Accurate Digital Terrain Models Research Articles

Creating a Lowland and Peatland Landscape Digital Terrain Model (DTM) from Interpolated Partial Coverage LiDAR Data for Central Kalimantan and East Sumatra, Indonesia

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines (‘strips’) covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7–96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications.

Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment

Digital stereo aerial photographs are periodically updated in many countries and offer a viable option for the regular update of information on forest variables. We compared the potential of image-based point clouds derived from three different sets of aerial photographs with airborne laser scanning (ALS) to assess plot-level forest attributes in a mountain environment. The three data types used were (A) high overlapping pan-sharpened (80/60%); (B) high overlapping panchromatic band (80/60%); and (C) standard overlapping pan-sharpened stereo aerial photographs (60/30%). We used height and density metrics at the plot level derived from image-based and ALS point clouds as the explanatory variables and Lorey’s mean height, timber volume, and mean basal area as the response variables. We obtained a RMSE = 8.83%, 29.24% and 35.12% for Lorey’s mean height, volume, and basal area using ALS data, respectively. Similarly, we obtained a RMSE = 9.96%, 31.13%, and 35.99% and RMSE = 11.28%, 31.01%, and 35.66% for Lorey’s mean height, volume and basal area using image-based point clouds derived from pan-sharpened stereo aerial photographs with 80/60% and 60/30% overlapping, respectively. For image-based point clouds derived from a panchromatic band of stereo aerial photographs (80%/60%), we obtained an RMSE = 10.04%, 31.19% and 35.86% for Lorey’s mean height, volume, and basal area, respectively. The overall findings indicated that the performance of image-based point clouds in all cases were as good as ALS. This highlights that in the presence of a highly accurate digital terrain model (DTM) from ALS, image-based point clouds offer a viable option for operational forest management in all countries where stereo aerial photographs are updated on a routine basis.

Comparison and integration of lidar and photogrammetric point clouds for mapping pre-fire forest structure

Lidar is an established tool for mapping forest structure, but its sparse spatial and temporal coverage often preclude its use in studying forest disturbance. In contrast, aerial imagery has been and continues to be regularly collected in many regions, and advances in stereo image matching have automated the creation of dense photogrammetric point clouds, which can also be used to map forest structure when paired with an accurate digital terrain model. As part of a study of the physical and ecological impacts of the 2012 High Park fire in Colorado, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and combined it with a digital terrain model generated from a 2013 post-fire lidar collection to produce canopy metrics commonly used in modelling of forest structure. We explore the correlation structure between the lidar and photogrammetry-derived canopy metrics, and the relationships between those metrics and forest structure attributes measured at unburned plots in the vicinity of the burn scar. Most corresponding lidar- and photogrammetry- derived canopy metrics had strong linear relationships between them (median r = 0.82), and metrics from both datasets yielded similar root mean square errors in multiple regression models of aboveground biomass (29.3% and 31.0%), basal area (29.8% and 27.7%), and several other forest structure attributes. We found the source of the canopy metrics (lidar or photogrammetry) to be a non-significant factor in some models of forest structure, suggesting that these datasets may be interchangeable in particular cases. Models derived from pre-fire aerial imagery were combined with burn severity information to quantify loss of biomass (2.31 ± 0.001 Tg) and examine possible relationships between forest structure and burn severity. These applications illustrate how the broad spatial and temporal coverage of aerial imagery and growing coverage of lidar can be utilized to improve understanding of changes in forest structure, including assessments of forest carbon flux.

