Geomorphometric Characteristics of Landslides in the Tinalah Watershed, Menoreh Mountains, Yogyakarta, Indonesia

AbstractA landslide is one of natural hazards that affect humans and their livelihood especially in the mountainous area. The increasing landslide risk due to global climate change and demographic pressure demands integration between disaster risk reduction and sustainability management, for instance, the recently increasing people's awareness of the landslide and its impacts. Landslides occur in particular location regarding both physical and non-physical features of an area, comprising geomorphology, geology, geomorphometry, human activities, earthquake probability, rainfall occurrence, and etc. This research aims to understand the characteristics of the specific land surface that bears susceptibility to landslides using a geomorphometric approach and to analyze the relationship between geomorphometric characteristics and landslide events. The Tinalah watershed is located in Menoreh Mountains, one of mountainous areas in Java where highly frequent landslides occur. Geomorphometric characteristics, derived from DEMs with 2x2-m2 grid resolution, consist of elevation, slope gradient, aspect, profile curvature, plan curvature, and general curvature. The inventory of landslide events, consisting of the location, time, area, perimeter, typology, and activity, is derived from the field maps, local government's report analysis, and interviews with local people. In this research, landslide distribution is mapped using the multi-temporal records of landslide events during 2006-2010. A raster-based spatial analysis reveals the relationship between landslide events and geomorphometric characteristics. Each variable shows the quantitative information of landslide distribution in the Tinalah watershed. As a result, geomorphometric characteristics have the most significant relationship with the landslide distribution in this study area

Multiscale Landforms Classification Based on UAV Datasets

The advance uses of Unmanned Aerial Vehicles (UAV) in geosciences by producing very high spatial resolution Digital Surface Models (DSMs), the various UAV flight altitudes led to different scales DSM. In this paper, we analyzed terrain forms using Topographic Position Index (TPI), landforms extracted by Iwahashi and Pike method and morphometric features of three different spatial resolutions DSM processed from different UAV flights height datasets of the same study area.Topographic Position Index (TPI) is an algorithm for measuring topographic slope positions and to automate landform classi?cations, Iwahashi and Pike had developed an unsupervised method for classification of Landforms and we have used the techniques developed by Peuker and Douglas, a method classifying terrain surfaces into 7 classes.Landforms extracted from the three indices listed above at the three flight heights of 120, 240 and 360 meters and compared with each other to understand the generalization of different scale and to highlight which landforms are more affected by the scale changes

Supervised intelligent committee machine method for hydraulic conductivity estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers

(FUZZY LOGIC AND APLLICATIONS IN GEOPHYSICS: A SEISMOLOGY EXAMPLE

Bulanık mantık, teknolojinin de etkisiyle son yıllarda birçok problemin çözümünde yaygın olarak kullanılan yöntemlerden biridir. Doğada kesin olarak tanımlanamayan birçok olayın bulanık mantık yardımıyla çözümleri mümkün hale gelmiştir. Uygulama alanının geniş olması ve birçok problemin çözümünde başarılı sonuçların elde edilmesi bu yönteme olan ilgiyi arttırmıştır.Bulanık mantığın jeofizik alanındaki uygulamaları da giderek artmaktadır. Özellikle sismik, elektromanyetik ve özdirenç gibi yöntemlerin ters çözümünde ayrıca parametre tayini ve ön kestirim gibi uygulamalarda kullanılmaktadır. Bu çalışmada bulanık mantığın günümüze kadar olan jeofizik uygulamaları derlenmiş ve yaygın olarak kullanım amaçları özetlenmeye çalışılmıştır. Batı Anadolu deprem katalog verilerinin Uyarlanabilir Yapay Sinir-Bulanık Mantık Çıkarım Sistemi (Adaptive Neurofuzzy Inference System) (UYBÇS) ile değerlendirilmesi üzerine örnek bir çalışmaya yer verilmiştir. With the effect of advancing technology, Fuzzy logic has become one of the most common methods used in solving problems during the recent years. Solutions of the many ill defined/unidentified events in nature/earth are made possible by means of fuzzy logic. Wide ranges of applications and obtaining successful results are caused the increasing interest on this method.Applications of Fuzzy logic on Geophysics are also increasing day by day. It is used on particularly inversion of seismic, electromagnetic and resistivity data, prediction of some physical parameters and estimation studies. The aim of this study is to compile the articles which are about Fuzzy logic application on geophysics and to summarize its intended purpose. Analyzing of the Earthquake data of Western Anatolia Using with Adaptive Neurofuzzy Inference System, is given an example of this method as a seismological application

