Abstract
Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.
Highlights
Despite the recent and rapid increase in the number and range of Earth observing satellites [1,2,3], the temporal resolution and availability of current very high spatial resolution satellite sensors are generally not sufficient nor flexible enough for many quantitative remote sensing applications, and they are of limited use in detecting and monitoring the dynamics of surficial environmental processes
In addition to Processing, the increasing of Unmanned Aerial Systems (UAS), recent advances in sensor technologies and analytical capabilities are rapidly expanding the potential UAS insights applications
Given the research and technological advances in recent years and the rapidly evolving landscape with respect to UAS applications, the aim of this paper is to review the current state of the art in the field of UAS applications for environmental monitoring, with a particular focus on hydrological variables, such as vegetation conditions, soil properties and moisture, overland flow, and streamflow
Summary
Despite the recent and rapid increase in the number and range of Earth observing satellites [1,2,3], the temporal resolution and availability of current very high spatial resolution satellite sensors (less than 10 m) are generally not sufficient nor flexible enough for many quantitative remote sensing applications, and they are of limited use in detecting and monitoring the dynamics of surficial environmental processes. Technology have created an alternative monitoring platform that provides an opportunity to capture the spatial, spectral, and temporal requirements across a range of applications with relatively small investment They offer high versatility, adaptability, and flexibility compared with manned airborne systems or satellites, and have the potential to be rapidly and repeatedly deployed for high spatial and temporal resolution data [6]. Beyond allowing the high spatial and temporal resolutions needed for many applications, UAS-mounted sensors have several additional advantages which are key across a range of applications They provide rapid access to environmental data, offering the near real-time capabilities required in many applications.
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