Hybrid Overlap Filter for LiDAR Point Clouds Using Free Software

[EN] Despite the large amounts of resources destined to developing filtering algorithms of LiDAR point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge. As a society advancing towards the democratization of information and collaborative processes, the researchers should not only focus on improving the efficacy of filters, but should also consider the users' needs with a view toward improving the usability and accessibility of the filters in order to develop tools that will provide solutions to the challenges facing this field of study. In this work, we describe the Hybrid Overlap Filter (HyOF), a new filtering algorithm implemented in the free R software environment. The flow diagram of HyOF differs in the following ways from that of other filters developed to date: (1) the algorithm is formed by a combination of sequentially operating functions (i.e., the output of the first function provides the input of the second), which are capable of functioning independently and thus enabling integration of these functions with other filtering algorithms; (2) the variable penetrability is defined and used, along with slope and elevation, to identify ground points; (3) prior to selection of the seed points, the original point cloud is processed with the aim of removing points corresponding to buildings; and (4) a new method based on a moving window, with longitudinal overlap between windows and transverse overlap between passes, is used to select the seed points. Our hybrid filtering method is tested using 15 reference samples acquired by the International Society of Photogrammetry and Remote Sensing (ISPRS) and is evaluated in comparison with 33 existing filtering algorithms. The results show that our hybrid filtering method produces an average total error of 3.34% and an average Kappa coefficient of 92.62%. The proposed algorithm is one of the most accurate filters that has been tested with the ISPRS reference samplesSIThis research was funded by the Project Red de Tecnoloxías LiDAR e de Información Xeoespacial (Plan Galego 2011–2015 (Plan I2C): Programa Consolidación e Estructuración (Redes)-CN 2012/323

Hybrid Overlap Filter for LiDAR Point Clouds Using Free Software

Despite the large amounts of resources destined to developing filtering algorithms of LiDAR point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge. As a society advancing towards the democratization of information and collaborative processes, the researchers should not only focus on improving the efficacy of filters, but should also consider the users’ needs with a view toward improving the usability and accessibility of the filters in order to develop tools that will provide solutions to the challenges facing this field of study. In this work, we describe the Hybrid Overlap Filter (HyOF), a new filtering algorithm implemented in the free R software environment. The flow diagram of HyOF differs in the following ways from that of other filters developed to date: (1) the algorithm is formed by a combination of sequentially operating functions (i.e., the output of the first function provides the input of the second), which are capable of functioning independently and thus enabling integration of these functions with other filtering algorithms; (2) the variable penetrability is defined and used, along with slope and elevation, to identify ground points; (3) prior to selection of the seed points, the original point cloud is processed with the aim of removing points corresponding to buildings; and (4) a new method based on a moving window, with longitudinal overlap between windows and transverse overlap between passes, is used to select the seed points. Our hybrid filtering method is tested using 15 reference samples acquired by the International Society of Photogrammetry and Remote Sensing (ISPRS) and is evaluated in comparison with 33 existing filtering algorithms. The results show that our hybrid filtering method produces an average total error of 3.34% and an average Kappa coefficient of 92.62%. The proposed algorithm is one of the most accurate filters that has been tested with the ISPRS reference samplesThis research was funded by the Project Red de Tecnoloxías LiDAR e de Información Xeoespacial (Plan Galego 2011–2015 (Plan I2C): Programa Consolidación e Estructuración (Redes)-CN 2012/323)S

MARLI: a mobile application for regional landslide inventories in Ecuador

[EN] The regions of Central and South America most susceptible to the occurrence of landslides will become even more vulnerable in the context of climate change. The Josefina disaster, in 1993, demonstrated both the vulnerability of local infrastructures and communities in the Paute River basin (Ecuador). Since this natural phenomena, several landslide inventories and susceptibility studies were developed, revealing the vulnerability of the Paute River basin to unstable terrain and the need for further studies throughout the basin. Despite this, no studies have been done since then to update the information generated. This paper describes a Mobile Application for Regional Landslide Inventories (MARLI), a simple but efficient open-access platform to report landslide events using the Open Data Kit system. Its design makes reporting fast, simple and cost-effective with an added benefit, and a specialized knowledge is not required for its use. MARLI was tested for the collection of landslides in Cuenca (Ecuador). From the data taken in the field, it was possible to analyze the performance and suitability of collected data and compare the results with regional inventories in the same area. Additionally, these results can be used for the elaboration and update of large-scale inventories or the training of automatic identification systems of landslides and later evaluation of their precision in a small-medium scale. Likewise, this product constitutes a fundamental input for the formulation of mitigation strategies, to formulate the appropriate response and in time, also the elaboration of reconstruction plans before the increase in the occurrence of such phenomenaSIThis study was supported by the Land Laboratory Research Group (G.I.-1934-TB) (Universidade de Santiago de Compostela, Spain) and the University of Azuay (Cuenca, Ecuador) (Project No: 2016-53)

Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain

[EN] While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-auto-matic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.SIThis study was funded by the SCALyFOR project (R&D Projects “Research Challenges”, Spanish Ministry of Economy and Competitivenes

