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This technical note aims to present a method for developing a Digital Terrain Model (DTM) of the coastal zone based on topobathymetric data from remote sensors. This research was conducted in the waterbody adjacent to the Vistula Śmiała River mouth in Gdańsk, which is characterised by dynamic changes in its seabed topography. Bathymetric and topographic measurements were conducted using an Unmanned Aerial Vehicle (UAV) and two hydrographic methods (a Single-Beam Echo Sounder (SBES) and a manual survey using a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver). The result of this research was the development of a topobathymetric chart based on data recorded by the above-mentioned sensors. It should be emphasised that bathymetric data for the shallow waterbody (less than 1 m deep) were obtained based on high-resolution photos taken by a UAV. They were processed using the "Depth Prediction" plug-in based on the Support Vector Regression (SVR) algorithm, which was implemented in the QGIS software as part of the INNOBAT project. This plug-in allowed us to generate a dense cloud of depth points for a shallow waterbody. Research has shown that the developed DTM of the coastal zone based on topobathymetric data from remote sensors is characterised by high accuracy of 0.248 m (p = 0.95) and high coverage of the seabed with measurements. Based on the research conducted, it should be concluded that the proposed method for developing a DTM of the coastal zone based on topobathymetric data from remote sensors allows the accuracy requirements provided in the International Hydrographic Organization (IHO) Special Order (depth error ≤ 0.25 m (p = 0.95)) to be met in shallow waterbodies. [ABSTRACT FROM AUTHOR]

High-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to <20 m. The evaluations of ASTER, SRTM, and TDX DEMs by ATL08 strong and weak beams are close to those assessed by LiDAR DTM points within 0.6 m for the strong beams. These findings indicate that ATL08 strong beams can be used as a height reference over vegetated mountainous regions. [ABSTRACT FROM AUTHOR]

ABSTRACTHigh-quality height reference data are embedded in the accuracy verification processes of most remote sensing terrain applications. The Ice, Cloud, and Land elevation Satellite 2 (ICESat-2)/ATL08 terrain product has shown promising results for estimating ground heights, but it has not been fully evaluated. Hence, this study aims to assess and enhance the accuracy of the ATL08 terrain product as a height reference for the newest versions of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and TanDEM-X (TDX) DEMs over vegetated mountainous areas. We used uncertainty-based filtering method for the ATL08 strong and weak beams to enhance their accuracy. Then, the results were evaluated against a reference airborne LiDAR digital terrain model (DTM), by selecting 10,000 points over the entire area and comparing the accuracy of ASTER, SRTM, and TDX DEMs assessed by the LiDAR DTM to the accuracy of the ASTER, SRTM, and TDX DEMs assessed by the ATL08 strong beams, weak beams, and all beams. We also detected the impact of the terrain aspect, slope, and land cover types on the accuracy of the ATL08 terrain elevations and their relationship with height errors and uncertainty. Our findings show the accuracy of the ATL08 strong beams was enhanced by 43.91%; while the weak beams accuracy was enhanced by 74.05%. Furthermore, slope strongly influenced ATL08 height errors and height uncertainty; especially on the weak beams. The errors induced by the slope significantly decreased when the uncertainty levels were reduced to

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych – BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.

High-resolution Digital Terrain Models (DTMs) of Mars are both limited and crucial for studying surface processes. The available DTMs generated from Mars Orbiter Laser Altimeter (MOLA) and High-Resolution Stereo Camera (HRSC) data offer limited resolution for working on a metre scale. The MER-A mission’s exploration of the Gusev crater was based on the data provided by MOLA and HRSC. However, with the introduction of the stereoscopic images obtained by the Context Camera (CTX) on board the Mars Reconnaissance Orbiter (MRO), it has been possible to generate topographic data with greater detail, significantly improving the MOLA and HRSC data. This has resulted in the generation of new DTMs with a resolution of ~5 m/pixel. Comparative analysis of these CTX DTMs with the MOLA and HRSC data provides an updated perspective of the Gusev topography and its geological features. The prominent advantage of using CTX stereo images lies in their wide coverage, as they have mapped 99.9% of the Martian surface. This wide coverage allows the creation of high-resolution models that will prove invaluable for future studies and missions.

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons. [ABSTRACT FROM AUTHOR]

Landslides are multi-phase mass flows often triggered by incessant rainfall and pose a severe threat to infrastructure and human lives in mountainous regions. A high-quality landslide assessment for a vast geographic expanse is economically unviable. Therefore, this study proposes a multi-scale approach to identify potential shallow landslide zones for a large area using the Transient Rainfall Infiltration Grid-Based Regional Slope Stability (TRIGRS) model, followed by targeted landslide assessment of the resulting vulnerable area and subsequent mass flow using a multi-phase mass flow framework. Finally, a preliminary design of a protective structure implemented as a rigid barrier is carried out based on obstacle flow interaction. A recurring landslide site in Kaliasaur, Uttarakhand, India, is analyzed using a 12.5 m resolution Digital Terrain Model (DTM) from the public domain and resampled DTM of 1 m resolution obtained from the aerial survey using an unmanned aerial vehicle (UAV). Based on the analysis of a 3-day rainfall event, 25.43% of the total area was unstable, and the percentage of landslide ratio class (LRclass) for validation of TRIGRS model parameters was 65.62% which can be referred to as a moderate success rate. The failed mass flow from the unstable zones in Kaliasaur has a maximum height and velocity of 6.93 m and 27.1 m/s, respectively. A rigid barrier of height 10 m adjacent to a national highway (NH-7) reduced the flow velocity by 17.73% and stopped the mass flow satisfactorily. This research advocates a multi-scale approach landslide hazard assessment to build a resilient infrastructure against landslides. [ABSTRACT FROM AUTHOR]

