High Quality Digital Terrain Models Research Articles

Sensitivity Testing of Stereophotoclinometry for the OSIRIS-REx Mission. II. Effective Observation Geometry for Digital Terrain Modeling

The OSIRIS-REx mission used stereophotoclinometry (SPC) to generate digital terrain models (DTMs) of its target asteroid, Bennu. Here we present a suite of preflight tests conducted to identify the observing geometry and number of images needed to create DTMs that would enable successful navigation around and to the surface of the asteroid. We demonstrate that high-quality DTMs can be generated by using only five images: four that are focused on topography, in which the spacecraft’s viewing geometry brackets the target (north, south, east, and west), and a fifth that measures the target’s albedo variation, taken from near local noon. We further show that the first 10 iterations of the SPC process can meaningfully improve DTM quality, including in the case of a suboptimal input image set, whereas after 10 iterations the DTM quality approaches an asymptotic maximum. We distill our findings into recommendations for observation planning that can be applied by other missions intending to use SPC to model the shape and terrain of their target.

New Bouguer Anomaly Map for the Territory of the Slovenia

The existing Bouguer anomaly map, which covers the territory of the Republic of Slovenia is a few decades old. Since then, quite a few new gravimetric measurements (data) for the territory of Slovenia as well as high quality digital terrain models that are needed for creating such a map have been made available. The methodology and standards for creating gravity anomaly maps are also changing. Thus, the national Bouguer anomaly map was updated. There were some gross errors detected in the set of old gravimetric data. Additionally, the influence of new updated gravimetric data was analyzed. The comparison of the various maps and the analysis of the influence of input gravimetric data indicates that the new gravimetric data of Slovenia has a significant influence on the creation of the gravimetric anomaly maps for Slovenia (even over 30 mGals at some points).

Modelling above-ground biomass stock over Norway using national forest inventory data with ArcticDEM and Sentinel-2 data

Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2).Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t ha−1). This result highlights the synergy between 3D and multispectral data in AGB modelling.Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.

A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model

The accuracy of vertical position information can be degraded by various sources of error in digital aerial photogrammetry (DAP) based point clouds. To address this issue, we propose a relatively straightforward method for automated correction of such point clouds. This method can be used in conjunction with any 3D reconstruction method in which a point cloud is generated from a pair of aerial images. The crux of the method involves separately co-registering each DAP point cloud (formed by the overlap of two or more images) to a common airborne laser scanning (ALS) based digital terrain model. The proposed method has the following essential steps: (1) Ground surface patches are identified in the normalized DAP point clouds by selecting areas in which standard deviation of vertical height is low, (2) height differences between the DAP and ALS point clouds are calculated at these patches, and (3) a correction surface is interpolated from these height differences and is then used to rectify the entire DAP point cloud. The performance of the proposed method is verified using plot data (n = 250) from a forested study area in Eastern Finland. We observed that DAP data from the area corrected using our proposed method resulted in significant increases in prediction accuracy of key forest variables. Specifically, the root mean squared error (RMSE) values for dominant height predictions decreased by up to 23.2%, while the associated model R2 values increased by 16.9%. As for stem volume, RMSEs dropped by 20.6%, while the model R2 improved by 14.6%, respectively. Hence, prediction accuracies were almost as good as with ALS data. The results suggest that vertically misaligned DAP data, if rectified using an algorithm such as the one presented here, could deliver near ALS data quality at a fraction of the cost.

UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador.

