Applicability of low-resolution digital terrain models in preliminary forest road design: A case study in Knezevo, Bosnia and Herzegovina

This research looked at the differences in forest road planning techniques and the existing software products (RoadEng and Arcinfo softwares) that have been developed to assist in forest road location. Forest road designing is conducted based on route survey using NIVO CTM mapping camera (for RoadEng software) and Garmin tools (for Arcinfo software) in Birenjestanak forest of Iran. In order to evaluate the accuracy, the outputs of RoadEng were compared to outputs of Arcinfo software. Results showed that the designed road in RoadEng software was geometrically more accurate than that of digital elevation models (DEM) 20 and 50 m intervals. The difference between real length of road and designed routes on DEM 20 and 50 m was 260 and 360 m, respectively. Also, the slope map achieved from DEM 50 m is not a good indicator for slope status in research area. The total earth working volume computed by Arcinfo software was more than that of RoadEng software. These differences for computed volume of cutting and filling were 1653 and 638 m3, respectively. The results showed DEM 50 m and DEM 20 m is not a good indicator for designing road. We can solve this problem by using the RoadEng software. With use of survey data (using mapping camera) we can transfer (Survey_Map) the route of a forest road into the plan, longitudinal, horizontal and vertical profiles, and economic calculations. Key words: RoadEng, Arcinfo, Garmin tools, digital elevation models (DEM), forest road, accuracy.

In this study we developed a forest road design program based on a high-resolution digital elevation model (DEM) from a light detection and ranging (LIDAR) system. After a designer has located the intersection points on a horizontal plane, the model first generates the horizontal alignment and the ground profile. The model precisely generates cross-sections and accurately calculates earthwork volumes using a high-resolution DEM. The model then optimizes the vertical alignment based on construction and maintenance costs using a heuristic technique known as tabu search. As the distance between cross-sections affects the accuracy of earthwork volume calculations, the results were examined by comparing them with the exact earthwork volume calculated by the probabilistic Monte Carlo simulation method. The earthwork volumes calculated by the Pappus-based method were similar to those calculated by the Monte Carlo simulation when the distance between cross-sections was within 10m. The model was applied to a high-resolution DEM from the LIDAR of Capitol Forest in Washington State, USA. The model generated a horizontal alignment, length 827m, composed of five horizontal curves. We examined the number of grade change points. The results indicated that tabu search found the best solution ($61.42/m) with five grade change points. This was composed of two vertical curves that almost followed the ground profile. As the accuracy of a high-resolution DEM from LIDAR increases, the model would become a useful tool for a forest road designer because it eliminates or at least reduces the time-consuming process of road surveys.

Although forest road networks are an important infrastructure for forestry, recreation, and sustainable forest management, they have a considerable effect on the environment. Therefore, a detailed analysis of the various benefits and associated costs of road network construction is needed. The cost of earthwork in road construction can be estimated based on the change in topography before and after construction. However, accurate estimation of the earthwork volume may not be possible on steep terrain where soil placement is limited. In this study, an unmanned aerial vehicle was flown under the tree canopy six times during a road repair work to measure the changes in topography using structure from motion analysis. Comparing the obtained 3D model with the measurement results from the total station, the average vertical error and root mean square error were −0.146 m and 0.098 m, respectively, suggesting its good accuracy for measuring an earthwork volume. Compared to the amount of earthwork estimated from the topographic changes before and after the repair work, the actual earthwork volume was 3.5 times greater for cutting and 1.9 times greater for filling. This method can be used to calculate the earthwork volume accurately for designing forest road networks on steep terrain.

