scholarly journals UAS for Wetland Mapping and Hydrological Modeling

2019 ◽  
Vol 11 (17) ◽  
pp. 1997 ◽  
Author(s):  
Justyna Jeziorska
Keyword(s):  
Spatial Data ◽  
Hydrological Modeling ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Regulatory Environment ◽  
Unmanned Aerial Systems ◽  
Wetland Mapping ◽  
Monitoring Strategies ◽  
Sensing Technology ◽  
Aerial Systems

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.

scholarly journals Unmanned Aerial System Imagery, Land Data and User Needs: A Socio-Technical Assessment in Rwanda

2019 ◽  
Vol 11 (9) ◽  
pp. 1035 ◽  
Author(s):  
Claudia Stöcker ◽  
Serene Ho ◽  
Placide Nkerabigwi ◽  
Cornelia Schmidt ◽  
Mila Koeva ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Land Tenure ◽  
Spatial Data ◽  
Information Needs ◽  
Institutional Environment ◽  
Social Study ◽  
Unmanned Aerial Systems ◽  
Quality Of Data ◽  
Priority List ◽  
Aerial Systems

Unmanned Aerial Systems (UAS) are emerging as a tool for alternative land tenure data acquisition. Even though UAS appear to represent a promising technology, it remains unclear to what extent they match the needs of communities and governments in the land sector. This paper responds to this question by undertaking a socio-technical study in Rwanda, aiming to determine the match between stakeholders’ needs and the characteristics of the UAS data acquisition workflow and its final products as valuable spatial data for land administration and spatial planning. A needs assessment enabled the expression of a range of land information needs across multiple levels and stakeholder sectors. Next to the social study, three different UAS were flown to test not only the quality of data but the possibilities of the use of this technology within the current institutional environment. A priority list of needs for cadastral and non-cadastral information as well as insights into operational challenges and data quality measures of UAS-based data products are presented. It can be concluded that UAS can have a significant contribution to match most of the prioritized needs in Rwanda. However, the results also reveal that structural and capacity conditions currently undermine this potential.

scholarly journals Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

2020 ◽  
Vol 12 (22) ◽  
pp. 3831
Author(s):  
Marvin Ludwig ◽  
Christian M. Runge ◽  
Nicolas Friess ◽  
Tiziana L. Koch ◽  
Sebastian Richter ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Spatial Data ◽  
Forest Canopy ◽  
Cost Effective ◽  
Temporal Analysis ◽  
Unmanned Aerial Systems ◽  
Wide Range ◽  
Very High Spatial Resolution ◽  
Multi Temporal ◽  
Aerial Systems

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.

Soil Moisture Retrievals from Unmanned Aerial Systems (UAS)

2020 ◽  
Author(s):  
ruodan zhuang ◽  
Salvatore Manfreda ◽  
Yijian Zeng ◽  
Zhongbo Su ◽  
Nunzio Romano ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Near Infrared ◽  
Spatial Scales ◽  
Standard Procedure ◽  
Thermal Inertia ◽  
Unmanned Aerial Systems ◽  
Natural Ecosystems ◽  
Resources Management ◽  
Cropland Area ◽  
Aerial Systems

<p>Quantification of the spatial and temporal behavior of soil moisture is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Unmanned Aerial Systems (UAS) offer a great potential in monitoring this parameter at sub-meter level and at relatively low cost. The standardization of operational procedures for soil moisture monitoring with UAS can be beneficial to understanding and quantify the quality of retrieved soil moisture (e.g., from different platforms and sensors).</p><p>In this study, soil moisture retrieved from UAS using different retrieval algorithms was compared to collocated ground measurements. The thermal inertia model builds upon the dependence of the thermal diffusion on soil moisture. The soil thermal inertia is quantified by processing visible and near-infrared (VIS-NIR) and thermal infrared (TIR) images, acquired at two different times of a day. The temperature–vegetation trapezoidal model is also used to map soil moisture over vegetated pixels. This trapezoidal model depicts the soil moisture dependence of the surface energy balance. The comparison of the two algorithms helps define a preliminary standard procedure for retrieving soil moisture with UAS.</p><p>As a case study, a typical cropland area with olive orchard, cherry and walnut trees in the region of Monteforte Cilento (Italy, Salerno) is used, where optical and thermal images and in situ data were simultaneously acquired. In the Alento observatory, long-term studies on vadose zone hydrology have been conducting across a range of spatial scales. Our findings provide an important contribution towards improving our knowledge on evaluating the ability of UAS to map soil moisture, in support of sustainable natural resources management and climate change studies.</p><p>This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.</p><p><strong>Keywords:</strong> soil moisture, Unmanned Aerial Systems, thermal inertia, HARMONIOUS</p>

scholarly journals BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

Agriculture ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Andrew Young ◽  
James Mahan ◽  
William Dodge ◽  
Paxton Payton
Keyword(s):  
Plant Growth ◽  
Growth And Development ◽  
Environmental Changes ◽  
Spatial Scales ◽  
Computer Software ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Plant Growth And Development ◽  
Temporal And Spatial ◽  
Aerial Systems

