Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

LiDAR simulation in modelled orchards to optimise the use of terretrial laser scanners and derived vegetative measures

Light detection and ranging (LiDAR) technology is beginning to have an impact on agriculture. Canopy volume and/or fruit tree leaf area can be estimated using terrestrial laser sensors based on this technology. However, the use of these devices may have different options depending on the resolution and scanning mode. As a consequence, data accuracy and LiDAR derived parameters are affected by sensor configuration, and may vary according to vegetative characteristics of tree crops. Given this scenario, users and suppliers of these devices need to know how to use the sensor in each case. This paper presents a computer program to determine the best configuration, allowing simulation and evaluation of different LiDAR configurations in various tree structures (or training systems). The ultimate goal is to optimise the use of laser scanners in field operations. The software presented generates a virtual orchard, and then allows the scanning simulation with a laser sensor. Trees are created using a hidden Markov tree (HMT) model. Varying the foliar structure of the orchard the LiDAR simulation was applied to twenty different artificially created orchards with or without leaves from two positions (lateral and zenith). To validate the laser sensor configuration, leaf surface of simulated trees was compared with the parameters obtained by LiDAR measurements: the impacted leaf area, the impacted total area (leaves and wood), and th impacted area in the three outer layers of leaves.

SIMLIDAR Simulation of LiDAR performance in artificially simulate orchards

SIMLIDAR is an application developed in Cþþ that generates an artificial orchard using a Lindenmayer system. The application simulates the lateral interaction between the artificial orchard and a laser scanner or LIDAR (Light Detection and Ranging). To best highlight the unique qualities of the LIDAR simulation, this work focuses on apple trees without leaves, i.e. the woody structure. The objective is to simulate a terrestrial laser sensor (LIDAR) when applied to different artificially created orchards and compare the simulated characteristics of trees with the parameters obtained with the LIDAR. The scanner is mounted on a virtual tractor and measures the distance between the origin of the laser beam and the nearby plant object. This measurement is taken with an angular scan in a plane which is perpendicular to the route of the virtual tractor. SIMLIDAR determines the distance measured in a bi-dimensional matrix N M, where N is the number of angular scans and M is the number of steps in the tractor route. In order to test the data and performance of SIMLIDAR, the simulation has been applied to 42 different artificial orchards. After previously defining and calculating two vegetative parameters (wood area and wood projected area) of the simulated trees, a good correlation (R2 ¼ 0.70e0.80) was found between these characteristics and the wood area detected (impacted) by the laser beam. The designed software can be valuable in horticulture for estimating biomass and optimising the pesticide treatments that are performed in winter.

Characterization of underground cellars in the Duero Basin by GNSS, LIDAR and GPR techniques

The underground cellars of the Duero River basin are part of spread and damaged agricultural landscape which is in danger of disappearing. These architectural complexes are allocated next to small towns. Constructions are mostly dug in the ground with a gallery down or "barrel" strait through which you access the cave or cellar. This wider space is used to make and store wine. Observation and detection of the winery both on the outside and underground is essential to make an inventory of the rural heritage. Geodetection is a non-invasive technique, suitable to determinate with precision buried structures in the ground. The undertaken works include LIDAR survey techniques, GNSS and GPR obtained data. The results are used to identify with centimetric precision construction elements forming the winery. Graphic and cartographic obtained documents allow optimum visualization of the studied field and can be used in the reconstruction of the place.

Algoritmo para la clasificación de nubes de puntos LiDAR en entornos urbanos: discriminación entre vegetación y edificaciones.

Los sistemas de registro aerotransportados que utilizan láser (LiDAR) se están convirtiendo en el principal instrumental para la recogida de la información cartográfica debido, principalmente, a la gran densidad de puntos, precisión alcanzada y rapidez en la obtención de modelos digitales. Sin embargo, sería importante disponer de algoritmos que permitan filtrar la información, seleccionando aquellos puntos medidos en zonas deseadas. Cuando se miden zonas urbanas, los elementos más importantes son las edificaciones. Por ello, se propone un nuevo algoritmo que permite clasificar y diferenciar aquellos puntos medidos sobre edificios, extrayendo, como resultado, el límite exterior que definen, de tal forma que se podría calcular la zona edificada. Abstarct: Registration systems using airborne laser (LIDAR) are becoming the main device for the collection of cartographic information, mainly due to the high density of points, accuracy and rate achieved in obtaining digital models. However, it would be important to have algorithms that filter the information by selecting those points measured in targeted areas. When measuring urban areas, buildings are the most important objects. Therefore, a new algorithm is proposed to classify those measured points on buildings and to compute their outer boundaries, so the built up area can be computed.

Propuesta de metodología de trabajo para la obtención de cartografía a partir de datos LiDAR registrados en una zona rústica.

Tras la llegada de la medición mediante LiDAR, la obtención de cartografía se ha visto facilitada, obteniendo modelos digitales con gran rapidez y precisión. No obstante, para poder tratar la gran cantidad de información registrada, se necesita emplear un conjunto de algoritmos que permita extraer los detalles importantes y necesarios de la zona registrada. Por ello, se presenta este trabajo donde se expondrá una metodología de actuación para obtener cartografía a escala 1/1000 de una zona rústica, basada en el cálculo de mapas de curvas de nivel y ortofotografías, generadas a partir de los MDT y MDS de la zona. Todas las pruebas se han realizado mediante el software MDTopX. Abstract: After the arrival of the LiDAR measurement, mapping has been facilitated, obtaining digital models very quickly and accurately. However, in order to manage the great amount of recorded information, a set of algorithms is required which allows the extracting of important and necessary details of the recorded area. Therefore, a methodology is presented for mapping at 1/1000 scale of a rural area, based on contour maps and orthophotos, generated from the DTM and DSM of the area. All tests were performed using MDTopX software.

Observation and Detection of Underground Cellars in the Duero Basin By GNSS, Lidar and Ground Penetrating Radar Techniques

The underground cellars that appear in different parts of Spain are part of an agricultural landscape dispersed, sometimes damaged, others at risk of disappearing. This paper studies the measurement and display of a group of wineries located in Atauta (Soria), in the Duero River corridor. It is a unique architectural complex, facing rising, built on a smooth hillock as shown in Fig. 1. These constructions are excavated in the ground. The access to the cave or underground cellar has a shape of a narrow tube or down gallery. Immediately after, this space gets wider. There, wine is produced and stored [1]. Observation and detection of the underground cellar, both on the outside and underground, it is essential to make an inventory of the rural patrimony [2]. The geodetection is a noninvasive technique, adequate to accurately locate buried structures in the ground. Works undertaken include topographic work with the LIDAR techniques and integration with data obtained by GNSS and GPR.