DEMs (grid digital elevation models) are used for a broad spectrum of applications, some of which require deriving features such as drainage networks (rivers, creek, etc.). A precise positioning in the features taken from DEMs is necessary, but frequently DEMs are not homogeneous (e.g. the mapmaker sources vary, cell sizes differ), so that the expected precision fluctuates. Therefore, a measure to estimate the discrepancy between features built from different DEMs would be useful. In particular, we focus on the horizontal discrepancy (HD), which is the lesser studied discrepancy in the literature. Our approach is based in the optical flow (OF) algorithm, which has been used successfully in object movement detection in consecutive images or video records. We establish the analogy between an image and a DEM because both are composed of regular elements, pixels, and cells. The variation in the pixel value between two consecutive images is used by OF to compute movement. We use the variation in the DEM cell value (height) to apply the OF and to estimate the HD between rivers and creeks existing in our DEMs examples. In our study, OF proved to be a good estimator of HD when features were derived from hill and mountain terrain, but was not reliable when the terrain was almost flat. However, most studies in the research literature have indicated that nearly flat terrain poses the most difficulties in forecasting positioning errors. Therefore, we conclude that the OF is a good estimator of the HD between features derived from DEMs.

Relative forest accessibility percent of accessible forest area by forest roads in comparison with total forest area is the most important indicator of the quality of primary forest accessibility. The accessible forest area by forest roads is determined by the bounded area around forest roads. Today, in the area with steep and variable slopes of terrain, a double targeted geometrical extraction distance of timber is used for the width of the bounded area around forest roads, and the forest road spacing is used in the area with a mild and uniform slope of the terrain. Both parameters depend on the targeted density of forest roads. Modern information technologies (IT) like geographical information systems (GIS) enable the quality spatial and statistical analysis of different kinds of data whose result is not accessible forest areas by current primary forest traffic infrastructure only, but also an insight into the spatial distribution of insufficient accessible areas into the forest area. The spatial distribution of these areas is significant for spatial distribution of the new routes of forest roads. The research is done in the area of Forest Management Unit (FMU) Prosara, for which the spatial analysis of a digital terrain model (DTM) determined the mostly hilly relief area. The average relative forest accessibility, based on double targeted geometrical extraction distance of timber, is 35% for the actual network of forest roads, and targeted forest road spacing is 60% for the upgraded network of forest roads.

Digital Elevation Models (DEMs) have become a widely used tool and product in the last 20 years. They provide a snapshot of the landscape and landscape features while also providing elevation values. They have allowed us to better visualize and interrogate topographic features. A landscape feature is a road and the location of the road is the “foundation” of any road. A road constructed in a poor location can fail and cause serious environmental damage, as well as add financial strain from continuous and costly maintenance problems. The forest managers know that forest roads must be inspected regularly and maintained as necessary to minimize erosion of the road surface, otherwise, they will require costly repairs. Forest roads represent a significant investment by forest owners and as such, this investment must be preserved. Nowadays forest managers expect the field survey also use DEM and orthophotos datasets to better understand the condition of the road network and to set maintenance and upgrade priorities. In this study, we validate several DEMs and we compare them to the measurements of a detailed road surveying with geodesic GPS. The aim is to use DEMs to spot troubles in the forest road network and relieve the forest owner from the cost of periodic field survey of the forest roads.

The design and management of national parks and other protected areas requires a broad base of physiographic and geo-ecological information about the landscape. This paper evaluates the effectiveness of satellite remote sensing for photogrammetric stereo-mapping and digital elevation model (DEM) extraction within remote mountainous terrain. As a case study, a landscape analysis of the Makalu Barun National Park and Conservation Area of east Nepal (27.5° N, 87.0° E) was examined. The study area is a highly complex and rugged mountain landscape, with extreme topographic relief and an elevation gradient spanning more than 8300 m. A DEM extracted from stereo SPOT imagery resulted in a median disagreement of 58 m when compared to a DEM generated from a conventionally digitized GIS dataset of topographic contours (scale=1:250 000). Visual comparison of the two DEMs showed substantial agreement at the landscape scale, while larger scale comparison of 100 m contours revealed some localized differences. The SPOT extracted DEM provided equal or better basis for orthorectification of satellite imagery when compared to the conventional DEM. Derivative landscape analysis outputs, such as hydrological modelling, drainage networks and watershed boundaries, compared well with results based upon the conventional dataset. Intermediate map products useful for field research and mapping included production of an orthorectified satellite base-map image. Additionally, a fused multisensor high resolution image of the study area, combining Landsat Thematic Mapper (TM) and SPOT imagery at 10 m resolution, was orthorectified to produce a false-colour satellite image map highlighting the spectral discrimination between land cover classes.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15 m and 20 m in hilly terrain and about 30 m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10 m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.