Morphometric characterisation of landform from DEMs

We describe a method of morphometric characterisation of landform from DEMs. The method is implemented by first classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single-scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi-scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi-scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single- and multi-scale terrain indices perform well when characterising the different geomorphometric features

Visualization of Uncertain Boundaries of Undersea Features

There have been several studies that detect, measure, analyze, and visualize the undersea features by using technologies in multiple disciplines including geography and oceanography. However, definitions of the undersea features often vary among the existing leading literature. Due to this reason the geographical boundary for a certain undersea feature is sometimes not identical among the definitions. In this study, we explore semantic uncertainty in the definitions of some undersea features and apply approaches from fuzzy-set theory and geographic information science on empirical bathymetric data to visualize the uncertain boundaries of the undersea features. Results from this study demonstrate that the representation based on the fuzzy-set approach can be useful for dealing with the semantic uncertainty of the undersea features

Mapping the physiography of Michigan with GIS

Abstract: We present a new physiographic map of Michigan, that is also available interactively, online. Only four, small-scale physiographic maps of Michigan had been previously published. Our mapping project made use of a wide variety of spatial data, in a GIS environment, to visualize and delineate the physical landscape in more detail than has been done previously. We also examined many of the unit boundaries in the field, using a GIS running on a GPS-enabled laptop. Unlike previous physiographic maps, the online version of the map enables users to query the criteria used to define each of the 224 boundaries of its 10 major and 91 minor physiographic units. The interactive nature of the online version of the map is a unique enhancement to physiographic maps and mapping. Our study also provides data on the number and types of criteria used to define each of the 224 unit boundaries within the map. Most of our unit boundaries are based on data derived from 10-m raster elevation data and NRCS soils data, e.g., relief, soil wetness, escarpments, landscape fabric, and parent material characteristics. Data gleaned from NRCS SSURGO county-scale soil maps were a strength of the project

Morphometric analysis of differently degraded simple craters on the moon

The main focus of this PhD research is the morphologic characterization of simple impact craters on lunar maria in order to find out a correlation between craters morphological degradation and absolute model ages of the surfaces where they were emplaced. Crater degradation can be indeed used to constrain the chronological evolution of planetary surfaces. The crater degradation is usually retrieved through visual inspection by subdividing craters into 4 classes: C1 represents the freshest ones, C2 are the ones with the first evidence of degradation (smoothed rim), C3 and C4 are related to morphologies ranging from heavily eroded to totally flattened respectively [Arthur, 1963]. We firstly conducted a morphometric analysis of craters representative of the four classes starting from the freshest one represented by the Linné crater. Craters were chosen on a homogeneous geological unit, the S28 unit in mare Serenitatis, with an absolute model age of 2.84 Gy [Hiesinger et al., 2011]. This analysis allowed us to establish the thresholds of mean slope from craters inner wall, in order to constrain the morphometric characterization of the four degradation classes. Successively we have extracted all impact craters (383) from a unique geological unit and we have defined the morphologic relationships among the degradation classes in function of the craters diameters. Finally, we expanded our analysis to six lunar maria, considering six lunar maria with different average absolute model ages, in order to perform this analysis with the wider range of ages. For each mare we considered a unique surface (dataset) derived from the merging of geological units with similar absolute model ages within the basin, in order to guarantee the most homogeneous possible surfaces, both in terms of impact rheology and absolute age. From the six surfaces we have extracted inner wall mean slopes from over 1000 impact craters. The mean slope values of the inner walls have shown a relation between crater morphology and the absolute model ages of the geological units where they are located. Older basins are characterized by craters with lower mean slope values, suggesting a dominance of older craters in their population, whereas the younger units have shown higher mean slope values of their simple craters, suggesting a population dominated by recent impacts. This tendency is the expression of the morphological alteration strictly connected to the lunar maria age. Since the geomorphometry of impact craters is influenced by the absolute age of the target area, we have constrained potential isochrones by fixing absolute age thresholds based on the morphological variations of impact craters

Fuzzy logic-based digital soil mapping in the Laurel Creek Conservation Area, Waterloo, Ontario

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited