Real-time Kinematic Global Positioning Research Articles

Structural control on the landscape evolution and avulsive behavior of rivers at mountain exits: The example of the Kosi River in eastern Nepal Himalaya

The Kosi River flows from the eastern Nepal Himalaya into the state of Bihar (India) and has experienced frequent avulsions, causing extensive flood-related damage. Because of this avulsive behavior, the Kosi is called the “Sorrow of Bihar.” The avulsion of 2008 was the most catastrophic avulsion event recorded for the Kosi and has been attributed primarily to hydrological and sedimentological processes that formed a super-elevated river channel and caused avulsion. Detailed topographic analysis of the region near the Kosi exit from the Himalaya, using mean-corrected and resampled 1-arc, Shuttle Radar Topography Mission (SRTM) and Real Time Kinematic Global Positioning System (RTK-GPS) datasets, reveals that the Kosi channel is super-elevated only relative to its eastern floodplain. The western floodplain elevation is similar to or higher than the Kosi channel in the region between the Kosi River exit from the main eastern Nepal Himalaya and the Kosi barrage at the Indo-Nepal border. Structurally, the Kosi exits the Himalaya in the transition zone between the closed Trijuga dun to the west and the Dharan salient to the east. The Trijuga dun is closed by the Main Frontal thrust (MFT)-related frontal topography or the Outer Churia Hills. The eastern slopes of these hills induce west-to-east topographic slope in the channel, such that topographic avulsion indices are highest only in the Outer Churia Hills affected parts of the Kosi Channel and the 2008 avulsion region. Therefore, our preferred model for the primary control on the channel's asymmetric, metastable, super-elevation is the influence of the tectonically controlled MFT-related Outer Churia Hills on the Kosi River channel. Geomorphological processes have operated in the Kosi channel in this backdrop. This study emphasizes that detailed structural and topographic analysis of river exits from mountain belts like the Himalaya can provide better insights into river channel metastability and avulsion worldwide.

Morphological Dune Mapping in Shallow Alluvial Stream Using UAV-based Hyperspectral Images

Characterizing morphological features in shallow streams such as dunes and ripples is vital to studies on fluvial geomorphology and in-stream habitat assessments of stream ecology. The paper aimed to examine the feasibility of a conventional hyperspectral method called linear optimal band ratio analysis for capturing the detailed morphologies in shallow small streams, allowing the identification of ripples and dunes. The present study involved a dedicated field experiment at the Gam stream, which is a tributary of the Nakdong River, South Korea. An unmanned aerial vehicle based hyperspectral image was obtained with a spatial resolution of <10 cm and developed an optimal depth-band ratio rating by densely scattered in situ bathymetry measurements with a portable Real Time Kinematic Global Positioning System. The derived hyperspectral bathymetric map with a 7 cm spatial resolution successfully captured the detailed bed morphology, where dunes with sizes of 1.5 m were clearly identifiable. The correlation with depth measured by RTK-GPS was found to be 0.956, with Root Mean Square Error of 0.033 meter. The research confirmed that the conventional linear OBRA used for low-altitude UAV-based hyperspectral images can capture morphological features in shallow streams with a high spatial resolution.

Quality assessment of open sourced digital elevation models in southeast coast of India

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.

Development of a high-throughput plant disease symptom severity assessment tool using machine learning image analysis and integrated geolocation

Tomato spotted wilt virus (TSWV) has the potential to cause severe yield losses in peanut (Arachis hypogaea L.), an important annual legume grown around the world. The most effective approach to manage the disease caused by TSWV is to grow disease resistant peanut varieties. One of the key challenges to breeding for disease resistance is to develop an accurate, reproducible and efficient disease assessment method. Accurate field-based assessment of disease incidence and severity is technically challenging and time-consuming. To address this challenge, a field-based, high-throughput assessment tool was developed to quantify the spatial distribution of disease symptoms over experimental peanut plots using a Real Time Kinematic Global Positioning System (RTK-GPS), consumer-grade cameras, a microcontroller, and an open-source machine learning software. A field experiment was designed to establish a range of disease incidence and severity scenarios. This field experiment was imaged for two seasons to develop and validate the tool. Using transfer learning, an existing Convolutional Neural Network (CNN) was trained from supervised training imagery to classify and quantify areas within the plot-level imagery as, symptomatic, asymptomatic, or ground. Multiple images were assessed by the machine learning model and georeferenced to individual experimental plots using RTK-GPS data. The CNN model trained to detect the symptom, “stunting and mottling”, was evaluated using Receiver Operating Characteristic (ROC) curve analysis and yielded an Area Under the Curve (AUC) of 0.97, sensitivity of 0.77, and specificity of 0.98 on the test set. Results from the disease assessment tool were compared with results from visual disease assessments, conducted by a trained plant pathologist. Field plot level means from CNN-based assessment of stunting and mottling correlated with plot level means from visual assessment of severity (r = 0.78; P < 0.0001). To further validate the CNN-based method, the TSWV field experiment was analyzed using linear mixed models with both visual severity and CNN-based severity assessments used as responses. Both models (visual or CNN-based assessment) identified the same main effects as being significant and post hoc analysis resulted in the same separation of varieties for their severity of TSWV symptoms. The results of this study demonstrate the successful application of this tool for high-throughput disease severity assessment in peanut under field conditions.

