GIS-based rockfall hazard zones modeling along the coastal Gulf of Aqaba Region, Egypt

AbstractRockfall is a natural hazard in mountainous areas not to be underestimated. Mass activities differing in rock volume may cause considerable economic damage. Accomplishing qualitative appraisal of high-potential zones for rockfall is a first step towards implementing mitigation strategies. Nowadays, Geographical Information Systems (GIS) are the state-of-the-art tool for a fast and economic approach of identifying potential hazard zones rather than using conventional mapping with in-situ field data. Primarily, current research focuses on designing and implementing user-friendly tools delineating potential rockfall hazard zonation (RHZ). The constructed model examines triggering factors like slope, aspect, elevation, lithology, structural lineament, rainfall intensity, and seismic activity focal depth of a mountainous coastal region (Gulf of Aqaba, Egypt). The extracted geomorphological parameters were based on a high-resolution TanDEM-X Digital Elevation Model. The enhanced Landsat ETM + 7 was used to generate the lithological and structural lineament parameters, while the rainfall data were collected from NASA project tool. The zonation model was implemented by means of ESRI’s ArcGIS Pro ModelBuilder. Google Earth Pro orthophotos compared with the generated rockfall hazard zonation map indicate the potential RHZ with high reliability. The achieved results show that 15 % of the study area qualifies as a high rockfall hazard zone. As the RHZs generated by the model depend on the input data and the selected rating scores and weights, obtaining ground truth is essential to get a trustworthy result. Finally, this study recommends employing the built RHZ model on similar terrains worldwide to support decision-makers involving any sustainable development projects.

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Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

Sociedade & Natureza ◽

10.14393/sn-v33-2021-59124 ◽

2021 ◽

Vol 33 ◽

Author(s):

Mohammed El-Fengour ◽

Hanifa El Motaki ◽

Aissa El Bouzidi

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Slope Angle ◽

Google Earth ◽

Slope Aspect ◽

Multivariate Logistic Regression Model ◽

Susceptibility Map ◽

Potential Hazard ◽

Topographic Wetness Index ◽

Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

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Understanding rockfalls along the national road G318 in China: from source area identification to hazard probability simulation

10.5194/nhess-2021-303 ◽

2021 ◽

Author(s):

Lixia Chen ◽

Yu Zhao ◽

Yuanyao Li ◽

Lei Gui ◽

Kunlong Yin ◽

...

Keyword(s):

Slope Angle ◽

Source Area ◽

Field Investigation ◽

Slope Aspect ◽

Multivariate Logistic Regression Model ◽

Potential Hazard ◽

Rockfall Hazard ◽

Source Areas ◽

Slope Structure ◽

National Road

Abstract. Rockfall hazard is frequent along the national road (G318) in west Hubei, China. To understand the distribution and potential hazard probability, this study combines the result of a 3-years engineering geological investigation, statistical modeling, and kinemics-based method to identify risky road sections. Rockfall hazard probability is calculated by integrating spatial, temporal, size probability, and reaching probabilities of source areas. Rockfall source areas are preliminarily identified first by slope angle threshold (SAT) analysis. Random Forest model (RFM) and multivariate logistic regression model (MLRM) are then applied and compared to get the final susceptible source areas, considering eight factors, including slope, aspect, elevation, lithology, joint density, slope structure, land-use type, distance to the road. Temporal and size probability of source areas are separately obtained by Poisson distribution and power-law distribution theory. An important parameter (reach angle) for rockfall trajectory simulation was determined by back analysis in Flow-R and validated by field investigation. The results show good fitness with the measurements by field investigation. In the conditions of 5, 20, and 50 years return period, potential risky road sections are found out under two size scenarios (larger than 1 000 m3, 10 000 m3). This research helps the local government to completely understand the rock falls from source area existence and potential risk to roads.

