DEM Densification Using Perspective Shape From Shading Through Multispectral Imagery

<p>Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.</p><p>In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.</p><p>Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon.     </p><p>Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.</p><p>The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.</p>

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

The aim of this thesis is to explore computational methods for the shape from shading problem as formulated through the image irradiance equation. We seek to develop robust, efficient methods and test our algorithms on synthetic images ranging from simple smooth surfaces to complex digital terrain model data. We consider three different approaches. The first approach revisits the method of characteristic strips with a view to using more stable integration schemes than had been used in earlier works. Stable schemes coupled with projections onto the image irradiance equation are used. Although the effects of noise are thereby reduced, the solution is still deemed unsatisfactory even for very simple surfaces. The second approach considers Horn's variational technique as a basis for producing a fast solver. We devise a discretization scheme coupled with a special continuation multigrid method for this formulation. We also allow for multiple image data and explicit knowledge of the location of discontinuities in surface height and orientation. Given multiple image data, we obtain excellent results even in the presence of discontinuities. The third approach examines a class of solution techniques based on fluid flow which are new to the shape from shading literature. This formulation is ill posed in general, so we propose a regularization of the problem. We observe that the algorithm is prone to producing spurious results. Analysis shows that these are due to the non-random accumulation of errors in the computed solution. Of the three approaches considered, the variational method yields the most promising results. Efficient, good quality reconstructions are obtained, especially when data from more than one image are available.

Interferometric synthetic aperture radar (InSAR) has become a key technology for producing high-precision digital surface models (DSMs) and digital orthophoto maps (DOMs) in full time and all weathers. Airborne millimeter-wave InSAR, with large-scale and high-resolution imaging, is characterized by high spatial resolution, flexibility, and immunity to loss-of-correlation. This paper introduces our modeling experiments with airborne dual-antenna, Ka-band InSAR regarding typical topographies of China. Ka-band SAR data were acquired in designated experimental areas in flat (Heyang area in Shaanxi) and mountainous areas (Shibing area in Guizhou and Qionglai area in Sichuan). The key processing of the experimental data for DSMs and DOMs is demonstrated in the paper, especially the proposed robust and efficient phase unwrapping (PU) method for the interferometric data and block adjustment method of strip calibration. The results show that the proposed unwrapping method can provide reliable unwrapped phase results in undulating areas, and the block adjustment can carry out consistent calibration for strips with sparse ground control points (GCPs). The accuracy assessment of the DSM shows that the coordinate root mean square error (RSME) of the obtained DSM is less than 2 m in height, and 2.5 m horizontally, which meets the 1:5000 requirement for topographic mapping in difficult areas.

Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing offers wide coverage, existing optical and Synthetic Aperture Radar (SAR)-based techniques face challenges in direct volume estimation due to resolution constraints and rapid terrain changes. This study proposes a Super-Resolution Shape from Shading (SRSFS) approach enhanced by a Non-local Piecewise-smooth albedo Constraint (NPC), hereafter referred to as NPC SRSFS, to estimate debris-flow erosion volume using single high-resolution optical satellite imagery. By integrating publicly available global Digital Elevation Model (DEM) data as prior terrain reference, the method enables accurate post-disaster topography reconstruction from a single optical image, thereby reducing reliance on stereo imagery. The NPC constraint improves the robustness of albedo estimation under heterogeneous surface conditions, enhancing depth recovery accuracy. The methodology is evaluated using Gaofen-6 satellite imagery, with quantitative comparisons to aerial Light Detection and Ranging (LiDAR) data. Results show that the proposed method achieves reliable terrain reconstruction and erosion volume estimates, with accuracy comparable to airborne LiDAR. This study demonstrates the potential of NPC SRSFS as a rapid, cost-effective alternative for post-disaster debris-flow assessment.

Shading-based DEM refinement under a comprehensive imaging model

Abstract. Topographic mapping, e.g. the generation of Digital Elevation Models (DEM), is of general interest to the remote sensing community and scientific research. Commonly, photogrammetric methods, e.g. stereo image analysis methods (SIAM) or bundle adjustment methods (BAM), are applied to derive 3D information based on multiple images of an area. These methods require the detection of control points, i.e. common points within multiple images, which relies on a similarity measure and usually yields a sparse map of 3D points. The full spatial DEM is then obtained by interpolation techniques or imposed restrictions, e.g. smoothness constraints. Since BAM utilizes all images of the area, it is assumed to provide a more accurate DEM than SIAM which utilizes only pairs of images. Intensity-based shape recovery, e.g. shape from shading (SfS), utilizes the reflectance behavior of the object surface and thus provides a dense map of relative height changes, which provide the possibility to refine the photogrammetric DEMs. Based on Rosetta NavCam images of 67P/Churyumov-Gerasimenko we compare intensity-based DEM refinement methods which use DEMs obtained based on SIAM and BAM as a reference. We show that both the SIAM based DEM refinement and the BAM based DEM refinement are of similar quality. It is thus possible to derive DEMs of high lateral resolution by applying the intensity-based refinement to the less complex SIAM.

Abstract. Lunar topographic information, e.g., lunar DEM (Digital Elevation Model), is very important for lunar exploration missions and scientific research. Lunar DEMs are typically generated from photogrammetric image processing or laser altimetry, of which photogrammetric methods require multiple stereo images of an area. DEMs generated from these methods are usually achieved by various interpolation techniques, leading to interpolation artifacts in the resulting DEM. On the other hand, photometric shape reconstruction, e.g., SfS (Shape from Shading), extensively studied in the field of Computer Vision has been introduced to pixel-level resolution DEM refinement. SfS methods have the ability to reconstruct pixel-wise terrain details that explain a given image of the terrain. If the terrain and its corresponding pixel-wise albedo were to be estimated simultaneously, this is a SAfS (Shape and Albedo from Shading) problem and it will be under-determined without additional information. Previous works show strong statistical regularities in albedo of natural objects, and this is even more logically valid in the case of lunar surface due to its lower surface albedo complexity than the Earth. In this paper we suggest a method that refines a lower-resolution DEM to pixel-level resolution given a monocular image of the coverage with known light source, at the same time we also estimate the corresponding pixel-wise albedo map. We regulate the behaviour of albedo and shape such that the optimized terrain and albedo are the likely solutions that explain the corresponding image. The parameters in the approach are optimized through a kernel-based relaxation framework to gain computational advantages. In this research we experimentally employ the Lunar-Lambertian model for reflectance modelling; the framework of the algorithm is expected to be independent of a specific reflectance model. Experiments are carried out using the monocular images from Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) (0.5 m spatial resolution), constrained by the SELENE and LRO Elevation Model (SLDEM 2015) of 60 m spatial resolution. The results indicate that local details are largely recovered by the algorithm while low frequency topographic consistency is affected by the low-resolution DEM.

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

In this paper, we discuss the measurements of spectral surface reflectance (rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lambda</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ) in the wavelength range 350-2500 nm measured using a spectroradiometer onboard a low-flying aircraft over Bangalore (12.95degN, 77.65deg E), an urban site in southern India. The large discrepancies in the retrieval of aerosol properties over land by the Moderate-Resolution Imaging Spectroradiometer (MODIS), which could be attributed to the inaccurate estimation of surface reflectance at many sites in India and elsewhere, provided motivation for this paper. The aim of this paper was to verify the surface reflectance relationships assumed by the MODIS aerosol algorithm for the estimation of surface reflectance in the visible channels (470 and 660 nm) from the surface reflectance at 2100 nm for aerosol retrieval over land. The variety of surfaces observed in this paper includes green and dry vegetations, bare land, and urban surfaces. The measured reflectance data were first corrected for the radiative effects of atmosphere lying between the ground and aircraft using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The corrected surface reflectance in the MODIS's blue (rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">470</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ), red (rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">660</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ), and shortwave-infrared (SWIR) channel (rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2100</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ) was linearly correlated. We found that the slope of reflectance relationship between 660 and 2100 nm derived from the forward scattering data was 0.53 with an intercept of 0.07, whereas the slope for the relationship between the reflectance at 470 and 660 nm was 0.85. These values are much higher than the slope ( ~0.49) for either wavelengths assumed by the MODIS aerosol algorithm over this region. The reflectance relationship for the backward scattering data has a slope of 0.39, with an intercept of 0.08 for 660 nm, and 0.65, with an intercept of 0.08 for 470 nm. The large values of the intercept (which is very small in the MODIS reflectance relationships) result in larger values of absolute surface reflectance in the visible channels. The discrepancy between the measured and assumed surface reflectances could lead to error in the aerosol retrieval. The reflectance ratio (rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">660</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> /rho <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2100</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> ) showed a clear dependence on the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NDVI</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SWIR</sub> where the ratio increased from 0.5 to 1 with an increase in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NDVI</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SWIR</sub> from 0 to 0.5. The high correlation between the reflectance at SWIR wavelengths (2100, 1640, and 1240 nm) indicated an opportunity to derive the surface reflectance and, possibly, aerosol properties at these wavelengths. We need more experiments to characterize the surface reflectance and associated inhomogeneity of land surfaces, which play a critical role in the remote sensing of aerosols over land.