Deriving Land and Water Surface Elevations in the Northeastern Yucatán Peninsula Using PPK GPS and UAV-Based Structure from Motion

While UAV-based imaging methods such as drone lidar scanning (DLS) and Structure from Motion (SfM) are now widely used in geographic research, accurate water surface elevation (WSE) measurement remains a difficult problem, as water absorbs wavelengths commonly used for lidar and SfM feature matching fails on these dynamic surfaces. We present a methodology for measuring WSE in a particularly challenging environment, the Yucatán Peninsula, where cenotes – exposed, water-filled sinkholes – provide an observation point into the critically important regional groundwater supply. In the northeastern Yucatán, elevations are very close to sea level, the area is of low relief, and the near-vertical edges of the walls of the cenotes complicate the use of the so-called “water edge” technique for WSE measurement. We demonstrate how post-processing kinematic (PPK) correction of even a single Real Time Kinematic (RTK) Global Positioning System (GPS) unit can be used to finely register the SfM-derived point cloud, and present evidence from both simulations and an empirical study that quantify the effect of “dip” in SfM-based environmental reconstructions. Finally, we present a statistical analysis of the problem of “thick” or “fuzzy” point clouds derived from SfM, with particular emphasis on their interactions with WSE measurement.

The technical challenges and outcomes of ground-penetrating radar: A site-specific example from Joggins, Nova Scotia

The Carboniferous Joggins Formation is known for its complete succession of fossil-rich, coal-bearing strata, deposited in a fluvial meanderbelt depositional setting. Hence, the Joggins Formation outcrop is an excellent analogue for studying the 2D geological complexities associated with meanderbelt systems. In this research, a conventional ground-penetrating radar system was tested with the intent of imaging near-surface, dipping, strata of the Joggins Formation (potentially with subsequent repeats as annual erosion provides new visual calibrations). The survey was unsuccessful in its primary goal, and for future reference we document the reasons here. However, the overlying near-surface angular unconformity was successfully imaged enabling mapping of the approximately 8 m of overlying glacial till. A successful outcome would have allowed observations from the 2D outcrop to be extended into 3D space and perhaps lead to an increased understanding of the small (e.g., bedform baffles and barriers) and large (e.g., channel bodies) scale architectural elements, meanderbelt geometry, and aspect ratios. The study comprises a 42-line, 3.46 km ground-penetrating radar survey using a Sensors and Software pulseEKKO Pro SmartCart system. It was combined with a real-time kinematic differential global positioning system for the georeferencing of survey lines. The 50 MHz antenna frequency, with a 1 m separation, was chosen to maximize the depth of penetration, while still maintaining a reasonable resolution. The results show that many of the lines are contaminated with diffraction hyperbolae, possibly caused from buried objects near or under the survey lines or surface objects near the survey lines. A total of thirteen unique radar reflectors are described and interpreted from this work. The thick clay-rich soil overlying the Joggins Formation probably contributed to significant signal attenuation and the nature of the Carboniferous strata (dip of the beds, pinching and swelling of the beds, bed thickness, etc.) also contributed to imaging difficulties.