A novel automated method for the improvement of photogrammetric DTM accuracy in forests

Digitalni model reljefa (DTM, engl. Digital Terrain Model) ima široku i važnu primjenu u mnogim djelatnostima, uključujući i šumarstvo. Međutim, precizno modeliranje terena, odnosno izrada DTM-a u šumama, bilo korištenjem terenskih metoda ili metoda daljinskih istraživanja, izazovan je i vrlo zahtjevan zadatak. U većini razvijenih zemalja svijeta, zračno lasersko skeniranje (ALS, engl. Airborne Laser Scanning) bazirano na LiDAR (engl. Light Detection and Ranging) tehnologiji trenutno predstavlja glavnu metodu za izradu DTM-a. Uslijed mogućnosti laserskog zračenja da penetrira kroz krošnje drveća, LiDAR tehnologija se pokazala kao efektivna i brza metoda za izradu DTM-a u šumskim područjima s vrlo velikom točnošću. Međutim, u mnogim zemljama svijeta, uključujući i Hrvatsku, zračno lasersko skeniranje nije u potpunosti provedeno, tj. samo su manji dijelovi zemlje pokriveni s podacima zračnog laserskog skeniranja. U tim slučajevima, DTM temeljen na stereo-fotogrametrijskoj izmjeri aerosnimaka potpomognut s terenskim podacima najčešće predstavlja glavni izvor informacija za izradu DTM-a. Poznato je da tako izrađen DTM u šumskim predjelima ima manju točnost od DTM-a dobivenog na temelju zračnog laserskog skeniranja zbog pokrivenosti terena vegetacijom. Također, u okviru nedavno provedenog istraživanja (Balenović i dr., 2018) utvrđeno je da takvi službeni fotogrametrijski digitalni podaci terena u šumskim predjelima sadrže određen broj tzv. grubih grešaka, koje mogu značajno utjecati na točnost izrađenog DTM-a. Nakon vizualnog detektiranja i manualnog uklanjanja tih pogrešaka, Balenović i dr. (2018) utvrdili su značajno poboljšanje točnosti fotogrametrijskog DTM-a. Stoga je glavni cilj ovoga rada razviti automatsku metodu za detekciju i eliminaciju vertikalnih pogrešaka u fotogrametrijskim digitalnim podacima terena te na taj način poboljšati točnost fotogrametrijskog DTM-a u nizinskim šumskim područjima Hrvatske. Ideja je razviti brzu, jednostavnu i učinkovitu metodu koja će biti primjenjiva i za druga šumska područja sličnih karakteristika, a za koja ne postoje DTM dobiven zračnim laserskim skeniranjem. Istraživanje je provedeno u nizinskim šumama na području gospodarske jedinice Jastrebarski lugovi, u neposrednoj blizini Jastrebarskog (Slika 1). Istraživanjem je obuhvaćena površina od 2.005,74 ha, na kojoj su u najvećoj mjeri zastupljene jednodobne sastojine hrasta lužnjaka (Quercus robur L.), a u ma­njoj mjeri jednodobne sastojine poljskog jasena (Fraxinus angustifolia L.) te jednodobne sastojine običnoga graba (Carpinus betulus L.). Nadmorska visina područja istraživanja kreće se u rasponu od 105 do 121 m. Fotogrametrijski DTM (DTM<sub>PHM</sub>) je izrađen iz digitalnih vektorskih podataka terena (prijelomnice, linije oblika, markantne točke terena i pravokutne mreže visinskih točaka) nabavljenih iz Državne geodetske uprave (Slika 2). Ti podaci predstavljaju nacionalni standard i jedini su dostupni podaci za izradu DTM-a u Hrvatskoj. Detaljan opis vektorskih podataka dan je u radu Balenović i dr. (2018). Prvo je iz digitalnih terenskih podataka izrađena nepravilna mreža trokuta, koja je potom linearnom interpolacijom pretvorena u rasterski DTM<sub>PHM</sub> prostorne rezolucije (veličine piksela) 0,5 m. Automatska metoda za detekciju i eliminaciju vertikalnih pogrešaka fotogrametrijskog DTM-a u nizinskim šumskim područjima razvijena je u slobodnom programskom paketu Grass GIS (Slika 3). Kombinacijom vrijednosti nagiba i tangencijalne zakrivljenosti terena rasterskog DTM<sub>PHM</sub> (Slika 4), automatskom metodom su detektirane 91 grube greške (engl. outliers). Drugim riječima, utvrđeno je da 91 točkasti vektorski objekt pogrešno prikazuje stvarnu visinu terena. Navedeni broj čini 3,2 % od ukupnog broja točkastih objekata korištenih za izradu DTM<sub>PHM</sub>-a. Nakon eliminacije detektiranih pogrešaka izrađen je novi, korigirani fotogrametrijski DTM (DTM<sub>PHMc</sub>). Za ocjenu vertikalne točnosti izvornog (DTM<sub>PHM</sub>) i korigiranog DTM-a (DTM<sub>PHMc</sub>) korišten je visoko precizni DTM dobiven zračnim laserskim skeniranjem (DTM<sub>LiD</sub>). U tu svrhu su izrađeni rasteri razlika između DTM<sub>PHM </sub>i DTM<sub>LiD</sub>, te između DTM<sub>PHMc </sub>i DTM<sub>LiD</sub>. Kako je preliminarnom analizom utvrđeno da vertikalne razlike između DTM<sub>PHM </sub>i DTM<sub>LiD</sub> nisu normalno distribuirane (Slika 5), za ocjenu točnosti su uz normalne mjere točnosti korištene i tzv. robusne mjere točnosti (Tablica 2). Dobiveni rezultati ukazuju na poboljšanje vertikalne točnosti fotogrametrijskog DTM-a primjenom razvijene automatske metode. To je posebice uočljivo na podpodručjima 2 i 3 (Slika 6 i 7) u kojima se nakon uklanjanja detektiranih grešaka, korijen srednje kvadratne pogreške (RMSE, engl. root mean square error) smanjio za 8 % odnosno 50 % (Tablica 2). Na temelju dobivenih rezultata i usporedbe s DTM<sub>LiD</sub>, može se zaključiti da predložena metoda uspješno detektira i eliminira vertikalne pogreške fotogrametrijskog DTM-a u nizinskim šumskim područjima, te slijedom toga poboljšava njegovu vertikalnu točnost.

Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models

Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.

The effect of user-defined parameters on DTM accuracy—development of a hybrid model

User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.

IMPLEMENTING SIFT AND BI-TRIANGULAR PLANE TRANSFORMATION FOR INTEGRATING DIGITAL TERRAIN MODELS

Abstract. Since their inception in the middle of the twentieth century, Digital Terrain Models (DTMs) have played an important role in many fields and applications that are used by geospatial professionals, ranging from commercial companies to government agencies. Thus, both the scientific community and the industry have introduced many methods and technologies for DTM generation and data handling. These resulted in a high volume and variety of DTM databases, each having different coverage and data-characteristics, such as accuracy, resolution, level-of-detail – amongst others. These various factors can cause a dilemma for scientists, mappers, and engineers that now have to choose a DTM to work with, let alone if several of these representations exist for a specified area. Traditionally, researchers tackled this problem by using only one DTM (e.g., the most accurate or detailed one), and only rarely tried to implement data fusion approaches, combining several DTMs into one cohesive unit. Although to some extent this was successful in reducing errors and improving the overall integrated DTM accuracy, two prominent problems are still scarcely addressed. The first is that the horizontal datum distortions and discrepancies between the DTMs are mostly ignored, with only the height dimension taken into account, even though in most cases these are evident. The second is that most approaches operate on a global scale, and thus do not address the more localized variations and discrepancies that are presented in the different DTMs. Both problems affect the resulting integrated DTM quality, which retains these unresolved distortions and discrepancies, resulting in a representation that is to some extent inferior and ambiguous. In order to tackle this, we propose an image based fusion approach: using the SIFT algorithm for matching and registration of the different representations, alongside localized morphing. Implementing the proposed approach and algorithms on various DTMs, the results are promising, with the capacity correctly geospatially align the DTMs, thus reducing the mean height difference variance between the databases to close to zero, as well as reducing the standard deviation between them by more than 30 %.

Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions

ABSTRACTUnmanned aerial vehicle (UAV) platforms are rapidly becoming popular in many research and industry sectors. Due to their relatively low purchase price and the fact they can be used to monitor areas that are difficult or even unsafe to access, they have been increasingly used in land surveying and mapping of smaller areas. Numerous UAV platforms equipped with various cameras are increasingly available on the market, differing in their suitability for environmental mapping. Surveyors therefore face a question whether to buy or assemble their own UAV. The objective of this study is to assess the performance of two fixed-wing UAV systems for land survey and mapping applications. In particular, we: (1) compared a commercial eBee platform equipped with a Sony Cybershot DSC-WX220 camera with zoom lens and a home assembled EasyStar II equipped with Nikon Coolpix A with a lens of fixed focal length to find out if a home-assembled solution can compete with specialized commercial platform; (2) investigated the utilization of UAV images acquired under leaf-off conditions for digital terrain model (DTM) generation with respect to vegetation cover (steppes and forests); (3) assessed whether an increase in the image quantity can compensate for a lower quality of images; and (4) compared the DTM derived from UAV imagery with the official Czech Republic airborne laser scanning (ALS)-derived DTM. One flight with Easystar II and two perpendicular flights with eBee were performed. From these three flights, four point clouds were derived (one from each flight, and one resulting from a combination of two eBee flights), supplemented with four ground filtered point clouds. The accuracy of point clouds and DTM was assessed through a comparison with a conventional GNSS survey. We successfully identified the bare ground during the leaf-off period in the deciduous forest using images from both platforms. Point densities of point clouds acquired with Easystar II exceeded the densities of those acquired with eBee even after combining images from two eBee flights. Root mean square error of all derived point clouds ranged between 0.11 and 0.19 m, exceeding the accuracy of a nationwide ALS-derived DTM in both forest and open steppe areas. The most accurate point cloud was acquired using Easystar II. This is likely due to a combined effect of the quality of onboard cameras, camera settings and environmental conditions during the flight. For users who prefer to have greater control over their options rather than being dependent on the commercially available kit solution, home-assembled kits utilizing drones capable of carrying any camera available on the market may be an advantage.

Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

High-resolution topography for Digital Terrain Model (DTM) in Keller Peninsula, Maritime Antarctica.

High resolution topography (HRT) surveys is an important tool to model landscapes, especially in zones subjected to strong environmental changes, such as Antarctica, where landform is highly influenced by cryoclasty and permafrost melting. The aim of this work was to obtain a high accurate DTM for Keller Peninsula, Maritime Antarctica. The survey study was assessed in the 2014/2015 and 2015/2016 during the austral summer, by using Terrestrial Laser Scanner (TLS). In order to cover 8 km² of the Peninsula, the TLS equipment was installed in 81 different points. Results of the DTM generated by TLS (hereafter, HRT-DTM), and the terrain variables Aspect, Slope and Hillshade obtained were compared with previous models generated by aerophotographic survey (hereafter, APG-DTM). RMSE for the HRT and APG-DTM were 0.726 and 2.397 m, respectively. Spatial resolution of the DTMs was 0.20 m. Morphometric variables obtained from the two methods presented visual differences on the thematic maps, especially related to the Aspect. Generalization was the main process, whereas interpolation occurred for the HRT survey, being the process of choice for the APG method. A large number of points are obtained by the TLS, providing a dense cloud of points, spatially well-distributed, enabling the generalization process to obtain surface models with high performance.

FILTERING PHOTOGRAMMETRIC POINT CLOUDS USING STANDARD LIDAR FILTERS TOWARDS DTM GENERATION

Abstract. Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.

