Stand delineation based on laser scanning data and simulated annealing

The use of airborne laser scanning (LS) is increasing in forestry. Scanning can be conducted from manned aircrafts or unmanned aerial vehicles (UAV). The scanning data are often used to calculate various attributes for small raster cells. These attributes can be used to segment the forest into homogeneous areas, called segments, micro-stands, or, like in this study, stands. Delineation of stands from raster data is equal to finding the most suitable stand number for each raster cell, which is a combinatorial optimization problem. This study tested the performance of the simulated annealing (SA) metaheuristic in the delineation of stands from grids of UAV-LS attributes. The objective function included three criteria: within-stand variation of the LS attributes, stand area, and stand shape. The purpose was to create delineations that consisted of homogeneous stands with a low number of small stands and a regular and roundish stand shape. The results showed that SA is capable of producing stand delineations that meet these criteria. However, the method tended to produce delineations where the stands often consisted of disconnected parts and the stand borders were jagged. These problems were mitigated by using a mode filter on the grid of stand numbers and giving unique numbers for all disconnected parts of a stand. Three LS attributes were used in the delineation. These attributes described the canopy height, the height of the bottom of the canopy and the variation of echo intensity within 1-m2 raster cells. Besides, a texture variable that described the spatial variation of canopy height in the proximity of a 1-m2 raster cell was found to be a useful variable. Stand delineations where the average stand area was about one hectare explained more than 80% of the variation in canopy height.

Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

Periodic measurement and identification of the presence and severity of pavement rutting are crucial for pavement management programs conducted by state transportation agencies. This paper proposes a novel analytical method for identifying pavement rutting locations using data collected by mobile terrestrial LiDAR scanning (MTLS). Four vendor MTLS systems were evaluated based on their ability to accurately reproduce a roadway’s transverse profile. To establish ground-truth measurements, 2 in. interval pavement transverse profiles, which included rutting sections, were collected using traditional surveying techniques. MTLS transverse profiles were evaluated using partial curve mapping, Fréchet distance, area, curve length, and dynamic time warping techniques. Resultant pavement transverse profiles were compared between vendors and a profile created from traditional surveying. Results indicate that calibrated MTLS systems can provide accurate transverse profiles for potential identification of pavement rut areas. Based on this determination, a novel method was developed for use in identifying locations of pavement rutting through analysis of the curvature of MTLS raster surfaces. After evaluating three grid cell sizes for elevation raster surfaces, a raster grid cell size of 1 ft × 1 ft was determined to be most suitable for identifying continuous concave raster cell groups along wheel path trajectories. These cell groupings were found to reliably identify pavement rutting locations. The analytical procedures employed through application of this method consist of an efficient workflow process that is not reliant on a time-consuming continuous comparison with an MTLS-modeled uniform surface.

COMPARISON OF FOREST STRUCTURE METRICS DERIVED FROM UAV LIDAR AND ALS DATA

<p><strong>Abstract.</strong> Point clouds derived from airborne laser scanning (ALS) and from LiDAR sensors mounted on unmanned aerial vehicles (ULS) reveal differences caused by the different sensor systems and acquisition geometries. These differences in the system characteristics are reflected in forest structure metrics that are derived from the respective point clouds. In our study, we investigate the completeness of scene coverage between the two systems and address differences between structure metrics derived from ULS and ALS, namely in point height quantiles, fractional cover (<i>fc</i>), the vertical complexity index (<i>VCI</i>) and the number of canopy layers (<i>nLayers</i>). The metrics are evaluated for raster cell sizes of 1&amp;ndash;10&amp;thinsp;m in order to investigate the spatial scale on which the sensor systems provide comparable metrics. We found highest correspondences between ALS and ULS in the <i>VCI</i>- and the <i>nLayers</i>-metrics, while fc revealed large differences. For the height quantiles, the absolute differences were larger for the 10%- (<i>h</i>10) and the 50%- (<i>h</i>50) than for the 90%- (<i>h</i>90) height quantile. Furthermore, we found differences between ALS- and ULS-metrics to decrease for larger cell sizes, except for <i>fc</i>, for which the differences increased, and <i>h</i>50 and <i>h</i>90, respectively, for which the differences were relatively stable for all cell sizes.</p>

Analysis of spatial and temporal changes of soil erosion in Anning river basin

Anning River basin as an important soil erosion monitoring and reserve in Sichuan Province. It is of great significance to grasp the temporal and spatial changes of soil erosion in this area in time to realize the comprehensive treatment of regional soil erosion. Based on the revision of the general soil erosion equation (RUSLE), the present situation of soil erosion erosion in 1995-2015 years in this area was monitored, and the classification was completed according to the national grading criteria, and the following results were mainly achieved:(1) Soil erosion in Anning river basin as a whole presents a low gradient change in the eastern High West.(2) The proportion of raster in soil erosion area of each grade also showed different structural differences, which showed that the degree of micro > Mild > Moderate > Strong > Extremely > severe, and the proportion of raster in mild and below intensity erosion area reached more than 80%, and the whole basin was at mildly below erosion level.(3) One-dimensional linear regression analysis shows, within 20 years, about 90% of the raster cell slope in this area is negative, the overall change trend of soil erosion in this area shows a significant improvement