This study aims to produce a 3D terrain visualization from the Digital Terrain Model from UAV-Fixed Wing data. In this study, data acquisition was performed by utilizing a Fixed Wing UAV equipped with a Sony Alpha 6000 camera. The study area is a predominantly hilly area, with a height approximately up to 327 meters from mean sea level as part of acquisition process, the flight mission planning was rigorously designed by using the Mission Planner software. A total of six flying missions were conducted to collect the necessary aerial photo data. In addition, Aerial photo data is processed by generating photo ortho mosaics through the Structure from the Motion Photogrammetry technique. Ground Control Points (GCP) and Independent Check Points (ICP) are integrated with in the process to enhance data accuracy and precision. ICP has an essential role as a verification tool, ensuring the conformity of the geometric position of the map with the actual coordinates on the earth's surface. The results of this study are Digital Terrain Model (DTM) data which is represented in a three-dimensional topography visualization. Subsequently, Raster data from DTM can be incorporated with BIM-based software, such as Autodesk Infraworks, where it more applicable for advanced planning and design. the results of this study discover that the accuracy of horizontal and vertical positions complies with the established standards as stated by Geospatial Information Agency Regulation Number 6 in 2018, where the 1:10,000 scale map have class 1 horizontal accuracy and class 3 vertical accuracy. [ABSTRACT FROM AUTHOR]

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych -- BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes. [ABSTRACT FROM AUTHOR]

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green–Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region. [ABSTRACT FROM AUTHOR]

Virtual 3D city modelling has several applications such as population estimation, navigation, visibility analysis. For accurate 3D city modelling, digital elevation model (DEM) data and digital terrain models (DTMs) are used as an important input. In order to estimate accurate height of building structures, ground filtering of DEM is required in order to obtain precise DTM. In this study, satellite-derived high resolutions (1 m) DEM data over different urban regions is used as an input and four different ground-filtering algorithms, i.e. thresholding-based ground filtering, morphological operation-based filtering, slope-based filtering, multidirectional ground-filtering (MGF) algorithm are implemented for filtering of DEM. Based on the detailed analysis and evaluations, It is concluded that the MGF algorithm outperforms other algorithms with an accuracy of > 99% over all the datasets. [ABSTRACT FROM AUTHOR]

Landslides, as one of the main problems in mountainous areas, are a challenging issue for modern geophysics. The triggers that cause these phenomena are diverse (including geological, geomorphological, and hydrological conditions, climatic factors, and earthquakes) and can occur in conjunction with each other. Human activity is also relevant, undoubtedly contributing to the intensification of landslide phenomena. One of these is the production of artificial snow on ski slopes. This paper presents a multimethod approach for imaging the landslide structure in Cisiec, in southwestern Poland, where such a situation occurs. In the presented work, the integration of remote sensing with multi-method geophysical imaging was used to visualize landslide zones, and to estimate ground motion. To verify the uncertainty of the obtained data, the combination of electrical resistivity tomography (ERT), multi-channel analysis of surface waves (MASW), and seismic refraction method (SRT) was supported by synthetic modeling. Using geophysical data with accurate GPS-based topography and a terrestrial laser scanning-based digital terrain model (DTM), it was possible to model the spatial variability and surface area of the landslide more precisely, as well as to estimate the velocity field in the nearest surface more accurately. The final result shows displacement up to 1 m on the ground surface visible on the DTM models, while the geophysical methods confirm the change in internal structure. The proposed methodology is fast, cost-effective, and can be used to image the structure of landslides, where the shallowest parts are usually complex and thus difficult to observe seismically. [ABSTRACT FROM AUTHOR]

Natural and man-made disasters are a justified concern causing life-threats damages, affects existing budgets or disrupts national economic and social development plans. In this paper, a digital terrain model (DTM) and a geographic information system (GIS) are used to identify areas with a high risk of avalanches in the Râmeț mountain area of Alba County. Avalanches are the result of a complex interaction between terrain, weather conditions and snow cover characteristics. The most important factor among those listed is the terrain, being the only constant parameter over time. Therefore, the present study focused in particular on the determination of topographic parameters and the realization of a digital terrain model with a resolution of 0.5 m, which allows the small-scale identification of slope angle variations, which are of great interest for avalanche study. This digital terrain model was generated after processing LiDAR data obtained from a scanning campaign for the analyzed area with the Riegl LMS Q680i LiDAR system, located aboard the Hawker Beechcraft King Air C90 GTx aircraft from INCAS. The result of the analysis consisted in mapping the risk of avalanches in the Râmeț mountain area in Alba County.

Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy between point clouds and DTMs. To promote data-driven DTM extraction, this article collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a deep neural network to extract DTMs directly from ALS point clouds via rasterization techniques, coined DeepTerRa. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds. The experimental results show the interest of the agnostic data-driven approach, with submetric error level compared to methods designed for DTM extraction. The data and source code are available online at https://lhoangan.github.io/deepterra/ for reproducibility and further similar research.

Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often been neglected. Referring to the historical disaster event, forest landslides have vast potential to cause unexpected ecological and social damage. This study reveals the terrain characteristics of the complex mountainous forest area of Cameron Highlands (CH), Malaysia, and demonstrates an approach to evaluate the terrain sensitivity of CH. Terrain assessment can be a powerful tool to prevent or reduce the risk of landslides. In this study, terrain features; elevation, slope gradient, aspect, topography wetness index (TWI), and length-slope factor (LS Factor) were extracted using a Digital Terrain Model (DTM) at 10 m resolution. The selected terrain features were incorporated using weighted overlay analysis to derive a terrain sensitivity map (TSM) using SAGA GIS software. The map identified five types of terrain sensitivity classified as very high sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity; these areas have a coverage of 0.78 km2, 114.31 km2, 107.50 km2, 102.99 km2, and 0.65 km2, respectively. The findings suggest that the sensitive areas are scattered throughout all of the mountainous forests of CH; thus, this enhanced the risk of landslide. Results showed 79.25% accuracy, which is satisfactory to be a guideline for forest management planning and assist decision making in the respective region.

Kentsel alanlarda uzaktan algılama görüntülerinden bina, ağaç, araç, vb. coğrafi nesnelerin otomatik olarak tespiti oldukça gerekli ve önemlidir. Bu çalışmada, çok yüksek konumsal çözünürlüklü renkli (Kırmızı, Yeşil, Mavi) stereo insansız hava aracı (İHA) görüntülerinden kentsel alanlarda sabit araçların tespiti yapılmıştır. Kullanılan yaklaşımın ilk adımında stereo İHA görüntülerinden sayısal yüzey modeli (SYM) oluşturulmaktadır. Sonra, SYM verisinden sayısal arazi modeli (SAM) ve SYM kullanılarak İHA görüntülerinden ortofoto oluşturulmaktadır. Ardından, yalnız yer üstü nesneleri elde etmek için SYM ve SAM verilerinin farkı alınarak normalize edilmiş sayısal yüzey modeli (nSYM) hesaplanmaktadır. Daha sonra, elde edilen nSYM verisi ek bant olarak kullanılmak suretiyle ortofotonun çoklu çözünürlük segmentasyonu ve ardından nesne-tabanlı sınıflandırması yapılmaktadır. Yaklaşım, Hacettepe Üniversitesi, Beytepe Yerleşkesi’nde farklı özelliklere sahip iki alan üzerinde uygulanmıştır. Oluşturulan referans veriyle yapılan karşılaştırma neticesinde, araç tespiti doğruluğu birinci test alanı (Alan#1) için %78.53 ve ikinci test alanı (Alan#2) için %92.15 olarak hesaplanmıştır. Elde edilen sonuçlar, önerilen yaklaşımla sabit araçların çok yüksek konumsal çözünürlüklü İHA görüntülerinden tespitinin yüksek doğrulukla yapılabildiğini göstermiştir.

Kentsel alanlarda uzaktan algılama görüntülerinden bina, ağaç, araç, vb. coğrafi nesnelerin otomatik olarak tespiti oldukça gerekli ve önemlidir. Bu çalışmada, çok yüksek konumsal çözünürlüklü renkli (Kırmızı, Yeşil, Mavi) stereo insansız hava aracı (İHA) görüntülerinden kentsel alanlarda sabit araçların tespiti yapılmıştır. Kullanılan yaklaşımın ilk adımında stereo İHA görüntülerinden sayısal yüzey modeli (SYM) oluşturulmaktadır. Sonra, SYM verisinden sayısal arazi modeli (SAM) ve SYM kullanılarak İHA görüntülerinden ortofoto oluşturulmaktadır. Ardından, yalnız yer üstü nesneleri elde etmek için SYM ve SAM verilerinin farkı alınarak normalize edilmiş sayısal yüzey modeli (nSYM) hesaplanmaktadır. Daha sonra, elde edilen nSYM verisi ek bant olarak kullanılmak suretiyle ortofotonun çoklu çözünürlük segmentasyonu ve ardından nesne-tabanlı sınıflandırması yapılmaktadır. Yaklaşım, Hacettepe Üniversitesi, Beytepe Yerleşkesi’nde farklı özelliklere sahip iki alan üzerinde uygulanmıştır. Oluşturulan referans veriyle yapılan karşılaştırma neticesinde, araç tespiti doğruluğu birinci test alanı (Alan#1) için %78.53 ve ikinci test alanı (Alan#2) için %92.15 olarak hesaplanmıştır. Elde edilen sonuçlar, önerilen yaklaşımla sabit araçların çok yüksek konumsal çözünürlüklü İHA görüntülerinden tespitinin yüksek doğrulukla yapılabildiğini göstermiştir.

The solutions of vegetation height and the mapping of terrain in forest have always been concerning and are still challenging problems. The purpose of this article is to provide a new technical approach for the digital terrain model (DTM) and forest topographic survey by aerial photogrammetry, in consideration of the forest vegetation height modeling problem. Based on an aerial digital orthophoto map and a digital surface model (DSM), the spectral features and geometric features that are related to forest vegetation height are analyzed and extracted. The nonlinear correlation maximal information coefficient, maximum asymmetry score, and Pearson linear correlation coefficient between feature factors and vegetation height are listed, and the correlations are evaluated as the basis for factors selection. Two kinds of support vector regression algorithms were adopted to establish the machine learning for forest vegetation height model (VHM). Therefore, the DSM can be corrected to DTM. The experimental results show that the accuracy of the forest VHM is better than 1 m. Thus, the proposed method is proved to be feasible and practical. It provides a low-cost and high-efficiency method for the VHM and DTM in forest areas by photogrammetry. [ABSTRACT FROM AUTHOR]

Low elevation outliers (LEOs) in TanDEM-X (TDX) digital elevation models (DEMs) have a great effect on the accuracy of digital terrain models (DTMs) generated by filtering DEM data. A novel method, combining local elevation histograms and adaptive triangulated irregular network (HATIN), was developed to detect LEOs in TDX DEMs. The performance of HATIN was evaluated by comparing LEOs identified by median, morphological, HATIN, and iterative inverse distance weighted (IIDW) interpolation methods with LEOs defined by differences between TDX DEMs and light detection and ranging (LiDAR) DTMs. The HATIN method detected almost all large clustered LEOs in the downtown area of Miami, USA with many high-rise buildings, and reduced the root-mean-square error (RMSE) of the resulting DTM from 10.37 to 1.11 m. In the same area, the IIDW method missed some large LEOs, producing a DTM with an RMSE slightly larger than that of the HATIN method. In contrast, the median and morphological methods made more omission and commission errors, failing particularly in the detection of many large LEOs, and producing larger RMSEs (2.43–2.91 m). In low-rise residential areas with isolated LEOs, the HATIN method also identified all large LEOs, producing the fewest commission errors of all methods. However, differences among DTM RMSEs for all four methods were minor. Therefore, it is recommended that, before TDX DEMs are filtered to generate DTMs for urban areas, isolated LEOs should be removed using the computationally simple median or morphological methods, whereas clustered LEOs should be removed by the complex and computationally intensive HATIN or IIDW methods. [ABSTRACT FROM AUTHOR]

Due to the ever-better accessibility of the digital terrain model (DTM), it is enjoying growing popularity as a source of information about the surrounding world. It became even more approachable when DTM data covering the whole of Poland was made available free of charge on the Geoportal website. The GRID with a resolution of 1 meter per 1 meter covers virtually the entire country. The data on the Geoportal does not only refer to the measurements processed into a grid of squares, but also includes measurement data. They can be accessed through a systematic division into section sheets in the 1992 system. The paper presents research on assessing the accuracy of these publicly available data, compared to measurements obtained with the GNSS observations. The analysis involved data directly from the measurement device, not yet processed into a grid of squares. As part of the research, the height of 3 objects was compared. It was decided to measure the terrain profiles. To get as close as possible to the source data provided at the Geoportal website, the selected points of profiles previously identified in the point cloud were measured. This approach freed the final results from the need for height interpolation for the data selected for analysis. As result, the potential source of errors in such an approach was eliminated. Although during a classical profile measurement with this technology the selection of points would certainly be different, for research purposes this approach seems optimal. For this reason, in addition to the expected deviations resulting from the height differences of the tested points, deviations from the data from the point cloud of the XY coordinates, related to the adopted technology, are also presented. [ABSTRACT FROM AUTHOR]

Natural and man-made disasters are a justified concern causing life-threats damages, affects existing budgets or disrupts national economic and social development plans. In this paper, a digital terrain model (DTM) and a geographic information system (GIS) are used to identify areas with a high risk of avalanches in the Râmet mountain area of Alba County. Avalanches are the result of a complex interaction between terrain, weather conditions and snow cover characteristics. The most important factor among those listed is the terrain, being the only constant parameter over time. Therefore, the present study focused in particular on the determination of topographic parameters and the realization of a digital terrain model with a resolution of 0.5 m, which allows the small-scale identification of slope angle variations, which are of great interest for avalanche study. This digital terrain model was generated after processing LiDAR data obtained from a scanning campaign for the analyzed area with the Riegl LMS Q680i LiDAR system, located aboard the Hawker Beechcraft King Air C90 GTx aircraft from INCAS. The result of the analysis consisted in mapping the risk of avalanches in the Râmet mountain area in Alba County. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

Airborne raw light detection and ranging (LiDAR) measurements are georeferenced three-dimensional coordinates of ground surface, including all natural and man-made features. Extracting terrain surface measurements from raw LiDAR data is referred to as “filtering.” Many filtering algorithms have been published, indicating the difficulty of the task; however, none performs equally well on all kinds of landscapes. This article presents a new algorithm, automatic weighted splines filter (AWSF), to extract the terrain points from raw LiDAR measurements. The mathematical model of the AWSF algorithm utilizes both the cubic smoothing splines (to interpolate and fit raw LiDAR data) and z-shaped function (to estimate the weight value of each point). The AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions. Diverse landscape scenarios are used, ranging from open fields, rural land, and urban areas to dense forests on mountains where most of the filtering algorithms struggle as these areas represent the most difficult and challenging landscapes because the canopy prevents LiDAR pulses from reaching the ground surface. A total of 19 samples were tested; the results clearly show that the filtered terrain measurements are accurate and that the performance of the AWSF algorithm is stable for all the LiDAR data samples in comparison with the other filtering approaches.

Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often been neglected. Referring to the historical disaster event, forest landslides have vast potential to cause unexpected ecological and social damage. This study reveals the terrain characteristics of the complex mountainous forest area of Cameron Highlands (CH), Malaysia, and demonstrates an approach to evaluate the terrain sensitivity of CH. Terrain assessment can be a powerful tool to prevent or reduce the risk of landslides. In this study, terrain features; elevation, slope gradient, aspect, topography wetness index (TWI), and length-slope factor (LS Factor) were extracted using a Digital Terrain Model (DTM) at 10 m resolution. The selected terrain features were incorporated using weighted overlay analysis to derive a terrain sensitivity map (TSM) using SAGA GIS software. The map identified five types of terrain sensitivity classified as very high sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity; these areas have a coverage of 0.78 km2, 114.31 km2, 107.50 km2, 102.99 km2, and 0.65 km2, respectively. The findings suggest that the sensitive areas are scattered throughout all of the mountainous forests of CH; thus, this enhanced the risk of landslide. Results showed 79.25% accuracy, which is satisfactory to be a guideline for forest management planning and assist decision making in the respective region. [ABSTRACT FROM AUTHOR]

The study of flood risk involves the knowledge of the spatial variability in the characteristics of the vegetation cover, terrain, climate and changes induced by the intervention of humans in watersheds. The increased needs of the actors in land management mean that static maps no longer meet the requirements of scientists and decision-makers. Access is needed to the data, methods and tools to produce complex maps in response to the different stages of risk evaluation and response. The availability of very high spatial resolution remote sensing data (VHSR) and digital terrain model (DTM) make it possible to detect objects close to human size and, therefore, is of interest for studying anthropogenic activities. The development of new methods and knowledge using detailed spatial data, coupled with the use of GIS, naturally becomes beneficial to the risks analysis. Indeed, the extraction of information from specific processes, such as vegetation indices, can be used as variables such as water heights, flow velocities, flow rates and submersion to predict the potential consequences of a flood. The functionalities of GIS for cartographic overlay and multi-criteria spatial analysis make it possible to identify the flood zones according to the level of risk from the flood, thus making it a useful decision-making tool.

The sequestration of carbon in the forest is very high after soil and ocean; this is one of the major factors which makes monitoring of forests very much essential. The traditional inventory techniques were very much exhaustive and needed encroachment into the inaccessible forest area, which create lot of difficulties to foresters and other researchers, that is why, most of the areas of intense forests are untouched for monitoring purposes. The degradation of the forest area is highly prominent in the hilly terrain; activities such as lopping and forest fire are very common in these areas. In this research, UAV optical photogrammetric technique is used for the extraction of canopy metrics as well as for the generation of DSM and DTM, which is further used for the validation of the results obtained with the Sentinel-2 and Sentinel-1A data. The Sentinel-1A of C-band data is used for the generation of backscatter image, texture images. Sentinel-2 is used for the calculation of various vegetation-based indices for the evaluation of the proficiency of the degradation in the forest area. On the basis of various vegetation indices and texture images calculated, the degradation of the forest can be quantified. The random forest classifier is used for the classification of the forest into degraded and non-degraded classes. The classification accuracy obtained for three classes, i.e. degraded, non-degraded, non-forest is 0.86 and precision is 0.75. The correlation is 0.72 for the classes, whereas error rate is found to be 0.13. Hence, the integration of UAV optical photogrammetry and SAR data can give much appreciated results; also, this technique is very much helpful in monitoring and management of inaccessible forest areas. [ABSTRACT FROM AUTHOR]

It is very essential and important in urban areas for the automatic detection of geographical objects such as buildings, trees, and vehicles by using remotely sensed images. In this study, the stationary vehicles were detected from very high spatial resolution stereo color (Red, Green, Blue) unmanned aerial vehicles (UAV) images in urban areas. In the first step of the approach used, digital surface model (DSM) is generated from the stereo images. Then, digital terrain model (DTM) is generated from the DSM, and by using the DSM orthophotos are generated from IHA images. Next, the normalized digital surface model (nDSM) is calculated by taking the difference between the DSM and DTM to obtain only the ground objects. After that, using the obtained nDSM data as an additional band, the multi-resolution segmentation and then object-based classification of the orthophoto are carried out. The approach was applied on two areas with different characteristics at Hacettepe University, Beytepe Campus. After comparing the results with the reference data, the vehicle detection accuracy was computed as 78.53% for the first test field (Field # 1) and it was computed as 92.15% for the second test field (Field # 2). The results show that the detection of stationary vehicles from very high spatial resolution UAV images can be performed with high accuracy using the proposed approach. [ABSTRACT FROM AUTHOR]

Wetlands are sensitive and complex systems whose conservation is a priority. For the correct understanding of their hydrological dynamics, it is necessary to determine the different elements of the water budget and, in particular, the geometry of the wetland basin in order to estimate the variations in storage capacity. This paper presents a novel, low-cost, user-friendly photogrammetric technique to obtain high-resolution datasets using non-metric cameras located in unmanned aerial systems (UAS) and structure-from-motion algorithms for producing high-precision 3D point clouds. The accuracy of the cartographic products obtained is evaluated using 59 checkpoints and comparing with the available LiDAR models. Best results are obtained using a full frame RGB sensor, which results in an orthomosaic with a pixel size of 1.38 cm and a positional RMSE of 3.8 cm in horizontal and a digital surface model (DSM) with a 3.5 cm RMSEZ. From the DSM, eliminating the influence of vegetation through masks, a digital terrain model (DTM) with a 5.9-cm RMSEZ that allows defining the filling curve of the wetland basin is obtained. This curve relates the stored volume and the surface exposed to evaporation with the water level, which allows to perform simulations in the balance models.