Large Scale Topographic Map Comparison Using Unmanned Aerial Vehicle (UAV) Imagers and Real Time Kinematic (RTK)

Large scale topographical mapping is important in geospatial industries in providing high resolution 3D geospatial data sources. The field of aerial photogrammetry also plays an important role in the surveying scope and map production. Nowadays, the use of Unmanned Aerial Vehicle (UAV) platform is a quick way for low altitude aerial data acquisition for mapping. For collecting ground details data at the land-scape level, real time kinematic global positioning system (RTK GPS) is a useful tool for Ground-based topographic mapping. RTK GPS is mobile, collects data quickly at field, and measures elevation within an accuracy of 1 – 5 cm. This work aims to do a comparison of topo-graphic map generated by UAV imagers and by RTK GPS in term of accuracy. The study was conducted using UAV with high resolution non-metric digital compact camera while the ground control point were established using static observation method and ground detail survey using RTK observation method. Vertical accuracy for digital elevation model (DEM) obtained from this study was 8.015 cm at the altitude of 116 m.

Estimation of water table depth using DUALEM-2 system

Most of the agricultural fields are generally irrigated or drained uniformly without considering the spatial and temporal variation in water table depth (WTD). Investigating WTD is important for scheduling irrigation, drainage system designs and water balance models. The objective of this study was to develop a software interface to estimate the variations in WTD via electromagnetic induction (EMI) methods using frequencies of a DUALEM-2 system. Two fields (Field 1: 45.38°N, 63.23°W and Field 2: 45.37°N, 63.25°W) were selected and thirty perforated observation wells were installed to calibrate the DUALEM-2 for predicting WTD. Boundaries of the selected sites and location of the wells were marked using a real time kinematics global positioning system (RTK-GPS). The user interface program was developed in Delphi 5.0 software and imported in a laptop computer to retrieve data from the DUALEM-2 system. The horizontal co-planar (HCP) geometry, perpendicular co-planar (PRP) geometry and WTD were recorded simultaneously from each well before and after every significant rainfall for three consecutive days. Comprehensive surveys were conducted to measure apparent ground conductivity (ECa) with DUALEM-2 and corresponding locations of the sampling points using Trimble Ag GPS 332. The regression model showed significant correlation between the HCP and WTD with co-efficient of determination (R2 = 0.71) for field 1 and (R2 = 0.53) for field 2. Maps were generated in ArcGIS 10 software to examine the accuracy of predicted WTD in comparison with actual values. Results indicated that the DUALEM-2 system was efficient in mapping variation rapidly and reliably in WTD in a non-destructive fashion rather than following conventional way of repeated well drilling for WTD determination. This information could be used for measuring the depletion of WTD during dry periods (droughts), site-specific irrigation and drainage design with the aided advantage of labor and time savings in case of observing precision water management practices at large fields.

Spatially explicit quantification of total soil erosion by RTK GPS in wind and water eroded croplands

Quantification of total soil erosion in wind and water eroded croplands is essential for assessing their contributions and the interaction between them. However, it is difficult to quantify total soil erosion amounts by the traditional monitoring and modelling approaches of wind erosion and water erosion. To address this problem, a Real Time Kinematic Global Positioning System (RTK GPS) was applied for a series of wind and water eroded croplands in the Bashang area in North China to quantify the total soil erosion amount over a period of 44 years. By comparing the elevation of the croplands with a reference surface without erosion, the total soil erosion modulus and its spatial variation were determined. Results showed that the erosion moduli of the six croplands ranged from 1.09 to 45.34 Mg ha−1 y−1 with an average modulus of 17.02 Mg ha−1 y−1. The croplands in the west suffered from more intense wind erosion compared to the middle and eastern areas; this was due to the presence of forest-grasslands, which served as wind breaks for the croplands in the middle and eastern regions. However, the croplands in the east showed the highest total erosion modulus, which was due to the influences of a gully. Within the croplands, the slope areas suffered from intense soil erosion which was mainly owing to water erosion. The reliability and uncertainty of this approach were discussed in terms of the equipment precision, results accuracy, and possible deposition on the reference surface. This study shows that when a suitable reference surface is identified and the erosion amount is considerable, RTK GPS survey can be used as a reliable and effective method to assess the spatially explicit total soil erosion in croplands influenced by both wind and water erosion.