Classification of Aerial Photogrammetric 3D Point Clouds

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labeling this kind of data is important for tasks such as environmental modeling, object classification, and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on four real-world photogrammetry datasets that were generated with Pix4Dmapper, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than three minutes on a desktop computer. We also demonstrate that our approach can be used to generate accurate Digital Terrain Models, outperforming approaches based on more simple heuristics such as Maximally Stable Extremal Regions.

Laser Scanning Systems and Techniques in Rockfall Source Identification and Risk Assessment: A Critical Review

Rockfall poses risk to people, their properties and to transportation ways in mountainous and hilly regions. This catastrophe shows various characteristics such as vast distribution, sudden occurrence, variable magnitude, strong fatalness and randomicity. Therefore, prediction of rockfall phenomenon both spatially and temporally is a challenging task. Digital Terrain model (DTM) is one of the most significant elements in rockfall source identification and risk assessment. Light detection and ranging (LiDAR) is the most advanced effective technique to derive high-resolution and accurate DTM. This paper presents a critical overview of rockfall phenomenon (definition, triggering factors, motion modes and modeling) and LiDAR technique in terms of data pre-processing, DTM generation and the factors that can be obtained from this technique for rockfall source identification and risk assessment. It also reviews the existing methods that are utilized for the evaluation of the rockfall trajectories and their characteristics (frequency, velocity, bouncing height and kinetic energy), probability, susceptibility, hazard and risk. Detail consideration is given on quantitative methodologies in addition to the qualitative ones. Various methods are demonstrated with respect to their application scales (local and regional). Additionally, attention is given to the latest improvement, particularly including the consideration of the intensity of the phenomena and the magnitude of the events at chosen sites.

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

Unmanned aerial vehicles (UAVs) and digital photogrammetric techniques are two recent advances in remote sensing (RS) technology that are emerging as alternatives to high-cost airborne laser scanning (ALS) data sources. Despite the potential of UAVs in forestry applications, very few studies have included detailed analyses of UAV photogrammetric products at larger scales or over a range of forest types, including mixed conifer–broadleaf forests. In this study, we assessed the performance of fixed-wing UAV photogrammetric products of a mixed conifer–broadleaf forest with varying levels of canopy structural complexity. We demonstrate that fixed-wing UAVs are capable of efficiently collecting image data at local scales and that UAV imagery can be effectively utilized with digital photogrammetric techniques to provide detailed automated reconstruction of the three-dimensional (3D) canopy surface of mixed conifer–broadleaf forests. When combined with an accurate digital terrain model (DTM), UAV photogrammetric products are promising for producing reliable structural measurements of the forest canopy. However, the performance of UAV photogrammetric products is likely to be influenced by the structural complexity of the forest canopy. Furthermore, we highlight the potential of fixed-wing UAVs in operational forest management at the forest management compartment level, for acquiring high-resolution imagery at low cost. A future direction of this research would be to address the issue of how well the photogrammetric products can predict the actual structure of mixed conifer–broadleaf forests.

A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small- to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis–triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m2 and 1378 m2, respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.

Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs.

Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest

Developments in airborne LiDAR data acquisition have provided better horizontal and vertical ground information in the form of 3D point clouds. This has led to satisfactory results of LiDAR-derived digital terrain models (DTMs), also across complex ecosystems like natural forest stands. However, data and site-driven factors such as spatial resolution (point density), topography (slope and aspect), and variation in forest habitat types affect the DTM accuracy. In addition, processing steps like ground filtering and interpolation of ground points may also result in differences in DTM quality. Here, a comparative study was designed by extracting DTMs from two LiDAR data sources (high- and low-density point clouds) and three ground-filtering algorithms (adaptive TIN algorithm with and without the use of mirror points as well as an interpolation-based algorithm). The accuracy of the DTMs was assessed in association with terrain parameter and forest habitat types across heterogeneous forest sites of Bavarian Forest National Park in southeastern Germany. Qualitative analysis was carried out by taking 8300 independent sets of DGPS-recorded sample points. In addition to deriving root-mean-square error (RMSE) and bias, analysis of variance (ANOVA) type II was conducted in a factorial design to assess the influential factors on the observed DTM random error. Results revealed these errors in the DTMs with occasional over- and underestimations up to 1.98 m compared to reference elevation values. DTMs produced from high pulse density LiDAR data were more accurate than those extracted from low pulse density. Furthermore, topographic and forest habitat-type factors significantly contributed to the DTM accuracy. Slope increment showed a direct relationship with DTM error, with higher errors observed in south, southwest, and west aspects. Furthermore, stands dominated by deciduous trees were associated with higher DTM error than other forest habitat types. The applied adaptive TIN ground-filtering algorithms with mirror points and the interpolation-based algorithm both produced comparatively lower error rates, which are, therefore, suggested to reduce interpolation error in DTMs across rugged and heterogeneous forested terrains.