Airborne raw light detection and ranging (LiDAR) measurements are georeferenced three-dimensional coordinates of ground surface, including all natural and man-made features. Extracting terrain surface measurements from raw LiDAR data is referred to as “filtering.” Many filtering algorithms have been published, indicating the difficulty of the task; however, none performs equally well on all kinds of landscapes. This article presents a new algorithm, automatic weighted splines filter (AWSF), to extract the terrain points from raw LiDAR measurements. The mathematical model of the AWSF algorithm utilizes both the cubic smoothing splines (to interpolate and fit raw LiDAR data) and z-shaped function (to estimate the weight value of each point). The AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions. Diverse landscape scenarios are used, ranging from open fields, rural land, and urban areas to dense forests on mountains where most of the filtering algorithms struggle as these areas represent the most difficult and challenging landscapes because the canopy prevents LiDAR pulses from reaching the ground surface. A total of 19 samples were tested; the results clearly show that the filtered terrain measurements are accurate and that the performance of the AWSF algorithm is stable for all the LiDAR data samples in comparison with the other filtering approaches. [ABSTRACT FROM AUTHOR]

This paper focuses on the retrieval of terrain topography below dense tropical forests by means of synthetic aperture radar (SAR) systems. Low-frequency signals are needed to penetrate such a thick vegetation layer; however, this expedient alone does not guarantee proper retrieval. It is, here, demonstrated that the phase center of P-band backscatter may lie several meters above the ground, depending on the slope and incidence angle. SAR tomography is shown to overcome this problem and retrieves the actual topography even in the presence of dense trees up to 50 m tall. Digital terrain models returned by SAR tomography are, here, put in comparison with light detection and ranging (LiDAR) terrain models: the accuracy of radar-derived maps is found to be at least comparable with the one offered by LiDAR systems. Moreover, the discrepancy between tomography and LiDAR is larger if large-footprint LiDAR is considered thus suggesting that, in this case, tomographic maps should be considered the reference height. Analyses are carried out by processing three data sets gathered over different tropical forests in western Africa. The robustness of the radar estimates is assessed with respect to both ground slope and treetop height. [ABSTRACT FROM AUTHOR]

Large-area urban ecology studies often miss information on vertical parameters of vegetation, even though they represent important constituting properties of complex urban ecosystems. The new globally available digital elevation model (DEM) of the spaceborne TanDEM-X mission has an unprecedented spatial resolution (12 × 12 m) that allows us to derive such relevant information. So far, suitable approaches using a TanDEM-X DEM for the derivation of a normalized canopy model (nCM) are largely absent. Therefore, this paper aims to obtain digital terrain models (DTMs) for the subsequent computation of two nCMs for urban-like vegetation (e.g., street trees) and forest-like vegetation (e.g., parks), respectively, in Berlin, Germany, using a TanDEM-X DEM and a vegetation mask derived from UltraCam-X data. Initial comparisons between morphological DTM-filter confirm the superior performance of a novel disaggregated progressive morphological filter (DPMF). For improved assessment of a DTM for urban-like vegetation, a modified DPMF and image enhancement methods were applied. For forest-like vegetation, an interpolation and a weighted DPMF approach were compared. Finally, all DTMs were used for nCM calculation. The nCM for urban-like vegetation revealed a mean height of 4.17 m compared to 9.61 m of a validation nCM. For forest-like vegetation, the mean height for the nCM of the weighted filtering approach (9.16 m) produced the best results (validation nCM: 13.55 m). It is concluded that an nCM from TanDEM-X can capture vegetation heights in their appropriate dimension, which can be beneficial for automated height-related vegetation analysis such as comparisons of vegetation carbon storage between several cities.

Sometimes when connecting a crest vertical curve, followed by a sag and another crest, a road disappears from the view of a driver to reappear later. Then, there is a loss of path or a hidden dip in a road. It is essential to avoid losses when they hide dangerous points, such as intersections or unexpected changes in direction. In addition, this loss disrupts drivers and its effect depends on quantitative relationships between the variables involved in the problem. This paper presents a quantitative procedure for studying hidden dips in roads. The method is based on calculating the sections visible and hidden by a driver using a Geographic Information System. An application to a Spanish road is presented. Procedure results were compared with in situ carried out studies and with a video of the highway recorded using a Global Positioning System equipped video camera embarked in a vehicle. The main quantitative issues related to hidden dips are discussed.

Building an accurate and large digital terrain model (DTM) of the seabed is a key issue in various applications, especially for covert rapid environment assessment using autonomous underwater vehicles (AUVs). New AUV generations are capable of acquiring bathymetry with multiple acoustic sensors: single-beam echosounder, Doppler velocity log, multibeam echosounder (MBES), interferometric sidescan sonar (ISSS), etc. As these sensors acquire the seabed with different geometries, they can be combined to produce a DTM in shorter time. For example, ISSS can reach a wide swath in shallow water but it shows an information gap at nadir, which is usually covered by completing an additional track. Simultaneously using the MBES to acquire the nadir removes the need for this additional track and reduces the energy consumption of the whole survey. This paper focuses on fusion algorithms to extract best information of the two sensors (MBES and ISSS) to feed DTM production software with optimal bathymetric information. This problem may be solved by taking into account the average information of the two sensors. However, the sensors do not always give accurate information and the average information therefore becomes biased. Another way to tackle the problem is to select a priori information given by the better of the two sensors based on a given geometric parameter (e.g., grazing angle). In this case, when the assumed best sensor fails, the information produced by the second sensor cannot be used to compensate for the erroneous information. Our approach consists in using all the available information and fuses it ahead of producing the DTM. Based on the theory of belief functions, this paper presents a framework to fuse the information coming from the two swath bathymetric sensors (MBES and ISSS). The belief theory, applied successfully to other fields, has been extended to handle the bathymetric information. The reliability and the uncertainty of each sonar are introduced in the fusion process to improve the estimation and the accuracy of the final terrain model. First, simulated sonar data, with perfectly known ground truth, are used to quantitatively assess the performance of the fusion process by comparing DTM obtained with and without fusion. Then, the experimental validation is conducted on actual data, acquired simultaneously by the two sonars systems (Klein K5000 ISSS, Reson 8125 MBES) mounted on the DAURADE AUV. Our evaluation of the fusion method shows significant quantitative and qualitative improvement in the production of the final DTM. [ABSTRACT FROM AUTHOR]