Abstract. Greenland digital elevation models (DEMs) are indispensable to fieldwork, ice velocity calculations, and mass change estimations. Previous DEMs have provided reasonable estimations for all of Greenland, but the time span of applied source data may lead to mass change estimation bias. To provide a DEM with a specific time stamp, we applied approximately 5.8×108 ICESat-2 observations from November 2018 to November 2019 to generate a new DEM, including the ice sheet and glaciers in peripheral Greenland. A spatiotemporal model fit process was performed at 500 m, 1 km, 2 km, and 5 km grid cells separately, and the final DEM was posted at the modal resolution of 500 m. A total of 98 % of the grids were obtained by the model fit, and the remaining DEM gaps were estimated via the ordinary Kriging interpolation method. Compared with IceBridge mission data acquired by the Airborne Topographic Mapper (ATM) lidar system, the ICESat-2 DEM was estimated to have a maximum median difference of −0.48 m. The performance of the grids obtained by model fit and interpolation was similar, both of which agreed well with the IceBridge data. DEM uncertainty rises in regions of low latitude and high slope or roughness. Furthermore, the ICESat-2 DEM showed significant accuracy improvements compared with other altimeter-derived DEMs, and the accuracy was comparable to those derived from stereophotogrammetry and interferometry. Overall, the ICESat-2 DEM showed excellent accuracy stability under various topographic conditions, which can provide a specific time-stamped DEM with high accuracy that will be useful to study Greenland elevation and mass balance changes. The Greenland DEM and its uncertainty are available at https://doi.org/10.11888/Geogra.tpdc.271336 (Fan et al., 2021).

Land‐use maps produced with high accuracy are extremely important as a basis for better use of the land as they are widely utilized in many areas such as agricultural policies, natural resources management, and environmental operations. This study aimed to produce a high accuracy land‐use map using digital surface models (DSMs) produced using different interpolation methods and a random forest (RF) classifier. High spatial resolution Triplesat‐2 images, Worldview‐2 (WV‐2) images, and unmanned aerial vehicle (UAV) images were used in this study. First, DSMs were produced from the point cloud using different interpolation methods. The image fusion process was applied and these fused images were classified using an RF classifier together with the DSMs. Overall results showed that the DSM obtained by the kriging method yielded better results than the other methods by increasing the classification accuracy by 7%–13%. In addition, the McNemar test was applied to the images resulting from the classification of fused images with and without using DSMs to investigate the statistical significance of the differences. McNemar test demonstrated that the values were greater than 3.84, which revealed that the DSM produced via kriging interpolation had significantly increased the classification accuracy at a 95% confidence interval.

This article presents a review of the combined analysis of digital terrain models (DTMs) and remotely sensed data in landscape investigations. The utilization of remotely sensed data with DTMs has become an important trend in geomatics in the past two decades. Models of more than ten quantitative topographic variables are employed as ancillary data in the treatment of images. The article reviews the methods for DTM derivation and the basic problems of DTM operation that are important for handling DTMs with imagery, namely: 1) the choice of a DTM network type; 2) DTM resolution; 3) DTM accuracy; and 4) the precise superimposition of DTMs and images. The processing of remotely sensed data and DTMs in combination is used in the following procedures: 1) the image correction of the topographic effect; 2) the correction of geometric image distortion; 3) image classification; 4) statistical and comparative analyses of landscape data; and 5) three-dimensional landscape modelling. These procedures are applied to solve a wide range of problems in geobotany, geochemistry, soil science, geology, glaciology and other sciences. The joint use of imagery and DTMs can increase the total amount of information extracted from both types of data. The trend has been towards the incorporation of the combined analysis of remotely sensed data and DTMs into mixed environmental models. The following potential applications of the treatment of imagery in association with DTMs are identified: 1) the prediction of the migration and accumulation zones of water, mineral and organic substances moved by gravity along the land surface and in the soil; 2) the investigation of the relationships between topographically expressed geological structures and landscape properties; 3) the improvement of geological engineering in industrial planning (e.g., the construction of nuclear power stations, oil and gas pipelines and canals); and 4) the monitoring of existing industries. Digital models of plan, profile, mean and total accumulation curvatures, and nonlocal and combined topographic attributes should be included in data processing both to solve the problems indicated and to improve the outcome of some regular tasks (for example, the prediction of soil moisture distribution and fault recognition).

A method for simulating delay-Doppler maps (DDMs) of global navigation satellite system signals reflected from land surfaces with heterogeneous terrain is developed from first principles. The method follows previous work for ocean DDMs in the geometric optics limit of the Kirchhoff approximation. Unlike the ocean method, however, where surface heights are assumed to be random with homogeneous statistics, this method decomposes the surface heights into a deterministic part obtained from a digital elevation map (DEM) and a random part representing the residual between the surface and the DEM. The method accounts for the displacement of reflected power into bins of lower delay due to raised surface terrain. The method also provides for the modulation of the normalized bistatic radar cross section by DEM-derived surface slopes over the glistening zone of the DDM. A technique to register Cyclone Global Navigation Satellite System (CYGNSS) DDM bins in delay-Doppler space for land applications is also proposed. The DEM-based method is applied to a CYGNSS track over the Soil Moisture Sensing Controller And oPtimal Estimator (SoilSCAPE) site at Tonzi Ranch, CA, USA. The DEM-based method has potential application for spaceborne monitoring of a variety of environmental parameters.

A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments