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Terrain models are widely used to depict the shape of the Earth's surface. With the development of photogrammetric methods, point cloud data have become one of the most popular data sources for terrain modelling. However, the obtained point clouds are of high density, which often increases redundancy rather than improving accuracy. Therefore, point cloud simplification should be a core component of terrain modelling. This paper proposes a point cloud simplification method by integrating topographic knowledge into terrain modelling (TKPCS). The method contains two steps: (1) topographic knowledge recognition and construction and (2) point cloud simplification using this topographic knowledge for terrain modelling. The proposed approach is benchmarked against improved versions of existing methods to validate its capability and accuracy in digital elevation model construction and terrain derivative extraction. The results show that the simplified points of the TKPCS method can generate finer resolution terrain models with higher accuracy and greater information entropy. The good performance of the TKPCS method is also stable at different scales. This work endeavours to transform perceptive topographic knowledge into a process of point cloud simplification and can benefit future research related to terrain modelling. [ABSTRACT FROM AUTHOR]

In recent years, applications and analyses based on slope units have become increasingly widespread. Compared with grid units, slope units can better represent terrain features and boundaries and allow a more complete view of the morphology of the Earth's surface. Maps based on slope units also offer significant improvements for disaster prediction and the analysis of slope land resources. Therefore, we need a reasonable method of slope unit classification. Although some methods have been proposed for slope unit classification, they have been too focused on morphological variations and have not fully considered the importance of geomorphology, and the geomorphological and physical significance of slope partitioning remain unclear. Therefore, we propose a novel slope unit classification method by combining terrain feature lines (CTFL) derived from the meaning of geomorphology ontology that use several terrain feature lines, such as geomorphic water division lines, valley shoulder lines, slope toe lines, and shady/sunny slope boundary lines, to classify slopes. The Jiuyuangou and Lushan study areas were selected to test the CTFL method. Compared with the traditional hydrological method, the CTFL method can effectively overcome topographic abruptness and distortions, improve the uniformity of slope and aspect within individual units, and increase the accuracy of slope unit applications and analyses. This work fully considers the importance of geomorphology and is conducive to future studies of slope unit division. [ABSTRACT FROM AUTHOR]

Hillslope surface classification via digital terrain analysis (DTA) is a current research focus in geomorphology and geographic information science (GIS) studies. However, traditional methods are generally based on raster digital elevation models (DEMs) and window difference methods, which suffer from terrain description and analysis scale mismatch issues. In this study, we propose a hillslope surface classification approach based on the vector structure. Triangulated irregular networks (TINs) are used as an example. Benefiting from free sampling with TINs, we apply terrain surface reconstruction to the original point cloud or DEM and then calculate the terrain derivatives based on the normal vectors of the optimized TINs. Finally, the fuzzy inference method is used to classify hillslope surface elements. We select two cases to evaluate the proposed method: a small watershed with dense point cloud data and a large region with complex landforms and a 30 m resolution Copernicus DEM. The results show that the proposed approach can effectively reduce the influence of DEM errors on classification and mitigate the scale mismatching problem in terrain generalization and analysis. A novel hillslope surface classification method with a new data structure is proposed to extend the application of vector methods and structures in DTA and GIS. [ABSTRACT FROM AUTHOR]

Super-resolution (SR), also called downscaling, has been widely explored in hydrology, climate, and vegetation distribution models, among others. Digital elevation model (DEM) SR aims to reconstruct terrain at a finer resolution than available measurements. The raw terrain data are often non-stationary and characterized by trends, while terrain residuals are generally stationary in geomorphologically heterogeneous areas. Here, we develop a multiple-point statistics approach that decomposes the target low-resolution DEM into a deterministic low-frequency trend component and a stochastic high-frequency residual component. Our simulation is focusing on the residual component. A training image selection process is applied to determine locally appropriate high-resolution residual training images. The high-resolution residual of the target DEM is simulated with an open-source multiple-point statistics (MPS) framework named QuickSampling. The residual of the low-resolution target DEM is used as conditioning data to ensure local accuracy. The deterministic trend component is then added to obtain the final downscaled DEM. The proposed algorithm is compared with the bicubic interpolation, a convolutional neural network(CNN), a generative adversarial network (GAN), a modified super-resolution residual network (MSRResNet), and geostatistical area-to-point-kriging. The results show that the proposed approach maintains the statistical properties of the fine-scale DEM with its spatial details, and can be easily extended to other fields such as the super-resolution/downscaling of precipitation, temperature, land use/cover, or satellite imagery. • DEM super-resolution is considered with both MPS and DL. • This is the first formal comparison between MPS and DL. • We use a multi-training image selection scheme for efficient MPS. • We use a detrending strategy that performs well for both MPS and DL. [ABSTRACT FROM AUTHOR]

Terrain derivative calculations from triangulated irregular network (TIN)-based digital elevation models (DEMs) have been extensively explored in geomorphometry. However, most calculation methods focus on the triangulation facets of TIN-based DEMs and ignore the vertices. In fact, these vertices are the original sampling points from the terrain surface and serve as the basis for triangulation. In this study, we argue that terrain derivative calculations using TIN-based DEMs should focus on the vertices. Employing examples with slope and aspect, we applied the TIN vertex-based method to a mathematical surface and a real topography using TIN-based DEMs with a range of sampling point densities. We performed a comparative analysis of the TIN vertex-based, TIN facet-based, and grid-based methods. Assessments on the mathematical surface showed that the TIN vertex-based method achieved the highest accuracy among the three methods. Error analysis for the real landform case indicated that the TIN vertex-based method performed slightly better than the grid-based method for slope calculation and slightly worse than the grid-based method for aspect calculation. Among the three methods, the TIN facet-based method was most sensitive to error. The TIN vertex-based method can provide a reference for the slope and aspect calculation based on point clouds. [ABSTRACT FROM AUTHOR]