Statistical methods are applied to determine earthwork quantities. First, cut and fill areas are delineated and planimetered. Then, a table of random numbers is used to locate sample items of depths of earthwork. The volume of earthwork is the product of the planimetered area and the weighted average of the sampled depths. The precision (error) of the answer is expressed as a function of confidence limit; average depth; contour interval; and number of sample items. This method is rapid and accurate and yields the most meaningful results when compared with any other method in use today for computing earthwork quantities. The distribution of cut and fill areas can also be determined from the sample items used to find the depth of earthwork. This introduces a second kind of error. Errors are quantified.

Computer-assisted forest road design mainly relies on a high-resolution digital elevation model (DEM), which provides terrain data for supporting the analysis of road design features. The resolution and accuracy of the DEM in representing the terrain structures vary depending on the preferred dataset, which then reflects some of the essential road features such as alignment, road slope, and earthwork. In this study, three forest road sections were designed by using high-resolution DEMs generated from UAV photogrammetry data, GNSS-GPS data and Total Station data. NetCAD 7.6 software, developed in Turkey and mostly used in road design applications, was used to perform the road design while calculating horizontal profiles, vertical profiles, curves, cross sections, and earthwork. The DEM generation capabilities for three datasets were compared based on spatial resolution, data collection and data processing stage. Then, the differences between three road sections were evaluated by considering specified road features such as alignment properties, road slope, and earthwork. The results indicated that the UAV (Unmanned Aerial Vehicles) based DEM generation method provided the highest resolution (10 cm), followed by the Total Station (56 cm) and GNSS-GPS (61 cm) based methods. When comparing the time for data collection procedure, it took 14 minutes, 70 minutes, and 110 minutes for UAV data, GNSS-GPS data, and Total Station data, respectively. On the other hand, UAV based method falls into a disadvantageous situation in data processing stage, due to high data processing time (3 hours). However, GNSS-GPS and Total Station based methods work only with spatial point data, so they require less processing time of 15 minutes and 25 minutes, respectively. The results indicated that road lengths were 294.8, 272.4 and 282.1 m and the average road slopes were 3.41%, 3.39%, and 3.31% for the road sections designed by using UAV, GNSS-GPS, and Total Station based DEMs, respectively. The excavation and landfill volumes were 369.16 m3 and 166.98 m3, 285.86 m3 and 201.83 m3, and 433.17 m3 and 183.95 m3, respectively. The results indicated that UAV photogrammetry data generates high-resolution DEMs that can be effectively used to design forest roads.

A previous study introduced a forest road design model developed to simultaneously optimize horizontal and vertical alignments of forest roads using a Tabu Search optimization technique and a high-resolution Digital Elevation Model (DEM). In this study, surface erosion prediction was incorporated into the road design model, so that users can optimize horizontal and vertical alignments of forest roads while constrained by maximum allowable sediment delivery from roads to streams. The road alignment optimization model was applied to a part of the Capitol State Forest in western Washington state. The application confirms the potential of the model to determine forest road alignments in a way to reduce total road costs as well as sediment delivery to streams. This paper also discusses the effects of DEM resolution on forest road alignment optimization. The accuracy of generating ground profile and forest road alignments depends on the resolution and accuracy of the DEM. The study results suggest that a 10-m grid DEM might be inappropriate to use for the purpose of road design and alignment optimization due to the lower accuracy in its elevation representation.

