Figure 1: Schematic summarising the key functions for processing groups of images (left) or a single image (right).
ThermStats is designed for biologists using thermography to quantify
thermal heterogeneity. It uses the
Thermimage package (Tattersall,
2019) to batch process data from
FLIR thermal cameras, and takes inspiration from FRAGSTATS (McGarigal et
SDMTools (VanDerWal et al.,
2014), Faye et al.
(2016) and Shi et al.
(2016) to facilitate the calculation of
various metrics of thermal heterogeneity for any gridded temperature
The package is available to download from GitHub using
Once loaded, the code below can be followed step-by-step.
Data are extracted from FLIR images using
batch_extract. This is a
batch implementation of the
readflirJPG function from
requires only the path to the directory of FLIR thermal images, and the
freely available external software
Besides raw data, this step also retrieves camera-specific calibration
parameters which are required later to convert raw data to temperature
# Batch extract thermal images included in ThermStats installation flir_raw <- batch_extract(in_dir = system.file("extdata", package = "ThermStats"), write_results = FALSE)
Raw data are encoded in each thermal image as a 16 bit analog-to-digital
signal, which represents the radiance received by the infrared sensor.
batch_convert converts these raw data to temperature
values using equations from infrared thermography, via a batch
implementation of the function
Thermimage. It uses the
calibration constants extracted in
batch_extract and environmental
parameters defined by the user:
# Define raw data raw_dat <- flir_raw$raw_dat # Define camera calibration constants dataframe camera_params <- flir_raw$camera_params # Define metadata metadata <- flir_metadata # Create vector denoting the position of each photo within metadata photo_index <- match(names(raw_dat), metadata$photo_no) # Batch convert flir_converted <- batch_convert( raw_dat = raw_dat, # Emissivity = mean of range in Scheffers et al. 2017 E = mean(c(0.982,0.99)), # Object distance = hypotenuse of right triangle where # vertical side is 1.3 m (breast height) & angle down is 45° OD = (sqrt(2))*1.3, # Apparent reflected temperature & atmospheric temperature = # atmospheric temperature measured in the field RTemp = metadata$atm_temp[photo_index], ATemp = metadata$atm_temp[photo_index], # Relative humidity = relative humidity measured in the field RH = metadata$rel_humidity[photo_index], # Calibration constants from 'batch_extract' PR1 = camera_params[,"PlanckR1"], PB = camera_params[,"PlanckB"], PF = camera_params[,"PlanckF"], PO = camera_params[,"PlanckO"], PR2 = camera_params[,"PlanckR2"], # Whether to write results or just return write_results = FALSE)
Statistics can be calculated for individual thermal images (in a matrix or raster format), or across multiple images within a specified grouping. The latter is useful for sampling designs where multiple images are collected at each sampling event to capture temperature across a wider sampling unit, such as a plot. In either case, statistics can include summary statistics specified by the user – for example, mean, minimum and maximum – as well as thermal connectivity (based on the climate connectivity measure of McGuire et al., 2016) and spatial statistics for hot and cold spots, identified using the G* variant of the Getis-Ord local statistic (Getis and Ord, 1996).
For an individual image,
get_stats requires the user to specify the
image and the desired statistics. Statistics can be calculated for
geographic temperature data, in which case the user should also define
the extent and projection of the data.
flir_stats <- get_stats( # The temperature dataset img = flir_converted$`8565`, # The ID of the dataset id = "8565", # Whether or not to calculate thermal connectivity calc_connectivity = FALSE, # Whether or not to identify hot and cold spots patches = TRUE, # The image projection (only relevant for geographic data) img_proj = NULL, # The image extent (only relevant for geographic data) img_extent = NULL, # The data to return return_vals = c("df", # Temperature data as dataframe "patches", # Patch outlines "pstats"), # Patch statistics dataframe # The summary statistics of interest sum_stats = c("median", "SHDI", "perc_5", "perc_95"))
For grouped images,
stats_by_group requires the user to supply a list
of matrices or a raster stack, and (optionally) the metadata and the
name of the variable in the metadata that defines the grouping. Table 1
shows the metadata used in the code snippet, where photo number
(‘photo_no’) defines individual temperature matrices, and the replicate
identity (‘rep_id’) defines the grouping of photos. There are two
replicates, ‘T7P1’ and ‘T7P2’, and each has two associated photos.
stats_by_group. photo_no rep_id atm_temp rel_humidity 8565 T7P1 24.00 96 8583 T7P1 24.00 96 8589 T7P2 23.25 98 8613 T7P2 23.50 96
By default, both
stats_by_group return a dataframe
with patch statistics (Table 2) for each image or group, respectively.
stats_by_group, which implements
get_statswithin groups. img_median img_perc_5 img_perc_95 img_SHDI hot_shape_index hot_aggregation 23.5 23 24.5 1.16 7.54 0.895 24.0 23 25.0 1.68 7.80 0.855
In addition to patch statistics,
get_stats can return (1) the
temperature dataset in a dataframe format, and (2) a
SpatialPolygonsDataFrame of its hot and cold spots. The function
plot_patches can then recreate the original thermal image overlaid
with outlines of hot and cold spots, as well as the temperature
plot_distribution = TRUE (Figure 2).
plot_patches( # The raw temperature data df = flir_stats$df, # The patch outlines patches = flir_stats$patches)
Figure 2: The output of
plot_patches includes a histogram and the
original temperature data overlaid with outlines of hot and cold spots,
identified using the G* variant of the Getis-Ord local statistic.
Faye, E., Rebaudo, F., Yánez-Cajo, D., Cauvy-Fraunié, S., Dangles, O., 2016. A toolbox for studying thermal heterogeneity across spatial scales: From unmanned aerial vehicle imagery to landscape metrics. Methods in Ecology and Evolution 7, 437–446. doi:10.1111/2041-210X.12488
Getis, A., Ord, J.K., 1996. Local spatial statistics: An overview. Spatial analysis: modelling in a GIS environment 374, 261–277.
McGarigal, K., Cushman, S.A., Ene, E., 2012. FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at: Http://www.umass.edu/landeco/research/fragstats/fragstats.html.
McGuire, J.L., Lawler, J.J., McRae, B.H., Theobald, D.M., 2016. Achieving climate connectivity in a fragmented landscape. Proceedings of the National Academy of Sciences 113, 7195–7200. doi:10.1073/pnas.1602817113
Shi, H., Wen, Z., Paull, D., Guo, M., 2016. A framework for quantifying the thermal buffering effect of microhabitats. Biological Conservation 204, 175–180. doi:10.1016/j.biocon.2016.11.006
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., Storlie, C., 2014. SDMTools: Species distribution modelling tools: Tools for processing data associated with species distribution modelling exercises. Available at: Https://cran.r-project.org/package=SDMTools.
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