DOI: 10.5061/dryad.m905qfv9s
title: Data and code for: A novel method for mapping high-precision animal locations using high-resolution imagery

description: Investigating ecological questions at the scale of individual organisms is
 necessary to understand and predict the biological consequences of
 changing environmental conditions. For small organisms this can be
 challenging because ecologists need tools with the appropriate accuracy,
 precision, and resolution to record and quantify their ecological
 interactions. Unfortunately, many existing tools are only appropriate for
 medium to large organisms or those that are wide ranging, inhibiting our
 ability to investigate the spatial ecology of small organisms at fine
 scales. Here, we tested a novel workflow for recording animal locations at
 very fine (decimeter) spatial scales, which we refer to as High-resolution
 Orthomosaic Location Recording (HOLR). The workflow for HOLR combined
 direct observations with data collection of locations on high-resolution
 uncrewed aerial vehicle (UAV) imagery loaded on smartphones. Observers
 identified landscape features they recognized in the imagery and estimated
 positions relative to these visual landmarks. We found HOLR was
 approximately twice as accurate as consumer-grade GPS devices, with a mean
 error of 0.75 m and a median error 0.30 m. We also found that performance
 varied across landscape features, with the highest accuracy in areas that
 had more visual landmarks for observers to use as reference points. In
 addition to sub-meter accuracy, HOLR was cost-effective and practical in
 the field, requiring no bulky equipment and allowing observers to easily
 record locations away from their own location. This workflow can be used
 to record locations in a variety of situations, but it will be
 particularly cost-effective when users simultaneously utilize the
 high-resolution environmental data contained within UAV imagery. Together,
 these tools can expand the application of spatial ecology research to
 smaller organisms than ever before.
description: Target Deployment We deployed 2.5 cm x 15 cm
 painted copper pipes across the study site. These targets were operative
 thermal models (OTMs) that were primarily being deployed to measure
 temperature across the study site as part of a separate study, but they
 made suitable targets because the pipes were designed to approximate the
 size and appearance of a small, elongate animal, such as a lizard. We
 deployed approximately 28 targets in each of four sessions, each lasting
 roughly 4 weeks (total n = 111). We used a stratified random design to
 ensure the targets were located in or around a variety of landscape
 features present at the site, including on <em>D. ingens</em>
 burrow mounds (n = 63), off <em>D. ingens</em> burrow mounds
 (n = 12), under shrubs (n = 24), and in a wash (n = 12; Figure 5). The
 off-burrow feature types had few easily identifiable visual landmarks that
 observers could use while mapping locations (Figure 6a and b). The
 on-burrow feature type had some potential landmarks, including burrow
 entrances and disturbed dirt, but these were sometimes very similar to one
 another, reducing their utility as visual landmarks (Figure 6a, b and e).
 The shrub feature type always had at least one shrub present, but often
 more than one shrub was nearby, providing a number of landmarks to use
 when mapping (Figure 6c and e). Finally, the wash feature type had many
 visual landmarks, including shrubs, large rocks, and clear banks and
 ledges (Figure 6d and e). While most of the targets used were placed
 individually, the wash sites were placed in four groups of three (one
 group per session), with one target in the middle of the wash channel and
 one on each bank to better capture the effect of these topographic changes
 on temperature for a separate study. These targets were far enough apart
 that recording the location of one did not influence where an observer
 would record the other wash targets, so they were treated as independent
 targets. We used the Eos Arrow Gold RTK GNSS receiver to record the
 locations of all targets. All locations were recorded in ArcGIS Collector,
 averaging across 30 locations, and had an estimated error of 3 cm or
 less. Target Mapping Two observers
 recorded the locations of all targets at the beginning of each of the four
 sessions. This design allowed for the comparison of accuracy between
 observers and through time. Observers worked independently, without
 communicating about visual references around target locations during the
 survey. One observer had extensive prior experience using the Avenza app
 and a small amount of experience mapping onto UAV imagery from a pilot
 study. The other observer only had a brief training session on the app
