scholarly journals Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Due to the record-breaking amount of rainfall, several river banks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor onboard the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in Joso city, Ibaraki prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10-m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal Synthetic Aperture Radar (SAR) images. The extracted results were modified by considering the land-cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and they showed a good level of agreement.

scholarly journals Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Vol 18 (7) ◽  
pp. 1905-1918 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015. Due to the record-breaking amount of rainfall, several riverbanks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor on board the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in the city of Joso, Ibaraki Prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10 m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal synthetic aperture radar (SAR) images. The extracted results were modified by considering the land cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and more than 85 % of the maximum inundation areas were extracted successfully.

scholarly journals Using machine learning to produce a very high-resolution land-cover map for Ireland and beyond

2021 ◽  
Author(s):  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Eoin Walsh
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Weather Forecasting ◽  
Land Cover Classification ◽  
Surface Processes ◽  
Land Cover Map ◽  
Very High

<p>An accurate representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. The Numerical Weather Prediction (NWP) system, to represent these exchanges, requires a land-cover classification map to calculate the surface parameters used in the turbulent, radiative, heat, and moisture fluxes estimations.</p><p>The land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting is ECOCLIMAP. ECOCLIMAP-SG (ECO-SG), the latest version of ECOCLIMAP, was evaluated over Ireland to prepare ECO-SG implementation in HARMONIE-AROME. This evaluation suggested that sparse urban areas are underestimated and instead appear as vegetation areas in ECO-SG [1], with an over-classification of grassland in place of sparse urban areas and other vegetation covers (Met Éireann internal communication). Some limitations in the performance of the current HARMONIE-AROME configuration attributed to surface processes and physiography issues are well-known [2]. This motivated work at Met Éireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.</p><p>In terms of accuracy, resolution, and the future production of time-varying land-cover map, the use of a convolutional neural network (CNN) to create a land-cover map using Sentinel-2 satellite imagery [3] over Estonia [4] presented better potential outcomes than the use of local datasets [5]. Consequently, this method was tested over Ireland and proven to be more accurate than ECO-SG for representing CORINE Primary and Secondary labels and at a higher resolution [5]. This work is a continuity of [5] focusing on 1. increasing the number of labels, 2. optimising the training procedure, 3. expanding the method for application to other HIRLAM countries and 4. implementation of the new land-cover map in HARMONIE-AROME.</p><p> </p><p>[1] Bessardon, G., Gleeson, E., (2019) Using the best available physiography to improve weather forecasts for Ireland. In EMS Annual Meeting.Retrieved fromhttps://presentations.copernicus.org/EMS2019-702_presentation.pdf</p><p>[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . Køltzow, M. Ø. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5),1919–1935.https://doi.org/10.1175/mwr-d-16-0417.1</p><p>[3] Bertini, F., Brand, O., Carlier, S., Del Bello, U., Drusch, M., Duca, R., Fernandez, V., Ferrario, C., Ferreira, M., Isola, C., Kirschner, V.,Laberinti, P., Lambert, M., Mandorlo, G., Marcos, P., Martimort, P., Moon, S., Oldeman,P., Palomba, M., and Pineiro, J.: Sentinel-2ESA’s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012</p><p>[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1–11,http://arxiv.org/abs/2003.02899, 2020</p><p>[5] Walsh, E., Bessardon, G., Gleeson, E., and Ulmas, P. (2021). Using machine learning to produce a very high-resolution land-cover map for Ireland. Advances in Science and Research, (accepted for publication)</p>

scholarly journals Environmental monitoring of soil pollution in urban areas (a case study from Heraklion city, Central Crete, Greece)

2017 ◽  
Vol 47 (2) ◽  
pp. 963
Author(s):  
E. Kokinou ◽  
C. Belonaki ◽  
D. Sakadakis ◽  
K. Sakadaki
Keyword(s):  
Magnetic Susceptibility ◽  
Urban Areas ◽  
Magnetic Measurements ◽  
Topographic Map ◽  
Near Surface ◽  
Thermomagnetic Analysis ◽  
Geological Features ◽  
Digital Elevation ◽  
Elevation Model

Main scope of the present study is to combine topographic and geological data, magnetic susceptibility and thermomagnetic analysis in order to investigate the magnetic properties of the near surface soils in possible polluted urban areas. For this purpose, a power plant with a dense traffic net around it, located in the NW section of Heraklion city in Crete was selected to be the study area. Surface soil samples have been collected from the area under investigation and they were analyzed in order to estimate the spatial distribution of the magnetic susceptibility. Loci of high values of the magnetic susceptibility within the study area gave rise to further proceed to thermomagnetic analysis of the selected samples. GIS techniques were used for mapping the magnetic measurements on the various topographic and geological features of the area. The digital elevation model was created by the digitization of the topographic map contours (1:5000 scale maps). The combination of the above techniques indicate high values of the magnetic susceptibility especially in the northeastern part of the investigated area, possibly related to pollution due to the presence of heavy metals.

scholarly journals Two Dimensional Model for Backwater Geomorphology: Darby Creek, PA

