Morphotectonic interpretation of the Makuyuni catchment in Northern Tanzania using DEM and SAR data

Flores-Prieto, Elio; Quénéhervé, Geraldine; Bachofer, Felix; Shahzad, Faisal; Maerker, Michael

doi:10.1016/j.geomorph.2015.07.049

Cited by 28 publications

(13 citation statements)

References 49 publications

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“…Four hundred ninety-eight ground reference points, which were collected in field campaigns, were used to train and validate the datasets. The UMB and LMB were identified with an overall accuracy (OA) of 80%, when merely applying the SVMs to delineate the LMB 91% OA were reached (Bachofer et al 2015). The results exhibit the surface distribution of the LMB and UMB (Fig.…”

Section: Delineation Of the Manyara Bedsmentioning

confidence: 86%

“…The ASTER system is composed of a sensor for visible and nearinfrared wavelengths (VNIR: 0.52-0.86 μm; 15 m ground resolution), a shortwave sensor (SWIR: 1.6-2.43 μm; 30 m ground resolution), and a thermal infrared sensor (TIR: 8.125-11.65 μm; 90 m ground resolution) (Fujisada 1995). For the bands of the VNIR and SWIR spectral region, 35 spectral indices were calculated (Bachofer et al 2015). Spectral indices are ratios of distinct spectral bands emphasizing the presence or absence of vegetation and different mineral compositions.…”

Section: Delineation Of the Manyara Bedsmentioning

confidence: 99%

“…Spectral indices are ratios of distinct spectral bands emphasizing the presence or absence of vegetation and different mineral compositions. Twenty-five additional topographic indices were calculated from a SRTM-X DEM (Bachofer et al 2015).…”

Section: Delineation Of the Manyara Bedsmentioning

confidence: 99%

See 2 more Smart Citations

Paleoenvironmental Research in the Semiarid Lake Manyara Area, Northern Tanzania: A Synopsis

Bachofer

Quénéhervé

Hertler

et al. 2017

Natural Science in Archaeology

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The Lake Manyara area is the focus of several paleo-archeological investigations. The Manyara basin is located approximately 70 km east of Olduvai Gorge, where important paleoanthropological artifacts are traced back to Homo habilis. In the Manyara basin itself, two hominin-bearing sites (0.78-0.633 Ma) and plenty of vertebrate bones and teeth as well as stone artifacts from different periods were discovered, especially close to the Makuyuni River. Different methodological approaches with a main emphasis on remote sensing were utilized to contribute to the understanding of the paleo-landscape development. In order to investigate the morphotectonic evolution of the study area, lineaments were detected from Synthetic Aperture Radar satellite scenes. The complex lacustrine development of the Lake Manyara and its paleo-stages was investigated by delineating the extent of paleo-lake sediments (older than 0.633 Ma) with multispectral ASTER data. In addition, lake terraces and shorelines on different levels (up to 80 m above today's lake level) and an outlet to the neighboring Engaruka basin were detected by analyzing the backscattered intensity of TerraSAR-X data. The distribution of topsoils, identified from multisensory remote sensing datasets, indicates soil formation as well as erosional and depositional processes. The fossils and artifacts were then

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Section: Delineation Of the Manyara Bedsmentioning

confidence: 86%

Section: Delineation Of the Manyara Bedsmentioning

confidence: 99%

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Paleoenvironmental Research in the Semiarid Lake Manyara Area, Northern Tanzania: A Synopsis

Bachofer

Quénéhervé

Hertler

et al. 2017

Natural Science in Archaeology

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“…Rainfall has a significant control in the erosion of landscape in rivers where rain is the only source of water. Hypsometric integral is a useful index that delivers a significant amount of information about the catchment erosion by various agents (Sternai et al 2011) and it has been widely applied to understand the evolution of the landscape (Perez-Pena et al 2010;Flores-Prieto et al 2015;Mathew et al 2016;Baumann et al 2018). Therefore, a relationship has been established to check if there is any significant association between the precipitation and HI in Indian catchments.…”

Section: Discussionmentioning

confidence: 99%

Landscape variables in the Indian (Peninsular) catchments: insights into hydro-geomorphic evolution

Das¹

2019

Preprint

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The river systems in peninsular India are remained unexplored in terms of hydro-geomorphic evolution, though a few works are carried out in order to understand the tectonic and structural evolution with paleoclimate. Morphometric analysis at catchment scale delivers insights into the dynamics, erosion capacity, probability of flood occurrence, lithological and structural control, and genetic response to the tectonics. The present study aimed to characterize the hydro-geomorphic evolution of 12 major catchments in Peninsular India via morphometric analysis. A total 25 morphometric parameters were computed and several statistical analyses performed in establishing the inter-correlation and making classification of Indian rivers. The results indicated that most of the rivers in peninsular India are 7th to 9th order catchments. A high variability of the rainfall was observed in these rivers where the northern catchments experience higher amount of precipitation contrasting to the southern basins. Almost all the basins showed a moderate relief ratio, hypsometric integral, ruggedness etc. Cauvery, Baitarni, and Brahmani showed exceptionally steeper gradient, high relief ratio, LS factor, and ruggedness index which may indicate higher erosion potential. A positive association between erosion rate and hypsometric integral was observed. All these rivers were classified in three categories based on cluster analysis where the medium and large sized catchments formed different groups. Despite of the extensive data and statistical analysis, the outcomes of this study are highly general due to large scale variation in lithology and climate. In foreseeable works dealing with such morphometric variables, a higher resolution sub-catchment scale analysis would be required for a better description of the hydro-geomorphic response of these large catchments in peninsular India.

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“…A digital elevation model (DEM) represents basic data for spatial analysis of geographic information systems, which are widely applied in the fields of hydrology [1], soil [2], landform [3,4], geology [5], agriculture [6], and military applications [7,8]. Topographic factors such as slope and aspect extracted based on DEM are not only important parameters for describing the structure and morphology of topographic surfaces but also basic variables of geo-analysis models, such as surface process simulation, hydrological, soil erosion, and land use classification models [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Effect of DEM Interpolation Neighbourhood on Terrain Factors

Zhu

Liu

Jing

et al. 2019

IJGI

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Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical.

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Morphotectonic interpretation of the Makuyuni catchment in Northern Tanzania using DEM and SAR data

Cited by 28 publications

References 49 publications

Paleoenvironmental Research in the Semiarid Lake Manyara Area, Northern Tanzania: A Synopsis

Paleoenvironmental Research in the Semiarid Lake Manyara Area, Northern Tanzania: A Synopsis

Landscape variables in the Indian (Peninsular) catchments: insights into hydro-geomorphic evolution

Effect of DEM Interpolation Neighbourhood on Terrain Factors

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