Forest Road Detection Using LiDAR Data and Hybrid Classification

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2This research was supported by: (1) the Project “Sistema de ayuda a la decisión para la adaptación al cambio climático a través de la planificación territorial y la gestión de riesgos (CLIMAPLAN) (PID2019-111154RB-I00): Proyectos de I+D+i - RTI”; and (2) “National Programme for the Promotion of Talent and Its Employability” of the Ministry of Economy, Industry, and Competitiveness (Torres-Quevedo program) via a postdoctoral grant (PTQ2018-010043) to Juan Guerra HernándezS

What Is the Most Suitable Height Range of ALS Point Cloud and LiDAR Metric for Understorey Analysis? A Study Case in a Mixed Deciduous Forest, Pokupsko Basin, Croatia

[EN] Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ɛ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.S

Detección automática de pistas forestales y evaluación de la precisión a partir de datos LiDAR de baja densidad en Asturias

[ES] Las pistas forestales son infraestructuras esenciales para la gestión sostenible de montes puesto que posibilitan el acceso a las masas forestales para su aprovechamiento o ejecución de tratamientos selvícolas, conectan distintas áreas, sirven de acceso para actividades recreativas y para la lucha contra incendios. Por ello, es imprescindible disponer de una cartografía actualizada de pistas, que permita conocer al menos su localización y longitud. El objetivo general de este estudio fue establecer una metodología para la detección automática del eje de las pistas forestales en un monte de Asturias, con rodales de pino marítimo, pino insigne, roble, castaño y otras frondosas. Para ello, se generaron distintas capas de información partiendo de los datos lidar de la zona de estudio (PNOA). A partir de ellas se evaluaron dos metodologías de clasificación de imágenes: la clasificación basada en píxeles y la orientada a objetos. Los resultados obtenidos se compararon con la cartografía real de las pistas del monte mediante indicadores de Corrección, Integridad y Calidad. Además también se evaluó la precisión del eje de las pistas combinando análisis GIS y métodos estadísticos. La clasificación basada en píxeles obtuvo valores más altos en los tres indicadores evaluados así como en la precisión posiciona

Diseño de inventarios forestales en poblaciones muy fragmentadas: un caso de estudiosobre el uso de información catastral en Galicia

[ES] Los inventarios forestales (IFs) permiten conocer y evaluar el estado de los sistemas forestales, lo que los convierte en una herramienta de apoyo fundamental para los procesos de toma de decisiones y planificación de una gestión forestal sostenible. La fase de diseño y planificación de un IF es clave a la hora de garantizar la calidad y fiabilidad de los resultados que de él se deriven. Dos de las principales cuestiones a establecer durante el diseño de un IF son (i) la población objetivo y el diseño muestral, y (ii) el diseño de parcela. Los diseños demuestra y parcela más habituales en IF pueden presentar ciertas limitaciones cuando las masas forestales objetivo están muy fragmentadas, como ocurre en el caso de Galicia, debido a la alta probabilidad de que las áreas de medición seleccionadas intersequen parcelas con distintos usos, especies o edades. En este trabajo se ha estudiado, desde el punto de vista teórico, el efecto sobre la variabilidad de la muestra de la utilización de información catastral en el proceso de optimización del diseño de un IF para Galicia, cuya población forestal se caracteriza por presentar un alto nivel de fragmentació

Aplicación da tecnoloxía LiDAR na análise do medio físico

[GA] Impártense os coñecementos básicos necesarios para o estudo do medio físico, a través do tratamento dos datos capturados polo sensor activo LiDAR (Light Detection and Ranging) aerotransportado, xa que este sensor permite a captura de datos xeográficos dunha forma áxil e precisa, mentres que o tratamento dos seus datos permite a xeración de modelos que describen o terreo e os obxectos situados enriba del. Así na actualidade, a tecnoloxía LiDAR revelouse como a máis efectiva para a produción de Modelos Xeográficos de alta resolución e calidade

Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

[EN] Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborneLiDAR, for the estimation of crown, stem and aboveground biomass fractions.To assess the effects of a reduction in LiDAR pulse densities on model precision.Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globulusstands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.Material and methods: Linear, multiplicative power and exponential models were used to establish empiricalrelationships between field measurements and LiDAR metrics.A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factorwere performed to study a possible loss of fit in these models.Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e.a reductionfrom 4 pulses m–2to 0.5 pulses m–2.Research highlights: Considering the results of this study, the low-density LiDAR data that are released by theSpanish National Geographic Institute will be an excellent source of information for reducing the cost of forestinventoriesSIGalician Government, Xunta de Galicia, DirecciónXeral de Montes(09MRU022291PR); Norvento (Mul-tinational energy company) (PGIDT09REM023E);Galician Government, Dirección Xeral de Ordenacióne Calidade do Sistema Universitario de Galicia (Con-sellería de Educación e Ordenación Universitaria)and European Social Fund (Official Journal of Galicia –DOG nº 9, p. 2246, exp. 2011/14