Robot-assisted measurement for hydrologic understanding in data sparse regions

This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as “tanks”) at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

Remote sensing technologies are integral to monitoring the mountain cryosphere in a warming world. Satellite missions and field-based platforms have transformed understanding of the processes driving changes in mountain glacier dynamics, snow cover, lake evolution, and the associated emergence of hazards (e.g. avalanches, floods, landslides). Sensors and platforms are becoming more bespoke, with innovation being driven by the commercial sector, and image repositories are more frequently open access, leading to the democratisation of data analysis and interpretation. Cloud computing, artificial intelligence, and machine learning are rapidly transforming our ability to handle this exponential increase in data. This review therefore provides a timely opportunity to synthesise current capabilities in remote sensing of the mountain cryosphere. Scientific and commercial applications were critically examined, recognising the technologies that have most advanced the discipline. Low-cost sensors can also be deployed in the field, using microprocessors and telecommunications equipment to connect mountain glaciers to stakeholders for real-time monitoring. The potential for novel automated pipelines that can process vast volumes of data is also discussed, from reimagining historical aerial imagery to produce elevation models, to automatically delineating glacier boundaries. Finally, the applications of these emerging techniques that will benefit scientific research avenues and real-world societal programmes are discussed