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2204 ◽  
Author(s):  
Hosseiny ◽  
Smith
Keyword(s):  
Urban Areas ◽  
Water Surface ◽  
Integrated Model ◽  
Shear Stresses ◽  
Two Dimensional ◽  
Morphological Alterations ◽  
Digital Elevation ◽  
Starting Point ◽  
Spatio Temporal ◽  
Elevation Model

Predicting morphological alterations in backwater zones has substantial merit as it potentially influences the life of millions of people by the change in flood dynamics and land topography. While there is no two-dimensional river model available for predicting morphological alterations in backwater zones, there is an absolute need for such models. This study presents an integrated iterative two-dimensional fluvial morphological model to quantify spatio-temporal fluvial morphological alterations in normal flow to backwater conditions. The integrated model works through the following steps iteratively to derive geomorphic change: (1) iRIC model is used to generate a 2D normal water surface; (2) a 1D water surface is developed for the backwater; (3) the normal and backwater surfaces are integrated; (4) an analytical 2D model is established to estimate shear stresses and morphological alterations in the normal, transitional, and backwater zones. The integrated model generates a new digital elevation model based on the estimated erosion and deposition. The resultant topography then serves as the starting point for the next iteration of flow, ultimately modeling geomorphic changes through time. This model was tested on Darby Creek in Metro-Philadelphia, one of the most flood-prone urban areas in the US and the largest freshwater marsh in Pennsylvania.

scholarly journals Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

2019 ◽  
Vol 8 (3) ◽  
pp. 120 ◽  
Author(s):  
Sara Shirowzhan ◽  
Samad Sepasgozar
Keyword(s):  
Digital Elevation Model ◽  
Urban Growth ◽  
Urban Areas ◽  
Future Directions ◽  
3D Data ◽  
Contour Lines ◽  
Digital Elevation ◽  
The Future ◽  
Elevation Model ◽  
Skewness And Kurtosis

Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

scholarly journals Flood depth estimation by means of high-resolution SAR images and LiDAR data

2018 ◽  
Author(s):  
Fabio Cian ◽  
Mattia Marconcini ◽  
Pietro Ceccato ◽  
Carlo Giupponi
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Depth Estimation ◽  
Flood Depth ◽  
Inundation Depth ◽  
Detection Approach ◽  
Digital Elevation ◽  
Radar Imagery ◽  
Elevation Model ◽  
Terrain Elevation

Abstract. When floods hit inhabited areas, great losses are usually registered both in terms of impacts on people (i.e., fatalities and injuries) as well as economic impacts on urban areas, commercial and productive sites, infrastructures and agriculture. To properly assess these, several parameters are needed among which flood depth is one of the most important as it governs the models used to compute damages in economic terms. This paper presents a simple yet effective semi-automatic approach for deriving very precise inundation depth. First, precise flood extent is derived employing a change detection approach based on the Normalized Difference Flood Index computed from high resolution Synthetic Aperture Radar imagery. Second, by means of a high-resolution Light Detection And Ranging Digital Elevation Model, water surface elevation is estimated through a statistical analysis of terrain elevation along the boundary lines of the identified flooded areas. Experimental results and quality assessment are given for the flood occurred in the Veneto region, North-Eastern Italy, in 2010. In particular, the method proved fast and robust and, compared to hydrodynamic models, it requires sensibly less input information.

scholarly journals A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

2020 ◽  
Vol 12 (3) ◽  
pp. 561 ◽  
Author(s):  
Bruno Adriano ◽  
Naoto Yokoya ◽  
Hiroyuki Miura ◽  
Masashi Matsuoka ◽  
Shunichi Koshimura
Keyword(s):  
Disaster Response ◽  
Large Scale ◽  
Reference Data ◽  
Mass Movement ◽  
Recovery Planning ◽  
Additional Information ◽  
Digital Elevation ◽  
Before And After ◽  
Intensity Images ◽  
Elevation Model

The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively.

scholarly journals DEMNAS: MODEL DIGITAL KETINGGIAN NASIONAL UNTUK APLIKASI KEPESISIRAN

OSEANA ◽  
2018 ◽  
Vol 43 (4) ◽  
Author(s):  
Marindah Yulia Iswari ◽  
Kasih Anggraini
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Digital Data ◽  
Coastal Hazards ◽  
Remote Sensing Images ◽  
Geospatial Information ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model

DEMNAS : NATIONAL DIGITAL ELEVATION MODEL FOR COASTAL APPLICATION. DEM is a digital data which contain information about elevation. In Indonesia, DEM can be generated from elevation points or contours in RBI (Rupabumi Indonesia). DEM can be performed to research of coastal application i.e. inundation or tsunami. DEM can help to analyze vulnerability or evacuation zone for coastal hazards. DEMNAS is one product of BIG (Geospatial Information Agency) which consist of elevation data from remote sensing images. DEMNAS data has not been widely used and is still being developed but DEMNAS has an advantage of spatial resolution. DEMNAS has spatial resolution 0.27 arc-second, which is bigger than the spatial resolution of global DEM.

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