Digital Elevation Model Interpolation Research Articles

Spatial interpolation of global DEM using federated deep learning

The digital elevation model (DEM) provides important data support for 3D terrain modeling. However, due to the complex and changeable terrain in the real world and the high cost of field measurement, it is extremely difficult to obtain continuous and high-density elevation data directly. Therefore, it is necessary to rely on spatial interpolation technology to restore the DEM overall picture in the original sampling area. The traditional spatial interpolation method usually has the characteristics of low model complexity and high computational cost, which leads to low real-time performance and low precision of the interpolation process. The interpolation operation based on DEM data can be considered as a special image generation process where the input is a DEM image with missing values and the output is a complete DEM image. At present, a large number of studies have proved that deep learning methods are very effective in image generation tasks. However, the training of deep learning models requires the support of a large number of high-quality data sets. DEM data in various countries, especially in key regions, are usually restricted by privacy protection regulations and cannot be disclosed. The emergence of Federated Learning (FL) provides a new solution, which supports local training on multiple end nodes, without sending local data to a remote center server for centralized training, effectively protecting data privacy. In this study, we propose a DEM interpolation model based on FL and multiScale U-Net. The experimental results show that compared with the traditional method, this model has faster processing speed and lower interpolation precision. At the same time, this research result provides a new way for efficient and secure use of terrain information, especially in those application scenarios that have strict requirements for DEM data privacy and security.

Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management

Abstract Soil resource management is fundamentally integral to environmental sustainability and agricultural productivity. The digital elevation model (DEM) is the fundamental data for analyzing landform surfaces, which introduces an opportunity to obtain a broad spectrum of terrain factors to simplify interpreting the patterns and processes in the geoscience field. The accuracy and resolution of DEM are crucial for their effective use, and many algorithms have been developed to interpolate digital elevation data from a set of known points. Although primary topographic variables derived from grid datasets are important, secondary variables, such as the relief index (RFI), play a more critical role in understanding the complicated relationship between soil properties and landform attributes. The RFI is attained from a DEM by calculating the elevation range within a given neighborhood surrounding a central cell. It is an essential predictor of soil natural resource management that measures the degree of differentiation surface relief. In addition, it is beneficial for perceiving the landscape and its management. This study presents a comprehensive zonal analysis comparing the RFI values derived from multiple interpolation-based DEMs. It investigates deterministic and geostatistical interpolators, such as inverse distance weighted and natural neighbor across distinct zones with diverse topographical characteristics. The findings indicated a high correlation between the RFI and the reliability of the DEM, and the natural neighbor technique provided superior performance against others. The results revealed that the choice of spatial interpolation technique significantly affects the accuracy and reliability of RFI models.

Climate zoning of asphalt pavement based on spatial interpolation and Fuzzy C-Means algorithm

ABSTRACT Natural conditions tend to affect the performance of the asphalt pavement. Climatic factors must be taken into account in the structural design of the asphalt pavement. Thus it is important to classify asphalt scientifically and reasonably based on the local climatic conditions. Based on 56-year meteorological data from 747 representative meteorological stations in China, temperature and precipitation were divided into eight meteorological indices. Three spatial interpolation methods are used for interpolation, namely Inverse Distance Weight, Ordinary Kriging and Collaborative Kriging considering Digital Elevation Model (DEM). After the comparison, the Collaborative Kriging considering DEM interpolation results of temperature and the Inverse Distance Weight interpolation results of precipitation were used to establish the standard climate zoning system in China. The classification of asphalt pavement is introduced on the basis of performance grading. According to the pavement high and low temperature design formula obtained by comparison, climatic suitability zoning of road asphalt is performed, and the classification table with performance grading of road asphalt in China is presented. The research results provide a theoretical basis for the selection of road asphalt and provide guidance for scientific and reasonable design of asphalt pavement.

High Accuracy Interpolation of DEM Using Generative Adversarial Network

Digital elevation model (DEM) interpolation is aimed at predicting the elevation values of unobserved locations, given a series of collected points. Over the years, the traditional interpolation methods have been widely used but can easily lead to accuracy degradation. In recent years, generative adversarial networks (GANs) have been proven to be more efficient than the traditional methods. However, the interpolation accuracy is not guaranteed. In this paper, we propose a GAN-based network named gated and symmetric-dilated U-net GAN (GSUGAN) for improved DEM interpolation, which performs visibly and quantitatively better than the traditional methods and the conditional encoder-decoder GAN (CEDGAN). We also discuss combinations of new techniques in the generator. This shows that the gated convolution and symmetric dilated convolution structure perform slightly better. Furthermore, based on the performance of the different methods, it was concluded that the Convolutional Neural Network (CNN)-based method has an advantage in the quantitative accuracy but the GAN-based method can obtain a better visual quality, especially in complex terrains. In summary, in this paper, we propose a GAN-based network for improved DEM interpolation and we further illustrate the GAN-based method’s performance compared to that of the CNN-based method.

