A high-precision liDAR-based method for surveying and classifying coastal notches

Formation of notches is an important process in the erosion of seaside cliffs. Monitoring of coastal notch erosion rate and processes has become a prime research focus for many coastal geomorphologists. Observation of notch erosion rate considers a number of characteristics, including cliff collapse risk, distinction of historical sea levels, and recognition of ongoing erosional mechanisms. This study presents new approaches for surveying and classifying marine notches based on a high-precision light detection and ranging (LiDAR)-based experiment performed on a small region of a coastal cliff in southern Portugal. A terrestrial LiDAR scanner was used to measure geometrical parameters and surface roughness of selected notches, enabling their classification according to shape and origin. The implemented methodology proved to be a highly effective tool for providing an unbiased analysis of marine morphodynamic processes acting on the seaside cliffs. In the analyzed population of voids carved into Miocene calcarenites in a coastal cliff section, two types of notch morphology were distinguished, namely U-shaped and V-shaped. The method presented here provides valuable data for landscape evaluation, sea-level changes, and any other types of analyses that rely on the accurate interpretation of cliff morphological features.National Science Centre [UMO-2015/17/D/ST10/02191

The evaluation of unmanned aerial systems-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds

Agricultural watersheds tend to be places of intensive farming activities that permanently modify their microtopography. The surface characteristics of the soil vary depending on the crops that are cultivated in these areas. Agricultural soil microtopography plays an important role in the quantification of runoff and sediment transport because the presence of crops, crop residues, furrows and ridges may impact the direction of water flow. To better assess such phenomena, 3-D reconstructions of high-resolution agricultural watershed topography is essential. Fine-resolution topographic data collection technologies can be used to discern highly detailed elevation variability in these areas. Knowledge of the strengths and weaknesses of existing technologies used for data collection on agricultural watersheds may be helpful in choosing an appropriate technology. This study assesses the suitability of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for collecting the fine-resolution topographic data required to generate accurate, high-resolution digital elevation models (DEMs) in a small watershed area (12 ha). Because of farming activity, 14 TLS scans (≈ 25 points m− 2) were collected without using high-definition surveying (HDS) targets, which are generally used to mesh adjacent scans. To evaluate the accuracy of the DEMs created from the TLS scan data, 1,098 ground control points (GCPs) were surveyed using a real time kinematic global positioning system (RTK-GPS). Linear regressions were then applied to each DEM to remove vertical errors from the TLS point elevations, errors caused by the non-perpendicularity of the scanner’s vertical axis to the local horizontal plane, and errors correlated with the distance to the scanner’s position. The scans were then meshed to generate a DEMTLS with a 1 × 1 m spatial resolution. The Agisoft PhotoScan and MicMac software packages were used to process the aerial photographs and generate a DEMPSC (Agisoft PhotoScan) and DEMMCM (MicMac), respectively, with spatial resolutions of 1 × 1 m. Comparing the DEMs with the 1,098 GCPs showed that the DEMTLS was the most accurate data product, with a root mean square error (RMSE) of 4.5 cm, followed by the DEMMCM and the DEMPSC, which had RMSE values of 9.0 and 13.9 cm, respectively. The DEMPSC had absolute errors along the border of the study area that ranged from 15.0 to 52.0 cm, indicating the presence of systematic errors. Although the derived DEMMCM was accurate, an error analysis along a transect showed that the errors in the DEMMCM data tended to increase in areas of lower elevation. Compared with TLS, UAS is a promising tool for data collection because of its flexibility and low operational cost. However, improvements are needed in the photogrammetric processing of the aerial photographs to remove non-linear distortions

Monitoring of large landslides by Terrestrial Laser Scanning techniques: field data collection and processing

We have monitored a large landslide that causes extensive damage by using Terrestrial Laser Scanners (TLS) and Global Positioning System (GPS) receivers. Our surveys have confirmed that the slope undergoes a continuous change. When using TLS some operational difficulties arise. We have used different TLSs types to better evaluate the reliability of our surveys; a full wave TLS has allowed to make easier the data filtering. All surveys have been framed in the same absolute reference system; this has been done by connecting both targets and laser stations to a Global Navigation Satellite System (GNSS) Permanent Reference Stations network. A direct comparison among the DEMs allows to infer the movements of the landslide

Automatic segmentation and reconstruction of traffic accident scenarios from mobile laser scanning data

Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools

An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near )natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p> hard plotso << located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in "easy plots". By improving the algorithmic performances, the accuracy levels can be further increased by future research

Automated road extraction from terrestrial based mobile laser scanning system using the GVF snake model

Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental applications. High quality road geometry and road side features can now be extracted from dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable route network information is gaining the attention of road safety and maintenance engineers. Road points are needed to be correctly identified, classified and extracted from LiDAR data before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we present a method to automatically extract the road from terrestrial based mobile laser scanning system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing spline that moves towards the desired feature or object boundary under the influence of internal forces within the curve itself and external GVF forces derived typically from 2D imaging data by minimizing certain energy such as edges or high frequency information. In our novel method, we initialise the snake contours over point cloud data based on the trajectory information produced by the MMS navigation sub-system. The internal energy term provided to the snake contour is based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes. Our method primarily differs from the traditional snake models in initialisation and in deriving the energy terms from the 3D LiDAR data

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes

Estimation of gap fraction and clumping index with Terrestrial and Airborne Laser Scanner data

El dosel forestal es una zona de intercambio de flujos y energía entre la superficie de la tierra y la atmósfera. Su estructura está representada por la organización espacial de todos los elementos vegetales que se encuentran sobre la superficie. La estructura del dosel condiciona una serie de variables microclimáticas en el interior de este espacio, las que influyen en la disponibilidad de los recursos y el comportamiento de las especies que cohabitan en él. Existe una serie de variables que permiten describir la estructura del dosel. Entre las más importantes se encuentran el índice de área foliar, cuyo cálculo y corrección depende de otros parámetros como la fracción de huecos (gap fraction, GF) y el índice de agrupamiento foliar (clumping index, CI). En este documento se estudian y desarrollan métodos para la estimación de GF y CI a partir de escáneres láser terrestres y aerotransportados (Terrestrial (TLS) and Airborne (ALS) Laser Scanners). Para lograr esto, se llevaron a cabo mediciones con TLS en Las Majadas del Tiétar (Cáceres, España) en el año 2009 y con ALS en Jasper Ridge (California, EE.UU.) en el 2007. En el caso de la estimación de GF a partir TLS, se desarrolló un nuevo método que calculaba la proporción entre píxeles vacíos y su totalidad a partir de imágenes angulares, una vez que se conocía su resolución. La validación del método fue realizada mediante simulaciones de datos con diversas resoluciones angulares y patrones de huecos en el dosel. El método se comparó también con los resultados de GF a partir de fotografías hemisféricas (hemispherical photography, HP), una vez que los datos TLS se reproyectaron para simular HP (TLS-SHP). La estimación del CI se llevó a cabo aplicando la teoría de la distribución del tamaño de los huecos de Chen y Cihlar (1995) sobre las TLS-SHP, que se contrastó con los valores de CI de las HP. En la zona de Jasper Ridge las estimaciones de GF se realizaron empleando métricas basadas en la ley de transmisividad de Beer-Lambert que miden el porcentaje de retornos láser que llegan al suelo, considerando parcelas circulares de datos ALS con diferentes tamaños de radio, para compararlas con la GF estimado de las HP. Del mismo modo, se probó también con la relación entre las intensidades de los retornos del suelo y las de todos ellos al interior de las parcelas. El CI se estimó a partir de métricas ALS derivadas de la altura de la vegetación y se relacionaron con el CI de las HP. Además, se adaptó con el mismo propósito el índice de segregación espacial de Pielou (1962), que se aplicó sobre imágenes de GF generadas para parcelas de datos ALS con distintos tamaños de radio y que fueron comparadas con el CI generado desde las HP. Para los experimentos llevados a cabo con los datos TLS, la GF fue sobreestimada en un 14% respecto a las HP, siendo las correlaciones estadísticamente significativas. El algoritmo desarrollado es operativo siempre y cuando el ruido en los datos angulares sea inferior al 6% de la resolución angular. Por encima de este umbral el método presentó un alto error, especialmente en los datos simulados con una estructura de huecos agrupados (cluster). El CI se subestimó en 27% respecto a los valores obtenidos por las HP. Los principales problemas vienen dados por la diferencia en la distribución del tamaño de los huecos registrados por las HP y las TLS-SHP. Por otra parte, la GF derivada de los datos ALS subestimó en un 3% y sobrestimó en un 43% comparado con las HP, para las parcelas de bosque y matorral, respectivamente. La GF obtenida presentó una clara dependencia del radio de los datos ALS considerados, que varió según el tipo de vegetación. Respecto a las estimaciones del CI, las métricas ALS de las alturas de la vegetación no mostraron buenos resultados. Esta circunstancia es contraria a estudios previos, lo que parece indicar que estas relaciones empíricas sólo funcionarían para el tipo de vegetación y sitio para el que fueron desarrolladas. Sin embargo, la modificación del algoritmo de Pielou subestimó el CI en sólo 6% y 4% para las parcelas de bosques y matorrales, respectivamente. Las posibles causas de estas diferencias radican en las distintas perspectivas y resolución espacial que poseen los datos ALS y HP

