solutions (Trimble Inc.,
 Sunnyvale, California, USA) with horizontal accuracy up to 8 mm and
 vertical accuracy up to 15 cm.
 Composite rasters were processed in Pix4D v. 3.2
 (Pix4D, Lausanne, Switzerland) using structure-from-motion (SfM)
 techniques that mosaicked images across all dates. Although Pix4D created
 a three-dimensional point cloud that is used to tie images together we did
 not use the height data in our classification of vegetation. All bands,
 across all time periods, were co-aligned in Pix4D resulting in an average
 horizontal positional precision of 1.26 cm. The eight ground control
 points obtained from the Trimble were used to georeferenced the images
 into a NAD83 UTM Zone 11 projection. Calibration to reflectance was
 achieved with an empirical line approach (Smith and Milton, 203 1999) that
 took advantage of four spectrally diverse field targets that were laid out
 prior to each flight. Reference reflectance data were collected in the
 field on July 20, 2017 during cloud-free conditions just prior to the UAV
 flight using a Spectral Evolution SR-3500 (Spectral Evolution Inc.,
 Haverhill, Massachussetts, USA). The SR-3500 collects data at 3-nm in the
 visible range. These data were resampled to match the bandwidths of the
 Micasense RedEdge and the Sony A5100 cameras and a linear regression was
 used to convert camera digital numbers to reflectance for each band
 individually. Field vegetation data was collected using sixteen 1-meter
 square quadrat frames on the day of each flight. To ensure that quadrat
 frames were visible in each image we permanently placed four metal
 five-inch spikes at the corner of each frame, and we placed the frames on
 each of the metal spikes prior to each flight. Quadrat frame locations
 were initially collected with a Trimble GeoXT GPS unit (Trimble Inc.,
 Sunnyvale, California, USA), and quadrats were re-photographed upon each
 subsequent visit. Initial vegetation measurements included estimating the
 aerial cover of each plant species as well as bare soil, rock, animal
 feces, medusahead litter, cheatgrass litter, and other plant litter. Each
 quadrat frame was classified into one of the following nine cover types
 based on its dominant vegetation type: annual forb, cheatgrass, crested
 wheatgrass, medusahead, litter, perennial forb, perennial grass, shrub,
 and bare soil. Slight differences in quadrat frame placement were
 accounted for such that pixels that had quadrat frames visible in them
 during any of the flights were removed. We randomly selected 9168 points
 from the resulting polygons to use for model training extracting each
 spectral band for each flight date. To obtain a fully independent
 validation we digitized polygons of dominant vegetation types and
 extracted 258,554 random points to use as validation. We manually
 digitized polygons using all eight dates of imagery as a background image
 coupled with extensive field knowledge of the site.
 We used the Random Forest algorithm to classify each
 pixel in the composite image into one of the nine dominant vegetation
 types. Random Forest is an ensemble decision tree classification in which
 trees are trained using bootstrapped sampling. Random Forest has been
 extensively used for image classification because of its high performance,
 lack of reliance on an underlying data distribution, and its ability to
 handle both continuous and categorical predictors. For this study we used
 the RandomForestSRC package in R. Models were run in classification mode
 using 1000 trees (ntree) and with the number of variables for splitting
 (mtry) set to the square 11 root of the number of predictors. In order to
 compare the importance of different spectral wavelengths and different
 dates on the classification accuracy we ran models for all combinations of
 dates and bands. Importance values for individual predictors (band*date
 combinations) were calculated using the mean decrease in impurity (Gini
 Index). Models were evaluated by creating a classification confusion
 matrix and assessing overall accuracy, Cohen’s kappa, and by comparing the
 overall accuracy to that of a null model in which the probability of all
 classes are equal.
description: The data are split into three folders. Occurrence contains
 training and validation points obtained from field sampling that were used
 to build and validate models derived from UAV imagery. Images contain
 orthomosaics from each of the eight flight dates and each of the five
 spectral bands. The classification folder contains the final classified
 vegetation map using the "best" three dates of imagery.  Each
 folder contains its own readme file explaining what each individual file
 is.