Improvements to stream extraction and soil wetness mapping within a forested catchment by increasing airborne LiDAR data density – a case study in Parkano, western Finland

The pulse density of airborne Light Detection and Ranging (LiDAR) is increasing due to technical developments. The trade-offs between pulse density, inventory costs, and forest attribute measurement accuracy are extensively studied, but the possibilities of high-density airborne LiDAR in stream extraction and soil wetness mapping are unknown. This study aimed to refine the best practices for generating a hydrologically conditioned digital elevation model (DEM) from an airborne LiDAR -derived 3D point cloud. Depressionless DEMs were processed using a stepwise breaching-filling method, and the performance of overland flow routing was studied in relation to a pulse density, an interpolation method, and a raster cell size. The study area was situated on a densely ditched forestry site in Parkano municipality, for which LiDAR data with a pulse density of 5 m were available. Stream networks and a topographic wetness index (TWI) were derived from altogether 12 DEM versions. The topological database of Finland was used as a ground reference in comparison, in addition to 40 selected main flow routes within the catchment. The results show improved performance of overland flow modeling due to increased data density. In addition, commonly used triangulated irregular networks were clearly outperformed by universal kriging and inverse-distance weighting in DEM interpolation. However, the TWI proved to be more sensitive to pulse density than an interpolation method. Improved overland flow routing contributes to enhanced forest resource planning at detailed spatial scales.–2

Evaluation of DEM interpolation techniques for characterizing terrain roughness

The emergence of modern geospatial techniques has brought a considerable change in data capturing, processing, and visualization tools. Digital elevation models (DEMs) are a 3D mathematical representation of the earth’s surface. Due to enormous advancement in geographic information system (GIS) tools and technologies, DEMs have a wide range of applications in natural resources management, environmental science, and engineering. In general, primary topographic variables extracted from DEMs are frequently less useful than secondary ones, e.g., surface roughness. Therefore, the precise values of secondary terrain features need to be derived from base DEMs. Generally, topographic roughness is an essential geomorphological variable that is a vital issue in geoscience. This research presents a comparison of deterministic interpolation methods, inverse distance weighted (IDW) and a natural neighbor (NN), with geostatistical technique, ordinary kriging (OK), to investigate the influence of generated DEM on quantifying terrain roughness relied on synthetic data and applying the zonal statistics tool. At the same time, a quantitative and qualitative approach is adopted to assessing the results through statistical methods and visual representations. The findings indicated that roughness values' behavior is broadly related to the quality of the built DEM. The NN approach yielded the greatest DEM accuracy, standard deviation, from a quantitative perspective (±0.930 m) than IDW (±3.748 m) and OK (SD = ±5.544 m) methods.

The effects of DEM interpolation on quantifying soil surface roughness using terrestrial LiDAR

Soil surface roughness (SSR) is often calculated based on the Digital Elevation Models (DEMs) obtained by interpolating points from terrestrial LiDAR measurements. This study aimed to investigate the effects of DEM interpolation and interpolation methods on quantification of SSR. A series of rainfall at the intensity of 59 and 178 mm hr−1 were simulated on a 2 by 6.1 m stony soil plot under 12 % and 20 % slope treatments. LiDAR measurements were conducted after each rainfall simulation at six positions around the soil plot. DEMs gridded at 5 and 10 mm were generated from LiDAR points using Inverse Distance Weighting, Natural Neighbors and Universal Kriging methods implemented in ESRI ArcGIS 10.5. Random roughness index (RR) was calculated based on the LiDAR-interpolated DEMs and LiDAR points directly and then compared. Results showed: 1) average values of RR from the DEMs were under-predicted compared to those directly calculated from LiDAR points by 7%–20%, indicating smoothing of the modeled surface during the interpolation process; 2) RR from LiDAR points indicated that soil surface became rougher as rainfall was progressed; 3) DEM errors increased as the surfaces evolved to rougher states and when 10 mm resolution was used; 4) the temporal SSR variations using the DEMs were -0.27 and -0.19 mm at 20 % slope and 0.30 and 0.36 mm at 12 % slope for the resolution 10 and 5 mm, respectively, while they were 0.22 mm at 20 % slope and 0.65 mm at 12 % slope when LiDAR points were directly used, the differences are due to the resulting increased DEM errors as the surface became rougher that masked the true changes in soil surface roughness. This study shows that at plot scale, DEMs generated through interpolations from LiDAR points underestimate soil surface roughness and are ineffective at tracking changes in soil surface roughness over time, and that LiDAR point data must be used instead at present scale.