Estimation of gap fraction and clumping index with Terrestrial and Airborne Laser Scanner data

El dosel forestal es una zona de intercambio de flujos y energía entre la superficie de la tierra y la atmósfera. Su estructura está representada por la organización espacial de todos los elementos vegetales que se encuentran sobre la superficie. La estructura del dosel condiciona una serie de variables microclimáticas en el interior de este espacio, las que influyen en la disponibilidad de los recursos y el comportamiento de las especies que cohabitan en él. Existe una serie de variables que permiten describir la estructura del dosel. Entre las más importantes se encuentran el índice de área foliar, cuyo cálculo y corrección depende de otros parámetros como la fracción de huecos (gap fraction, GF) y el índice de agrupamiento foliar (clumping index, CI). En este documento se estudian y desarrollan métodos para la estimación de GF y CI a partir de escáneres láser terrestres y aerotransportados (Terrestrial (TLS) and Airborne (ALS) Laser Scanners). Para lograr esto, se llevaron a cabo mediciones con TLS en Las Majadas del Tiétar (Cáceres, España) en el año 2009 y con ALS en Jasper Ridge (California, EE.UU.) en el 2007. En el caso de la estimación de GF a partir TLS, se desarrolló un nuevo método que calculaba la proporción entre píxeles vacíos y su totalidad a partir de imágenes angulares, una vez que se conocía su resolución. La validación del método fue realizada mediante simulaciones de datos con diversas resoluciones angulares y patrones de huecos en el dosel. El método se comparó también con los resultados de GF a partir de fotografías hemisféricas (hemispherical photography, HP), una vez que los datos TLS se reproyectaron para simular HP (TLS-SHP). La estimación del CI se llevó a cabo aplicando la teoría de la distribución del tamaño de los huecos de Chen y Cihlar (1995) sobre las TLS-SHP, que se contrastó con los valores de CI de las HP. En la zona de Jasper Ridge las estimaciones de GF se realizaron empleando métricas basadas en la ley de transmisividad de Beer-Lambert que miden el porcentaje de retornos láser que llegan al suelo, considerando parcelas circulares de datos ALS con diferentes tamaños de radio, para compararlas con la GF estimado de las HP. Del mismo modo, se probó también con la relación entre las intensidades de los retornos del suelo y las de todos ellos al interior de las parcelas. El CI se estimó a partir de métricas ALS derivadas de la altura de la vegetación y se relacionaron con el CI de las HP. Además, se adaptó con el mismo propósito el índice de segregación espacial de Pielou (1962), que se aplicó sobre imágenes de GF generadas para parcelas de datos ALS con distintos tamaños de radio y que fueron comparadas con el CI generado desde las HP. Para los experimentos llevados a cabo con los datos TLS, la GF fue sobreestimada en un 14% respecto a las HP, siendo las correlaciones estadísticamente significativas. El algoritmo desarrollado es operativo siempre y cuando el ruido en los datos angulares sea inferior al 6% de la resolución angular. Por encima de este umbral el método presentó un alto error, especialmente en los datos simulados con una estructura de huecos agrupados (cluster). El CI se subestimó en 27% respecto a los valores obtenidos por las HP. Los principales problemas vienen dados por la diferencia en la distribución del tamaño de los huecos registrados por las HP y las TLS-SHP. Por otra parte, la GF derivada de los datos ALS subestimó en un 3% y sobrestimó en un 43% comparado con las HP, para las parcelas de bosque y matorral, respectivamente. La GF obtenida presentó una clara dependencia del radio de los datos ALS considerados, que varió según el tipo de vegetación. Respecto a las estimaciones del CI, las métricas ALS de las alturas de la vegetación no mostraron buenos resultados. Esta circunstancia es contraria a estudios previos, lo que parece indicar que estas relaciones empíricas sólo funcionarían para el tipo de vegetación y sitio para el que fueron desarrolladas. Sin embargo, la modificación del algoritmo de Pielou subestimó el CI en sólo 6% y 4% para las parcelas de bosques y matorrales, respectivamente. Las posibles causas de estas diferencias radican en las distintas perspectivas y resolución espacial que poseen los datos ALS y HP

Seamless integration of above- and under-canopy unmanned aerial vehicle laser scanning for forest investigation

BackgroundCurrent automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost.ResultsIn the experiment, an approximately 0.5ha forest was covered in ca. 10min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2-4cm RMSE of the diameter at the breast height estimates, and a 4-7cm RMSE of the stem curve estimates.ConclusionsResults of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.Peer reviewe