Digital elevation models (DEM) used to assess soil erosion risks: a case study in Boyaca, Colombia

The objective of this research was to develop a model for assessing the risk of erosion, exploring the potential of DEMs from SRTM, ASTER, ALOS PALSAR and one made with interpolation of a 1:25,000 contour map to calculate the variables of the relief that have greater impact on erosion. Several geomorphometric parameters, such as slope, aspect, profile and plan curvature, topographic wetness index, stream power index, and sediment transport capacity were computed from the DEM's elevation, some fuzzy logic functions proposed to evaluate the incidence of each parameter on erosion risk in a mountainous area of Colombia. The results showed that the use of DEM data is a relatively easy, uncostly method to identify, in a qualitative way, the risk of erosion and contribute to the enhancement of erosion information that is obtained with conventional general soil surveys.

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Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽

10.3390/s19163590 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3590 ◽

Cited By ~ 38

Author(s):

Bui ◽

Moayedi ◽

Kalantar ◽

Osouli ◽

Gör ◽

...

Keyword(s):

Landslide Susceptibility ◽

Characteristic Curve ◽

Absolute Error ◽

Slope Aspect ◽

Stream Power ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Performance Error ◽

Stream Power Index ◽

Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

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GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques

Journal of Geographical Studies ◽

10.21523/gcj5.20040201 ◽

2020 ◽

Vol 4 (2) ◽

pp. 45-63

Author(s):

Ishaku Bashir ◽

Rachel Sallau ◽

Abubakar Sheikh ◽

Zuni Aminu ◽

Shu’aib Hassan

Keyword(s):

Vulnerability Index ◽

Drainage Density ◽

Disaster Mitigation ◽

Vulnerability Mapping ◽

Slope Aspect ◽

Land Area ◽

Stream Power ◽

Topographic Wetness Index ◽

Vulnerability Class ◽

Stream Power Index

This paper explores the potentiality of GIS-based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) for gully vulnerability mapping. Multilayer information of basin characteristics, such as drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), slope aspect and land use land cover (LULC), were used in this study to develop a Gully Vulnerability Index (GVI). A weighted approach was implemented on each criterion relative to their inferred influence on gully vulnerability and validated by determining the Consistency Ratio (CR). Findings show a varying magnitude of gully vulnerability across the study area. The low to medium gully vulnerability class was dominant covering a land area of 6557ha (21.25%), and mostly confined to developed areas. Still, it is noteworthy to observe that the severe gully vulnerability class covers a substantial land area of 5825ha (18.88%), which presents a great risk to infrastructural development and human settlements in the study area. The study has a model predictive capability with accuracy rate of 84.62%. The integration of the MCDA and AHP into GIS workflow is an effective approach critical to minimize the limitations associated with gully occurrence analysis, using a singular basin characteristic. The results obtained in the study will equally be important in determining gully risk zones, circumspect urban development, tracking and proper infrastructure construction plans for long-term gully disaster mitigation.

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Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Sensors ◽

10.3390/s19163451 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3451 ◽

Cited By ~ 8

Author(s):

Usman Salihu Lay ◽

Biswajeet Pradhan ◽

Zainuddin Bin Md Yusoff ◽

Ahmad Fikri Bin Abdallah ◽

Jagannath Aryal ◽

...

Keyword(s):

Data Mining ◽

Debris Flow ◽

Airborne Lidar ◽

Multivariate Adaptive Regression Splines ◽

Slope Aspect ◽

Support Vector ◽

Mountainous Area ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Debris Flow Susceptibility

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

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Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil

Scientia Agricola ◽

10.1590/s0103-90162006000300008 ◽

2006 ◽

Vol 63 (3) ◽

pp. 262-268 ◽

Cited By ~ 35

Author(s):

Elvio Giasson ◽

Robin Thomas Clarke ◽

Alberto Vasconcellos Inda Junior ◽

Gustavo Henrique Merten ◽

Carlos Gustavo Tornquist

Keyword(s):

Land Use Planning ◽

Soil Types ◽

Sources Of Information ◽

Topographic Wetness Index ◽

Digital Elevation ◽

Terrain Parameters ◽

Logistic Regressions ◽

Elevation Model ◽

Soil Surveys ◽

Soil Distribution

Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.

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Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece.

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.10940 ◽

2016 ◽

Vol 47 (1) ◽

pp. 264 ◽

Cited By ~ 1

Author(s):

I. Ilia ◽

D. Rozos ◽

I. Koumantakis

Keyword(s):

Spatial Analysis ◽

Power Index ◽

Stream Power ◽

Topographic Wetness Index ◽

Wetness Index ◽

Topographic Position Index ◽

Landform Classification ◽

Essential Parameter ◽

Position Index ◽

Stream Power Index

The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter

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Stream Power determination along of a basin: First trial in the Chancay-Lambayeque basin.

