Advancing remote sensing of biocrusts with drone imagery and machine learning

Biocrusts are a major ground cover type in drylands, driving ecosystem function and contributing to biodiversity at large scales. However, their small size and similar colour to background soils and vegetation make them challenging to monitor with remote sensing. We developed a simple and accurate field method for large scale surveys of biocrust, using drone imagery and machine learning, guided by visual ground survey data. We compared the accuracy of three different camera sensors- RGB, multispectral, and thermal. We used XGBoost predictive modelling to classify groundcover into six classes including three biocrust community morphology types (bare ground, cyanobacteria-lichen biocrust, crustose and foliose lichen biocrust, moss biocrust, dead vegetation, live vegetation). Visual ground-based survey data and fine-scale photography were used to ground truth drone imagery to develop training datasets. Modelled outputs demonstrated that Multispectral was the best drone camera sensor type, with the highest accuracy of 97.0 %, with NDVI the most important band for the model. When we applied the model to 50 m² plots to validate its predictions, we had similar results to visual classification from field surveys and fine-scale photographs, successfully separating biocrust from bare ground. Our relatively simple method can be applied to biocrusts using readily available, low-cost technology. Considerable opportunities exist for using this approach to provide landscape-level biocrust assessment, using remote sensing, leading to improved restoration and management of drylands for conservation.