PARAMETERIC ANALYSIS FOR AUTOMATED EXTRACTION OF ROAD EDGES FROM MOBILE LASER SCANNING DATA

The applicability of Mobile Laser Scanning (MLS) systems continue to prove their worth in route corridor mapping due to the rapid, continuous and cost effective 3D data acquisition capability. LiDAR data provides a number of attributes which can be useful for extracting various road features. Road edge is a fundamental feature and its accurate knowledge increases the reliability and precision of extracting other road features. We developed an automated algorithm for extracting left and right edges from MLS data. The algorithm involved several input parameters which are required to be analysed in order to find their optimal values. In this paper, we present a detailed analysis of the dimension parameters of input data and raster cell in our algorithm. These parameters were analysed based on temporal, completeness and accuracy performance of our algorithm for their different sets of values. This analysis provided the estimation of an optimal values of parameters which were used to automate the process of extracting road edges from MLS data.

VALIDATION OF BIM COMPONENTS BY PHOTOGRAMMETRIC POINT CLOUDS FOR CONSTRUCTION SITE MONITORING

Construction progress monitoring is a primarily manual and time consuming process which is usually based on 2D plans and therefore has a need for an increased automation. In this paper an approach is introduced for comparing a planned state of a building (as-planned) derived from a Building Information Model (BIM) to a photogrammetric point cloud (as-built). In order to accomplish the comparison a triangle-based representation of the building model is used. The approach has two main processing steps. First, visibility checks are performed to determine whether or not elements of the building are potentially built. The remaining parts can be either categorized as free areas, which are definitely not built, or as unknown areas, which are not visible. In the second step it is determined if the potentially built parts can be confirmed by the surrounding points. This process begins by splitting each triangle into small raster cells. For each raster cell a measure is calculated using three criteria: the mean distance of the points, their standard deviation and the deviation from a local plane fit. A triangle is confirmed if a sufficient number of raster cells yield a high rating by the measure. The approach is tested based on a real case inner city scenario. Only triangles showing unambiguous results are labeled with their statuses, because it is intended to use these results to infer additional statements based on dependencies modeled in the BIM. It is shown that the label built is reliable and can be used for further analysis. As a drawback this comes with a high percentage of ambiguously classified elements, for which the acquired data is not sufficient (in terms of coverage and/or accuracy) for validation.

Development and application of the Riparian Mapping Tool to identify priority rehabilitation areas for nitrogen removal in the Tully - Murray basin, Queensland, Australia

One feature of riparian zones is their ability to significantly reduce the nitrogen loads entering streams by removing nitrate from the groundwater. A novel GIS model was used to prioritise riparian rehabilitation in catchments. It is proposed that high-priority areas are those with a high potential for riparian denitrification and have nearby land uses that generate high nitrogen loads. For this purpose, we defined the Rehabilitation Index, which is the product of two other indices, the Nitrate Removal Index and the Nitrate Interception Index. The latter identifies the nitrate contamination potential for each raster cell in the riparian zone by examining the extent and proximity of agricultural urban land uses. The former is estimated using a conceptual model for surface–groundwater interactions in riparian zones associated with middle-order gaining perennial streams, where nitrate is removed via denitrification when the base flow interacts with the carbon-rich riparian sediments before discharging to the streams. Riparian zones that are relatively low in the landscape, have a flat topography, and have soils of medium hydraulic conductivity are most conducive to denitrification. In the present study, the model was implemented in the Tully–Murray basin, Queensland, Australia, to produce priority riparian rehabilitation area maps.

Spatial information for scheduling core area production in forest planning

Forest core area is the portion of the forested landscape that is free from edge effects from surrounding areas. Forest core area is important for specific plant communities and wildlife species. Identifying spatial interdependencies of site-specific management decisions is an important step for recognizing core area production in forest management scheduling models. A forest-wide map layer of influence zones can be used to identify the interdependencies. Each influence zone is a potential area for producing core area. Each is unique in terms of the specific combination of management units that interact to influence core area production. A raster-based approach is presented for identifying influence zones and estimating their area. Tests considered the need for precision in terms of the size of the raster cells for accurately identifying influence zones and estimating their size. Tests, using a 100 m buffer width for defining core area, show that scheduling results were relatively insensitive to gains in precision from using raster cell widths less than 30 m.