Terrain Segments Research Articles

Исследование сходимости результатов обработки радиолокационных снимков Sentinel-1А с натурными наблюдениями сельскохозяйственных участков

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

Geomorphometric analysis and terrain evaluation for environmental management in the Kurseong hill subdivision of the Darjeeling district, West Bengal, India

Kurseong hill subdivision of the Darjeeling district, West Bengal, is associated with complex geomorphic set-up stemmed from form–process interaction on varied slope, relief, elevation, aspects, ridge–valley orientation, density and frequency of drainage, dissection, ruggedness and topographic wetness over different stratigraphy of geological rocks having different hardness. This complexity provides ample scope to evaluate the terrain and categorize the terrain into different sensitivity zones according to land utilization point of view so that different terrain segments can be classified into different grades and therefore can be rationally utilized and managed for sustainable use. Therefore, the present paper takes an attempt to evaluate the terrain sensitivity of the Kurseong hill subdivision based on multi-criteria analysis method to identify different grades of terrain according to their ability to hold and sustain the pressure of land utilization for settlements, agriculture and other land uses. Terrain sensitivity evaluation of the Kurseong hill subdivision has been done on the basis of the study of hard rock geology, some selected geomorphometric parameters derived from 3-arc-second SRTM digital elevation data as well as SOI toposheets having RF of 1:50,000 and the existing land use factors after necessary ground truthing on some selected sites. The procedure adopted for multi-criteria analysis is 1 km2 grid-based rating value assignment algorithm of the selected parameters and determination of different classes of sensitive terrain and their gradation according to the capacity of the terrain to withstand present pressure of land utilization. Some measures for rational land utilization and management have also been proposed for optimal land use planning for the study area.

AUTOMATIC EXTRACTION OF DTM FROM LOW RESOLUTION DSM BY TWOSTEPS SEMI-GLOBAL FILTERING

Automatically extracting DTM from DSM or LiDAR data by distinguishing non-ground points from ground points is an important issue. Many algorithms for this issue are developed, however, most of them are targeted at processing dense LiDAR data, and lack the ability of getting DTM from low resolution DSM. This is caused by the decrease of distinction on elevation variation between steep terrains and surface objects. In this paper, a method called two-steps semi-global filtering (TSGF) is proposed to extract DTM from low resolution DSM. Firstly, the DSM slope map is calculated and smoothed by SGF (semi-global filtering), which is then binarized and used as the mask of flat terrains. Secondly, the DSM is segmented with the restriction of the flat terrains mask. Lastly, each segment is filtered with semi-global algorithm in order to remove non-ground points, which will produce the final DTM. The first SGF is based on global distribution characteristic of large slope, which distinguishes steep terrains and flat terrains. The second SGF is used to filter non-ground points on DSM within flat terrain segments. Therefore, by two steps SGF non-ground points are removed robustly, while shape of steep terrains is kept. Experiments on DSM generated by ZY3 imagery with resolution of 10-30m demonstrate the effectiveness of the proposed method.

AUTOMATIC EXTRACTION OF DTM FROM LOW RESOLUTION DSM BY TWOSTEPS SEMI-GLOBAL FILTERING

Abstract. Automatically extracting DTM from DSM or LiDAR data by distinguishing non-ground points from ground points is an important issue. Many algorithms for this issue are developed, however, most of them are targeted at processing dense LiDAR data, and lack the ability of getting DTM from low resolution DSM. This is caused by the decrease of distinction on elevation variation between steep terrains and surface objects. In this paper, a method called two-steps semi-global filtering (TSGF) is proposed to extract DTM from low resolution DSM. Firstly, the DSM slope map is calculated and smoothed by SGF (semi-global filtering), which is then binarized and used as the mask of flat terrains. Secondly, the DSM is segmented with the restriction of the flat terrains mask. Lastly, each segment is filtered with semi-global algorithm in order to remove non-ground points, which will produce the final DTM. The first SGF is based on global distribution characteristic of large slope, which distinguishes steep terrains and flat terrains. The second SGF is used to filter non-ground points on DSM within flat terrain segments. Therefore, by two steps SGF non-ground points are removed robustly, while shape of steep terrains is kept. Experiments on DSM generated by ZY3 imagery with resolution of 10-30m demonstrate the effectiveness of the proposed method.

