PLS-PM analysis of forest fires using remote sensing tools. The case of Xurés in the Transboundary Biosphere Reserve

Forest fires have environmental, social and economic impacts in many areas. Various factors related to territory directly influence both the number and the surface area of each fire. The link between different variables (climate, social and environmental) in the risk of fire and in the characteristics of fires is studied here through Partial Least Squares - Path Models. In addition, images from the Sentinel-2 sensor and geographic information systems are used to create a cartographic base of fires in the Transboundary Biosphere Reserve of Galicia and the Site of Community Importance of Xurés (Galicia) between 2015 and 2020. In all, seven variables are analyzed in this study area using the partial least squares-path modeling method: climate, topography, land use, type of environmental protection, the anthropogenic factor, fire defense, and fire data (severity and area). The parameters for each variable are used to obtain weights and thus determine the importance of each one. The areas where the problem of forest fires is greatest are those with the greatest environmental protection. Up to 31% of the surface area of the Natura 2000 Network was burned in the 6-year study period. Topography and land use are also shown to be relevant factors in the effects of forest fires in this territory. By contrast, higher population density and the development of infrastructures such as roads and water tanks mitigate the impact of fires. The problem of forest fires encompasses many variables that need to be studied. By contextualizing each study area as far as possible, specific measures to prevent and reduce damage can be drawn up.Agencia Estatal de Investigación | Ref. PCI2020–120705-2Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Realistic correction of sky-coloured points in Mobile Laser Scanning point clouds

The enrichment of the point clouds with colour images improves the visualisation of the data as well as the segmentation and recognition processes. Coloured point clouds are becoming increasingly common, however, the colour they display is not always as expected. Errors in the colouring of point clouds acquired with Mobile Laser Scanning are due to perspective in the camera image, different resolution or poor calibration between the LiDAR sensor and the image sensor. The consequences of these errors are noticeable in elements captured in images, but not in point clouds, such as the sky. This paper focuses on the correction of the sky-coloured points, without resorting to the images that were initially used to colour the whole point cloud. The proposed method consists of three stages. First the region of interest where the erroneously coloured points are accumulated, is selected. Second, the sky-coloured points are detected by calculating the colour distance in the Lab colour space to a sample of the sky-colour. And third, the colour of the sky-coloured detected points is restored from the colour of the nearby points. The method is tested in ten real case studies with their corresponding point clouds from urban and rural areas. In two case studies, sky-coloured points were assigned manually and the remaining eight case studies, the sky-coloured points are derived from the acquisition errors. The algorithm for sky-coloured points detection obtained an average F1-score of 94.7%. The results show a correct reassignment of colour, texture, and patterns, while improving the point cloud visualisation.Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGXunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Agencia Estatal de Investigación | Ref. PID2019-108816RB-I0

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU

From BIM to scan planning and optimization for construction control

Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158Ministerio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-7Xunta de Galicia | Ref. ED481B 216/079-

Remote sensing approach to evaluate post-fire vegetation structure

Spain is included in the top five European countries with the highest number of wildfires. Forest fire can produce significant impacts on the structure and functioning of natural ecosystems. After a forest fire, the evaluation of the damage severity and spatial patterns are important for forest recovery planning, which plays a critical role in the sustainability of the forest ecosystem. The process of forest recovery and the ecological and physiological functions of the burned forest area should be continuously monitored. Remote sensing technologies and in special LiDAR are useful to describe the structure of vegetation. The vegetation modelling and the initial changes of forest plant composition are studied in the forest after mapping the burned areas using Landsat-7 images and Sentinel-2 images. Normalized Burn Ratio (NBR) index and Normalized Difference Vegetation Index (NVVI) is calculated as well as the difference before and after fire. The evaluation of temporal changes of vegetation are analysed by statistical variables of the point cloud, average height, standard deviation and variance. Fraction Canopy Cover (FCC) also is calculated and the point cloud is classified following the fuel model by Prometheus. An analysis method based on satellite images was completed in order to analyse the evolution of vegetation in areas that suffer forest fire.TOPACIO Project | Ref. IN852a 2018/37Aeromedia UAVExtraco Construcciones e ProxectosConexiona TelecomSan2 Sustainable Innovatio

Canopy detection over roads using mobile lidar data

A high percentage of forest fires take place around roads. These infrastructures provide an escape route for the population in case of fire. Optimization of forest management in the surroundings of roads is a necessary task in terms of wildfires prevention and mitigation of their effects. Therefore, it is essential to avoid the horizontal continuity of vegetation across roads. A methodology for the measurement of canopy area over roads is developed and based on mobile LiDAR (Light Detection and Ranging) point clouds. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from the University of Vigo. The methodology is automated using LiDAR data processing (M-estimator Sample Consensus and near neighbour algorithms) and image processing techniques (rasterization and binarization). The developed algorithms are tested on a study area, the DP-3606 road (Spain). Results are compared with ground truth data of the canopy projected on the road. The best obtained results present a mean geometric error of 2.82% for 0.25 m resolution and 104.02% for 2 m resolution. Furthermore, the higher the pixel size, the greater the error was obtained with a linear correlation value of 0.99.Diputación de Pontevedra | Ref. 17/410.1720.789.0

