scholarly journals Landslide Trail Extraction Using Fire Extinguishing Model

2022 ◽  
Vol 14 (2) ◽  
pp. 308
Author(s):  
Zhao Zhan ◽  
Wenzhong Shi ◽  
Min Zhang ◽  
Zhewei Liu ◽  
Linya Peng ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Parameter Tuning ◽  
Landslide Risk ◽  
Excellent Performance ◽  
Fire Extinguishing ◽  
Manual Intervention ◽  
Digital Elevation ◽  
Landslide Mapping ◽  
Cost Efficient ◽  
Elevation Model

Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a challenge to existing skeletonization methods is that expert knowledge and manual intervention are required to obtain a branchless skeleton, which limits the applicability of these methods. To address this problem, a new workflow for landslide trail extraction (LTE) is proposed in this study. To avoid generating redundant branches and to improve the degree of automation, two endpoints, i.e., the crown point and the toe point, of the trail were determined first, with reference to the digital elevation model. Thus, a fire extinguishing model (FEM) is proposed to generate skeletons without redundant branches. Finally, the effectiveness of the proposed method is verified, by extracting landslide trails from landslide polygons of various shapes and sizes, in two study areas. Experimental results show that, compared with the traditional grassfire model-based skeletonization method, the proposed FEM is capable of obtaining landslide trails without spurious branches. More importantly, compared with the baseline method in our previous work, the proposed LTE workflow can avoid problems including incompleteness, low centrality, and direction errors. This method requires no parameter tuning and yields excellent performance, and is thus highly valuable for practical landslide mapping.

The potential of Self-Organising maps clustering to characterise a harvested peatland using airborne radiometric data and OS digital elevation model.

2021 ◽  
Author(s):  
Eve daly ◽  
David O Leary
Keyword(s):  
Water Content ◽  
Digital Elevation Model ◽  
Expert Knowledge ◽  
Self Organizing Map ◽  
Near Surface ◽  
Restoration Project ◽  
Raised Bogs ◽  
Digital Elevation ◽  
Elevation Model ◽  
Radiometric Data

<p>Peatlands are becoming recognized as important carbon sequestration centres. Through restoration projects of peatlands in which the water table is raised, they may become carbon neutral or possibly carbon negative. Restoration projects require a knowledge of intra-peat variation across potentially large spatial areas. This is often difficult with traditional in-situ point measurements. The integration of multidimensional geophysical datasets and digital elevation models, combined with modern data analytical techniques, may provide a rapid means of accessing intra-peat variation. In this study, an airborne radiometric survey, being flown nationally over the Republic of Ireland, combined with a digital elevation model, is used to delineate areas within an industrial peatland where peat thickness is less than 1m. Radiometric data are particularly suited to peat studies as they are sensitive to water content and peat thickness and require relatively little expert knowledge to utilise. Peat, as a mostly organic material, acts as a low signal environment where variations in the signal are linked to intra-peat variation of thickness, density and/or water content. This study uses an unsupervised machine learning, self-organizing map clustering methodology to group the study site into three zones interpreted as 1) the edge of the bog where peat layer is thinning or there is influence on the radiometric signal from non-peat soils outside of the bog, 2) the normal peat conditions where thickness and saturation appear as a relative constant in the radiometric response, and 3) areas where the peat is either thinner or drier. A ground geophysical survey was conducted to verify this interpretation. The delineation of such spatial variations in the radiometric response could aid any restoration project in the initial stages or act as a baseline study to monitor changes to the peatland during and after a restoration project is complete. Future work will see this methodology extended to other peatland types such as blanket bogs and natural raised bogs, as well as the integration of concurrent airborne electromagnetic data to link the near-surface radiometric response to the deeper vadose zone and define a more comprehensive classification scheme for these peatland sites.</p>

scholarly journals Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2160
Author(s):  
Daniel Kibirige ◽  
Endre Dobos
Keyword(s):  
Soil Moisture ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Kriging ◽  
Environmental Covariates ◽  
Observatory Data ◽  
Digital Elevation ◽  
Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

scholarly journals LANDSLIDE SUSCEPTIBILITY MODELLING IN SELECTED STATES ACROSS SE. NIGERIA

2020 ◽  
Vol 4 (1) ◽  
pp. 23-27
Author(s):  
R. O. E. Ulakpa ◽  
V.U.D. Okwu ◽  
K. E. Chukwu ◽  
M. O. Eyankware
Keyword(s):  
Landslide Susceptibility ◽  
Landsat Imagery ◽  
Control Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Landsat 8 ◽  
Aerial Photos ◽  
Anambra State ◽  
Digital Elevation ◽  
Elevation Model

