DOI: 10.5061/dryad.pnvx0k6nr
title: Data from: Evaluating the use of lidar to discern snag characteristics important for wildlife

description: Standing dead trees (known as snags) are historically difficult to map and
 model using airborne laser scanning (ALS), or lidar. Specific snag
 characteristics are important for wildlife; for instance, a larger snag
 with a broken top can serve as a nesting platform for raptors. The
 objective of this study was to evaluate whether characteristics such as
 top intactness could be inferred from discrete-return ALS data. We
 collected structural information for 198 snags in closed-canopy conifer
 forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter
 point clouds to serve as predictor variables in random forest (RF) models
 to classify snags into four groups by size (small [&lt;40 cm
 diameter] or large [≥40 cm diameter]) and intactness (intact or
 broken top) across multiple iterations. We conducted these models first
 with all snags combined, and then ran the same models with only small or
 large snags. Overall accuracies were highest in RF models with large snags
 only (77%), but kappa statistics for all models were low (0.29–0.49). ALS
 data alone were not sufficient to identify top intactness for large snags;
 future studies combining ALS data with other remotely sensed data to
 improve classification of snag characteristics important for wildlife is
 encouraged.
description: The study was conducted in closed canopy forest stands within the
 Idaho Panhandle National Forest (IPNF), Idaho, USA. Discrete-return
 airborne lidar (also airborne laser scanning, ALS) data were acquired as
 part of an ongoing effort by the U.S. Forest Service Rocky Mountain
 Research Station (USFS RMRS; Moscow, ID, USA) to develop advanced
 protocols for forest mapping and monitoring. The locations of ALS
 acquisitions were used as the basis for the two study sites (based on IPNF
 Ranger Districts): Coeur d’Alene River (CdA) and Saint Joe (StJ). The
 lidar extent of CdA spanned 78,706 ha to the east of Coeur d'Alene,
 ID, USA (centered at 47°44'40.0"N, 116°36'38.8"W). The
 lidar extent of StJ spanned 7,598 ha to the east of Avery, ID, USA
 (centered at 47°08'28.5"N, 116°05'38.9"W).
 All snags evaluated for this study were located within 25 m
 fixed-radius survey plots. Survey plot locations were selected through a
 stratified random sampling design based on canopy cover and height metrics
 from ALS and constrained using criteria to enable subsequent point count
 surveys of forest birds, including a 300 m minimum distance from other
 plot centers and a road buffer (minimum of 60 m and maximum of 250 m away
 from roads). Plot centers were recorded using a global navigation
 satellite system (Trimble GNSS) receiver and differentially corrected in
 post-processing. Snags identified within a 25 m radius survey plot were
 measured, provided they satisfied the criteria, including (1) a minimum
 height equal to breast height (used to measure diameter at breast height,
 DBH; 1.37m above ground), (2) a minimum DBH of 0.15 m, (3) less than a 45º
 lean in any direction, and (4) possessed no visible live parts of the tree
 (primarily the presence of green needles; red/brown needles were accepted
 and noted). The minimum DBH was selected based on criteria for woodpecker
 use. Snag locations were determined using the same Trimble GNSS receiver
 as used for plot center, which was positioned on the north side of the
 bole. The georeferenced ALS data were processed at the
 survey plot-level using the R environment (version 4.0.3). Point clouds
 were preprocessed using the lidR package at a 50 m-radius around plot
 centers (0.8 ha plots) to generate buffered, height-normalized point
 clouds. All ground, near-ground, and understory returns below 1.37 m were
 excluded to retain only canopy returns. Outliers below 50 m height above
 ground were not removed as they may represent snag hits. Snag center
 coordinates were calculated by subtracting half the measured DBH from the
 northing value collected in the field, to correct for the GNSS receiver
 being positioned on the north side of the bole. Snag center coordinates
 were used as locations to clip the height-normalized ALS point clouds from
 within each survey plot. Individual snag point clouds were clipped to a
 2.5 m circular radius buffer around each snag center, in order to
 encompass the full tree footprint, offset any error in GPS location, and
 preserve enough returns to compute all metrics.
 Lidar-derived metrics were calculated for each 2.5 m-radius snag
 point cloud, with a minimum height set to 1.37 m above ground. A subset of
 the full suite of standard height and return-based metrics (as provided in
 the lidR package) was chosen alongside additional structural and
 topographic metrics for this study, in order to best evaluate differences
 among the four classes of snag structural characteristics. We focused on
 lidar metrics previously shown to be effective indicators of forest
 biomass across different forest types and those which would have specific
 utility for individual snags, as compared to individual live tree or
 plot-level analyses. In total, 13 metrics were used for our
 evaluation. To analyze whether airborne lidar metrics
 could be used to discriminate between snag classes, we conducted Random
 Forest (RF) classification using the R environment and with the packages
 randomForest & caret. For our response variable, we focused on two
 aspects from field inventory data for each snag: diameter and intactness.
 We divided the candidate snags into two categories for each of these
 variables, in order to have distinct groups to compare lidar metrics
 against. Snag diameter was divided into small (DBH less than 40 cm) and
 large (DBH greater than or equal to 40 cm) groups, based on literature
 suggesting these are ecologically relevant distinctions, as well as
 important for ALS detectability. Snag intactness was divided into intact
 (where the snag bole still had a visible top) and broken (where at least
 part of the bole had broken off). Therefore, the distinct snag classes we
 used for evaluation were “small intact” (SI), “small broken” (SB), “large
 intact” (LI), and “large broken” (LB). A total of 198 snags were used for
 these analyses. For validation purposes, RF models were
 run within a bootstrap with 20 iterations. For each iteration, we drew 60%
 of the snags (n<sub>train</sub> = 118) from the total
 available snag population (n<sub>total</sub> = 198) to train
 the RF model. The remaining snags not drawn per iteration
 (n<sub>test</sub> = 80) were used for independent validation
 of the RF model. For each bootstrap iteration, confusion matrices of
 observed versus predicted classifications were used to calculate overall
 accuracy and the kappa statistic (a coefficient ranging from 0 to 1, where
 0 is equated with random chance and 1 is perfect concordance), as well as
 producer’s accuracy (inverse of omission error) and user’s accuracy
 (inverse of commission error) for each class. In
 addition to the confusion matrix, an evaluation of predictor variable
 importance was reported for the top RF model
 (RF<sub>ALL</sub>) in terms of mean decrease in the Gini Index
 (a measure of the decrease in node impurity). To evaluate the importance
 of each predictor relative to one another among the four snag classes (and
 across models), the class-specific variable importance scores were
 standardized using the Model Improvement Ration (MIR) whereby all raw
 variable importance scores are divided by the maximum.
 We conducted additional RF modeling on subsets of the snags by
 diameter, modeling only small snags (n<sub>total</sub> = 82)
 and only large snags (n<sub>total</sub> = 116) against the
 same suite of predictor variables and using the same RF parameters and
 validation methods. The only change was that classification was reduced
 from the four snag classes to only two based on snag intactness (intact
 versus broken top) for each model. The confusion matrix and predictor
 variable importance tables for the top RF models (small snags =
 RF<sub>SM</sub>; large snags = RF<sub>LG</sub>)
 were also reported, which let us further evaluate which of the 13
 variables were most important for each size class.
description: Please see ReadMe files for additional information.