Digital Elevation Model (DEM) and its resulting parameters are essential terrain related information. DEM and the extracted information (slope, aspect, roughness etc.) have been identified as one of the most important and fundamental variables to various streams of engineering and planning designs which are the hall marks of development all over the world. Thus, to delineate the major surface and subsurface structures for evaluating the Planning framework for the Federal Capital City of Nigeria, analyzing the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM data is very crucial. Hence this paper aimed at examining the effects of terrain configuration of Shuttle Radar Topographic Mission (SRTMV3) and ALOS PALSAR DEM. The methodology involved data acquisition of ALOS PALSAR, SRTMV3 and Ortho DEMs, after which the ALOS PALSAR and SRTMV3 DEMs were resampled to 10m of the Ortho DEM, image classification and then an assessment of the impact of terrain configuration on DEM performance with horizontal profiles was carried out. The results revealed that SRTMV3 v3 performed better with close resemblance with the Ortho DEM on flat and undulating terrain while it underestimated the rolling terrain and overestimated the hilly and mountainous terrain. ALOS PALSAR DEM when compared against the Ortho DEM grossly overestimated all the terrain configuration in the study area. In all, the overall performance of SRTMV3 v3 had a close resemblance in performance to that of the Ortho DEM, while ALOS PALSAR had a significant difference in performance. It was therefore recommended that SRTMV3 v3 should be used as an alternative DEM source where high-resolution elevation data are not readily available. Keywords: ALOS PALSAR, Digital Elevation Model, SRTM, Terrain modelling. DOI: 10.7176/JEES/10-6-14 Publication date: June 30 th 2020

The study site is selected in a Malaysian tropical rainforest area, which consists of a mixture of plain, hilly and mountainous terrain. Digital Elevation Model (DEM) images were generated from nine RADARSAT-1 imageries (F, S and W beam modes) which make up six stereo pair combinations. The DEM accuracies for all the stereo combinations have been validated and compared to each other. The results show that numerous factors affect the final DEM accuracy. In flat areas, the final DEM accuracy is highly correlated to the stereo intersection geometry of the different image combinations. The higher the stereo intersection angle of the same beam mode, the better the accuracy of the final DEM.

Earthwork volumes represent the basis on which contractors are paid for highway construction. The earthwork volumes between successive roadway stations are also used in determining the economic distribution of earthwork. It is essential that the volumes should be accurately computed because disagreements related to earthwork volumes often cause the owner and the contractor to look to the courts for settlement. The traditional model for estimating earthwork volumes of curved roadways (flat horizontal curves) is suitable only for level terrains. For moderately fluctuating terrains, a mathematical model has been developed. This model, however, assumes that the longitudinal ground profile between successive stations is linear and the ground cross slope is constant. The mathematical model is not accurate for greatly fluctuating profiles, such as those in hilly and mountainous terrains. This paper develops a model for estimating earthwork volumes for such profiles using Monte Carlo simulation. The paper illustrates how a completely deterministic problem can be solved using a probabilistic simulation. The results show that the simulation model improves the estimates of earthwork volumes compared to traditional and mathematical models.

Uncertainty can be apprehended as lack of knowledge about a certain phenomenon. Decisions about whether and how to react to this uncertainty depend on a number of factors. These factors include the ability to estimate the amount of uncertainty and thus estimate the involved risk, available options to decrease either the uncertainty or its relevance, and the costs for responding or ignoring uncertainty.
\nIn GIScience, the modelling of processes is subject to uncertainties from a number of sources. Above all, the abstraction inherent in any model results in uncertainty,
\ncreated from the assumptions made to simplify complex processes and interrelations in order to formalise and model them. Additionally, uncertainty in any input data
\npropagates through a model into the results. For topography-based models, i.e. models characterising and detecting topographic form, or models simulating processes that act upon this topography, digital elevation models (DEMs) are a potential source of uncertainty. DEMs consist of measured or digitised elevation values, and as such are
\nsubject to any error in the data capturing process. Widespread DEMs such as GLOBE or SRTM are distributed with accuracy figures that only give global measures such as
\nroot mean square error (RMSE) lacking any information on the spatial distribution of error. Where uncertainty from DEM accuracy has to be modelled to assess its impact on the results of associated topographic models, assumptions have to be made about the spatial distribution of uncertainty. Within this dissertation it has been shown that these assumptions influence the impact of uncertainty on modelled ice sheets. Besides DEM accuracy, a number of factors in handling DEM data introduce additional uncertainty. These factors include the choice of data model, processing such as projecting and resampling of a DEM data, as well as algorithms used to extract and process elevation based information.
\nWithin this dissertation, the influence of resampling on uncertainty in topography has been explored. This was done by assessing the variation in resampled DEMs introduced
\nby changing the source and target resolution, choice of resampling algorithms and resampling origin. When these uncertainties were modelled and added to input topographies for the GLIMMER ice sheet model, they had noticeable influence on modelled ice sheet configurations. Where higher accuracy reference data for a DEM is available, error can be derived and analysed to provide information about spatial autocorrelation and possible dependencies of error with topographic attributes such as elevation, slope or roughness. Within the course of this dissertation, an uncertainty model was developed which allows modelling of GLOBE DEM uncertainty for areas without higher accuracy reference data such as Scandinavia. The model is based on derived dependencies of GLOBE error with topographic attributes, derived from areas where SRTM data was available to be used as a reference. The model includes both deterministic and stochastic components and reproduces GLOBE DEM uncertainty well for different test areas.
\nThe developed uncertainty model was applied to investigate the impact of DEM uncertainty on different types of models in three case studies. The first case study applied a geomorphologic and hydrologic model (TARDEM), the second case study used two snow melt models, and in the third case study the GLIMMER ice sheet model was employed. Results showed the impact of uncertainty to be depending on a number of facts. Generally, modelled DEM uncertainty had less impact on derived global topographic variables such as mean slope length or the number of derived watersheds when
\napplied to a hydrological model. Higher impacts were recorded where the model focus was on local processes, such as the delineation of a certain watershed and calculation
\nof associated parameters such as hypsometry. For process models like the ice sheet model, factors such as terrain configuration (smooth vs. rough topography, abundant
\nridges or valleys) influenced the impact of DEM uncertainty on ice sheet model (ISM)results.
\nAdditionally, the amount of uncertainty and its spatial correlation, as well as the relative influence of topography within a model were found to play key roles. This implies that for process models, the impact of uncertainty can vary over time. In the case of the ice sheet model, uncertainty had the greatest impact on ice sheet configuration during phases of inception and retreat, and its impact was shown to be dependent on the overall size of the ice masses.
\nIn another set of experiments, a range of sensitivity tests using different ISM parameters and input data were conducted, and the results of these tests were used to
\nconduct a full parametric uncertainty analysis (PUA) for a steady-state climate scenario on Fennoscandia. Results from this analysis allowed the comparison of the influence
\nof uncertainty in other parameters to that of DEM uncertainty, which was found to be equivalent to a 1degC change in climate. The impact of DEM uncertainty was found to be comparable to that of various ‘internal’ ISM parameters. However modelled DEM uncertainty resulted in significantly different ice sheet configurations. This underlines the importance of DEM uncertainty to be considered in ice sheet modelling.
\nUsing different temperature index models (TIM) to model potential snow melt across different resolutions revealed significant impact of scale and resampling on modelled melt rates. This effect was substantially decreased by the use of subgrid model approaches. While it was shown that these subgrid approaches are subject to an increased susceptibility to DEM uncertainty, this effect was more than compensated for by an increased performance in terms of modelled melt rates.
\nIn summary, the results of this dissertation underline the necessity of detailed information on the statistical and spatial distribution of DEM uncertainty to be included
\nwith the data. Additionally, in topographic modelling, uncertainty from other sources such as resampling have shown to be of importance, and modellers and end-users
\nshould account for these uncertainties introduced into model results.