Bathymetric Survey and Topography Changes Investigation of Part of Badagry Creek and Yewa River, Lagos State, Southwest Nigeria

Bathymetry survey of Part of Badagry Creek and Yewa River was conducted with the aim of investigating the topography changes in depth between two epoch dataset by calculating the volume of sediment and dredged material between the two periods. Depth sounding at 100m interval, data grid of 100 m interval, constant vessel speed of 2.2 m/secs (8Km/hr) for data capture, data storage at interval of 30 Secs was ensured at both the creek and the river. Seabed topography changes of Part of Badagry Creek and Yewa River was investigated. The two dataset investigated was the data acquired in September 2008 and November 2015. Data acquisition was done using digital echo sounder SDE28, Handheld Real time Kinematic (RTK) Global Positioning System (GPS) in acquiring the spatial coordinates (x, y) and water depth (z). Six (6) wrecks (Shipping Boat) were determined along both the creek and the river. The initial processing was carried out with the use of HYPACK 2008 software for data sorting. ArcGIS 10.2.1 was used for data analysis as well as graphics design. The processed depths were presented in form of tables, map/plan and charts. The result showed that In 2008, the depth observed ranges from (-0.5 to 10.4) m while in 2015, the depth observed ranges from (-0.32 to 10.85) m which implies that some area have been cut. Also, the results of the volume of deposited sediments and dredged material are computed as 10.55 x 106 m3 and 7.24 x 106 m3. It showed from the result that volume of accreted sediment is greater than volume of the material dredged. Increase in volume of sediment deposited could be as a result of the adjoining river Yewa flowing into the larger river (tributaries) of the Badagry Creek. Therefore, further studies need to carry out in order to know the source of accreted mass through integrated coastal management plan.

Quantification of river bank erosion by RTK GPS monitoring: case studies along the Ningxia-Inner Mongolia reaches of the Yellow River, China.

The Ningxia-Inner Mongolia reaches of the Yellow River suffer from bank erosion problems; in order to identify the bank erosion dynamics, Real Time Kinematic Global Positioning System (RTK GPS) was applied to monitor bank morphology at three sites: Taole Cropland (TC), Maobula Shrubland (MS), and Maobula Cropland (MC). The measured data were analyzed using the Geographical Information System (GIS) to quantify the volume and amount of bank erosion. To verify the feasibility of other means quantifying bank erosion including remote sensing image interpretation and Bank-Stability and Toe-Erosion Model (BSTEM) simulation, their results were compared with the directly monitored results by RTK GPS. Results show that the bank erosion moduli at the TC, MS, and MC sites are 12,762, 6681 and 44,142tkm-1a-1 respectively based on RTK GPS measurements from 2011 to 2014, with the bank erosion amount varying between flood and non-flood seasons and among different years. The bank erosion quantified by remote sensing interpretation and BSTEM simulation agreed well with results from RTK GPS measurement. The main factors that influence bank erosion on the upper reaches of the Yellow River include land use in the bank area, bank height, and bank curvature. More rational land use along the Yellow River and stabilization of the river bank are required for this area. This study shows that RTK GPS monitoring is reliable and useful for bank erosion research, which has not yet been fully exploited. There is potential of applying remote sensing and model simulation to determine bank erosion of large rivers, while they should be combined and supported by field investigated data.

An Augmented Reality Geo-Registration Method for Ground Target Localization from a Low-Cost UAV Platform.

This paper presents an augmented reality-based method for geo-registering videos from low-cost multi-rotor Unmanned Aerial Vehicles (UAVs). The goal of the proposed method is to conduct an accurate geo-registration and target localization on a UAV video stream. The geo-registration of video stream requires accurate attitude data. However, the Inertial Measurement Unit (IMU) sensors on most low-cost UAVs are not capable of being directly used for geo-registering the video. The magnetic compasses on UAVs are more vulnerable to the interferences in the working environment than the accelerometers. Thus the camera yaw error is the main sources of the registration error. In this research, to enhance the low accuracy attitude data from the onboard IMU, an extended Kalman Filter (EKF) model is used to merge Real Time Kinematic Global Positioning System (RTK GPS) data with the IMU data. In the merge process, the high accuracy RTK GPS data can be used to promote the accuracy and stability of the 3-axis body attitude data. A method of target localization based on the geo-registration model is proposed to determine the coordinates of the ground targets in the video. The proposed method uses a modified extended Kalman Filter to combine the data from RTK GPS and the IMU to improve the accuracy of the geo-registration and the localization result of the ground targets. The localization results are compared to the reference point coordinates from satellite image. The comparison indicates that the proposed method can provide practical geo-registration and target localization results.

Small catchments DEM creation using Unmanned Aerial Vehicles

Digital elevation models (DEM) are an important source of information on the terrain, allowing researchers to evaluate various exogenous processes. The higher the accuracy of DEM the better the level of the work possible. An important source of data for the construction of DEMs are point clouds obtained with terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAV). In this paper, we present the results of constructing a DEM on small catchments using UAVs. Estimation of the UAV DEM showed comparable accuracy with the TLS if real time kinematic Global Positioning System (RTK-GPS) ground control points (GCPs) and check points (CPs) were used. In this case, the main source of errors in the construction of DEMs are the errors in the referencing of survey results.