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Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Water ◽

10.3390/w13213115 ◽

2021 ◽

Vol 13 (21) ◽

pp. 3115

Author(s):

Hadi Farhadi ◽

Mohammad Najafzadeh

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Flood Risk ◽

Google Earth ◽

Risk Mapping ◽

Geographical Information ◽

Slope Aspect ◽

Landsat 8 ◽

Risk Indices ◽

Flood Risk Mapping

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

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Pengembangan Prototipe Sistem Informasi Jaringan Telekomunikasi PT. Telkom Untuk Penentuan Rute Optimal Dalam Penanganan Gangguan Berdasarkan Algoritma Floyd - Warshall

Jurnal Penelitian Pos dan informatika ◽

10.17933/jppi.2017.0701001 ◽

2017 ◽

Vol 7 (1) ◽

pp. 1

Author(s):

Ircham Habib Anggara ◽

Florence Elfriede Silalahi ◽

Barandi Sapta Widartono

Keyword(s):

Information System ◽

Travel Time ◽

Geographical Information Systems ◽

Google Earth ◽

Travel Distance ◽

Telephone Network ◽

Geographical Information ◽

Optimal Route ◽

Error Handling ◽

Route Network

ABSTRAKSaat ini banyak operator telekomunikasi yang bermunculan di Indonesia sehingga menyebabkan terjadinya persaingan yang tinggi antar operator telekomunikasi. PT. Telkom selaku badan usaha yang berwenang dalam pembangunan dan pengembangan sektor telekomunikasi khususnya untuk telepon kabel, juga menyadarinya dan berupaya untuk meningkatkan pelayanan kepada pelanggan. Penelitian ini bertujuan membuat suatu basis data spasial dan model sistem informasi jaringan telepon PT. Telkom yang interaktif dengan memanfaatkan citra Quickbird yang bersumber dari Google Earth, Global Positiong System (GPS) dan Sistem Informasi Geografis (SIG) untuk penentuan rute optimal penanganan gangguan jaringan telepon PT. Telkom berdasarkan Algoritma Floyd-Warshall. Penentuan rute optimal didasarkan atas variabel impedensi, berupa jarak tempuh dan waktu tempuh yang diturunkan dari panjang jalan dibagi dengan kecepatan rata-rata kendaraan per ruas jalan. Hasil penelitian ini berupa Sistem Informasi Rute Optimal Telkom Bantul (SIROTOL) yang berbasis dekstop dan dapat berdiri sendiri tanpa adanya software SIG yang lain. Rute optimal program SIROTOL mampu digunakan untuk menentukan rute optimal penanganan gangguan jaringan telepon PT. Telkom Bantul dengan hasil yang akurat atau mendekati kondisi di lapangan. Hal tersebut dibuktikan dengan hasil validasi lapangan yang memiliki nilai uji akurasi rute optimal berdasarkan jarak tempuh sebesar 97.06% dan nilai uji akurasi rute optimal berdasarkan waktu tempuh sebesar 96.14%. ABSTRACTNowdays, many providers are emerging in Indonesia so that they lead high competition among telecommunication operators. As a state owned company that has authorities on the development of telecommunications sector, especially for cables telephone, PT. TELKOM also realize that, so they strive for a better service to the customers.This research aims to create a spatial database and interactive telephone network information system model of PT. Telkom by using Quickbird imagery derived from Google Earth, Global Position System (GPS) and Geographical Information Systems (GIS) to determine the optimal route telephone network for error handling based on Floyd-Warshall algorithm. Determination of the optimal route is based on the variable impedance of the travel distance and travel time derived from the length of road divided by the average speed of vehicles per road segment. Subsequent tissue analysis results are integrated with GPS navigation technology to help a network technician search for location of interference and network technicians to assist the movement towards the location of the phone to crash in the field. The result of the research is Telkom Bantul Optimal Route Information System (SIROTOL) desktop based and stand alone application. SIROTOL optimal route program can be applied to determine the optimal route accurately on Telkom Bantul’s error handling or at least close to field conditions. It can be proved by field validation results which resulted in accurate optimal route test value based on travel distance of 97.06% and travel time of 96.14%.Keywords: optimal route, network analysis, Floyd-Warshall algorithm, telephone network

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LANDSLIDE HAZARD ZONATION MAPPING OF CHAMOLI LANDSLIDES IN REMOTE SENSING AND GIS ENVIRONMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-475-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 475-480

Author(s):

M. K. Tripathi ◽

H. Govil ◽

P. K. Champati ray ◽

I. C. Das

Keyword(s):

Remote Sensing ◽

Remote Sensing And Gis ◽

Landslide Hazard ◽

Information Value ◽

Slope Aspect ◽

Hazard Zonation ◽

Landslide Hazard Zonation ◽

Thematic Layers ◽

Zonation Map ◽

Gis Environment

Abstract. Landslides are very common problem in hilly terrain. Chamoli region of Himalaya is highest sensitive zone of the landslide hazards. The purpose of Chamoli landslide study, to observe the important terrain factors and parameters responsible for landslide initiation. Lithological, geomorphological, slope, aspect, landslide, drainage density and lineament density map generated in remote sensing and GIS environment. Data information of related geological terrain obtain through topographic maps, remote sensing images, field visits and geological maps. Geodatabases of all thematic layers prepared through digitization of topographic map and satellite imageries (LISS-III, LISS-IV &amp; ASTER DEM). Integrated all thematic layers applying information value method under GIS environment to map the zonation of landslide hazard zonation map validation and verification completed by field visit. The landslide hazard zonation map classified in four classes very high, high, medium and low.

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The Geospatial Web

Advances in Electronic Government, Digital Divide, and Regional Development - Citizen E-Participation in Urban Governance ◽

10.4018/978-1-4666-4169-3.ch009 ◽

2013 ◽

pp. 144-171 ◽

Cited By ~ 10

Author(s):

Karl Atzmanstorfer ◽

Thomas Blaschke

Keyword(s):

Public Participation ◽

Geographical Information Systems ◽

Urban Governance ◽

Emerging Countries ◽

Google Earth ◽

Geographical Information ◽

Software Packages ◽

Internet Users ◽

Geospatial Tools ◽

Spatial View

This chapter introduces a spatial view to e-participation in urban governance which is based on the technological core of Geographical Information Systems (GIS) and their more recent transformation into service architectures. The chapter begins with the premise that the technological realms are available today in professional software packages and in open source software environments. It focuses on the utilization of GIS and various methodologies in participatory planning projects. The technical descriptions are limited to a degree that the reader can understand the applications envisaged. The chapter describes developments in the GIS domain which are summarized under the term ‘Public Participation GIS’ (PPGIS) since the 1990s. In 2005 however, the launch of Google Earth changed the situation significantly: such mapping platforms—including Microsoft Bing and others—brought mapping functionality to the computers of hundreds of millions of internet users and soon after, the term “volunteered geographic information” was created. It refers to the two-way communication possibilities using geospatial tools and to the participation of citizens in planning initiatives. The chapter highlights a few of such applications in urban planning and administration and discusses the situation in developing and emerging countries, while posing the question of whether or not such options may lead to an empowerment of citizens.

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Using Google Earth and Google Street View To Rate Rock Slope Hazards

Environmental and Engineering Geoscience ◽

10.2113/eeg-1922 ◽

2018 ◽

Vol 24 (2) ◽

pp. 237-250

Author(s):

William Swanger ◽

Yonathan Admassu

Keyword(s):