Identification and mapping of landslide is essential for landslide risk and hazard assessment. This paper gives information on the uses of landsat imagery for mapping landslide areas ranging in size from safe area to highly prone areas. Landslide mitigation largely depends on the understanding of the nature of the factors namely: slope, soil type, lineament, lineament density, elevation, rainfall and vegetation. These factors have direct bearing on the occurrence of landslide. Identification of these factors is of paramount importance in setting out appropriate and strategic landslides control measures. Images for this study was downloaded by using remote sensing with landsat 8 ETM and aerial photos using ArcGIS 10.7 and Surfer 8 software, while Digital Elevation Model (DEM) and Google EarthPro TM were used to produce slope, drainage, lineament and elevation. From the processed landsat 8 imagery, landslide susceptibility map was produced, and landslide was category into various class; low, medium and high. From the study, it was observed that Enugu and Anambra state ranges from high to medium in terms of landslide susceptibility, Imo state ranges from medium to low.

scholarly journals APPLICATION OF AHP METHOD FOR MAPPING SLOPE GEOMORPHIC PHENOMENA

2017 ◽  
Author(s):  
Florin Constantin MIHAI
Keyword(s):  
Risk Mitigation ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Performance ◽  
Landslide Risk ◽  
Good Correspondence ◽  
Topographic Features ◽  
Digital Elevation ◽  
Elevation Model ◽  
Hierarchy Process

Landslides are common and frequent geomorphic phenomena for the plateau regions in Romania having important consequences, especially economic ones, that needs designing scientific and technical plans for landslide risk mitigation. For this, an important preliminary step is assessing and mapping the landslide susceptibility. This paper examines a plateau zone in eastern Romania providing such a map, based on the landslides inventory, the digital elevation model (DEM) and the thematic layers of several factors thought to be potential predictors of landslides occurrence: topographic features, land use, and lithology. The methodological framework is based on the analytical hierarchy process (AHP) principles and factors weights attributed based on frequency of landslides. The predictive performance of the model was assessed using the confusion matrix, the ROC (receiver operating characteristic) curve and the AUC (area under curve) parameter. The results indicate a good correspondence between the susceptibility estimated for the test samples and for the validation samples

scholarly journals Prediction of soil classes in a complex landscape in Southern Brazil

2019 ◽  
Vol 54 ◽  
Author(s):  
Jean Michel Moura-Bueno ◽  
Ricardo Simão Diniz Dalmolin ◽  
Taciara Zborowski Horst-Heinen ◽  
Luciano Campos Cancian ◽  
Ricardo Bergamo Schenato ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Model Performance ◽  
Spatial Prediction ◽  
Support Vector ◽  
Kappa Index ◽  
Digital Elevation ◽  
Complex Landscape ◽  
Elevation Model

Abstract: The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression, and support vector machine. The accuracy of the models was evaluated by the kappa index (K), general accuracy (GA), and class accuracy. The prediction models were sensitive to the disproportionate sampling of soil classes. The best predicted map achieved a GA of 71% and K of 0.59. The use of the covariate set chosen by expert knowledge improves model performance in predicting soil classes in a complex landscape, and random forest is the best model for the spatial prediction of soil classes.

scholarly journals Assessment of landslide risk in the mountainous area. Case study: Bumiaji Sub-District

2021 ◽  
Vol 916 (1) ◽  
pp. 012009
Author(s):  
A R A Prasetya ◽  
T A Rachmawati ◽  
F Usman
Keyword(s):  
Secondary Data ◽  
Risk Level ◽  
Landslide Risk ◽  
Mountainous Area ◽  
Tourist Destinations ◽  
Digital Elevation ◽  
Medium Level ◽  
Hazard Level ◽  
Elevation Model ◽  
High Level

Abstract Throughout 2016-2021, there were 31 landslides that have caused physical, economic, and social damages. Bumiaji Sub-District has several tourist destinations that are potentially exposed to landslides. This study aims to create a landslide risk map in Bumiaji Sub-District. This research was conducted during the COVID-19 pandemic situation. Therefore, the data collected was secondary data obtained from Google satellite images, Google Street View, the digital elevation model from the National Geospatial Institution, and other literature reviews. The data was then analysed using a landslide risk assessment based on Perka BNPB Number 2/2012. The results of this risk analysis show that Bumiaji Sub-District is dominated by low-level risk (48%), followed by high-level risk (30%), and medium-level risk (15%). High-risk level is affected by high hazards and vulnerabilities, especially in Giripurno Village. High hazard level is affected by high intensity of rainfall, slope degree, the sensitivity of soil to erosion, and the type of land cover. High vulnerabilities are affected by physical, social, and economic aspects susceptible to losses.