Testing ground-based robotics as remote-sensing platforms for Structure from Motion – implications for planetary science

ABSTRACTWe assessed robotic performance by performing Structure from Motion (SfM) mapping surveys of a small gully in New South Wales, Australia, using two rovers of different sizes that each captured photographic images from a high-resolution, 12 megapixel (MP) camera and a low-resolution (<1 MP) camera. Results from these trials were compared with a simulated unmanned aerial vehicle (UAV) SfM survey of the same gully feature using the high-resolution camera on a boom pole. We conducted this in order to determine whether small, ground-based vehicles would be suitable remote-sensing platforms for collecting this data where UAV operations would be impractical or impossible. Accuracy of the resulting point clouds and the time taken to complete the surveys were used as the key metrics to assess the performance of the respective methods. The high-resolution camera surveys produced digital elevation models (DEMs) that corresponded closely to the control points surveyed using Real Time Kinematic (RTK) global positioning system (GPS) technology, although the robotic surveys took longer to complete. Additionally, camera resolution and height above ground were the major factors when determining the success of generating SfM data. We found that despite these limitations, ground-based vehicles are capable of generating point clouds accurate enough to be used to investigate small-scale geomorphology.

Glacier elevation changes in the western Nyainqentanglha Range of the Tibetan Plateau as observed by TerraSAR-X/TanDEM-X images

ABSTRACTGlobal warming greatly affects glacier retreat, which in turn influences local water resources and sea level altitudes. We used Landsat Operational Land Imager (OLI) images to extract glacier outlines in the western Nyainqentanglha Range, then used TerraSAR-X/TanDEM-X images and SRTM-C DEM to calculate changes in glacier elevation using a differential interferometric synthetic aperture radar (DInSAR) approach. The decreasing rate of glacier elevation in the western Nyainqentanglha Range was −0.30 ± 0.07 m per year (m yr−1) from 2000 to 2014. The annual thinning rate of the northern slope (−0.46 ± 0.07 m yr−1) was faster than that of the southern slope (−0.24 ± 0.07 m yr−1). We investigated two exemplary glacier sites in details: Zhadang glacier on the northern slope and Gurenhekou glacier on the southern slope. The annual elevation change of 39 points on Zhadang glacier calculated by the DInSAR and Real Time Kinematic Global Position System (RTK-GPS) seperately have a correlation coefficient of 0.88. The mass balance change of Gurenhekou glacier is −0.21 ± 0.06 m water equivalent per year (m w.e. yr−1) from DInSAR, which is similar to the value of −0.31 m w.e. yr−1 determined via stakes and snow pits from 2005 to 2010.

Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems

ABSTRACTAccurate positioning of single trees registered automatically during harvesting operations opens up new possibilities for reducing the field sampling effort in forest inventories utilising remotely sensed data. In the present study, we propose to use a harvester to collect single-tree data during regular harvest operations and use these data to substitute or supplement traditional measurements on sample plots. Today’s harvesters are capable of recording single-tree information such as species and diameter at breast height, and a cut-to-length harvester was equipped with an integrated accurate positioning system based on real-time kinematic global satellite positioning, as well as a low-cost global navigation satellite system (GNSS) receiver mounted directly on the harvester head. Positions from 73 trees were evaluated and compared to coordinates obtained using a total station. At the single-tree level, the mean error for the integrated positioning system was 0.94 m. The low-cost GNSS receiver mounted on the harvester head yielded a mean error of 7.00 m. The sub-meter accuracy obtained with the integrated system suggests that data acquired with a harvester using this positioning system may have a great potential as a method for single-tree field data acquisition.

Hydrographic survey of a south Florida canal using a hydrolite

Abstract In this study, a hydrographic map of a South Florida canal is prepared using RTK GPS (Real Time Kinematic Global Positioning System) measurements combined with a hydrolite (single beam echo sounder). RTK GPS measurements are made using both single RTK and Network RTK systems. Digital sounding measurements taken by hydrolite are compared to manual lead line measurements. Single RTK and Network RTK results differed in the order of tenth of a foot accuracy for horizontal coordinates and for height measurements variations can go up to several tenths. Sounding results indicate a best-fit trend line with a slope of 0.993, and an R squared value of 0.972, demonstrating that hydrolite measurements and manually collected depths at this site are well correlated.