Google Earth ◽

Orientation Data ◽

Google Street View ◽

Rockfall Hazard ◽

Street View ◽

Discontinuity Orientation ◽

Measuring Tape ◽

Study Sites ◽

Weak Rock Masses ◽

Geologic Information

Abstract Rockfall hazard from cut slopes along highways are caused primarily by unfavorable orientations of discontinuities, presence of unconsolidated cobble/boulder deposits, undercutting of strong rocks by weaker rocks, or degradation of weak rock masses. The rockfall hazard rating system (RHRS) was introduced in Oregon to evaluate the hazard and associated risk to an adjacent transportation facility for a cut slope's potential for releasing rockfalls. RHRS is a numerical score–based rating of parameters that characterize rockfalls. The parameters include slope geometry (height, angle, roughness, orientation), geologic information (discontinuity characterization, undercutting susceptibility), driver's line of sight, and climate. Geologic information, such as discontinuity orientation data, is traditionally collected using a transit compass and measuring tape at the site. The method is time consuming and expensive and can be dangerous. This study tests the use of Google Earth and Google Street View tools to remotely collect data for selected parameters that characterize rockfall hazard. The selected parameters are categorized under slope profile, geologic characteristics, and impact factor parameters, which are quantitatively and qualitatively measurable using Google Street View and Google Earth. A section of U.S. 33 with a high density of road cuts and two more sites along Interstate 64, all located in Virginia, were selected for the study. Sites were evaluated by using a combination of measurement tools available in Google Earth and a visual inspection of the rock units in Google Street View. The results of seven of the sites were re-evaluated using field-derived data.

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MODELO DE ZONEAMENTO DE RISCO DE INCÊNDIOS PARA UNIDADES DE CONSERVAÇÃO BRASILEIRAS: O CASO DO PARQUE ESTADUAL DO CERRADO (PR)

FLORESTA ◽

10.5380/rf.v41i3.24049 ◽

2011 ◽

Vol 41 (3) ◽

Cited By ~ 1

Author(s):

Letícia Koproski ◽

Matheus Pinheiro Ferreira ◽

Johann Georg Goldammer ◽

Antonio Carlos Batista

Keyword(s):

Protected Areas ◽

Fire History ◽

Fire Risk ◽

Geographical Information ◽

Slope Aspect ◽

Integrated Analysis ◽

Risk Map ◽

Integration Model ◽

Forest Protection ◽

Few Data

Este trabalho teve como objetivo estabelecer um modelo de zoneamento de risco de incêndios pela análise dos fatores físicos, associados às fontes de ignição e aos fatores de propagação dos incêndios, que pudesse ser aplicado à realidade da gestão das áreas protegidas em território brasileiro. Para tanto, o Parque Estadual do Cerrado foi selecionado como área de estudo. Foram produzidos mapas de riscos referentes à cobertura vegetal (V), influências humanas (H), declividade (D), orientação das encostas (E) e altimetria (A). O zoneamento foi gerado pela superposição dos mapas de risco, em função da somatória ponderada dos riscos parciais, representado pela equação: RISCO: 4V +3H + 1D + 1E + 1A. A partir do zoneamento, foi possível identificar duas áreas prioritárias para o manejo do fogo com relação ao risco de incêndios na Unidade. O modelo de integração traduziu adequadamente os níveis de risco e pode ser aplicado em outras unidades de conservação, especialmente em locais onde não existam muitos dados disponíveis sobre o histórico do fogo, ou onde existam poucos dados disponíveis sobre as áreas de estudo. Recomenda-se a utilização do modelo em locais onde não existam diferenças climáticas significativas.Palavras-chave: Mapas de risco; incêndios florestais; SIG; proteção florestal; áreas protegidas. AbstractFire risk mapping for Brazilian protected areas: the case of Cerrado State Park (PR). The aim of this research was to develop a model of forest fire risk map for Brazilian protected areas. The Cerrado State Park, located in Jaguariaíva city, State of Paraná, south of Brazil, was the focused area. The fire risk map was built up through the integrated analysis of vegetation cover (V), slope gradient (G), slope aspect (A), elevation (E), and human activities (H). For this analysis the Geographical Information System (GIS) was used. The fire risk map was the result of the overlay of the preliminary risk maps, by the model represented by the equation: RISK: 4V + 3H + 1G + 1A + 1E. The results presented that the integration model worked successfully for the area, properly managing the variables according to local characteristics and indicated two priority fire management areas in the Park. The model can be applied to protected areas with few data about fire history or few data about the area itself. The model is not recommended to be used in areas with significantly different climates.Keywords: Fire risk map; wildfires; GIS; forest protection.

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Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment

Remote Sensing ◽

10.3390/rs12223805 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3805

Author(s):

Xihua Yang ◽

Mingxi Zhang ◽

Lorena Oliveira ◽

Quinn R. Ollivier ◽

Shane Faulkner ◽

...

Keyword(s):

Water Quality ◽

Drinking Water ◽

Erosion Rate ◽

Google Earth ◽

Geographical Information ◽

Rainfall Erosivity ◽

Landsat 8 ◽

Storm Events ◽

Erosion Risk ◽

Water Catchment

The Australian Black Summer wildfires between September 2019 and January 2020 burnt many parts of eastern Australia including major forests within the Sydney drinking water catchment (SDWC) area, almost 16.000 km2. There was great concern on post-fire erosion and water quality hazards to Sydney’s drinking water supply, especially after the heavy rainfall events in February 2020. We developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, remote sensing, Google Earth Engine (GEE), Geographical Information Systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE). The event-based rainfall erosivity was estimated from radar-derived rainfall accumulations for all storm events after the wildfires. Satellite data including Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to estimate the fractional vegetation covers and the RUSLE cover-management factor. The study reveals that the average post-fire erosion rate over SDWC in February 2020 was 4.9 Mg ha−1 month−1, about 30 times higher than the pre-fire erosion and 10 times higher than the average erosion rate at the same period because of the intense storm events and rainfall erosivity with a return period over 40 years. The high post-fire erosion risk areas (up to 23.8 Mg ha−1 month−1) were at sub-catchments near Warragamba Dam which forms Lake Burragorang and supplies drinking water to more than four million people in Sydney. These findings assist in the timely assessment of post-fire erosion and water quality risks and help develop cost-effective fire incident management and mitigation actions for such an area with both significant ecological and drinking water assets. The methodology developed from this study is potentially applicable elsewhere for similar studies as the input datasets (satellite and radar data) and computing platforms (GEE, GIS) are available and accessible worldwide.

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Using Google Earth™ and Geographical Information System data as method to delineate sample domains for an urban household surveys: the case of Maroua (Far North Region-Cameroon)

International Journal of Health Geographics ◽

10.1186/s12942-019-0186-8 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Ronald R. B. Ngom Vougat ◽

Steven Chouto ◽

Sylvain Aoudou Doua ◽

Rebecca Garabed ◽

André Zoli Pagnah ◽

...

Keyword(s):

Low Cost ◽

Cost Effective ◽

Google Earth ◽

Geographical Information ◽

Gps Tracking ◽

Low Incomes ◽

Global Positioning ◽

System Data ◽

The One ◽

Time Required

Abstract Background Getting a random household sample during a survey can be expensive and very difficult especially in urban area and non-specialist. This study aimed to test an alternative method using freely available aerial imagery. Methods A gridded map and random selection method was used to select households for interviews. A hundred numbered of points were put along the edges of an updated map of Maroua. Then two numbers were randomly draw at a time and a line was drawn between those two numbers. A lot of different kinds of shapes of different sizes obtained were numbered. Ten shapes were randomly draw and the one selected were considered as ‘neighbourhoods’. A grid of 30 m × 30 m was drawn over each and then numbered. 202 grids considered here as households were randomly selected from the ten neighbourhoods for interviews. Results Out of 202 households visited, only 4 were found to be something other than a house. In addition, 30 sampled households (14.85%) were abandoned or the occupants had relocated elsewhere. This method resulted in an accuracy level of 72%, its advantage is the ability to generate efficient random sample at relatively low cost as well the time required. Conclusions The method proposed in this study was efficient and cost-effective when compared to the infield generation of a household inventory or Global Positioning System (GPS) tracking of households. It can then be used by researchers in low-incomes countries where funding for research is a challenge. However, this method needs to train the investigators on how to use the GPS.

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