A digital terrain model of the Strzelin granite quarry was created from ISOK project data obtained from airborne laser scanning (ALS). Based on 2009, 2012, and 2014 data, the volume of water in the Strzelin quarry’s two excavations were determined. Additionally, the predicted volume of retained water after mining ceases was calculated, assuming a water level of 150.0 m a.s.l. In 2014, the two reservoirs retained, respectively, ≈ 0.14 and 0.01 million m3 of water. If the two pits are eventually connected, ≈ 6.6 million m3 of water could be retained. Including the 2014 water volume as a small-scale retention basin would increase the volume of retained water in the Ślęza catchment and Strzelin County by 19.0%, and 34.2%, respectively. The hypothetical reservoir created after mining ends would increase small-scale (reservoir and pond) retention ≈ 8 times in the Ślęza catchment and ≈ 15 times in the County, constituting a significant improvement. [ABSTRACT FROM AUTHOR]

Wetlands are sensitive and complex systems whose conservation is a priority. For the correct understanding of their hydrological dynamics, it is necessary to determine the different elements of the water budget and, in particular, the geometry of the wetland basin in order to estimate the variations in storage capacity. This paper presents a novel, low-cost, user-friendly photogrammetric technique to obtain high-resolution datasets using non-metric cameras located in unmanned aerial systems (UAS) and structure-from-motion algorithms for producing high-precision 3D point clouds. The accuracy of the cartographic products obtained is evaluated using 59 checkpoints and comparing with the available LiDAR models. Best results are obtained using a full frame RGB sensor, which results in an orthomosaic with a pixel size of 1.38 cm and a positional RMSE of 3.8 cm in horizontal and a digital surface model (DSM) with a 3.5 cm RMSEZ. From the DSM, eliminating the influence of vegetation through masks, a digital terrain model (DTM) with a 5.9-cm RMSEZ that allows defining the filling curve of the wetland basin is obtained. This curve relates the stored volume and the surface exposed to evaporation with the water level, which allows to perform simulations in the balance models. [ABSTRACT FROM AUTHOR]

In recent years, there has been a major development in the field of Unmanned Aerial Vehicles (UAVs) as well as a significant increase in the use of aerial photogrammetry, which is an affordable alternative to using LiDAR. However, the nature of the data obtained from photogrammetry differs from LiDAR data. Photogrammetry using the Structure from Motion (SfM) method is however computationally complicated, and results can be affected by many influences. In this paper, data from two UAVs were compared. The first one is a commercial eBee system produced by SenseFly equipped with a Sony Cyber-shot DCS-WX220 camera. The other is a home assembled solution consisting of EasyStar II motorised glider and 3DR Pixhawk B autopilot equipped with Nikon Coolpix A camera. The area of spoil heap was measured by both systems in the leaf-off period. Both systems were set up identically for data acquisition (overlapping, resolution), which made a comparison of the output quality possible. The ground control points (GCPs) were placed in the study area and their position determined by GNSS (RTK method). A traditional approach for point clouds accuracy validation is their comparison with data of greater accuracy. Unfortunately, the photogrammetry is often validated using GNSS points, the position of which is determined under different conditions than GCPs (different daytime, number, and visibility of satellites, etc.). The magnitude of photogrammetry errors is theoretically the same as that of GNSS. Therefore, in this study, we suggest a novel approach that can be used to compare the accuracy of UAV point clouds without the need for additional validation data (for example, GNSS survey). To exemplify this approach, we used data gathered by two UAV systems (eBee and Easy Star II). Particularly, we statistically estimated the accuracy of the UAV point clouds; used two approaches to estimate standard deviations (one of them using estimated dependencies between data); and investigated the influence of the vegetation cover. To determine the systematic and random errors of the UAV systems data, three areas were selected, each with a typical example of vegetation on the spoil heap (forest, grass, bush). A comparison of the individual data in a grassy area suggests that the accuracy of the differences is about 0.03 m, which corresponds to the actual pixel size. Average shift (systematic error) ranged from 0.01 m to 0.08 m. In the forest terrain, the accuracy of data differences is about 0.04 m, which is slightly worse than in the grassy area. Bushy terrain data achieves precision values between a grassy area and a forest area. [ABSTRACT FROM AUTHOR]

