Remote sensing of natural hazard-related disasters with small drones: Global trends, biases, and research opportunities

Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities. Here, we provide a critical review of drone-based remote sensing of natural hazard-related disasters to highlight research trends, biases, and expose new opportunities. We performed a systematic literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, resulting in 635 relevant articles from which we derived statistics relating to geography, drone hardware, disaster management application, and drone remote sensing data type and analysis method. Key findings include a bias towards: (i) mass movement hazards (38%); (ii) small (< 1 km2) (76%) and rural (79%) study areas in high-income countries and territories (64%); (iii) image-based observations of features from the natural environment (77%); and (iv) support of mitigation-related vulnerability assessment and risk modeling (54%) and environmental recovery (23%). We recommend that future studies focus on: (i) earthquakes, floods, and cyclones and other windstorms due to higher loss of life and economic impacts; (ii) larger and urban study areas in low, lower-middle, and upper-middle income countries and territories to support vulnerable populations; (iii) under-demonstrated (and especially response-related) disaster management activities, which generally require observations of built features from urban environments; and (iv) data standards for integrating drone-based remote sensing with international disaster management methodologies. •Of 635 studies, 87% performed mitigation- and recovery-related remote sensing.•Mass movements were the most commonly examined hazard type (38% of studies).•Study areas were small (76%), rural (79%), and high-income (64%).•We recommend more research on earthquakes, floods, and cyclones/windstorms.•More response-related studies in larger, urban, and lower-income areas are needed.