Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Grohmann, Carlos Henrique

doi:10.1016/j.rse.2018.04.043

Cited by 167 publications

(126 citation statements)

References 89 publications

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“…The global altimetric error of the SRTM‐GL1 DEM is reported to be less than 6 m (Farr et al, ; Rabus, Eineder, Roth, & Bamler, ). Bonfietti‐Marini et al () estimated the average altimetric error (considering TOPODATA DEM data) in the Pantanal sub‐basin to be 3.64 m. Grohmann () compared the SRTM‐GL1 in the PHB against the ASTER GDEM30 and ALOS AW3D30 datasets and found the SRTM dataset to have the least amount of absolute vertical error of any available dataset for the region.…”

Section: Methodsmentioning

confidence: 99%

Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil)

Campos

Tritico

Girard

et al. 2020

River Research & Apps

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There are 104 hydroelectric facilities proposed to be installed in the watersheds that feed the Pantanal, a vast floodplain wetland located mostly in Brazil. The Pantanal is host to 23 long‐distance migratory fish species that ascend upland tributaries to spawn. A Geographic Information System was used to predict the impact of hydroelectric dams on potential migration routes for these species. Both anthropogenic (hydroelectric dams) and natural barriers were included in the analysis. Natural barriers were identified by river slope. Critical river slopes of 10 and 25%, above which fish were predicted to be incapable of ascending, were modeled as natural barriers. Based on this model, we show that between 2 and 14% of rivers in the Pantanal watershed are naturally blocked to fish migration. An additional 5 to 9% of rivers are currently blocked due to 35 existing hydroelectric facilities. If all proposed dams are built, the area flooded by new reservoirs will triple and the river kilometers blocked will double, blocking 25 to 32% of the river system to fish migration. The Taquari and Cuiabá River sub‐basins will be the most impacted, each having more than 70% of their rivers blocked. The impact of individual proposed facilities on the loss of migration routes is related to their proximity to existing barriers. Fourteen of the proposed dams are upstream from existing barriers and will therefore not further restrict long‐distance fish migration routes while proposed dams are predicted to close an additional 11,000 to 12,000 km of river channels.

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Section: Methodsmentioning

confidence: 99%

Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil)

Campos

Tritico

Girard

et al. 2020

River Research & Apps

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“…Furthermore, 11 topographic parameters (including elevation, slope, aspect, shaded relief, profile convexity, plan convexity, longitudinal convexity, cross sectional convexity, minimum curvature, maximum curvature, and root-mean-square error) were generated from the ASTER GDEM V2 data using the Topographic Modeling tool in the ENVI v5.3 software package. The vertical datum of the TanDEM-X 90 m DEM is the WGS 84 ellipsoid, while SRTM DEM data are referenced to the EGM96 geoid [68]. The ellipsoidal height should be converted to the orthometric height through adding the geoid undulation [68].…”

Section: Other Datasetsmentioning

confidence: 99%

“…The vertical datum of the TanDEM-X 90 m DEM is the WGS 84 ellipsoid, while SRTM DEM data are referenced to the EGM96 geoid [68]. The ellipsoidal height should be converted to the orthometric height through adding the geoid undulation [68]. TanDEM-X data were then resampled to the spatial resolution of SRTM DEM data using a bilinear interpolation method.…”

Section: Other Datasetsmentioning

confidence: 99%

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

Zhang

Menenti

et al. 2019

Remote Sensing

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Glaciers in the Tibetan Plateau are an important indicator of climate change. Automatic glacier facies mapping utilizing remote sensing data is challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock. Most of the available glacier datasets do not provide the boundary of clean ice and debris-covered glacier facies, while debris-covered glacier facies play a key role in mass balance research. The aim of this study was to develop an automatic algorithm to distinguish ice cover types based on multi-temporal satellite data, and the algorithm was implemented in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. The classification method was built upon an automated machine learning approach: Random Forest in combination with the analysis of topographic and textural features based on Landsat-8 imagery and multiple digital elevation model (DEM) data. Very high spatial resolution Gao Fen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) imagery was used to select training samples and validate the classification results. In this study, all of the land cover types were classified with overall good performance using the proposed method. The results indicated that fully debris-covered glaciers accounted for approximately 20.7% of the total glacier area in this region and were mainly distributed at elevations between 4600 m and 4800 m above sea level (a.s.l.). Additionally, an analysis of the results clearly revealed that the proportion of small size glaciers (<1 km 2 ) were 88.3% distributed at lower elevations compared to larger size glaciers (≥1 km 2 ). In addition, the majority of glaciers (both in terms of glacier number and area) were characterized by a mean slope ranging between 20 • and 30 • , and 42.1% of glaciers had a northeast and north orientation in the Parlung Zangbo basin.In the past few decades, much work has been accomplished to map the extent of clean glacial ice and to quantify changes over time using satellite image data [6]. Methods applied range from visual interpretation [7] to segmentation of band ratio or spectral indices (e.g., the Normalized Difference Snow Index) images [8] and different unsupervised (e.g., the Iterative Self Organizing Data Analysis Techniques Algorithm, ISODATA) [9] and supervised (e.g., the Maximum Likelihood algorithm) classification [10] and decision tree methods [5,11]. For extracting debris-covered glaciers using multispectral imagery, fully manual onscreen digitizing is widely considered to be a common classification approach [12]. However, the accuracy of results using manual approaches depends greatly on the researcher's experience. Due to the laborious work of manual delineation, many researchers have further proposed semi-automated methods to extract the debris-covered glacial surface [13,14]. The use of Unmanned Aerial Vehicles (UAVs) and terrestrial remote sensing techniques offers new ways to monitor the debris-covered glaciers on a detailed spatial scale [15,16].Nevertheless, the spatial heterogeneity of the g...

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“…For DEM derivatives, which are prone to be distorted by noise and artefacts, the quality of the data affected the ability to identify large-scale spatial patterns. This question requires further investigation in the context of both DEM preprocessing techniques (Gallant et al, Dowling, 2012) and the potential to use newly available more detailed sources of elevation data (Grohmann, 2018).…”

Section: Resultsmentioning

confidence: 99%

The Application of Directional Univariate Structure Functions Analysis for Studying the Spatial Anisotropy of Environmental Variables

Svidzinska

2019

Ekológia (Bratislava)

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Svidzinska D.: The application of directional univariate structure functions analysis for studying the spatial anisotropy of environmental variables. Ekológia (Bratislava), Vol. 38, No. 2, p. 140-153, 2019. As anisotropy is a fundamental property of the real-world environmental spatial variables, the conventional omnidirectional variograms and correlograms do not provide means enough to characterise spatial dependence between observations. The purpose of this article is to introduce directional univariate structure functions analysis to explore and quantify the spatial anisotropy of environmental variables. Analysis of six environmental variables within three physical-geographical regions proved the leading role of relief for landscape differentiation; it also defined the size and extension of major landforms responsible for the organisation of spatial pattern. The arrangement of the vegetation patches demonstrated linkage with the major landforms. The other relief derivatives, being prone to noise and artefacts in the original data, showed a random-variable type of behaviour. In the lack of any particular spatially anisotropic structure, the results of the analysis can provide a clue about meaningful distances of interest at finer scales. The approach can also be an exploratory tool for discrete measurements to recognise the features of spatial continuity.

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Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

Cited by 167 publications

References 89 publications

Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil)

Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil)

Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study

The Application of Directional Univariate Structure Functions Analysis for Studying the Spatial Anisotropy of Environmental Variables

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