Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Abstract

Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground-based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite-based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high-resolution imagery from Unmanned Aerial Systems (UAS) to map fine-scale patterns of biocrusts. We collected sub-centimeter, true color 3-band imagery at 10 plots in sagebrush and pinyon-juniper woodland communities in a semiarid ecosystem in the southwestern US and used objectbased image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon-juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon-juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon-juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon-juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

Measuring reflectance of tiny organisms: The promise of species level biocrust remote sensing

“…textual variables, spectral indices and feature reduction approaches; Fassnacht et al, 2016). Initially, due to the small size of biocrust organisms, we suggest using low flying drones or portable frames to collect spectra within small plots, in conjunction with fieldbased sampling for validation (see Havrilla et al, 2020;Lopatin et al, 2017). This may involve using the pure signatures of species collected in this study to understand their contribution to the overall remote sensing reflectance (e.g.…”

Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland

“…With possible spatial resolutions in the range of centimeters, UAV-based multispectral and thermal observations of the study would enable for distinguishing individual grey hair-grass tussocks from BSCs, or even different types of biological soil crusts, e.g., lichen-and moss-dominated [58,59]. This information could be used for generating more precise reference data, as the protection status and the ammunition contamination do not allow accessing the study area.…”

UAV RGB, thermal infrared and multispectral imagery used to investigate the control of terrain on the spatial distribution of dryland biocrust

“…However, remote sensing of biocrusts has traditionally been relegated to other platforms, such as airborne (e.g., Weber et al, 2008 ; Rodríguez‐Caballero et al, 2014 ) and satellite sensors (e.g., Panigada et al, 2019 ). Only recently the potential of red, green and blue (RGB) imagery acquired from UAVs to identify dryland biocrusts has been explored (Havrilla et al, 2020 ; Jung et al, 2020 ; Sevgi et al, 2020 ). As biocrusts are difficult to distinguish from the background due to their small size and similar optical properties to vegetation and soil (Weber & Hill, 2016 ; Smith et al, 2019 ), UAVs integrated with multispectral cameras could greatly improve their identification.…”