Semivariogram fitting based on SVM and GPR for DEM interpolation

DEM (Digital Elevation Model) as a digital model of the earth’s surface elevation could be generated from remote sensing technology such as stereo imaging for various applications. To generate DEM from stereo imagery, interpolation or approximation process stage is required. Stochastic interpolation e.g. ordinary kriging uses semivariogram fitting to calculate weights of interpolation values based on known points. This research is applying regression types of machine learning for semivariogram fitting to interpolate DEM. Previous research conducted was LS-SVM (Least Square-Support Vector Machine) and SVR (Support Vector Regression) for semivariogram fitting process. Types of SVM and GPR (Gaussian Process Regression) are adopted for semivariogram fitting for ordinary kriging interpolation in this experiment. The result showed that in general SVM types could predict accuracy better than other types of regression, and GPR types produce better DEM accuracy based on the experiment.

Road grade estimates for bicycle travel analysis on a street network

Road grade is a major factor influencing walking and cycling speed, energy expenditure, and travel behaviour. Bicycling research and commercial travel applications typically use digital elevation models to estimate road grades. The goal of this research is to determine the best methods of obtaining road grade data for bicycle travel analysis on an urban street network. Multiple elevation data sources are collected for eight sample locations in the city of Vancouver, Canada. Road grade extraction algorithms are applied and compared to precise ground surveying data. Estimates of cycling power and energy are used to assess the road grade accuracy in a specific analytical context. Road grades on all 35 elevated roadways of at least 30 m in the city are also characterized and classified into 3 modalities of road grade distribution. Results show that road elevations and grades extracted from raw Light Detection and Ranging (LIDAR) data are the most accurate where directly measured grades are unavailable. Interpolation of digital elevation models (DEM) and digital surface models (DSM) can provide adequate grade estimates on non-elevated roads, but are highly inaccurate on elevated structures, leading to substantial errors in cycling power and energy estimates. Short elevated network links (under 100 m) often have unimodal grade distributions which can be approximated with straight-line elevation profiles, but longer elevated structures (such as arched spans) have more grade variability. Recommendations are made for estimating grades on a network with and without LIDAR data, with the caveat that propagation of errors in grade data should be considered in walking and cycling analysis.

Effect of DEM Interpolation Neighbourhood on Terrain Factors

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.

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris flows are among the most hazardous phenomena in mountain areas. To cope with debris flow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris flow routing models are the topographic data, in the form of Digital Elevation Models (DEMs). The quality of the DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the gridding method used to obtain them. Therefore, the choice of the DEMs interpolation method affects the realistic representation of the channel and fan morphology, and thus reasonably the debris flow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e. linear triangulation, natural neighbour, nearest neighbour, inverse distance to a power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris flow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full-waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination among statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the influence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris flows. In detail we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are inadequate in building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbour algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris flow routing modeling reveals that the choice of the interpolation algorithm does not significantly affect the model outcomes.

A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation

It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

Robust Interpolation of DEMs From Lidar-Derived Elevation Data

Light detection and ranging (lidar)-derived elevation data are commonly subjected to outliers due to the boundaries of occlusions, physical imperfections of sensors, and surface reflectance. Outliers have a serious negative effect on the accuracy of digital elevation models (DEMs). To decrease the impact of outliers on DEM construction, we propose a robust interpolation algorithm of multiquadric (MQ) based on a regularized least absolute deviation (LAD) technique. The objective function of the proposed method includes a regularization-based smoothing term and an LAD-based fitting term, respectively, used to smooth noisy samples and resist the influence of outliers. To solve the objective function of the proposed method, we develop a simple scheme based on the split-Bregman iteration algorithm. Results from simulated data sets indicate that when sample points are noisy or contaminated by outliers, the proposed method is more accurate than the classical MQ and two recently developed robust algorithms of MQ for surface modeling. Real-world examples of interpolating 1 private and 11 publicly available airborne lidar-derived data sets demonstrate that the proposed method averagely produces better results than two promising interpolation methods including regularized spline with tension (RST) and gridded data-based robust thin plate spline (RTPS). Specifically, the image of RTPS is too smooth to retain terrain details. Although RST can keep subtle terrain features, it is distorted by some misclassified object points (i.e., pseudooutliers). The proposed method obtains a good tradeoff between resisting the effect of outliers and preserving terrain features. Overall, the proposed method can be considered as an alternative for interpolating lidar-derived data sets potentially including outliers.

An Automated Processing Algorithm for Flat Areas Resulting from DEM Filling and Interpolation

Correction of digital elevation models (DEMs) for flat areas is a critical process for hydrological analyses and modeling, such as the determination of flow directions and accumulations, and the delineation of drainage networks and sub-basins. In this study, a new algorithm is proposed for flat correction/removal. It uses the puddle delineation (PD) program to identify depressions (including their centers and overflow/spilling thresholds), compute topographic characteristics, and further fill the depressions. Three different levels of elevation increments are used for flat correction. The first and second level of increments create flows toward the thresholds and centers of the filled depressions or flats, while the third level of small random increments is introduced to cope with multiple threshold conditions. A set of artificial surfaces and two real-world landscapes were selected to test the new algorithm. The results showed that the proposed method was not limited by the shapes, the number of thresholds, and the surrounding topographic conditions of flat areas. Compared with the traditional methods, the new algorithm simplified the flat correction procedure and reduced the final elevation increments by 5.71–33.33%. This can be used to effectively remove/correct topographic flats and create flat-free DEMs.