 prior to this work, but otherwise had the education and experience typical
 of an ecology research assistant. We used this difference in experience to
 investigate the influence of experience on accuracy. In addition to
 mapping the target locations every four weeks (i.e., mapping sessions
 1-4), both observers used the same HOLR method to regularly record
 locations of radio-collared <em>G. sila</em> at the study
 site, and so were gaining mapping experience for the duration of the
 study. The observers treated the targets as if they were live animals,
 staying at least 8 m away from the targets while recording their
 locations. It is important to avoid disturbing study organisms when
 collecting animal location data to avoid influencing their behavior or the
 data collected.             To provide a comparison to
 the most common method for recording locations in the field, we also
 recorded the location of each target using a Garmin eTrex Vista handheld
 GPS receiver for the first three sessions. During the third session this
 unit failed, so we switched to an eTrex Legend H (Garmin Ltd., Olathe, KS,
 USA) for the remainder of session 3 and all of session 4, such that we
 recorded 58 locations with the Vista and 53 with the Legend H. All
 locations were collected with the GPS receiver approximately two meters
 above the ground, clear of any obstructions. The receiver was held
 horizontally in this position for approximately ten seconds before
 collecting the location as a single point. We chose this approach to
 balance the desire for optimal GPS performance with the time constraints a
 researcher typically faces when tracking animals in the field. Full
 details on the HOLR workflow are available in supplemental information
 (Appendix S1). Accuracy Analysis
 Due to low sample sizes, we used summary statistics and visual
 assessments of the data to compare the accuracy of the GPS receivers
 against the two observers using HOLR. We used linear models and AIC scores
 to assess the influence of three variables on HOLR accuracy (Burnham and
 Anderson 2002). The distribution of error values was right skewed, so we
 used the natural log of error as the response variable for the models
 (Appendix S2). To explore how the presence of different landscape features
 might affect HOLR users’ performance, we included a categorical variable
 of landscape feature type with four levels (i.e., shrubs, wash,
 on-burrows, and off-burrows). We included observer as a categorical
 variable with two levels, to control for any differences in performance.
 Finally, to assess changes in performance over the duration of the study,
 we included session as a continuous variable. We created seven total
 models, using each unique combination of the three predictor variables. We
 used AIC scores to rank the models against each other, with lower scores
 indicating better models (Burnham and Anderson 2002).
 Other Processing Notes: The spatial data
 (Avenza, GNSS and GPS) is not processed other than the GNSS and GPS files
 merged across several collecting sessions. The OTM GPS Locations.csv file
 combines the location, target, session and device used for all GPS
 locations. The OTM Error Independent Long.csv file has the error for all
 mapped and GPS locations calculated in R, with targets that were close to
 another (i.e., not independent) removed and the data arranged in long
 format. See included R scripts for more processing details.
description: # HOLR Test Location Data and Code
 [https://doi.org/10.5061/dryad.m905qfv9s](https://doi.org/10.5061/dryad.m905qfv9s) This dataset includes data and code used to test the efficacy of the high-resolution orthomosaic location recording (HOLR) method. HOLR was designed as a means of collecting sub-meter location data in the field with less equipment and expense than a sub-meter GNSS device requires, making it more practical for ecological research applications. Test location data was collected using HOLR, consumer-grade GPS receiver (as a comparison) and a high-accuracy global navigation satellite system (GNSS) receiver (the "true location"). ## Description of the data and file structure Included here are the spatial data as shapefiles (.shp) files for the three types of location data (HOLR, GPS and GNSS), two .csv files that were produced as intermediate steps during data analysis (*OTM Error Independent Long.csv* and *OTM GPS Locations.csv*), and the R code used to process, analyze and visualize this data. Shapefiles (.shp) used in this study contain the geometry and attributes of geospatial features (e.g., points, lines, polylines, polygons). The file bundle contains the main file .shp and companion files including: .cpg, .dbf, .prj, .sbn, .sbx, .shx. To view the data you can open the .shp file with QGIS or similar. The *Avenza Data* folder holds shapefiles of the locations (points) collected by two observers (IA, LP) using the HOLR method across four test sessions (numbered 1-4); the *GPS OTM All Sessions* shapefile contains all the consumer-grade GPS locations (points) collected, and the *GNSS OTM Combined* shapefile contains all the high-accuracy GNSS locations (points) collected. **Variable Descriptions:** *OTM Error Independent Long.csv* * Site - Unique ID for each target site * Error - Distance in meters between location estimate and the actual location as measured by the high-accuracy GNSS reciever. * Session - Which session (1-4) the location estimate was collected in * Habitat - Landscape feature type class (Shrub, Wash, Precinct or Off-Precinct) * Observer - Which observer or device estimated the location * Type - Whether the estimate used HOLR (Mapping) or not (GPS) *OTM GPS Locations.csv* * Point - Target ID as recorded in the field (Note: some IDs were automatically converted to dates by Excel but this collumn is not necessary for the analyses; it has been retained so the R scripts work correctly.) * Easting - Easting in meters using EPSG: 26911 * Northing - Northing in meters using EPSG: 26911 * site_num - Unique ID assigned to the target site (matches 'Site' variable in the OTM Error Independent Long.csv file) * Session - Which session (1-4) the location estimate was collected in * Device - Which GPS receiver collected the location estimate *GPS OTM ALL Sessions.shp* * Point - Target ID as recorded in the field (Note: some IDs were automatically converted to dates by Excel but this collumn is not necessary for the analyses) * Easting - Easting in meters using EPSG: 26911 * Northing - Northing in meters using EPSG: 26911 * site_num - Unique ID assigned to the target site (matches 'Site' variable in the OTM Error Independent Long.csv file) * Session - Which session (1-4) the location estimate was collected in * Device - Which GPS receiver collected the location estimate *GNSS OTM Combined.shp* Note: this file has the raw output of the GNSS receiver collected using ESRI's now deprecated ArcGIS Collector app. Most collumns are full of zeros or other repeated values that are not used in any analysis. Defined below are the columns with relevant data. For more information on ESRI GNSS data fields see the ArcGIS Pro *Add GPS Metadata Fields (Data Management)* article at [https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/add-gps-metadata-fields.htm](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/add-gps-metadata-fields.htm) * esrignss_p - Position source type (0 - unknown, 1 - user defined, 2 - integrated (system) location provider, 3 - external GNSS receiver, 4 - network location provider) * esrignss_r - Receiver name * esrignss_l - Latitude in EPSG: 6318 (Note: the locations were collected in EPSG: 6318 by the GNSS and converted into EPSG: 3857 by the ArcGIS Collector app. The data was converted into EPSG:26911 during processing which is what the shapefile is in.) * esrignss_1 - Longitude in EPSG: 6318 (Note: the locations were collected in EPSG: 6318 by the GNSS and converted into EPSG: 3857 by the ArcGIS Collector app. The data was converted into EPSG:26911 during processing which is what the shapefile is in.) * esrignss_a - Altitude in EPSG: 6318 * esrignss_6 - Date the location was collected (Note: the 'CreationDa' and 'EditDate' columns have the same dates) * esrignss_7 - Average horizontal accuracy in meters * esrignss_8 - Average vertical accuracy in meters * esrignss_9 - Number of positions averaged * esrigns_10 - Standard deviation in meters. * site_num - Unique ID assigned to the target site (matches 'Site' variable in the OTM Error Independent Long.csv file) * Session - Which session (1-4) the location estimate was collected in * Habitat - Landscape feature type class (Shrub, Wash, Precinct or Off-Precinct) * AllMapped - Binary indicating whether both observers and one of the GPS receivers all estimated that target's location or not (1 is yes, 0 is no). Targets that were not mapped by all three were excluded from analyses. ## Sharing/Access information Data was derived from the following sources: * HOLR method using the Avenza Maps app with drone imagery (1.2 cm ground sampling distance or pixel size) * Consumer-grade GPS devices (Garmin eTrex Vista and Garmin eTrex Legend-H) * Eos Gold high-accuracy GNSS device with ESRI's ArcGIS Collector app ## Code/Software Two R scripts are included, numbered in order that they were used. The first processes the spatial data into a usable format, produces some intermediate .csv files and creates some preliminary figures. The second completes a formal accuracy analysis and a variaty of figures illustrating the analysis results.