The miniaturization and affordable production of integrated microelectronics have improved in recent years, making unmanned aerial systems (UAS) accessible to consumers and igniting their interest. Researchers have proposed UAS-based solutions for almost any conceivable problem, but the greatest impact will likely be in applications that exploit the unique advantages of the technology: work in dangerous or difficult-to-access areas, high spatial resolution and/or frequent measurements of environmental phenomena, and deployment of novel sensing technology over small to moderate spatial scales. Examples of such applications may be the identification of wetland areas and use of high-resolution spatial data for hydrological modeling. However, because of the large—and growing—assortment of aircraft and sensors available on the market, an evolving regulatory environment, and limited practical guidance or examples of wetland mapping with UAS, it has been difficult to confidently devise or recommend UAS-based monitoring strategies for these applications. This paper provides a comprehensive review of UAS hardware, software, regulations, scientific applications, and data collection/post-processing procedures that are relevant for wetland monitoring and hydrological modeling.

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Background: New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be updated at a low cost in selected study areas with newer, often higher resolution data using unmanned aerial systems (UAS) or terrestrial sensors. However, differences in spatial coverage and levels of detail often create discontinuities along the newly mapped area boundaries and subsequently lead to artifacts in results of DEM analyses or models of landscape processes. Methods: To generate a seamless updated DEM, we propose a generalized approach to DEM fusion with a smooth transition while preserving important topographic features. The transition is controlled by distance-based weighted averaging along the DEMs' blending overlap with spatially variable width based on elevation differences. Results: We demonstrate the method on two case studies exploring the effects of DEM fusion on water flow modeling in the context of precision agriculture. In the first case study, we update a lidar-based DEM with a fused set of two digital surface models (DSMs) derived from imagery acquired by UAS. In the second application, developed for a tangible geospatial interface, we fuse a georeferenced, physical sand model continuously scanned by a Kinect sensor with a lidar-based DEM of the surrounding watershed in order to computationally simulate and test methods for controlling storm water flow. Conclusions:The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.

River basins located in the Central Sudetes (SW Poland) demonstrate a high vulnerability to flooding. Four mountainous basins and the corresponding outlets have been chosen for modeling the streamflow dynamics using TOPMODEL, a physically based semi-distributed topohydrological model. The model has been calibrated using the Monte Carlo approach-with discharge, rainfall, and evapotranspiration data used to estimate the parameters. The overall performance of the model was judged by interpreting the efficiency measures. TOPMODEL was able to reproduce the main pattern of the hydrograph with acceptable accuracy for two of the investigated catchments. However, it failed to simulate the hydrological response in the remaining two catchments. The best performing data set obtained Nash-Sutcliffe efficiency of 0.78. This data set was chosen to conduct a detailed analysis aiming to estimate the optimal timespan of input data for which TOPMODEL performs best. The best fit was attained for the half-year time span. The model was validated and found to reveal good skills.

ABSTRACT:Today's methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

The objective of this paper is to present the concept of a novel system, known as HydroProg, that aims to issue flood warnings in real time on the basis of numerous hydrological predictions computed using various models.The core infrastructure of the system is hosted by the University of Wrocław, Poland. A newly-established computational centre provides in real time, courtesy of the project Partners, various modelling groups, referred to as "project Participants", with hydrometeorological data. The project Participants, having downloaded the most recent observations, are requested to run their hydrologic models on their machines and to provide the HydroProg system with the most up-to-date prediction of riverflow. The system gathers individual forecasts derived by the Participants and processes them in order to compute the ensemble prediction based on multiple models, following the approach known as multimodelling. The system is implemented in R and, in order to attain the above-mentioned functionality, is equipped with numerous scripts that manipulate PostgreSQL-and MySQL-managed databases and control the data quality as well as the data processing flow. As a result, the Participants are provided with multivariate hydrometeorological time series with sparse outliers and without missing values, and they may use these data to run their models. The first strategic project Partner is the County Office in Kłodzko, Poland, owner of the Local System for Flood Monitoring in Kłodzko County. The experimental implementation of the HydroProg system in the Nysa Kłodzka river basin has been completed, and six hydrologic models are run by scientists or research groups from the University of Wrocław, Poland, who act as Participants. Herein, we shows a single prediction exercise which serves as an example of the HydroProg performance.

Abstract. The Department of Geoinformatics and Cartography of the University of Wrocław, Poland, is host institution of a project, financed by the National Science Centre in Poland, whose objective is to predict riverflow in real-time. If inundation is predicted, the problem of the verification of the overbank flow prognosis arises. This verification can be attained by utilizing an unmanned aerial vehicle that may be used for remote sensing applications. The unmanned aerial vehicle in question can take sequential photos with the unprecedented resolution of 3 cm/pix. Both the resolution and the opportunity for frequent flights -due to the low cost of the entire operation -allow us to compare prediction maps showing the forecasted overbank flow during an extreme hydrological event with the true observation obtained from the air. Although such verification is site-and event-specific, it can provide us with an objective technique for checking our system in a spatial domain. The main part of the system, known as HydroProg, produces multimodel ensemble hydrograph predictions and compares single-model prognoses; visualizations of them are then published in a web map service. The spatial predictions, along with the aerial orthophoto images, will also be presented online so that the user is able to observe the functioning of the system. Regular research flights have been carried out in Kłodzko County since 2012. The study areas correspond to sites where our Partner, the County Office in Kłodzko (SW Poland) -owner of the Local System for Flood Monitoring in Kłodzko County -has automatic gauges, and thus spatially reflect the hydrologic observation network. The aforementioned aerial module is experimental and will be incorporated into the entire system.

ABSTRACT:With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.