10.5194/egusphere-egu21-36 ◽

2021 ◽

Author(s):

Joshua Er Addi Iparraguirre Ayala ◽

Estibene Pool Vásquez Choque ◽

Carlos Lenin Benavente Escobar ◽

Flor de María Zanini Maldonado ◽

Hugo Dulio Gómez Velásquez

Keyword(s):

Power Relationship ◽

Stream Power ◽

Process Data ◽

Rock Falls ◽

Alos Palsar ◽

Flow Rates ◽

Digital Elevation ◽

Peruvian Coast ◽

Critical Zones ◽

Gravitational Processes

The Peruvian coast is one of the driest in the world, but it is continuously affected by extraordinary rains associated with El Ni&#241;o and/or La Ni&#241;a phenomenon. During these periods of intense rainfall, high flow rates are registered and gravitational processes are reported along the valleys, such as: landslides, debris flow, rock falls, avalanches, among others.This work presents the first estimation of the Stream Power, relationship between the energy, the flow, the slope of the channel and the density of the flow of the Chancay - Lambayeque basin, with the objective of determining the energy of the main rivers in the basin and relating with gravitational processes and damage to infrastructures.We use two softwares: LSDTopoTools and ArcSWAT (version for ArcGIS 10.6). Using high resolution Digital Elevation Models (Alos Palsar, 12.5 m) we delimit the basin, its drainage area, water network and slope using LSDTopoTools. Subsequently, we use the SWAT program.First, the sub-basins were delimited. Second, the Hydrological Response Units (HRU) were obtained, applying the Land Use data and the FAO base guide on soil types updated by the Ministry of Agriculture and Irrigation of Peru (MINAGRI). Third, we process data on temperature, wind speed, humidity, solar radiation and rainfall from 1970 - 2018 from five meteorological stations distributed in the study basin, whose data were provided by the National Meteorology and Hydrology Service of Peru (SENAMHI). Next, we include in the analyzes the flow data from the Tinajones reservoir (6&#176; 38&#180;S, 79&#176; 29&#180;W). Finally, the annual flow rates (Hm3/s) were simulated and adjusted using SWATCup.The results show an average flow for the year 2018 that varies from 13 Hm3/s - 49 Hm3/s. This means that the Stream Power varies from 1.3x1012Kw-4.8x1012Kw, the maximum power coinciding with the location of the Tinajones reservoir in the middle basin.These results have allowed us to identify that 73% of the critical zones (zones with presence of gravitational processes) are in the sections where the rivers register high Stream Power; and in the same way in these sections geological dangers predominate such as flows and rock falls. In addition, infrastructures were located that may be susceptible to being damaged (e.g. three bridges, where flows range between ~22-35 Hm3/s) and/or may compromise the health of the inhabitants (e.g. five mining deposits located along the basin, considered high risk).And to conclude, because the Tinajones reservoir is reaching its maximum capacity, a possible area was identified where a new reservoir can be housed (complying with all technical conditions), whose location would be 20 km to the east, in the province of Chumbil Alto (Cajamarca - Peru).

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An illustrated introduction to general geomorphometry

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133317733667 ◽

2017 ◽

Vol 41 (6) ◽

pp. 723-752 ◽

Cited By ~ 21

Author(s):

Igor V Florinsky

Keyword(s):

Analytical Description ◽

Shape Index ◽

Shuttle Radar Topography Mission ◽

Slope Aspect ◽

Triaxial Ellipsoid ◽

Stream Power ◽

Mathematical Concepts ◽

Non Local ◽

Stream Power Index ◽

Horizontal Curvature

Geomorphometry is widely used to solve various multiscale geoscientific problems. For the successful application of geomorphometric methods, a researcher should know the basic mathematical concepts of geomorphometry and be aware of the system of morphometric variables, as well as understand their physical, mathematical and geographical meanings. This paper reviews the basic mathematical concepts of general geomorphometry. First, we discuss the notion of the topographic surface and its limitations. Second, we present definitions, formulae and meanings for four main groups of morphometric variables, such as local, non-local, two-field specific and combined topographic attributes, and we review the following 29 fundamental morphometric variables: slope, aspect, northwardness, eastwardness, plan curvature, horizontal curvature, vertical curvature, difference curvature, horizontal excess curvature, vertical excess curvature, accumulation curvature, ring curvature, minimal curvature, maximal curvature, mean curvature, Gaussian curvature, unsphericity curvature, rotor, Laplacian, shape index, curvedness, horizontal curvature deflection, vertical curvature deflection, catchment area, dispersive area, reflectance, insolation, topographic index and stream power index. For illustrations, we use a digital elevation model (DEM) of Mount Ararat, extracted from the Shuttle Radar Topography Mission (SRTM) 1-arc-second DEM. The DEM was treated by a spectral analytical method. Finally, we briefly discuss the main paradox of general geomorphometry associated with the smoothness of the topographic surface and the non-smoothness of the real topography; application of morphometric variables; statistical aspects of geomorphometric modelling, including relationships between morphometric variables and roughness indices; and some pending problems of general geomorphometry (i.e. analysis of inner surfaces of caves, analytical description of non-local attributes and structural lines, as well as modelling on a triaxial ellipsoid). The paper can be used as a reference guide on general geomorphometry.

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A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

Remote Sensing ◽

10.3390/rs12172688 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2688 ◽

Cited By ~ 2

Author(s):

Viet-Ha Nhu ◽

Phuong-Thao Thi Ngo ◽

Tien Dat Pham ◽

Jie Dou ◽

Xuan Song ◽

...

Keyword(s):

Soil Type ◽

Flash Flood ◽

Predictive Performance ◽

Slope Aspect ◽

Support Vector ◽

Landsat 8 ◽

Mountainous Area ◽

Random Subspace ◽

Topographic Wetness Index ◽

Inventory Map

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

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Relief parameters and fuzzy logic for land evaluations of mango crops (Mangifera indica L.) in Colombia

Agronomía Colombiana ◽

10.15446/agron.colomb.v32n2.43988 ◽

2014 ◽

Vol 32 (2) ◽

pp. 246-254 ◽

Cited By ~ 2

Author(s):

Oscar Javier Munar-Vivas ◽

Luis Joel Martínez M.

Keyword(s):

Fuzzy Logic ◽

Mangifera Indica ◽

Evaluation Study ◽

Shuttle Radar Topography Mission ◽

Land Evaluation ◽

Slope Aspect ◽

Detailed Data ◽

Topographic Wetness Index ◽

Realistic Approach ◽

Digital Elevation

The aim of this paper is to illustrate the use of digital elevation models (DEM) to calculate relief parameters and include them in suitability studies of land for mango crops in Colombia. Data from SRTM (Shuttle Radar Topography Mission) DEMs with 30 meter of spatial resolution and elevation in meters were used to calculate the slope, aspect, curvature, solar radiation, and topographic wetness index for inclusion in a land evaluation study. Fuzzy logic rules were developed and applied to define the degree of suitability by matching land use requirements with land characteristics. When integrated with geographic information systems, DEMs have significant potential for quantitatively defining and characterizing relief and for generating more detailed data to improve land evaluation processes. The Fuzzy logic proved to be a more realistic approach for evaluating the degree of land suitability than traditional bivalent logic, allowing for the use of membership degrees.

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Landslide susceptibility analysis using decision tree method, Phidim, Eastern Nepal

Bulletin of the Department of Geology ◽

10.3126/bdg.v15i0.7419 ◽

2013 ◽

Vol 15 ◽

pp. 69-76 ◽

Cited By ~ 2

Author(s):

Chandra Prakash Poudyal

Keyword(s):

Decision Tree ◽

Landslide Susceptibility ◽

Power Index ◽

Geographical Information ◽

Susceptibility Map ◽

Effective Parameters ◽

Stream Power ◽

Topographic Wetness Index ◽

Landslide Susceptibility Map ◽

Stream Power Index

The decision tree is one of the new methods used for the determination of landslide susceptibility in the study area. The Phidim area is selected for the application of this method. The total surface area is 168.07 sq. km, and is located at the eastern part of Nepal. There are total of 10 different data bases used for this study which are; geological formation, elevation, slope, curvature, aspect, stream power index, topographic wetness index, distance from drainage, lineaments, and slope length, and are considered as landslide conditioning factors. Geographical information system (GIS) is used as basic tools and ARC/View is used for the processing data analysis and final map preparation. For the decision tree analysis the PASW 18 (statistical tool) is used to generate values of each factor. According to the results of decision tree, two geological formations; stream power index and slope are found as the most effective parameters on the landslide occurrence in the study area. Using the predicted values, the landslide susceptibility map of the study area is produced. To assess the performance of the produced susceptibility map, the area under curve (AUC) is drawn. The AUC value of the produced landslide susceptibility map has been obtained as 95.9%. According to the results of the AUC evaluation, the produced map has showed a good performance. As to wrap up, the produced map is able to be used for medium scaled and regional planning purposes. DOI: http://dx.doi.org/10.3126/bdg.v15i0.7419 Bulletin of the Department of Geology, Vol. 15, 2012, pp. 69-76

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