Lidar remote sensing variables predict breeding habitat of a Neotropical migrant bird

Abstract

Abstract. A topic of recurring interest in ecological research is the degree to which vegetation structure influences the distribution and abundance of species. Here we test the applicability of remote sensing, particularly novel use of waveform lidar measurements, for quantifying the habitat heterogeneity of a contiguous northern hardwoods forest in the northeastern United States. We apply these results to predict the breeding habitat quality, an indicator of reproductive output of a well-studied Neotropical migrant songbird, the Blackthroated Blue Warbler (Dendroica caerulescens). We found that using canopy vertical structure metrics provided unique information for models of habitat quality and spatial patterns of prevalence. An ensemble decision tree modeling approach (random forests) consistently identified lidar metrics describing the vertical distribution and complexity of canopy elements as important predictors of habitat use over multiple years. Although other aspects of habitat were important, including the seasonality of vegetation cover, the canopy structure variables provided unique and complementary information that systematically improved model predictions. We conclude that canopy structure metrics derived from waveform lidar, which will be available on future satellite missions, can advance multiple aspects of biodiversity research, and additional studies should be extended to other organisms and regions.

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

“…For example, for the black-throated blue warbler (Dendroica caerulescens) in the northern hardwood forests of Hubbard Brook, New Hampshire, canopy height, elevation and canopy complexity were found to be key characteristics of frequently occupied habitat [32]. Horizontal structure (i.e., relative tree canopy cover) was demonstrated to be a key habitat variable determining the presence of capercaillie (Tetrao urogallus) in a forest reserve in the Swiss Pre-Alps [8].…”

“…Other frequently extracted canopy structure metrics from airborne lidar have included measures of skewness and kurtosis, height percentiles, and the percentage of returns (or return energy) within specified height bands [5,19]. More developed metrics include canopy cover or closure, canopy permeability or penetration ratio, foliage height diversity, and vertical distribution ratio [15,32]. The use of more complex canopy structure metrics has become more prevalent as studies have progressed from using lidar data in the form of rasterised canopy height models (CHMs) to working directly with terrain-normalised point clouds.…”

Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network

“…The 3D model gives the opportunity to test such relations at every level of the canopy for instance in the study of epiphytes which biomass and diversity were shown to be related to air humidity in our study zone Obregon et al 2011). Prediction of micro-climatic conditions can also serve to define habitat for animals (Goetz et al 2010).…”

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

“…The addition of passive data was often to represent compositional elements and/or patch dynamics to complement the forest structure represented by the active remote sensing metrics. Several of the studies compared leaf-on and leaf-off passive sensor images to differentiate between deciduous and coniferous forest patches (Goetz et al, 2010;Swatantran et al, 2012;Farrell et al, 2013). Data fusion is not always limited to one active and one passive sensor.…”