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Glaciers are an important source of fresh water for Central Asia as they release water during the summer months when precipitation is low and water demand highest. Many studies address glacier area changes but only changes in glacier mass can be directly linked to climate and runoff. Despite the importance, investigations of glacier mass changes have been restricted to only a few glaciers in the Tien Shan until now. Geodetic mass balance measurements are suitable to complement and extend existing in-situ measurements. In this study, both area and mass changes of the ~40 km² glacier ice in the Ala Archa Valley, Kyrgyz Tien Shan, were investigated using 1964 and 1971 stereo Corona, 2012 stereo ASTER, the SRTM digital terrain model and other optical data such as Landsat ETM+ or Rapid Eye. In addition, ice thickness was modeled taking the basal shear stress and the glacier surface topography into account. The results indicate an area loss of 18.3±5.0% from 1964 until 2010 with continuous shrinkage in all investigated periods. The glacier’s mass balance was −0.45±0.27 m w.e. a−1 for the period 1964–1999 and −0.42±0.66 m w.e. a−1 for 1999–2012. Golubin Glacier showed a possible slight mass gain for 1964– 1971 and a decelerated mass loss for the 1999–2012 period. This is in good agreement with existing in-situ measurements exiting from 1962 until 1994 and since 2010. The overall ice volume was estimated to be 1.56±0.47 km³ of ice in the year 2000. Hence, the entire ice would be lost by 2100 if the mass loss would continue at the same rate

The article focuses on the geodetic monitoring of the Rabenov landslide territory. The monitored slope is situated in the landscape deteriorated by mining activity in North Bohemia, West of Usti nad Labem, in Chabařovice, a reclaimed brown coal pit. The article presents a general description of the application and accuracy of geodetic methods used to measure on the movement of material in landslides. Also, the history, the problems and the plans for reclamation of the monitored territory are briefly mentioned. The text concentrates on the description of the network of three observed points which are formed by geotechnical instrumentations and on the description of a terrestrial and GNSS measurement in the network of monitored points. The assessment methods and the results from the eight-year phased monitoring process are also discussed. Different methods were tested during the phases. For terrestrial measurements, standard deviations of about 2 mm in position were made and for GNSS standard deviations of about 14 mm. Surveys in a local grid showed that there were continuous movements of observed station points of about 10 mm per year. The paper also discusses the use and assessment of digital terrain models in zones with on-going artificial as well as natural changes. Models indicate elevation changes of about 2–6 m and movement of about 10,000–100,000 m3 of material.

Terrestrial laser scanning has become an important surveying technique in many fields such as natural hazard assessment. To analyse earth surface processes, it is useful to generate a digital terrain model originated from laser scan point cloud data. To determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. In mountainous terrain with a small-structured topography, filtering is very difficult. Here, automatic filtering solutions usually designed for airborne laser scan data often lead to unsatisfactory results. In this work, we further develop an existing approach for automated filtering of terrestrial laser scan data, which is based on the assumption that no other surface point can be located in the area above a direct line of sight between scanner and another measured point. By taking into account several environmental variables and a repetitive calculation method, the modified method leads to significantly better results. The root-mean-square-error (RSME) for the same test measurement area could be reduced from 5.284 to 1.610. In addition, a new approach for filtering and interpolation of terrestrial laser scanning data is presented using a grid with horizontal and vertical angular data and the measurement length. [ABSTRACT FROM AUTHOR]

Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature. [ABSTRACT FROM AUTHOR]

Landslides, like other natural hazards, such as avalanches, floods, and debris flows, may result in a lot of property damage and human casualties. The volume of landslide deposits is a key parameter for landslide studies and disaster relief. Using remote sensing and digital terrain model (DTM) data, this paper analyzes errors that can occur in calculating landslide volumes using conventional models. To improve existing models, the mechanisms and laws governing the material deposited by landslides are studied and then the mass balance principle and mass balance line are defined. Based on these ideas, a novel and improved model (Mass Balance Model, MBM) is proposed. By using a parameter called the “height adaptor”, MBM translates the volume calculation into an automatic search for the mass balance line within the scope of the landslide. Due to the use of mass balance constraints and the height adaptor, MBM is much more effective and reliable. A test of MBM was carried out for the case of a typical landslide, triggered by the Wenchuan Earthquake of 12 May 2008.

The approach to the formation of common geo-informational space (GIS) of the territories on the base of using of space information with features of fuzzy approach to its processing is proposed in the paper. The method of correction of GIS parameters is proved and proposed. The direction of designing of digital plan-schemes is showed. [ABSTRACT FROM AUTHOR]

Data acquisition artefacts are commonly found in multibeam bathymetric data, but their effects on mapping methodologies using geographic information system techniques have not been widely explored. Artefacts have been extensively studied in terrestrial settings, but their study in a marine context has currently been limited to engineering and surveying technology development in order to reduce their amplitude during data collection and postprocessing. Knowledge on how they propagate to further analyses like environmental characterization or terrain analysis is scant. The goal of this paper is to describe the contribution of different types of artefacts to marine terrain attributes at multiple scales. Using multibeam bathymetric data from German Bank, off Nova Scotia (Canada), digital bathymetric models (DBMs) were computed at five different spatial resolutions. Ten different amplitudes of heave, pitch, roll, and time artefacts were artificially introduced to generate altered DBMs. Then, six terrain attributes were derived from each of the reference and altered DBMs. Relationships between the amplitude of artefacts and the statistical and spatial distributions of: 1) altered bathymetry and terrain attributes surfaces and 2) errors caused by the artefacts were modeled. Spatial similarity between altered and reference surfaces was also assessed. Results indicate that most artefacts impact spatial similarity and that pitch and roll significantly impact the statistical distribution of DBMs and terrain attributes while time and heave artefacts have a more subtle impact. Results also confirm the relationship between spatial data quality and spatial scale, as finer-scale data were impacted by artefacts to a greater degree than broader-scale data. [ABSTRACT FROM PUBLISHER]