Evaluation of Multiresolution Digital Elevation Model (DEM) from Real-Time Kinematic GPS and Ancillary Data for Reservoir Storage Capacity Estimation

Ouma, Yashon O.

doi:10.3390/hydrology3020016

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Moreover, DEMs are critical in their role for normalising digital surface models. In this study, errors in the DEM were minimized by increasing spatial resolution from 10 m to 0.5 m. This finding is in line with previous research showing that DEMs interpolated from LiDAR point clouds had accuracy proportional to spatial resolution [59][60][61]. Although those results were based on LiDAR data, which typically has much greater point density than the point density achieved with the GNSS approach, in this study, the underlying theory remains the same.…”

Section: Optimal Resolutionsupporting

confidence: 76%

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Salekin

Burgess

Morgenroth

et al. 2018

IJGI

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Abstract:It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m −2 ), 50% (0.259 points m −2 ), and 75% (0.129 points m −2 ) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m −2 ). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m −2 ). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

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Section: Optimal Resolutionsupporting

confidence: 76%

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Salekin

Burgess

Morgenroth

et al. 2018

IJGI

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“…With an initial capacity of approximately 2.4 million cubic meters, the volume has reduced to about 1.8 million. The dam is the main drinking water source for the surrounding community including Moi University and the growing neighbouring urban and rural populations [50]. Over the years, the use of the reservoir's water has diversified to include domestic drinking, irrigation, recreation, and fishing.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Reservoir Bio-Optical Water Quality Parameters Using Smartphone Sensor Apps and Landsat ETM+: Review and Comparative Experimental Results

et al. 2018

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This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll-a (Chl-a), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the remote reflectances of ETM+ green, blue, and NIR independently yielded the best fits for the respective predictions of Chl-a, turbidity, and SPM. The concentration of Chl-a was derived from the Landsat ETM+ and HydroColor with respective Pearson correlation coefficients r of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and HydroColor with respective r values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with the laboratory measurements with r value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater, and HydroColor. The empirical study results showed that the imaging models can be effectively applied in the estimation of the physical WQP.

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“…To apply this toolset, it is necessary to obtain the digital elevation model (DEM) data. Moreover, a decrease in accuracy of estimation due to the increase of DEM scale has been reported [29]. Hence, high resolution DEM data are required for the best estimation.…”

Section: Dam Operation Modulementioning

confidence: 99%

Impacts of Upstream Structures on Downstream Discharge in the Transboundary Imjin River Basin, Korean Peninsula

Kim

Bae

2020

Applied Sciences

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The transboundary river basin is a great challenge for water management and disaster reduction due to its specific characteristics. In this study, upstream impacts from natural and artificial sources on the downstream discharge on the Imjin river basin, the well-known transboundary region in the Korean peninsula, were evaluated using a hydrological model integrating a dam operation module at an hourly timescale. The module uses a concept of the AutoROM method as the operational rule to update the dam storage and decide water release. Dam storages were translated into water levels using a water level–storage curve. To quantify the impact of hydraulic structures on the Northern Imjin river basin, change in discharge was analyzed in four flood events (2009, 2010, 2011, and 2012). Dam failure scenarios were developed under conditions of the 2010 flood event, in which the releases of 100%, 80%, 50%, and 20% of water storage of Hwanggang dam were simulated. The results indicate that the amount of water released from upstream dams is the main cause of floods in the downstream region. To reduce the risk of floods in the downstream river basin, an optimal dam operation module and information on upstream dams play an important role and contribute to the effective use of water resources.

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Evaluation of Multiresolution Digital Elevation Model (DEM) from Real-Time Kinematic GPS and Ancillary Data for Reservoir Storage Capacity Estimation

Cited by 8 publications

References 26 publications

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

Estimation of Reservoir Bio-Optical Water Quality Parameters Using Smartphone Sensor Apps and Landsat ETM+: Review and Comparative Experimental Results

Impacts of Upstream Structures on Downstream Discharge in the Transboundary Imjin River Basin, Korean Peninsula

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