The use of aerial imagery in agriculture is increasing. Improvements in unmanned aerial systems (UASs) and the hardware and software used to analyze imagery are presenting new options for agricultural studies. One of the challenges associated with improving crop performance under water deficit conditions is the increased variability in the growth and development inherent in low water settings. The nature of plant growth and development under water deficits makes it difficult to monitor the response to environmental changes. Small field and plot-level experiments are often variable enough that averages of seasonal crop characteristics may be of limited value to the researcher. This variability leads to a desire to resolve fields on finer temporal and spatial scales. While UAS imagery provides an ability to monitor the crop on a useful temporal scale, the spatial scale is still difficult to resolve. In this study, an automated computer software framework was developed to facilitate resolving field and plot-level crop imagery to finer spatial resolutions. The method uses a Binary Large Object (BLOB)-based algorithm to automate the generation of areas of measurement (AOMs) as a tool for crop analysis. The use of the BLOB-based system is demonstrated in the analysis of plots of cotton grown in Lubbock, Texas, during the summer of 2018. The method allowed the creation and analysis of 1133 AOMs from the plots and the extraction of agronomic data that described plant growth and development.

scholarly journals Analysis of the Suitability of High-Resolution DEM Obtained Using ALS and UAS (SfM) for the Identification of Changes and Monitoring the Development of Selected Geohazards in the Alpine Environment—A Case Study in High Tatras, Slovakia

2020 ◽  
Vol 12 (23) ◽  
pp. 3901
Author(s):  
Ľudovít Kovanič ◽  
Peter Blistan ◽  
Rudolf Urban ◽  
Martin Štroner ◽  
Monika Blišťanová ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Morphological Changes ◽  
Current Trend ◽  
Unmanned Aerial Systems ◽  
Alpine Environment ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Model ◽  
Aerial Systems

The current trend in the use of remote sensing technologies is their use as a tool for monitoring hard-to-reach areas, objects or phenomena in the alpine environment. Remote sensing technology is also effectively used to monitor geohazards and the development of human-made changes in the country. Research presented in this study demonstrates the results for the usability of the publicly available national digital elevation model DEM 5.0 obtained by utilizing the airborne laser scanning (ALS) survey to monitor the development of erosion, morphological changes of talus cones, or the dynamics of movement of rock blocks between stages of measurement in the alpine environment of the High Tatras mountains. The reference methods for this study are the terrestrial laser scanning (TLS) and structure-from-motion (SfM) photogrammetric approach using unmanned aerial systems (UASs). By comparing the created DEMs, the ALS point cloud’s accuracy on mostly rocky areas of different sizes was verified. The results show that the standard deviation of the ALS point cloud ranges from 19 to 46 mm depending on the area’s size and characteristics. The maximum difference ranges from 100 to 741 mm. The value of systematic displacement of data obtained by different technologies ranges from 1 to 29 mm. This research confirms the suitability of the ALS method with its advantages and limits for the detection of movement of rock blocks or change of position of any natural or anthropogenic objects with a size from approximately 1 m2.

Use of Unmanned Aerial Systems for Hydrological Monitoring

2020 ◽  
Author(s):  
Salvatore Manfreda ◽  
Keyword(s):  
Best Practices ◽  
Environmental Studies ◽  
Global Network ◽  
Unmanned Aerial Systems ◽  
Monitoring Strategies ◽  
Working Groups ◽  
Definition Of ◽  
Aerial Systems ◽  
Hydrological Monitoring

<p>Unmanned Aerial Systems (UAS) are offering an extraordinary opportunity to improve our ability to monitor river basins. The wide use of UAS leaded to a significant grow of the number of applications and methodologies developed for specific scopes of environmental monitoring. For this reason, there is a serious challenge to harmonise and provide standardised guidance applicable across a broad range of environments and conditions. In this context, a network of scientists is cooperating within the framework of a COST (European Cooperation in Science and Technology) Action named “Harmonious - Given the wide use of UAS within environmental studies”. The intention of “Harmonious” is to promote monitoring strategies, establish harmonised monitoring practices, and transfer most recent advances on UAS methodologies to others within a global network. The working groups of Harmonious are currently working on the definition of practical guidance for environmental studies identifying critical processes and the interconnection of each step for a successful workflow. Given the number of environmental constraints and variables, it is impractical to provide a protocol that can be applied universally under all possible conditions, but it is possible to systematise the fragmented knowledge on this topic identifying the best-practices to improve the overall quality of the final products. Preliminary results of the HARMONIOUS COST Action will be given.</p>

Unmanned aerial vehicles can accurately, reliably, and economically compete with terrestrial mapping methods

2020 ◽  
Vol 8 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Orrin Thomas ◽  
Christian Stallings ◽  
Benjamin Wilkinson
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Structure From Motion ◽  
Spatial Data ◽  
Mean Squared Error ◽  
Laser Scanner ◽  
Point Clouds ◽  
Unmanned Aerial Systems ◽  
Squared Error ◽  
Similar Accuracy ◽  
Aerial Systems

Structure from motion (SfM) and imagery-derived point clouds (IDPC) are excellent tools for collecting spatial data. However, reported accuracies from unmanned aerial systems (UAS) commonly fall short of their theoretical potential. The research presented here, using a DJI Inspire 2 with post-processed kinematic direct geopositioning, demonstrates that UAS mapping can be consistently accurate enough for use in place of, or in concert with, terrestrial methods (2 cm vertical root mean squared error). We further demonstrate that features that are missing or distorted in IDPC (e.g., roof edges, break lines, and above-ground utilities) can be collected from UAS-imagery stereo models with similar accuracy. Accuracy in the experiments was verified by comparison to data from a total station and terrestrial laser scanner (TLS). Use of the recommended hardware and stereo compilation reduced mapping costs by 40%–75% on three test projects.

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