scholarly journals Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 816
Author(s):  
Dong Eon Kim ◽  
Shie-Yui Liong ◽  
Philippe Gourbesville ◽  
Ludovic Andres ◽  
Jiandong Liu
Keyword(s):  
Low Cost ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Shuttle Radar Topography Mission ◽  
Performance Criteria ◽  
Srtm Dem ◽  
Flood Modeling ◽  
High Quality ◽  
Improvement Scheme ◽  
Flood Maps

Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.

scholarly journals Exploring the potential of SRTM topographic data for flood inundation modelling under uncertainty

2013 ◽  
Vol 15 (3) ◽  
pp. 849-861 ◽  
Author(s):  
Kun Yan ◽  
Giuliano Di Baldassarre ◽  
Dimitri P. Solomatine
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Flood Inundation ◽  
Inundation Modelling ◽  
Actual Effectiveness ◽  
River Po ◽  
Topography Data

The desirable data for model building and calibration to support the decision-making process in flood risk management are often not sufficient or unavailable. A potential opportunity is now offered by global remote sensing data, which can be freely (or at low cost) obtained from the internet, for example, Shuttle Radar Topography Mission (SRTM) topography. There is a general sense that inundation modelling performance will be degraded by using SRTM topography data. However, the actual effectiveness and usefulness of SRTM topography is still largely unexplored. To overcome this lack of knowledge, we have explored the value of SRTM topography to support flood inundation modelling under uncertainty. The study was performed on a 98 km reach of the River Po in northern Italy. The comparison between a hydraulic model based on high-quality topography and one based on SRTM topography was carried out by explicitly considering other sources of uncertainty (besides topography inaccuracy) that unavoidably affect hydraulic modelling, such as parameter and inflow uncertainties. The results of this study showed that the differences between the high-resolution topography-based model and the SRTM-based model are significant, but within the accuracy that is typically associated with large-scale flood studies.

scholarly journals Potentiality and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Calò ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Satellite Data ◽  
Low Cost ◽  
Ground Truth ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Ground Truth Data ◽  
Flood Peak ◽  
Sar Data ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

scholarly journals Image-based surface reconstruction in geomorphometry – merits, limits and developments of a promising tool for geoscientists

2015 ◽  
Vol 3 (4) ◽  
pp. 1445-1508 ◽  
Author(s):  
A. Eltner ◽  
A. Kaiser ◽  
C. Castillo ◽  
G. Rock ◽  
F. Neugirg ◽  
...  
Keyword(s):  
Structure From Motion ◽  
High Performance ◽  
Scientific Practice ◽  
Low Cost ◽  
Image Data ◽  
Ground Truth ◽  
Scale Invariant ◽  
High Quality ◽  
Promising Tool ◽  
Wide Range

Abstract. Photogrammetry and geosciences are closely linked since the late 19th century. Today, a wide range of commercial and open-source software enable non-experts users to obtain high-quality 3-D datasets of the environment, which was formerly reserved to remote sensing experts, geodesists or owners of cost-intensive metric airborne imaging systems. Complex tridimensional geomorphological features can be easily reconstructed from images captured with consumer grade cameras. Furthermore, rapid developments in UAV technology allow for high quality aerial surveying and orthophotography generation at a relatively low-cost. The increasing computing capacities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by other fields of research (e.g. computer vision and visual perception) has extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure from motion methods offer algorithms, e.g. robust feature detectors like the scale-invariant feature transform for 2-D imagery, which allow for efficient and automatic orientation of large image sets without further data acquisition information. Nevertheless, the importance of carrying out correct fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors still need to be adapted in the common scientific practice. This review manuscript intends not only to summarize the present state of published research on structure-from-motion photogrammetry applications in geomorphometry, but also to give an overview of terms and fields of application, to quantify already achieved accuracies and used scales using different strategies, to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that the identification of common errors, "bad practices" and some other valuable information in already published articles, scientific reports and book chapters may help in guiding the future use of SfM photogrammetry in geosciences.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals Linum usitatisimum L. is the most important crop in Russia for the production of high-quality oil with low cost (review)

2021 ◽  
Vol 640 (4) ◽  
pp. 042014
Author(s):  
E N Turin ◽  
A N Susskiy ◽  
R S Stukalov ◽  
M V Shestopalov ◽  
E L Turina ◽  
...  
Keyword(s):  
Low Cost ◽  
High Quality ◽  
Important Crop

Teledermatology Can Produce High-Quality Care at Low Cost

2003 ◽  
Vol 11 (4) ◽  
pp. 209-215 ◽  
Author(s):  
Keng Chen ◽  
Stephen Shumack ◽  
Richard Wootton
Keyword(s):  
Low Cost ◽  
Quality Care ◽  
High Quality ◽  
High Quality Care

The Preparation of High Quality CIS Films by Spraying Citrate-Capped Cu11In9 Alloy Nanoparticles Ink and RTP Process

2013 ◽  
Vol 787 ◽  
pp. 382-387
Author(s):  
Li Zhou ◽  
Yuan Kui Ding ◽  
Pai Feng Luo
Keyword(s):  
Raw Materials ◽  
Low Cost ◽  
Chalcopyrite Structure ◽  
Alloy Nanoparticles ◽  
System Research ◽  
Spherical Nanoparticles ◽  
Particle Agglomeration ◽  
Energy Band Gap ◽  
High Quality ◽  
Selenization Process

A facile low-cost non-vacuum process for fabrication of high quality CuInSe2(CIS) films is described, which indicates a promising way for the application in thin film solar cells. First, citrate-capped Cu11In9alloy nanoparticles are synthesized by hot-injection method after a system research on the different reaction time and Cu-In ratio of the raw materials. From the TEM and XRD results, we can see that uniform spherical nanoparticles with dominant Cu11In9phase and less particle-to-particle agglomeration are successfully achieved in this study. Then, employing spray and RTP selenization process, high quality CIS films with dense and big grains are obtained, which show the single chalcopyrite structure and the preferred (112) orientation. An energy band gap about 1.01 eV is measured through the absorption spectroscopy measurement in our work.

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