Assessing the performance of object‐oriented LiDARpredictors for forest bird habitat suitability modeling

Abstract

Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point‐cloud (PC) area‐based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object‐oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area‐based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area‐based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO‐based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area‐based models. In sum, LiDAR‐derived PC area‐based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.

Disentangling LiDAR Contribution in Modelling Species–Habitat Structure Relationships in Terrestrial Ecosystems Worldwide. A Systematic Review and Future Directions

“…Mean height [36,37]; Mean outer canopy height [38]; 25th and 95th percentiles [39,40]; Maximum height [37,41].…”

“…Gini coefficient [48]; Simpson index [39]; Vertical gap index, measured as the total distance between individual canopy strata divided by maximum canopy height [43].…”

Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning

“…Habitat structure has also many indirect effects on invertebrates, for example by influencing microclimate, light availability and floristic composition (Aguirre‐Gutiérrez et al., 2017; Davies & Asner, 2014; Müller et al., 2014). The fine‐scale habitat suitability of invertebrates is typically driven by various aspects of vegetation structure, including vertical vegetation complexity (e.g., the density of specific strata), horizontal heterogeneity (e.g., canopy roughness) or the horizontal structure of vegetation at the landscape scale (e.g., the extent of edges and open spaces; Bakx et al., 2019; Davies & Asner, 2014; Glad et al., 2020; Simonson et al., 2014). Despite many local field studies on butterfly–habitat relationships, the generality of these relationships remains unclear because quantifying vegetation structure across broad spatial extents has often been limited by the difficulty to obtain detailed, high‐resolution data in a standardized, comparable and spatially contiguous way (Davies & Asner, 2014; Kissling et al., 2017; Valbuena et al., 2020).…”

Detecting overmature forests with airborne laser scanning (ALS)

“…The second type of metrics, object‐oriented metrics, relies on the use of geometrical criteria to identify objects such as trees, gaps and edges in the point cloud. It is also possible to derive summary statistics from these objects to supplement a set of explanatory variables (Glad et al., 2020). However, object‐oriented metrics, such as the number of detected trees of a certain height or the proportion of area covered by gaps, do not necessarily improve model predictive power (Marchi et al., 2018).…”

“…Applications to other ecosystems services, e.g . protection from rockfalls (Monnet et al., 2017) or biodiversity mapping (Bouvier et al., 2017; Glad et al., 2020), have also been tested. Using LiDAR data to identify mature forests is a recent technique that has mainly focused on the detection of one specific maturity attribute: deadwood.…”