Exposure Modelling of Extremely Low-Frequency Magnetic Fields from Overhead Power Lines and Its Validation by Measurements.

A three-dimensional model for calculating long term exposure to extremely low-frequency magnetic fields from high-voltage overhead power lines is presented, as well as its validation by measurements. For the validation, the model was applied to two different high-voltage overhead power lines in Iffwil and Wiler (Switzerland). In order to capture the daily and seasonal variations, each measurement was taken for 48 h and the measurements were carried out six times at each site, at intervals of approximately two months, between January and December 2015. During each measurement, a lateral transect of the magnetic flux density was determined in the middle of a span from nine measurement points in the range of ±80 m. The technical data of both the lines as well as the load flow data during the measurement periods were provided by the grid operators. These data were used to calculate 48 h averages of the absolute value of the magnetic flux density and compared with modelled values. The highest 48 h average was 1.66 µT (centre of the line in Iffwil); the lowest 48 h average was 22 nT (80 m distance from the centre line in Iffwil). On average, the magnetic flux density was overestimated by 2% (standard deviation: 9%) in Iffwil and underestimated by 1% (8%) in Wiler. Sensitivity analyses showed that the uncertainty is mainly driven by errors in the coordinates and height data. In particular, for predictions near the centre of the line, an accurate digital terrain model is critical.

An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data

In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN) model reported in the literature, we proposed an integrated approach where echo detection, terrain identification, and TIN generation were carried out iteratively. The proposed method was tested on a dataset collected by a Riegl LMS Q-560 scanner over a study area near Sault Ste. Marie, Ontario, Canada (46°33′56′′N, 83°25′18′′W). The results demonstrated that more terrain points under shrubs could be identified, and the generated DTMs exhibited more details in the terrain than those obtained using the progressive TIN method. In addition, 1275 points across this study area were surveyed on the ground and used to validate the proposed approach. The estimated elevations were shown to have a strong linear relationship with the measured ones, with R2 values above 0.98, and the RMSEs (Root Mean Squared Errors) between them were less than 0.15 m even for areas with hilly terrains underneath vegetation canopies.

A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

In this paper, we present and evaluate a new propagation model for heterogeneous networks. The designed model is multiband, multienvironment, and is usable for short and long distance. For this research, a measurement campaign was conducted in Tunis (Tunisia) using continuous wave analog technology. It concerns the most used bands (450, 850, 1800, 2100, and 2600 MHz) in rural, suburban, and urban environments. Measurements are split into two independent and random sets. The first one is used for model training, whereas the second is used for model validation. The new model is based on neural networks, uses back propagation algorithm, and obtains its inputs from Standard Propagation Model, to which we have added more parameters such as frequency, environment type, land use distribution, and diffraction loss. Model variables are computed from accurate Digital Terrain Model and Land Used maps with 2-m resolution. The statistical analysis has shown that the developed model is accurate as we obtained the following metrics: 0.235-dB absolute mean error, 6.850-dB standard deviation, and 85% correlation factor. The obtained simulation results are then compared to SPM and ITU-R P.1812-4 prediction, which are taken as reference to highlight the benefit of the new model.