Assessing UAV Landslide Mapping Using Unmanned Aerial Vehicle (UAV) for Landslide Mapping Activity

2014 ◽  
Vol 567 ◽  
pp. 669-674 ◽  
Author(s):  
Munirah Radin Mohd Mokhtar ◽  
Abdul Nasir Matori ◽  
Khamaruzaman Wan Yusof ◽  
Abdul Mutalib Embong ◽  
Muhammad Ikhwan Jamaludin
Keyword(s):  
Digital Elevation Model ◽  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
Research Area ◽  
3D Surface ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Landslide Mapping ◽  
Elevation Model ◽  
Digital Orthophoto

The purpose of this research is to improve the landslide mapping using unmanned aerial vehicle (UAV) for the area of slope displacement. It further presents the UAV namely multi-motor that being used to capture images at the research area located in Parit, Perak. The objective of this research paper is to develop a three dimensional of landslide area produced from the UAV images. For the whole process of image processing, thirty six control points are established using global positioning system (GPS) staic mehtod using Agisoft Photoscan. The results show that the digital elevation model (DEM), aspect Model, slope model, and digital orthophoto can be obtained using the procedure and method used in the study. The information is obtained through accurate assessment results and used to create a 3D model which is then used to monitor technique applications. The restitution stereo model is also by three dimensional rotations or transformation in 3D surface. From here, the landslide can be detected by calculation of three difference epoch data achieved from Digital Elevation Model (DEM) generation. Prior to that, this paper focuses on the monitoring of that area based on DEM area and volume generated from 3D surface analysis. To conclude this study shows that UAV can be used for producing digital map.

scholarly journals Inventory of landslides triggered by hurricane Matthew in Guantánamo, Cuba

2021 ◽  
Author(s):  
Yusmira Savón Vaciano ◽  
Ricardo Delgado Tellez ◽  
Enrique A. Castellanos Abella ◽  
Rafael Guardado Lacaba ◽  
Arisleidys Peña de la Cruz
Keyword(s):  
Wide Distribution ◽  
Rain Gauge ◽  
Landslide Risk ◽  
Eastern Region ◽  
Landslide Occurrence ◽  
Digital Elevation ◽  
Geological Map ◽  
Sentinel 2A ◽  
Elevation Model ◽  
Landslide Hazard Map

Abstract An inventory of landslides triggered by Hurricane Matthew (4–5 October 2016) through the eastern region of Cuba was carried out using Sentinel 2A satellite images. The inventory was compared with the slope map generated from the digital elevation model at 25 m per pixel and with the geological map at 1: 100 000 scale. The precipitation data from the 1-hour rain gauge records of four stations of the Cuban Institute of Meteorology (INSMET) and 24-hour rain gauge records of six stations of National Institute of Hydraulic Resources (INRH) were processed and analysed during this event. In total, 237 landslides were classified into rockslides, debrisflows and topples. A wide distribution of landslides was found within the selected slope classes, depending of the landslide type. Most of the landslides were generated in green schist of volcanic and vulcanoclastic rocks and rocks of the ophiolitic complex made up of ancient remains of oceanic crust. Findings increase understanding of landslide occurrence in this area in order to update landslide hazard map and to reduce landslide risk.

STUDY OF THE LANDSLIDE MORPHOLOGY BASED ON GNSS DATA AND AIRBORNE SOUNDING (ON THE EXAMPLE OF A SECTION OF THE PROTVA RIVER VALLEY)

2018 ◽  
Vol 12 (5-6) ◽  
pp. 50-57 ◽  
Author(s):  
I. S. Voskresensky ◽  
A. A. Suchilin ◽  
L. A. Ushakova ◽  
V. M. Shaforostov ◽  
A. L. Entin ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Geodetic Network ◽  
Aerial Survey ◽  
River Valley ◽  
Ease Of Use ◽  
Digital Elevation ◽  
Loose Deposits ◽  
Positioning Method ◽  
Landslide Body ◽  
Elevation Model

To use unmanned aerial vehicles (UAVs) for obtaining digital elevation models (DEM) and digital terrain models (DTM) is currently actively practiced in scientific and practical purposes. This technology has many advantages: efficiency, ease of use, and the possibility of application on relatively small area. This allows us to perform qualitative and quantitative studies of the progress of dangerous relief-forming processes and to assess their consequences quickly. In this paper, we describe the process of obtaining a digital elevation model (DEM) of the relief of the slope located on the bank of the Protva River (Satino training site of the Faculty of Geography, Lomonosov Moscow State University). To obtain the digital elevation model, we created a temporary geodetic network. The coordinates of the points were measured by the satellite positioning method using a highprecision mobile complex. The aerial survey was carried out using an unmanned aerial vehicle from a low altitude (about 40–45 m). The processing of survey materials was performed via automatic photogrammetry (Structure-from-Motion method), and the digital elevation model of the landslide surface on the Protva River valley section was created. Remote sensing was supplemented by studying archival materials of aerial photography, as well as field survey conducted immediately after the landslide. The total amount of research results made it possible to establish the causes and character of the landslide process on the study site. According to the geomorphological conditions of formation, the landslide refers to a variety of landslideslides, which are formed when water is saturated with loose deposits. The landslide body was formed with the "collapse" of the blocks of turf and deluvial loams and their "destruction" as they shifted and accumulated at the foot of the slope.

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