A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

Abstract. With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

Vibration Measurement and Spectral Analysis of Chassis Frame Mounted Structure for Off-Road Wheeled Heavy Vehicles

Chassis mounted structure is a base component for shelters or containers mounted on heavy transport vehicles. When the vehicle is driven in rough terrains or during off-road transportation this structure has a significant role in protecting the sophisticated cargo and intelligent tracking systems placed inside the shelters. During off-road transportation or warhead conditions the vehicle is subjected to large unevenness in load due to road or soil irregularities in rough terrains, which causes vibrations to be induced in the vehicle. As the nature of vibrations induced in vehicle during travel on off-road or cross-country terrains is random and unpredictable, there is a concern to analyse the vibration response of chassis and chassis mounted structures is needed. Present work deals with vibration measurement and spectral analysis of a chassis mounted structure designed for off-road and commercial transport vehicles. The road profile on which the vibration measurement has been carried out includes paved road and cross-country terrain segments. The vibration measurement has been carried at three different vehicle speeds. Signal analysis procedure for the acquired test data is discussed. The chassis mounted structure under concern is intended to hold two shelters or containers. From the vibration measurement at critical locations, g-(RMS) and g-(peak) values for paved and cross-country roads have been found out. Power spectral density values have also been found on chassis and structure for the same transport situations. Major inferences include the evaluation of minimum and maximum g-values (peak and RMS) on chassis and chassis mounted structure. Power spectral density graphs are constructed from which the dominant frequency for both road profiles is found out.

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

GEOECOLOGICAL EVALUATION OF TERRAIN IN NATIONAL PARK UNA

This paper has a geoecological evaluation of terrain in National Park Una as its study object. Aim of the research is to clarify how valuable and useful are particular terrain segments for tourism valorization, construction of various objects, certain economic activities etc. Geoecological evaluation of terrain is strongly relevant methodological procedure, which can be used for acquiring of results about real value of particular areas. Paper consists of several parts. First part defines the exact area of exploration and geographic position of National Park Una. Second part is concerned with geomorphological characteristics of explored area and possibilities of its valorization, by detailed analyses of hypsometric facts, angle of slope inclination, vertical articulation and terrain exposition. Geoecological evaluation of terrain is performed in third part, for which purpose a bonity categories are used. Geoecological evaluation of terrain is based on previous geomorphological analysis. Methods used in this paper are: analysis, synthesis, statistical method, cartographic method and GIS analysis. Method of terrain evaluation is also used, through four categories: hypsometric characteristics, angle of slopes, vertical articulation and terrain mobility.

Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

Mapping or “delimiting” landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results.

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.

Autonomous Robot Navigation Using Traversability Indices

The concepts of Local, Regional, and Global Traversability Indices have been introduced recently (Seraji, 1999, 2000, 2001). These indices represent the suitability of a terrain for traversal by a mobile robot at different scales of resolution. This paper utilizes these indices to develop a navigation strategy for a mobile robot traversing a challenging terrain. The traversability indices form the basis of three navigational behaviors; namely, Traverse-Local, Traverse-Regional, and Traverse-Global behaviors. These behaviors are blended with the Seek-Goal behavior to ensure that the mobile robot reaches the goal safely while avoiding obstacles and impassable terrain segments. The paper is concluded by an illustrative graphical simulation study.

