Airborne LiDAR and Aerial Imagery to Assess Potential Burrow Locations for the Desert Tortoise (Gopherus agassizii)

Abstract

Abstract:The Southwestern United States desert serves as the host for several threatened and endangered species, one of which is the desert tortoise (Gopherus agassizii). The goal of this study was to develop a fine-scale, remote-sensing-based approach that indicates favorable burrow locations for G. agassizii in the Boulder City (Nevada) Conservation Easement area (35,500 ha). This was done by analyzing airborne LiDAR data (5-7 points/m 2 ) and color imagery (four bands, 0.15-m resolution) and determining the percent vegetation cover; shrub height and area; Normalized Difference Vegetation Index (NDVI); and several geomorphic characteristics including slope, azimuth, and roughness. Other field data used herein include estimates of canopy area and species richness using 1271 line transects, and shrub height and canopy area using plant-specific measurements of~200 plants. Larrea tridentata and Ambrosia dumosa shrubs were identified using an algorithm that obtained an optimum combination of NDVI and average reflectance of the four bands (IR, R, G, and B) from pixels in each image. The results, which identified more than 65 million shrubs across the study area, indicate that percent vegetation cover from aerial imagery across the site (13.92%) compared favorably (14.52%) to the estimate obtained from line transects, though the LiDAR method yielded shrub heights approximately 60% those of measured shrub heights. Landscape and plant properties were combined with known locations of tortoise burrows, as visually observed in 2014. Masks were created using roughness coefficient, slope percent, azimuth of burrow openings, elevation, and percent vegetation cover to isolate areas more likely to host burrows. Combined, the masks isolated 55% of the total survey area, which will help target future field surveys. Overall, the approach provides areas where tortoise burrows are more likely to be found, though additional ecological data would help refine the overall method.

Efficacy of remote sensing technologies for burrow count estimates of a rare kangaroo rat

“…Although this approach would be a more expensive option, a customizable airborne campaign could collect <5 cm imagery over the entire suitable area and, in addition, provide topographic information from advanced laser-based systems such as Light Detection and Ranging (LiDAR). Point-cloud products from LiDAR would provide multiple return data that could help separate vegetation structure from mound topographic properties and predict burrow presence on the ground (Young et al 2017).…”

A dynamic and evidence-based approach to mapping burn potential

“…It provides high-resolution vertical measures of plants and other aboveground objects and can also produce detailed ground elevation models (Polat and Uysal 2018;Zhao et al 2021). These outputs, when analysed in conjunction with high-resolution optical imagery, have been used to produce accurate vegetation cover maps (Young et al 2017). Globally, large amounts of high-resolution (~50 cm) satellite imagery are now available, which provides another potential source of reference data.…”

Area-Wide Prediction of Vertebrate and Invertebrate Hole Density and Depth across a Climate Gradient in Chile Based on UAV and Machine Learning

“…If burrows were not visible in the images because they were partly or completely overlaid by vegetation, previous authors did not estimate the density and depth of burrows, but only defined areas with or without burrows. In these cases, MaxEnt models [29] based on predictors from vegetation [30] or Digital Surface Models (DSMs) generated from UAV images [31] have been applied. However, beyond hole detection, none of the previous studies has estimated the area-wide density and depth of holes created by all vertebrate and invertebrate burrowing animals, regardless of the vegetation coverage and taxa, or tested their approach in several climate zones.…”