Remote sensing is revolutionizing the way in which forests studies are conducted, and recent technological advances, such as Structure from Motion (SfM) photogrammetry from Unmanned Aerial Vehicle (UAV), are providing more efficient methods to assist in REDD (Reducing Emissions from Deforestation and forest Degradation) monitoring and forest sustainable management. The aim of this work was to develop and test a methodology based on SfM from UAV to generate high quality Digital Terrain Models (DTMs) on teak plantations (Tectona grandis Linn. F.) situated in the Coastal Region of Ecuador (dry tropical forest). UAV overlapping images were collected using a DJI Phantom 4 Advanced© quadcopter during the dry season (leaf-off phenological stage) over 58 teak square plots of 36 m side belonging to three different plantations located in the province of Guayas (Ecuador). A workflow consisting of SfM absolute image alignment based on field surveyed ground control points, very dense point cloud generation, ground points filtering and outlier removal, and DTM interpolation from labeled ground points, was accomplished. A very accurate Terrestrial Laser Scanning (TLS) derived ground points were employed as ground reference to estimate the UAV-SfM DTM vertical error in each reference plot. The plot-level obtained DTMs presented low vertical bias and random error (−3.1 cm and 11.9 cm on average, respectively), showing statistically significant greater error in those reference plots with basal area and estimated vegetation coverage above 15 m2/ha and 60%, respectively. To the best of the authors’ knowledge, this is the first study aimed at monitoring of teak plantations located in dry tropical forests from UAV images. It provides valuable information that recommends carrying out the UAV image capture during the leaf-off season to obtain UAV-SfM derived DTMs suitable to serve as ground reference in supporting teak plantations inventories.

IDENTIFYING SURFACE CHANGES ON HRSC IMAGES OF THE MARS SOUTH POLAR RESIDUAL CAP (SPRC)

Abstract. The surface of Mars has been an object of interest for planetary research since the launch of Mariner 4 in 1964. Since then different cameras such as the Viking Visual Imaging Subsystem (VIS), Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC), and Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) have been imaging its surface at ever higher resolution. The High Resolution Stereo Camera (HRSC) on board of the European Space Agency (ESA) Mars Express, has been imaging the Martian surface, since 25th December 2003 until the present-day. HRSC has covered 100 % of the surface of Mars, about 70 % of the surface with panchromatic images at 10-20 m/pixel, and about 98 % at better than 100 m/pixel (Neukum et. al., 2004), including the polar regions of Mars. The Mars polar regions have been studied intensively recently by analysing images taken by the Mars Express and MRO missions (Plaut et al., 2007). The South Polar Residual Cap (SPRC) does not change very much in volume overall but there are numerous examples of dynamic phenomena associated with seasonal changes in the atmosphere. In particular, we can examine the time variation of layers of solid carbon dioxide and water ice with dust deposition (Bibring, 2004), spider-like channels (Piqueux et al., 2003) and so-called Swiss Cheese Terrain (Titus et al., 2004). Because of seasonal changes each Martian year, due to the sublimation and deposition of water and CO2 ice on the Martian south polar region, clearly identifiable surface changes occur in otherwise permanently icy region. In this research, good quality HRSC images of the Mars South Polar region are processed based on previous identification as the optimal coverage of clear surfaces (Campbell et al., 2015). HRSC images of the Martian South Pole are categorized in terms of quality, time, and location to find overlapping areas, processed into high quality Digital Terrain Models (DTMs) and Orthorectified Images (ORIs) and projected into polar stereographic projection using DLR (Deutsches Zentrum für Luft- und Raumfahrt; German Aerospace Center)’s VICAR and GIS software with modifications developed by Kim & Muller (2009). Surface changes are identified in the Mars SPRC region and analysed based on their appearance in the HRSC images.

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR

Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard’s airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R2 > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R2 > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R2 > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.

Multi-resolution digital terrain models and their potential for Mars landing site assessments

One of the key issues for planetary, especially Mars surface exploration, is how to make an objective assessment of criteria for landing sites selection for future rovers, sample return missions and landers considering topographic variables such as slope, altitude and roughness as well as their physical scattering properties. For these purposes, stereo vision analysis is here proposed as the best possible solution to provide reliable topographic data. Recently, a number of successful orbital missions to Mars have taken place including the Mars Express mission with the High Resolution Stereo Camera (HRSC) as well as the Mars Reconnaissance Orbiter spacecraft equipped with two major cameras—Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE). A stereo processing chain has been developed to generate high quality Digital Terrain Models (DTMs)—up to a maximum grid spacing of 0.7m with HiRISE, 10m with CTX and 25m with HRSC along with terrain-corrected ortho rectified images. Applying this system, topographic datasets were produced over potential landing sites, which had been previously proposed based mainly on their purported geological significance. High (<10m) and ultra-high (<4m) resolution DTMs from stereo imagery were employed to confirm mainly topographical hazard free landing sites from an engineering standpoint as well as to assess the geology of the target areas. In particular, the Minimum Noise Fraction (MNF) approach has been applied to assess landing risks quantitatively based on the surface roughness of the resultant topographic products. In future, it is expected that topographic products can be integrated with other data sources such as hyperspectral imagery, radar backscattering and laser beam broadening in order to provide the physical properties for the landing site selection as well.