Matching horizon features using a Messy Genetic Algorithm

Three line segment matching techniques are compared on horizon line matching problems. The application is to reduce camera angle uncertainty for outdoor robots. Each of the three algorithms seeks an optimal many-to-many match between a subset of 2D line segments extracted from a rendered terrain map and segments extracted from an image. Two algorithms are non-deterministic, with one performing Random Starts Local Search and the other using a Messy Genetic Algorithm. The third algorithm searches a list of ranked key feature matches using a limited deterministic local search. The Random Starts Local Search Algorithm is the slowest of the three algorithms and fails to find the best match on 9 out of 54 problems. In contrast, the Messy Genetic Algorithm correctly solves all 54 test problems. The Key Feature Algorithm is the fastest, but it fails to find the optimal match in three cases. The results suggest the Messy Genetic Algorithm is superior to the other two algorithms on problems of this type. The test imagery was collected on one of the Unmanned Ground Vehicle Program's vehicles operating at the Lockheed Martin Demo C test site. Using the optimal matches it is shown that uncertainty in camera pointing angle is reduced from several degrees to less than a degree.

Water and energy balance of a forested Appalachian watershed

This study focuses on the water and energy balance of a central Appalachian watershed with an area of 39.2 ha, with upland oaks and cove hardwoods, 80 years old, located near Parsons, West Virginia (39°20'N, 79°40'W). For 40 hydrologic years (May–April, 1951–1990) the average yearly sum of precipitation ( P) was 145.5 cm, streamflow ( R) was 63.8 cm, and evapotranspiration ( Et = P − R) was 81.7 cm. Neglecting the energy used in photosynthesis, the yearly sum of net radiation of the watershed ( Rn) equals latent heat of evapotranspiration ( Et) plus sensible heat exchange with the atmosphere ( H). Rn was computed for 432 terrain segments covering the whole watershed area. Its average value was 2.2 GJ m −2 year −1. From the balance LEt = 2.02 GJ m −2 year −1, where L is the latent heat of evaporation of water. The sensible heat exchange ( H = Rn − Et) was 0.18 GJ m −2 year −1 or 8% of Rn.

Topography and Radiation Exchange of a Mountainous Watershed

Abstract This report deals with the radiation exchange of a complex terrain. A relatively simple network for computing topographic parameters global radiation, and net radiation of a mountainous terrain was developed and applied to a forested Appalachian watershed encompassing 39.2 ha (Watershed 4, U.S. Forest Service at Parsons, West Virginia, 39°20′N, 79°40′W). Monthly sums of global radiation and net radiation of the watershed were computed for the period of 40 years (1951–90). Topographic map of the watershed in scale 1:2500 and data on global radiation at a horizontal surface in the study area (1955–77), monthly average air temperature, moisture and cloudiness were used in the analysis. The topographic analysis showed that for the 432 terrain segments, covering the whole area of the watershed, the average daily possible sunshine duration varied from 9.3 to 11.9 h, with an average of 11.0 h, which is about 92% of the possible sunshine duration at a horizontal surface at the study area. The sky view fa...

Distribution of the radiative index of dryness and forest site quality in a mountainous watershed

The radiative aridity index, β (i.e., the ratio of yearly sums of net radiation to those of the latent heat of precipitation), and forest biomass were obtained for 245 terrain segments [Formula: see text] in an Appalachian watershed. A hypothesis was tested that β can be used as a site quality indicator in complex terrain. Regression analysis yielded the following relationship between the average forest biomass of the watershed (Mo = 15.94 kg m−2), the biomass (Mi), the radiative aridity index (βi), and the azimuth (Ai) of terrain segments: Mi/M0 = 2βi[l−Ai/2π + (Ai/2π)4] ± 0.27. The value of the expression in the bracket reaches its maximum for Ai = 0 (north facing slopes), and a minimum for Ai = 227° (southwest facing slopes). A possible interpretation of the obtained relationship is that p represents long term (e.g., daily and seasonal) effects of water and energy exchanges of terrain segments on growth, and the expression in the bracket represents the aspect-related effects of daily fluctuations of microclimate of terrain segments on growth.