Point clouds for direct pedestrian pathfinding in urban environments

Pathfinding applications for the citizen in urban environments are usually designed from the perspective of a driver, not being effective for pedestrians. In addition, urban scenes have multiple elements that interfere with pedestrian routes and navigable space. In this paper, a methodology for the direct use of point clouds for pathfinding in urban environments is presented, solving the main limitations for this purpose: (a) the excessive number of points is reduced for transformation into nodes on the final graph, (b) urban static elements acting as permanent obstacles, such as furniture and trees, are delimited and differentiated from dynamic elements such as pedestrians, (c) occlusions on ground elements are corrected to enable a complete graph modelling, and (d) navigable space is delimited from free unobstructed space according to two motor skills (pedestrians without reduced mobility and wheelchairs). The methodology is tested into three different streets sampled as point clouds by mobile laser scanning (MLS) systems: an intersection of several streets with ground composed of sidewalks at different heights; an avenue with wide sidewalks, trees and cars parked on one side; and a street with a single-lane road and narrow sidewalks. By applying Dijkstra pathfinding algorithm to the resulting graphs, the correct viability of the generated routes has been verified based on a visual analysis of the generated routes on the point cloud and on the knowledge of the urban study area. The methodology enables the automatic generation of graphs representing the navigable urban space, on which safe and real routes for different motor skills can be calculated.Universidade de Vigo. 00VI 131H 641.02Xunta de Galicia. ED481B 2016/079-0Xunta de Galicia. ED431C 2016-038Ministerio de Economía, Industria y Competitividad. TIN2016-77158-C4-2-R, RTC-2016-5257-

Optimal scan planning for surveying large sites with static and mobile mapping systems

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGSince the last two decades, the use of laser scanners for generating accurate and dense 3D models has been rapidly growing in multiple disciplines. The reliance on human-expertise to perform an efficient scanning in terms of completeness and quality encouraged the researchers to develop strategies for carrying out an optimized and automated scan planning. Nevertheless, due to the predominant use of static terrestrial laser scanners (TLS), the most of developed methods have been focused on scan optimization by fixing standpoints on basis of static scanning. The increasing use of portable mobile laser scanning systems (MLS) enables faster non-stop acquisition which demands the planning of optimal scan trajectories. Therefore, a novel method addressing the absence of dynamic scan planning is proposed considering specific MLS constraints such as maximum acquisition time or closed-loops requirement. First, an initial analysis is carried out to determinate key-positions to reach during data acquisition. From these positions a navigable graph is generated to compute routes satisfying specific MLS constraints by a three-step process. This starts by estimating the number of routes necessary to subsequently carry out a coarse graph partition based on Kmedoids clustering. Next, a balancing algorithm was implemented to compute a balanced graph partition by node exchanging. Finally, partitions are extended by adding key nodes from their adjacent ones in order to provide a desirable overlapping between scans. The method was tested by simulating three laser scanner configurations in four indoor and outdoor real case studies. The acquisition quality of the computed scan planning was evaluated in terms of 3D completeness and point cloud density with the simulator Helios++.Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigacion | Ref. PCI2022-132943Agencia Estatal de Investigación | Ref. RYC2020-029193-

Automatic detection and characterization of ground occlusions in urban point clouds from mobile laser scanning data

Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED481D 2019/020Agencia Estatal de Investigación | Ref. RTI2018-095893-B-C21Agencia Estatal de Investigación | Ref. PID2019-05221RB-C4

Individual tree segmentation method based on mobile backpack LiDAR point clouds

Individual tree (IT) segmentation is crucial for forest management, supporting forest inventory, biomass monitoring or tree competition analysis. Light detection and ranging (LiDAR) is a prominent technology in this context, outperforming competing technologies. Aerial laser scanning (ALS) is frequently used for forest documentation, showing good point densities at the tree-top surface. Even though under-canopy data collection is possible with multi-echo ALS, the number of points for regions near the ground in leafy forests drops drastically, and, as a result, terrestrial laser scanners (TLS) may be required to obtain reliable information about tree trunks or under-growth features. In this work, an IT extraction method for terrestrial backpack LiDAR data is presented. The method is based on DBSCAN clustering and cylinder voxelization of the volume, showing a high detection rate (∼90%) for tree locations obtained from point clouds, and low commission and submission errors (accuracy over 93%). The method includes a sensibility assessment to calculate the optimal input parameters and adapt the workflow to real-world data. This approach shows that forest management can benefit from IT segmentation, using a handheld TLS to improve data collection productivity.Agencia Estatal de Investigación | Ref. PID2019-108816RB-I0