A universal spectral analytical method for digital terrain modeling

ABSTRACTThere are three major mathematical problems in digital terrain analysis: (1) interpolation of digital elevation models (DEMs); (2) DEM generalization and denoising; and (3) computation of morphometric variables through calculating partial derivatives of elevation. Traditionally, these three problems are solved separately by means of procedures implemented in different methods and algorithms. In this article, we present a universal spectral analytical method based on high-order orthogonal expansions using the Chebyshev polynomials of the first kind with the subsequent Fejér summation. The method is intended for the processing of regularly spaced DEMs within a single framework including DEM global approximation, denoising, generalization, as well as calculating the partial derivatives of elevation and local morphometric variables.The method is exemplified by a portion of the Great Rift Valley and central Kenyan highlands. A DEM of this territory (the matrix 480 × 481 with a grid spacing of 30″) was extracted from the global DEM SRTM30_PLUS. We evaluated various sets of expansion coefficients (up to 7000) to approximate and reconstruct DEMs with and without the Fejér summation. Digital models of horizontal and vertical curvatures were computed using the first and second partial derivatives of elevation derived from the reconstructed DEMs. To evaluate the approximation accuracy, digital models of residuals (differences between the reconstructed DEMs and the initial one) were calculated. The test results demonstrated that the method is characterized by a good performance (i.e., a distinct monotonic convergence of the approximation) and a high speed of data processing. The method can become an effective alternative to common techniques of DEM processing.

An improved ANUDEM method combining topographic correction and DEM interpolation

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The experiments undertaken confirmed the superiority of the proposed method over the other DEM interpolation methods. It is shown that the proposed method can provide results with a higher accuracy, as well as a better visual quality.

Цифровое моделирование рельефа с использованием ортогональных полиномов

Background: Mathematical problems of digital terrain analysis include interpolation of digital elevation models (DEMs), DEM generalization and denoising, as well as computation of morphometric variables by calculation of partial derivatives of elevation. Traditionally, these procedures are based on numerical treatments of DEMs, two-dimensional (2D) discrete functions of elevation. Methods: We developed a spectral analytical method and algorithm based on high-order orthogonal expansions using the Chebyshev polynomials of the first kind with the subsequent

COMPARISON ANALYSIS OF INTERPOLATION TECHNIQUES FOR DEM GENERATION USING CARTOSAT-1 STEREO DATA

Digital Elevation Model (DEM) can be generated using several techniques such as photogrammetric technique, interferometry, Lidar, etc. In photogrammetric technique, a DEM generation using stereo images, accuracy of generated DEM is also dependent on interpolation techniques. The process of interpolation is conducted to generate DEM as a continuous data from the point map that contained height information as a discrete data. In this research, point map was extracted from Cartosat-1 stereo image and from geodetic single frequency GPS in differential mode. Different interpolation techniques were applied on these data sets with different combination within these data sets. In this study, analysis of DEM interpolation was conducted with deterministic interpolators such as inverse distance weighted (IDW), global polynomial, local polynomial, and radial basis functions (RBF); and probabilistic interpolators such as simple kriging, ordinary kriging, universal kriging, indicator kriging, probabilistic kriging, disjunctive kriging, and cokriging. The accuracy of generated DEMs through different interpolation techniques were evaluated with ground point data collected from geodetic single frequency GPS in differential mode. Based on the analysis, the range error of DEMs generated was between 1.29 m to 2.96 m. Interpolation method with the least error was ordinary kriging using point map data and GPS points, while the highest error was obtained from global polynomial method.

A terrain-based hybrid approach towards DEM interpolation

Visualization of geospatial entities generally adopts digital elevation models (DEMs) that are interpolated to establish 3D co-ordinates for the entire terrain. The accuracy of the generated terrain models depends on the type of interpolation mechanism adopted. Generally available methods in this context use point data values and their distribution; however, terrain characteristics such as shape are not generally considered in the interpolation. We propose an adaptive interpolation strategy that considers the terrain features for improving the estimation accuracy. In this research, we have compared the proposed approach with different contemporary interpolation techniques, such as inverse distance weighting, kriging (KRG), nearest neighbour and spline methods. Comparative analysis of the proposed technique with contemporary approaches revealed that considerable success has been achieved with the procedure. Investigations have also been done to analyse the sensitivity of the approach to terrain smoothness.

Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods

This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability.