Comparison of Terrain Indices and Landform Classification Procedures in Low-Relief Agricultural Fields

Landforms control the spatial distribution of numerous factors associated with agronomy and water quality. Although curvature and slope are the fundamental surface derivatives used in landform classification procedures, methodologies for landform classifications have been performed with other terrain indices including the topographic position index (TPI) and the convergence index (CI). The objectives of this study are to compare plan curvature, the convergence index, profile curvature, and the topographic position index at various scales to determine which better identifies the spatial variability of soil phosphorus (P) within three low relief agricultural fields in central Illinois and to compare how two methods of landform classification, e.g. Pennock et al. (1987) and a modified approach to the TPI method (Weiss 2001, Jenness 2006), capture the variability of spatial soil P within an agricultural field. Soil sampling was performed on a 0.4 ha grid within three agricultural fields located near Decatur, IL and samples were analyzed for Mehlich-3 phosphorus. A 10-m DEM of the three fields was also generated from a survey performed with a real time kinematic global positioning system. The DEM was used to generate rasters of profile curvature, plan curvature, topographic position index, and convergence index in each of the three fields at scales ranging from 10 m to 150 m radii. In two of the three study sites, the TPI (r ≥ -0.42) was better correlated to soil P than profile curvature (r ≤ 0.41), while the CI (r ≥ -0.52) was better correlated to soil P than plan curvature (r ≥ -0.45) in all three sites. Although the Pennock method of landform classification failed to identify footslopes and shoulders, which are clearly part of these fields’ topographic framework, the Pennock method (R² = 0.29) and TPI method (R² = 0.30) classified landforms that captured similar amounts of soil P spatial variability in two of the three study sites. The TPI and CI should be further explored when performing terrain analysis at the agricultural field scale to create solutions for precision management objectives

Comparison of Different Methods of Automated Landform Classification at the Drainage Basin Scale: Examples from the Southern Italy

In this work, we tested the reliability of two different methods of automated landform classification (ACL) in three geological domains of the southern Italian chain with contrasting morphological features. ACL maps deriving from the TPI-based (topographic position index) algorithm are strictly dependent to the search input parameters and they are not able to fully capture landforms of different size. Geomorphons-based classification has shown a higher potential and can represent a powerful method of ACL, although it should be improved with the introduction of additional DEM-based parameters for the extraction of landform classe

ANALYSIS OF RELIEF ELEMENTS THROUGH THE TOPOGRAPHIC POSITION INDEX (TPI) IN THE ARROIO PUITÃ WATERSHED –RIO GRANDE DO SUL/BRAZIL

O presente trabalho teve como objetivo a determinação de classes do Topographic Position Index (TPI) na bacia hidrográfica do arroio Puitã. O arroio Puitã localiza-se no sul do Brasil, oeste do estado do Rio Grande do Sul. A base altimétrica para a definição do Topographic Position Index, foram os dados de radar do “Shuttle Radar Topography Mission” (SRTM). O TPI é a base do sistema de classificação e, é simplesmente a diferença entre um valor de elevação de células e a altitude média da vizinhança em torno dessas células. Valores positivos significam que a célula é mais elevada do que os seus arredores, enquanto valores negativos significa que é mais baixa. A escala utilizada para a definição das classes de TPI, foi de 10 pixeis, ou seja, foi utilizado um raio de 10 pixeis para a análise da vizinhança que compõem a média de altitude e estabelece o valor de TPI do pixel central. As classes de TPI determinadas foram assim denominadas: vales; áreas planas; encostas suaves; encostas onduladas; encostas íngremes e topo das encostas. A área de encostas suaves predomina na bacia com 38,06% da área total. As áreas de encostas onduladas e áreas planas são as segunda e terceira, em área, com 27,15% e 27,11%, respectivamente. A área de topo das encostas é a que ocupa a menor área, com apenas 0,44% da área total. A aplicação da metodologia de determinação do relevo através Topographic Position Index apresentou um resultado que responde bem as feições de relevo observadas em campo, o que o potencializa para a aplicação em outras áreas.This study aimed to determine the Topographic Position Index (TPI) classes in the Arroio Puitã watershed. The Arroio Puitã is located in southern Brazil, western portion of the state of Rio Grande do Sul. The altimetric basis for defining the Topographic Position Index, were the radar data from the "Shuttle Radar Topography Mission" (SRTM). The TPI is the basis of the classification system, and is defined as the difference between a cell elevation value and the average elevation of the neighborhood around that cell. Positive values mean that the cell is higher than its surroundings, while negative values mean that it is lower. The scale used to define classes of TPI was 10 pixels, that is, we used a radius of 10 pixels for the neighborhood analysis composing the average altitude and sets the TPI value of the central pixel. TPI classes were so-called: Valleys, Lower Slopes, Gentle Slopes, Steep Slopes, Upper Slopes and Ridges. The area of Gentle Slopes predominates in the watershed with 38.06% of the total area. The areas of Steep Slopes and Lower Slopes, are the second and third in larger areas with 27.15% and 27.11% respectively. The area of the Ridges occupies the smallest area with only 0.44% of the total area. The implementation of the methodology for determining the relief through the Topographic Position Index presented a result that responds well to the major relief features observed in the field, potentializing its application to other areas

AN OBJECT BASED IMAGE ANALYSIS APPROACH FOR THE EXTRACTION OF THE KOLOUMBO VOLCANO AND ASSOCIATED DOMES-CONES FROM A DIGITAL SEABED ELEVATION MODEL

Η παρούσα μελέτη, αφορά στη μελέτη του θαλάσσιου πυθμένα από ψηφιακά μοντέλα αναγλύφου, με την ανάπτυξη μεθοδολογίας αντικειμενοστρεφούς ανάλυσης εικόνας. Έχει ως στόχο την αυτοματοποιημένη εξαγωγή γεωμορφολογικών χαρακτηριστικών πυθμένα, στον οποίο εντοπίζεται έντονη ηφαιστειακή δραστηριότητα. Η περιοχή μελέτης βρίσκεται στη λεκάνη της Ανύδρου, όπου δεσπόζει το υποθαλάσσιο ηφαίστειο του Κολούμπο, καθώς και μικρότεροι υποθαλάσσιοι ηφαιστειακοί κώνοι, 7 χλμ βορειοανατολικά της Σαντορίνης. Για το σκοπό αυτό, έγινε χρήση ψηφιακού μοντέλου αναγλύφου πυθμένα χωρικής ανάλυσης 50m και των παραγώγων αυτού: Slope, Topographic Position Index (TPI) και Terrain Ruggedness Index (TRI). Δημιουργήθηκαν συνολικά εννέα επίπεδα κατάτμησης και ταξινόμησης με στόχο την παραγωγή του τελικού επιπέδου κατάτμησης “level 5”, στο οποίο και ταξινομήθηκαν οι τελικές κατηγορίες γεωμορφολογικών χαρακτηριστικών. Τα αποτελέσματα της μεθόδου αξιολογήθηκαν με τη χρήση 1617 αλγορίθμων που αφορούν την ευστάθεια της ταξινόμησης, αλλά και με ποιοτική και ποσοτική σύγκριση των αποτελεσμάτων με υπάρχων χαρτογραφικό υλικό.This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map

Slavs and Proximity to Watercourses. The use of Corine Land Cover 2000, Local Drainage Direction Map and Topographic Position Index

Proximity to rivers, streams, lakes and bogs has been emphasized almost stereotypicallye as a specific Slavonic trait of character, but does this fit reality? We try to get further with the solution of this problem by a.) calculating the surface distances on basis of a Local Drainage Map and b.) using the Topographic Position Index

Unidades locais de Paisagem aplicadas à escala regional: área Alentejo, Centro e Extremadura

El presente artículo ofrece una visión general de los estudios realizados en la elaboración de las Unidades Locales de Paisaje (LLU) integrando los componentes de la litología, formas de relieve (TPI- Topographic Position Index) y ocupación/uso del suelo aplicada en el área OTALEX C. Los resultados indican que las LLUs más representativas son los "Cultivos temporales sobre Aluviales y coluviales en Llanura" (7,8%) y "Cultivos temporales sobre Pizarras en Llanura" (2,6%). Las "Zonas agroforestales - Dehesas sobre Pizarras en Llanura" y "Zonas agro-forestales - Dehesas sobre Aluviales y coluviales en Llanura" representan en conjunto el 8% de la superficie (748000 ha). También la zona de "Los Pastizales sobre Pizarras en Llanura", que representan el 2,6% y los matorrales densos en zonas de pendiente, valles aplanados y en zonas de llanura que ocupan aproximadamente el 14% de la superficie OTALEX C

Landslide Susceptibility Assessment in Mojokerto Regency Using Logistic Regression

The impact of landslides varies from place to place, including cutting off transportation routes, destroying agricultural land, and/or destroying houses. Due to the high threat of landslides, it is necessary to make efforts to improve community preparedness by disseminating information about landslide distribution. In this research, landslide assessment was conducted using logistic regression. Twelve landslide factors were assessed including topographic position index, stream power index, slope, aspect, elevation, profile curvature, distance to drainage, soil, rainfall, land use, and distance to road. The assessment of the landslide susceptibility level in this study was highly accurate, based on the AUC value obtained, which was 0.92. The results of the assessment of the landslide susceptibility level were divided into five classes with the following areas: very low 36%, low 4.4%, moderate 2.91%, high 4.1% and very high 52.5%. Keywords: scientific, approach, methodological, techniques, geograph

An earthquake-triggered submarine mass failure mechanism for the 1994 Mindoro tsunami in the Philippines : Constraints from numerical modeling and submarine geomorphology

Tsunamis have been known to result from a wide range of phenomena, such as earthquakes, volcanic eruptions, submarine mass failures, and meteorite impacts. Of earthquake-generated tsunamis, those arising from strike-slip mechanisms are less common, with the 1994 Mindoro tsunami in the Philippines among the few known examples. The 1994 Mindoro tsunami followed a Mw 7.1 earthquake along the right-lateral Aglubang River Fault. The tsunami affected the coasts surrounding the Verde Island Passage, one of the Philippines’ insular seas located between the islands of Luzon and Mindoro, and east of the West Philippine Sea margin. A total of 78 lives were lost due to the earthquake and tsunami, with 41 being directly attributed to the tsunami alone. Despite the close spatial and temporal association between the 1994 Mindoro earthquake and tsunami, previous numerical modeling suggests the need for other contributing mechanisms for the 1994 tsunami. In this study, we conducted submarine geomorphological mapping of the South Pass within the Verde Island Passage, with particular focus on identifying possible submarine mass failures. Identification of submarine features were based on Red Relief Image Map (RIMM), Topographic Position Index (topographic position index)-based landform classification, and profile and plan curvatures derived from high-resolution bathymetry data. Among the important submarine features mapped include the San Andres submarine mass failure (SASMF). The San Andres submarine mass failure has an estimated volume of 0.0483 km3 and is located within the Malaylay Submarine Canyon System in the Verde Island Passage, ∼1 km offshore of San Andres in Baco, Oriental Mindoro. We also explored two tsunami models (EQ-only and EQ+SMF) for the 1994 Mindoro tsunami using JAGURS. The source mechanisms for both models included an earthquake component based on the Mw 7.1 earthquake, while the EQ+SMF also included an additional submarine mass failure component based on the mapped San Andres submarine mass failure. Modeled wave heights from the EQ-only model drastically underestimates the observed wave heights for the 1994 Mindoro tsunami. In contrast, the EQ+SMF model tsunami wave height estimates were closer to the observed data. As such, we propose an earthquake-triggered, submarine mass failure source mechanism for the 1994 Mindoro tsunami