Agricultural terraces are important for agricultural production and soil-and-water conservation. They comprise treads and risers that require manual construction and maintenance. If managed improperly, risers will collapse, causing soil loss, gully erosion, and cultivation threats. However, mapping terrace risers remains a challenge. This study presents a novel approach to automatically map terrace risers by combining remote sensing images and digital elevation models (DEMs). First, a terraced hillslope was extracted via a hill-shading method and edges in the image were detected using a Canny edge detector. Next, the DEM was used to generate the contour direction, and edges along this direction were searched and coded as candidate terrace risers via directional detection. Finally, the results of directional detection and the edge image obtained from the Canny detector were overlaid to backtrack complete terrace risers. The approach was validated using four study areas with different topographic characteristics in the Loess Plateau, China. The results verify that the approach achieves outstanding performance and robustness in mapping terrace risers. The precision, recall, and F-measure were 90.81%–97.57%, 88.53%–94.10%, and 90.13%–95.80%, respectively. This approach is flexible and applicable with freely available images and DEM sources. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) contain some of the most important data for providing terrain information and supporting environmental analyses. However, the applications of DEMs are significantly limited by data voids, which are commonly found in regions with rugged terrain. We propose a novel deep learning-based strategy called a topographic knowledge-constrained conditional generative adversarial network (TKCGAN) to fill data voids in DEMs. Shuttle Radar Topography Mission (SRTM) data with spatial resolutions of 3 and 1 arc-seconds are used in experiments to demonstrate the applicability of the TKCGAN. Qualitative topographic knowledge of valleys and ridges is transformed into new loss functions that can be applied in deep learning-based algorithms and constrain the training process. The results show that the TKCGAN outperforms other common methods in filling voids and improves the elevation and surface slope accuracy of the reconstruction results. The performance of the TKCGAN is stable in the test areas and reduces the error in the regions with medium and high surface slopes. Furthermore, the analysis of profiles indicates that the TKCGAN achieves better performance according to a visual inspection and quantitative comparison. In addition, the proposed strategy can be applied to DEMs with different resolutions. This work is an endeavour to transform topographic knowledge into computer-processable rules and benefits future research related to terrain reconstruction and modelling. • Integrating topographic knowledge into deep learning-based algorithm. • A topographic knowledge-constrained conditional generative adversarial (TKCGAN) network is developed. • TKCGAN reconstructs terrains in voids according to input terrain features. • TKCGAN can generate low slope error in void areas. • TKCGAN fuses the remote sensing observation and geographic idea. [ABSTRACT FROM AUTHOR]

Terrain plan concave and convex parts are important topographical shapes that affect the convergence and divergence of flow. The plan concavity and convexity are generally calculated through the curvature method, which is limited by the serious sensitivity to digital elevation model (DEM) errors and scale dependence. In this study, we propose an aspect vector method to quantify the terrain plan concavity and convexity. This method considers different aspect changes of the plan concavity and convexity, which reduces the scale effect on the curve bending quantification. The proposed method is applied to a mathematical Gaussian surface and two different case studies using a DEM with resolution of 5 m. A comparative analysis is conducted between the proposed and existing curvature-based methods to assess the capability of the former. The results show that the proposed method achieves more realistic and accurate terrain plan concavity and convexity than does the existing method. Moreover, the aspect vector approach is less strongly affected by the spatial resolution of the DEM than is the existing curvature-based method, which demonstrates the application potential of the former to other studies. Furthermore, the concept of aspect vector can be expanded to quantify the terrain concavity and convexity from other directions and promote the development of existing curvature-based terrain quantification methods in geomorphometry. • We propose an aspect vector method to quantify plan concavity and convexity. • The vector method reduces the scale effect on curve bending quantification. • Vector method achieves better quantification result compared with curvature method • Vector method can be expanded and used in other geomorphometry researches. [ABSTRACT FROM AUTHOR]

Terrain derivatives exhibit surface morphology in various aspects. However, existing spatial change calculation methods for terrain derivatives are based on a mathematical scalar operating system, which may disregard the directional property of the original data to a certain extent. This situation is particularly true in second-order terrain derivatives, in which original data can be terrain derivatives with clear directional properties, such as slope or aspect. Thus, this study proposes a mathematical vector operation method for the calculation of second-order terrain derivatives. Given the examples of the first-order terrain derivatives of slope and aspect, their second-order terrain derivatives are calculated using the proposed vector method. Directional properties are considered and vectorized using the following steps: rotation-type judgment, standardization of initial direction, and vector representation. The proposed vector method is applied to one mathematical Gaussian surface and three different ground landform areas using digital elevation models (DEMs) with 5 and 1 m resolutions. Comparison analysis results between the vector and scalar methods show that the former achieves more reasonable and accurate second-order terrain derivatives than the latter. Moreover, the vector method avoids overexpression or even exaggeration errors. This vector operation concept and its expanded methods can be applied in calculating other terrain derivatives in geomorphometry. [ABSTRACT FROM AUTHOR]