Out-of-sequence reactivation of the Munsiari thrust in the Relli River basin, Darjiling Himalaya, India: Insights from Shuttle Radar Topography Mission digital elevation model-based geomorphic indices

Quantitative tectonic geomorphology has emerged as a powerful discipline for studying evolution of topography, landscapes, and neotectonics using geomorphic indices computed from digital elevation data such as the Shuttle Radar Topography Mission (SRTM) data. We computed SRTM-based geomorphic indices to study neotectonics in the Relli River basin in the Darjiling Himalaya. We also used Real Time Kinematic Global Positioning System (RTKGPS) independent checkpoints to assess the quality of the SRTM data used to compute the geomorphic indices along with their uncertainties. Our analysis revealed that though the SRTM C-Band 90-m resolution (C90) digital elevation data has been used extensively for geomorphic studies, the 30-m resolution (C30) data were significantly more accurate. Moreover, geomorphic indices computed using SRTM C30 and C90 elevations in the Relli basin indicate that normalized, nondimensional indices such as the relief ratio (Rh), hypsometric integral (HI), basin elongation (Re), and valley floor width-to-height ratio (Vf) are statistically indistinguishable with uncertainty (1σ) at least an order of magnitude below the index value. The geomorphic indices in the Relli basin reveal neotectonic activity in the Munsiari thrust (MT) fault zone and intraformational faults in its footwall in the Lesser Himalayan rocks and also indicate that the basin is at an early mature stage close to equilibrium between tectonic and erosional process. However, analysis of the uncertainties associated with the indices suggest that the normalized or nondimensional geomorphic indices have the lowest uncertainties and that neotectonics in the Relli basin may only be confined to reactivation in the MT. The reactivation in the MT fault zone by out-of-sequence neotectonics implies the possibility of large earthquake events in the Darjiling Himalaya and significant seismic and landslide hazard for populations in large towns specifically located on the MT. Our new approach of looking at geomorphic indices and their uncertainties delivers a novel perspective for improved understanding of out-of-sequence neotectonics in river basins that may be applied more broadly across the Himalaya and elsewhere.

Navigation of an Autonomous Tractor for a Row-Type Tree Plantation Using a Laser Range Finder—Development of a Point-to-Go Algorithm

It is challenging to develop a control algorithm that uses only one sensor to guide an autonomous vehicle. The objective of this research was to develop a control algorithm with a single sensor for an autonomous agricultural vehicle that could identify landmarks in the row-type plantation environment and navigate a vehicle to a point-to-go target location through the plantation. To enable such a navigation system for the plantation system, a laser range finder (LRF) was used as a single sensor to detect objects and navigate a full-size autonomous agricultural tractor. The LRF was used to control the tractor as it followed a path, and landmarks were detected “on-the-go” in real time. The landmarks were selected based on data for their distances calculated by comparison with the surrounding objects. Once the landmarks were selected, a target point was calculated from the landmarks, and the tractor was navigated toward the target. Navigation experiments were successfully conducted on the selected paths without colliding with the surrounding objects. A real time kinematic global positioning system (RTK GPS) was used to compare the positioning between the autonomous control and manual control. The results of this study showed that this control system could navigate the autonomous tractor to follow the paths, and the vehicle position differed from the manually driven paths by 0.264, 0.370 and 0.542 m for the wide, tight, and U-turn paths, respectively, with directional accuracies of 3.139°, 4.394°, and 5.217°, respectively, which are satisfactory for the autonomous operation of tractors on rubber or palm plantations. Therefore, this laser-based landmark detection and navigation system can be adapted to an autonomous navigation system to reduce the vehicle`s sensor cost and improve the accuracy of the positioning.

Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains

The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in the coastal floodplain. Coastal digital elevation model (DEM) data describing coastal topography are essential for assessing future flood-related damages and understanding the impacts of sea-level rise. The Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are currently the most accurate and freely available DEM data. However, an accuracy assessment specifically targeted at DEMs over low elevation coastal plains is lacking. The present study focuses on these areas to assess the vertical accuracy of SRTM and ASTER GDEM using Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic (RTK) Global Positioning System (GPS) field survey data. The findings show that DEM accuracy is much better than the mission specifications over coastal plains. In addition, optical remote sensing image analysis further reveals the relationship between DEM vertical accuracy and land cover in these areas. This study provides a systematic approach to assess the accuracy of DEMs in coastal zones, and the results highlight the limitations and potential of these DEMs in coastal applications.