scholarly journals Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 283
Author(s):  
Junzhe Zhang ◽  
Wei Guo ◽  
Bo Zhou ◽  
Gregory S. Okin
Keyword(s):  
Remote Sensing ◽  
Wind Erosion ◽  
Field Measurements ◽  
Spatially Explicit ◽  
Surface Model ◽  
High Resolution Imagery ◽  
Soil Elevation ◽  
3D Photogrammetry ◽  
Using Data ◽  
Sample Set

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.

scholarly journals Application of UAV in Topographic Modelling and Structural Geological Mapping of Quarries and Their Surroundings—Delineation of Fault-Bordered Raw Material Reserves

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 489 ◽  
Author(s):  
Ákos Török ◽  
Gyula Bögöly ◽  
Árpád Somogyi ◽  
Tamás Lovas
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Field Measurements ◽  
Fault Zones ◽  
Geological Mapping ◽  
Raw Material ◽  
Surface Model ◽  
Entire Study ◽  
Geological Information ◽  
Major Fault

A 3D surface model of an active limestone quarry and a vegetation-covered plateau was created using unmanned aerial vehicle (UAV) technique in combination with terrestrial laser scanning (TLS). The aim of the research was to identify major fault zones that dissect the inaccessible quarry faces and to prepare a model that shows the location of these fault zones at the entire study area. An additional purpose was to calculate reserves of the four identified lithological units. It was only possible to measure faults at the lowermost two meters of the quarry faces. At the upper parts of the quarry and on the vegetation-covered plateau where no field geological information was available, remote sensing was used. Former logs of core drillings were obtained for the modelling of the spatial distribution of four lithological units representing cover beds and various quality of limestone reserves. With the comparison of core data, field measurements and remote sensing, it was possible to depict major faults. Waste material volumes and limestone reserves were calculated for five blocks that are surrounded by these faults. The paper demonstrates that, with remote sensing and with localised control field measurements, it is possible: (a) to provide all geometric data of faults and (b) to create a 3D model with fault planes even at no exposure or at hardly accessible areas. The surface model with detected faults serves as a basis for calculating geological reserves.

scholarly journals Slope stability and rockfall assessment of volcanic tuffs using RPAS with 2-D FEM slope modelling

2018 ◽  
Vol 18 (2) ◽  
pp. 583-597 ◽  
Author(s):  
Ákos Török ◽  
Árpád Barsi ◽  
Gyula Bögöly ◽  
Tamás Lovas ◽  
Árpád Somogyi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cross Sections ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Field Measurements ◽  
Satellite System ◽  
Surface Model ◽  
Rockfall Hazard ◽  
Terrain Model ◽  
Remote Sensing Techniques

Abstract. Steep, hardly accessible cliffs of rhyolite tuff in NE Hungary are prone to rockfalls, endangering visitors of a castle. Remote sensing techniques were employed to obtain data on terrain morphology and to provide slope geometry for assessing the stability of these rock walls. A RPAS (Remotely Piloted Aircraft System) was used to collect images which were processed by Pix4D mapper (structure from motion technology) to generate a point cloud and mesh. The georeferencing was made by Global Navigation Satellite System (GNSS) with the use of seven ground control points. The obtained digital surface model (DSM) was processed (vegetation removal) and the derived digital terrain model (DTM) allowed cross sections to be drawn and a joint system to be detected. Joint and discontinuity system was also verified by field measurements. On-site tests as well as laboratory tests provided additional engineering geological data for slope modelling. Stability of cliffs was assessed by 2-D FEM (finite element method). Global analyses of cross sections show that weak intercalating tuff layers may serve as potential slip surfaces. However, at present the greatest hazard is related to planar failure along ENE–WSW joints and to wedge failure. The paper demonstrates that RPAS is a rapid and useful tool for generating a reliable terrain model of hardly accessible cliff faces. It also emphasizes the efficiency of RPAS in rockfall hazard assessment in comparison with other remote sensing techniques such as terrestrial laser scanning (TLS).

Evaluation of EPIC's wind erosion submodel using data from southern Alberta

1998 ◽  
Vol 78 (3) ◽  
pp. 485-492 ◽  
Author(s):  
K. N. Potter ◽  
J. R. Williams ◽  
F. J. Larney ◽  
M. S. Bullock
Keyword(s):  
Water Content ◽  
Wind Erosion ◽  
Field Measurements ◽  
Limited Data ◽  
Vegetative Cover ◽  
Southern Alberta ◽  
Erosion Models ◽  
Wind Velocities ◽  
Simulation Results ◽  
Using Data

Wind erosion models have been used to assess policy impacts on soil erosion, but validation of models has been difficult until recently. We evaluated the Environmental Policy Integrated Climate (EPIC) wind erosion submodel by comparing simulation results to field measured wind erosion sediment losses. Using standard model inputs and actual wind velocities, wind erosion was simulated for a field near Lethbridge, Alberta (49°37′N, 112°38′W) where field measurements of wind erosion were made in April 1992 on a Dark Brown Chernozemic soil. The EPIC submodel predicted erosion losses for each day that erosion was measured, and approximated the magnitude of erosion on six of the seven erosion events. EPIC significantly overestimated erosion for one event and also simulated erosion on 3 d when no erosion was recorded. Field length had a larger effect on simulation results during large erosion events than for smaller events. The effect of surface soil water content on wind erosion appeared to be captured by the model, but only limited data were available to evaluate this aspect. Other portions of the model such as the effects of surface roughness and vegetative cover could not be evaluated in this study. Key words: Field length, surface water content, sediment losses

System Information Model of Obtaining and Using Data of Remote Sensing the Earth

2008 ◽  
Vol 40 (11) ◽  
pp. 46-56
Author(s):  
Ludmila I. Samoilenko ◽  
Sergey A. Baulin ◽  
Tatyana V. Ilyenko ◽  
Margarita A. Kirnosova ◽  
Ludmila N. Kolos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Model ◽  
The Earth ◽  
System Information ◽  
Using Data

scholarly journals New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

2021 ◽  
Vol 13 (5) ◽  
pp. 860
Author(s):  
Yi-Chun Lin ◽  
Tian Zhou ◽  
Taojun Wang ◽  
Melba Crawford ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Control Strategies ◽  
Target Function ◽  
Canopy Cover ◽  
Surface Model ◽  
Flowering Date ◽  
Sensing Platforms ◽  
Geolocation Accuracy ◽  
New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

scholarly journals Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2862
Author(s):  
Yakun Xie ◽  
Dejun Feng ◽  
Sifan Xiong ◽  
Jun Zhu ◽  
Yangge Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Estimation Method ◽  
Absolute Error ◽  
Large Error ◽  
Estimation Methods ◽  
Height Estimation ◽  
Building Height ◽  
Remote Sensing Imagery ◽  
High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Monitoring plastic accumulation in water hyacinths using remote sensing

2021 ◽  
Author(s):  
Niels Janssens ◽  
Lauren Biermann ◽  
Louise Schreyers ◽  
Martin Herold ◽  
Tim van Emmerik
Keyword(s):  
Remote Sensing ◽  
Seasonal Dynamics ◽  
Water Hyacinth ◽  
Naive Bayes ◽  
Pearson Correlation ◽  
Field Measurements ◽  
Naïve Bayes ◽  
Plastic Waste ◽  
Water Hyacinths ◽  
Sentinel 2

<p>While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,  infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.</p>

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