An efficient method for automatic slope unit delineation from a huge region

2021 ◽  
Author(s):  
Pengfei Wu ◽  
Jintao Liu
Keyword(s):  
Tibetan Plateau ◽  
Efficient Method ◽  
Landslide Susceptibility ◽  
Large Scale ◽  
Large Area ◽  
Slope Unit ◽  
Digital Elevation ◽  
Automatic Methods ◽  
Yarlung Tsangpo ◽  
Quantitative Assessments

<p>Slope units have great potential for hydrological and geomorphological studies, especially for landslide susceptibility modelling. Digital Elevation Models are widely used to delineate slope units by connecting drainage and divide lines. However, it is difficult to estimate reasonable scale thresholds for drainage and divide lines, which limits the application of slope units. Most existing methods for slope unit delineation determine the scales manually. Recently, several automatic methods have been proposed, but these methods encounter the problem of low computing efficiency for large-scale region. In this study, a new efficient method is presented for automatic slope unit delineation. Similar to an existing method, our method divides the region into several sub-basins and delineate slope units for every sub-basin independently. Within a sub-basin, grid cells are classified into massive units, and neighboring units are merged according to several strategies when aspect homogeneity can be satisfied. The new method is tested in Yarlung Tsangpo Basin, southeastern Tibetan Plateau. This basin has a large area of nearly 250 000 km<sup>2</sup>, and can be divided into nearly 340 000 slope units within 3 hours using a general personal computers. The rationality of our method is proved by both visual and quantitative assessments.</p>

scholarly journals Evolution of eastern Tibetan river systems is driven by the indentation of India

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yi Chen ◽  
Baosheng Wu ◽  
Zhongyu Xiong ◽  
Jinbo Zan ◽  
Bangwen Zhang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Network Models ◽  
The Tibetan Plateau ◽  
River Systems ◽  
Channel Network ◽  
Erosion Rates ◽  
Digital Elevation ◽  
Tectonically Active ◽  
Elevation Model

AbstractThe main rivers that originate from the Tibetan Plateau are important as a resource and for the sedimentary and biogeochemical exchange between mountains and oceans. However, the dominant mechanism for the evolution of eastern Tibetan river systems remains ambiguous. Here we conduct geomorphological analyses of river systems and assess catchment-average erosion rates in the eastern Tibetan Plateau using a digital elevation model and cosmogenic radionuclide data. We find that major dividing ranges have northeast oriented asymmetric geometries and that erosion rates reduce in the same direction. This coincides with the northeastward indentation of India and we suggest this indicates a primarily tectonic influence on the large-scale configuration of eastern Tibetan river systems. In contrast, low-level streams appear to be controlled by fluvial self-organization processes. We propose that this distinction between high- and low-order channel evolution highlights the importance of local optimization of optimal channel network models in tectonically active areas.

scholarly journals An Automated Snow Mapper Powered by Machine Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4826
Author(s):  
Haojie Wang ◽  
Limin Zhang ◽  
Lin Wang ◽  
Jian He ◽  
Hongyu Luo
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Large Scale ◽  
Regional Climate ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Mountainous Area ◽  
Northern Tibetan Plateau ◽  
Digital Elevation ◽  
Elevation Model

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.

scholarly journals The performance of landslide susceptibility models critically depends on the quality of digital elevation models

2020 ◽  
Vol 11 (1) ◽  
pp. 1075-1092 ◽  
Author(s):  
Jonas Brock ◽  
Patrick Schratz ◽  
Helene Petschko ◽  
Jannes Muenchow ◽  
Mihai Micu ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Digital Elevation Models ◽  
Digital Elevation

Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

2021 ◽  
Author(s):  
Cahio Guimarães Seabra Eiras ◽  
Juliana Ribeiro Gonçalves de Souza ◽  
Renata Delicio Andrade de Freitas ◽  
César Falcão Barella ◽  
Tiago Martins Pereira
Keyword(s):  
Discriminant Analysis ◽  
Efficient Method ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment

scholarly journals Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

2021 ◽  
Vol 13 (15) ◽  
pp. 2877
Author(s):  
Yu Tao ◽  
Siting Xiong ◽  
Susan J. Conway ◽  
Jan-Peter Muller ◽  
Anthony Guimpier ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Imaging System ◽  
Landing Site ◽  
Parameter Tuning ◽  
Processing Parameters ◽  
Input Image ◽  
Large Area ◽  
Laser Altimeter ◽  
Imaging Science

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.

scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

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