A photographic transect method for field measurement of vegetation cover along an ecotone gradient in a desert environment

Vegetation cover is essential to the ecologic and biogeochemical functioning of drylands. These systems are marked by spatiotemporal variation in vegetation cover and biomass. Regular monitoring of vegetation cover in drylands is critical for their conservation and management. This study examines the effectiveness of photo-based grid point intercept field survey for quantifying vegetation characteristics in drylands. The field data provides ground truth data for space borne remote sensing to analyze changes in vegetation cover. The study integrates rapid field photo sampling, GPS, and GIS to maximize raw data collection in the field and allow for subsequent sampling in the laboratory setting. Paired T-tests and simple linear regression were employed to assess sample size. Quantification of vegetation cover using minimum distance (MD), spectral angle mapper (SAM), and support vector machine (SVM) automated classification were assessed using visual sampling as a reference. Fifty sampling points are sufficient for quantifying vegetation cover of steppe, but are less adequate for desert steppe. The assessment of automated classifications of photo plots revealed that SAM outperformed both MD and SVM. Photo-based Grid-point Intercept provides a cost-and time-effective technique for data collection to support satellite and air platform remote sensing analysis of vegetation dynamics.

Automated summary

Vegetation cover is crucial for the functioning of drylands, and regular monitoring of its changes is important for their preservation and management. This study explores the effectiveness of photo-based grid point intercept field survey for quantifying vegetation characteristics in drylands, and suggests that it can provide a cost-and time-effective technique for data collection to support remote sensing analysis of vegetation dynamics.