A Novel Filtering Algorithm for Bare-Earth Extraction From Airborne Laser Scanning Data Using an Artificial Neural Network

The extraction of bare-earth points from airborne laser scanning (ALS) data and the generation of high-quality digital terrain models (DTMs) are important research challenges. In this study, a novel filtering algorithm based on artificial neural networks (ANNs) is proposed to extract bare-earth points from ALS data efficiently. An efficient set of conditions were defined to choose the training data semi-automatically when an expert user is not available. Four standard study sites were used to evaluate the performance of the method. The obtained results were compared with four popular filtering algorithms based on type I error, type II error, the kappa coefficient and the total error. First echoes were used in the proposed method to increase the reliable detection of vegetated areas. The proposed algorithm has an easy implementation procedure and low computational costs. The results obtained for both semiautomatic and supervised training data selection reveal acceptable accuracies, especially for type II errors. Use of this algorithm would lead to high-quality DTM generation using accurately identified bare-earth points in urban areas.

Derivation and Validation of High-Resolution Digital Terrain Models from Mars Express HRSC Data

The High Resolution Stereo Camera (HRSC) onboard the Mars Express mission is the first photogrammetric stereo sensor system employed for planetary remote sensing. The derivation of high-quality digital terrain models is subject to a variety of parameters, some of which show a significant variability between and also within individual datasets. Therefore, adaptive processing techniques and the use of efficient quality parameters for controlling automated processing are considered to be key requirements for DTM generation. We present the general procedure for the derivation of HRSC high-resolution DTM, representing the core element of the systematic derivation of high-level data products by the Mars Express HRSC experiment team. We also analyze test series applying specific processing variations, including a new method for signal adaptive image preprocessing. The results are assessed based on internal quality measures and compared to external terrain data. Sub-pixel scale 3D point accuracy of better than 10 m and a DTM spatial resolution of up to 50 m can be achieved for large parts of the surface of Mars within a reasonable effort. This confirms the potentials of the applied along-track multiple stereo imaging principle and allows for a considerable improvement in our knowledge of the topography of Mars.

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

Abstract A method for the detection of buildings in densely built-up urban areas by the fusion of first and last pulse laser scanner data and multi-spectral images is presented. The method attempts to achieve a classification of land cover into the classes “building”, “tree”, “grassland”, and “bare soil”, the latter three being considered relevant for the subsequent generation of a high-quality digital terrain model (DTM). Building detection is accomplished by first applying a hierarchical rule-based technique for coarse DTM generation based on morphological filtering. After that, data fusion based on the theory of Dempster–Shafer is used at two different stages of the classification process. We describe the algorithms involved, giving examples for a test site in Fairfield (New South Wales).

Laser scanning—surveying and mapping agencies are using a new technique for the derivation of digital terrain models

A user group of the Surveying and Mapping Agencies (SMA) of the Federal States of Germany tested several datasets for the derivation of high-quality Digital Terrain Models (DTM) which were collected by laser scanning. Since the results were very promising, a standard procedure for verification and handling of the data was proposed. Because the ground points that are delivered by the contractor are the result of an automated filtering process, final editing is necessary to correct remaining misclassifications. This can be carried out using photogrammetric stereo models or through comparison of the results with large scale topographic maps. Both approaches lead to a high-quality DTM with much shorter production time and less costs as compared to the photogrammetric methods used up to now.