Regional and local topography subdivision and landform mapping using SRTM-derived data: a case study in southeastern Brazil

Manfré, Luiz Augusto; Nóbrega, Rodrigo Affonso de Albuquerque; Quintanilha, José Alberto

doi:10.1007/s12665-014-3869-2

Cited by 14 publications

(9 citation statements)

References 28 publications

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“…The applications using the widespread free distribution DEM have gained great importance, especially among academic activities, given the wide range of possible applications, such as mapping hilltops environmental protected areas (Oliveira 2015), monitoring forest biomass (Solberg et al 2010), hydrological modeling (Ludwig and Schneider 2006), soil losses estimation (Fornelos and Neves 2007), aid in detecting neotectonic reactivations (Fonseca and Corrêa 2011), geomorphologic mapping (Camargo et al 2011), relief shapes classification (Manfré, Nóbrega and Quintanilha 2015), didactic resource for teaching and learning .…”

Section: Introductionmentioning

confidence: 99%

ALTIMETRY ASSESSMENT OF ASTER GDEM v2 AND SRTM v3 DIGITAL ELEVATION MODELS: A CASE STUDY IN URBAN AREA OF BELO HORIZONTE, MG, BRAZIL

et al. 2017

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Abstract:This work is an altimetry evaluation study involving Digital Elevation Models ASTER GDEM version 2 and SRTM version 3. Both models are readily available free of charge, however as they are built from different remote sensing methods it is also expected that they present different data qualities. LIDAR data with 25 cm vertical accuracy were used as reference for assessment validation. The evaluation study, carried out in urbanized area, investigated the distribution of the residuals and the relationship between the observed errors with land slope classes. Remote sensing principles, quantitative statistical methods and the Cartographic Accuracy Standard of Digital Mapping Products (PEC-PCD) were considered. The results indicated strong positive linear correlation and the existence of a functional relationship between the evaluated models and the reference model. Residuals between -4.36 m and 3.11 m grouped 47.7% of samples corresponding to ASTER GDEM and 63.7% of samples corresponding to SRTM. In both evaluated models, Root Mean Square Error values increased with increasing of land slope. Considering 1: 50,000 mapping scale the PEC-PCD classification indicated class B standard for SRTM and class C for ASTER GDEM. In all analyzes, SRTM presented smaller altimetry errors compared to ASTER GDEM, except in areas with steep relief.Keywords: Digital Elevation Models; ASTER GDEM v2; SRTM v3; Altimetry Assessment.

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Section: Introductionmentioning

confidence: 99%

ALTIMETRY ASSESSMENT OF ASTER GDEM v2 AND SRTM v3 DIGITAL ELEVATION MODELS: A CASE STUDY IN URBAN AREA OF BELO HORIZONTE, MG, BRAZIL

et al. 2017

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“…Many macro landforms, such as volcanoes, mountains, hills, plains, and fans, and sub-classified landforms, such as high mountains, low plains, tablelands, and rough low hills, can be achieved through a designed classifier or object-based image analysis approach based on topographic variables derived from DEMs (Camargo et al, 2012; Drăguţ and Eisank, 2012; Iwahashi and Pike, 2007; Jasiewicz et al, 2014; Manfré et al, 2015; Prima et al, 2006; Saadat et al, 2008; Stepinski and Bagaria, 2009; Vannametee et al, 2014). In the aforementioned classifications, topographic variables, such as slope, curvature, local relief, roughness, etc., are calculated through neighborhood analysis, which only reflects the local topographic surface parameters and fails to reveal terrain structural features in the macro scale.…”

Section: Introductionmentioning

confidence: 99%

Regional topographic classification in the North Shaanxi Loess Plateau based on catchment boundary profiles

Yang

et al. 2017

Progress in Physical Geography: Earth and Environment

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Topographic classification and mapping are fundamental topics in geomorphology and have many promising applications. This study aimed to achieve landform classification using the catchment boundary profile (CBP) in the area of the North Shaanxi Province. CBPs were extracted, and seven indices were proposed and calculated to describe the profile features. Spatial maps of these indices were generated by interpolating 81 sample areas in the North Shaanxi Province of China. Finally, a topographic regional map was generated through multi-resolution segmentation after integrating the spatial maps of the seven indices. Results showed that the CBP indices were typical and representative to describe the terrain characteristics in the North Shaanxi Province and revealed their diverse spatial patterns. The investigated area was classified into 14 topographic units. The requirements of maximizing internal homogeneity while minimizing external homogeneity in terms of morphological characteristics were satisfied through the analysis of the terrain texture, topographic features, and land surface parameters. A comparison with the reference map showed that the regional classification differed from the geomorphologic map, given the different classification systems. However, the regional classification provided new knowledge of topographic features and spatial patterns in the macro scale. A comparison with other classification method indicated the advantage of the CBP-based method, which could distinguish the landforms of loess tableland, loess ridge, and loess hill that are the typical characteristics of loess landforms. The sensitivity analysis revealed that the differences in CBP indices, except for roughness and fractal dimension (FD) from different landforms, would increase with the digital elevation model (DEM) resolution becoming coarser, which indicates that the CBP-based method could be applied on a coarser resolution DEM if the differences of roughness and FD from different landforms could be identified at this coarse DEM level.

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“…The scale and shape parameters of the segmentation process were defined according to the method of Dragut and Eisank (2012), wherein the shape and compactness factors were set to zero, and the scale parameter was defined according to the difference between the local variances of the objects. Thus, a scale parameter of 50 was used, which is similar to the value used in Manfré et al (2014).…”

Section: Digital Image Processingmentioning

confidence: 99%

Landslide Hazard Mapping Near the Admiral Álvaro Alberto Nuclear Complex, Rio De Janeiro, Brazil.

2018

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Technological accidents can be vast in scope and require a rapid response to evacuate the affected region. Access routes to nuclear power stations are essential for the preparation of emergency plans in the event of technological disasters. The Admiral Álvaro Alberto Nuclear Power Plant (Central Nuclear Almirante Álvaro Alberto - CNAAA) in Angra dos Reis, Brazil, is located in a region with high rainfall and rugged terrain. This article presents digital image processing and geoprocessing procedures for mapping landslide-susceptible areas and landslide scars associated with the CNAAA access routes. Digital Elevation Models and their derivations were used to identify landslide-susceptible areas, and LANDSAT images were used to map the land cover. The information was superimposed, and the hazard areas and potential landslide scars were mapped. Most of the study area is medium or high risk for landslide events. Landslides scars mapping achieved over 50% of accuracy representing a potential methodology for the risk assessment and landslides monitoring in the study area. The results demonstrate that further and detailed studies must be performed in the areas in order to maintain the access roads available for eventual evacuations in a technological disaster event.

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Regional and local topography subdivision and landform mapping using SRTM-derived data: a case study in southeastern Brazil

Cited by 14 publications

References 28 publications

ALTIMETRY ASSESSMENT OF ASTER GDEM v2 AND SRTM v3 DIGITAL ELEVATION MODELS: A CASE STUDY IN URBAN AREA OF BELO HORIZONTE, MG, BRAZIL

ALTIMETRY ASSESSMENT OF ASTER GDEM v2 AND SRTM v3 DIGITAL ELEVATION MODELS: A CASE STUDY IN URBAN AREA OF BELO HORIZONTE, MG, BRAZIL

Regional topographic classification in the North Shaanxi Loess Plateau based on catchment boundary profiles

Landslide Hazard Mapping Near the Admiral Álvaro Alberto Nuclear Complex, Rio De Janeiro, Brazil.

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