Contributo dos dados lidar para o inventário florestal municipal

Relatório de Estágio apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Gestão do Território

Measuring gait using a ground laser range sensor

This paper describes a mesurement system designed to register the displacement of the legs using a two-dimensional laser range sensor with a scanning plane parallel to the ground and extract gait parameters. In the proposed methodology, the position of the legs is estimated by fitting two circles with the laser points that define their contour and the gait parameters are extracted applying a step-line model to the estimated displacement of the legs to reduce uncertainty in the determination of the stand and swing phase of the gait. Results obtained in a range up to 8 m shows that the systematic error in the location of one static leg is lower than 10 mm with and standard deviation lower than 8 mm; this deviation increases to 11 mm in the case of a moving leg. The proposed measurement system has been applied to estimate the gait parameters of six volunteers in a preliminary walking experiment.

Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: advances to improve pesticide application methods

Canopy characterization is a key factor to improve pesticide application methods in tree crops and vineyards. Development of quick, easy and efficient methods to determine the fundamental parameters used to characterize canopy structure is thus an important need. In this research the use of ultrasonic and LIDAR sensors have been compared with the traditional manual and destructive canopy measurement procedure. For both methods the values of key parameters such as crop height, crop width, crop volume or leaf area have been compared. Obtained results indicate that an ultrasonic sensor is an appropriate tool to determine the average canopy characteristics, while a LIDAR sensor provides more accuracy and detailed information about the canopy. Good correlations have been obtained between crop volume (CVU) values measured with ultrasonic sensors and leaf area index, LAI (R2 = 0.51). A good correlation has also been obtained between the canopy volume measured with ultrasonic and LIDAR sensors (R2 = 0.52). Laser measurements of crop height (CHL) allow one to accurately predict the canopy volume. The proposed new technologies seems very appropriate as complementary tools to improve the efficiency of pesticide applications, although further improvements are still needed.

Measuring oscillating walking paths with a LIDAR

This work describes the analysis of different walking paths registered using a Light Detection And Ranging (LIDAR) laser range sensor in order to measure oscillating trajectories during unsupervised walking. The estimate of the gait and trajectory parameters were obtained with a terrestrial LIDAR placed 100 mm above the ground with the scanning plane parallel to the floor to measure the trajectory of the legs without attaching any markers or modifying the floor. Three different large walking experiments were performed to test the proposed measurement system with straight and oscillating trajectories. The main advantages of the proposed system are the possibility to measure several steps and obtain average gait parameters and the minimum infrastructure required. This measurement system enables the development of new ambulatory applications based on the analysis of the gait and the trajectory during a walk.

Maneig de les dades d'un escàner làser (LiDAR) per a l'obtenció de paràmetres vegetatius en fruiters (perera) i vinya

Aquest projecte té com a finalitat desenvolupar un sistema no destructiu per a la caracterització de les plantacions de vinya i d’arbres fruiters mitjançant la utilització d’un sensor làser (LiDAR - Light Detection and Ranging). La informació obtinguda ha de permetre estudiar la resposta del cultiu a determinades accions (poda, reg, adobs, etc.); i també realitzar tractaments fitosanitaris adaptats a la densitat foliar del cultiu. La posada a punt del sistema (software i hardware) es va realitzar a escala reduïda mitjançant proves de laboratori sobre un arbre ornamental. Obtenint la configuració del sensor LiDAR més adequada i la calibració de tot el sistema. L’any 2004 van realitzar assajos en plantacions de pomera, perera, cítrics i vinya. L’objectiu era posar a prova el sistema i obtenir dades dels cultius. Amb la introducció de canvis i millores en el sistema i en la metodologia de treball, l’any 2005 es van realitzar nous assajos, però només en perera Blanquilla i en vinya Merlot. En tots els assajos s’escanejaven unes franges de vegetació concretes i posteriorment es desfullaven manualment per a calcular-ne l’Índex d’Àrea Foliar (IAF). Les dades obtingudes amb el sensor LiDAR s’han analitzat mitjançant l’aplicació de la metodologia desenvolupada per Walklate et al.(2002) i s’han obtingut determinats paràmetres vegetatius de cultiu, que posteriorment s’han correlacionat amb l’Índex d’Àrea Foliar (IAF) obtingut de forma experimental. La capacitat de predicció de l’Índex d’Àrea Foliar (IAF) per part dels diferents paràmetres calculats es diferent en cada cultiu, essent necessàries més proves i major nombre de dades a fi d’obtenir un model fiable per a l’estimació de l’IAF a partir de les lectures del sensor LiDAR. L’estudi de la variabilitat de la vegetació mitjançant l’anàlisi de la variabilitat del Tree Area Index (TAI) al llarg de la fila ha permès determinar el nombre mínim necessari d’escanejades acumulades per a l’estimació fiable de l’Índex d’Àrea Foliar. Finalment s’ha estudiat la incidència de l’alçada de col•locació del sensor LiDAR respecte la vegetació.

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.

Estimación y evolución temporal de variables forestales con tecnología LiDAR en el Valle de la Fuenfría (Cercedilla, Madrid)

Este estudio profundiza en la estimación de variables forestales a partir de información LiDAR en el Valle de la Fuenfría (Cercedilla, Madrid). Para ello se dispone de dos vuelos realizados con sensor LiDAR en los años 2002 y 2011 y en el invierno de 2013 se ha realizado un inventario de 60 parcelas de campo. En primer lugar se han estimado seis variables dasométricas (volumen, área basimétrica, biomasa total, altura dominante, densidad y diámetro medio cuadrático) para 2013, tanto a nivel de píxel como a nivel de rodal y monte. Se construyeron modelos de regresión lineal múltiple que permitieron estimar con precisión dichas variables. En segundo lugar, se probaron diferentes métodos para la estimación de la distribución diamétrica. Por un lado, el método de predicción de percentiles y, por otro lado, el método de predicción de parámetros. Este segundo método se probó para una función Weibull simple, una función Weibull doble y una combinación de ambas según la distribución que mejor se ajustaba a cada parcela. Sin embargo, ninguno de los métodos ha resultado suficientemente válido para predecir la distribución diamétrica. Por último se estimaron el crecimiento en volumen y área basimétrica a partir de la comparación de los vuelos del 2002 y 2011. A pesar de que la tecnología LiDAR era diferente y solo se disponía de un inventario completo, realizado en 2013, los modelos construidos presentan buenas bondades de ajuste. Asimismo, el crecimiento a nivel de pixel se ha mostrado estar relacionado de forma estadísticamente significativa con la pendiente, orientación y altitud media del píxel. ABSTRACT This project goes in depth on the estimation of forest attributes by means of LiDAR data in Fuenfria’s Valley (Cercedilla, Madrid). The available information was two LiDAR flights (2002 and 2011) and a forest inventory consisting of 60 plots (2013). First, six different dasometric attributes (volume, basal area, total aboveground biomass, top height, density and quadratic mean diameter) were estimated in 2013 both at a pixel, stand and forest level. The models were developed using multiple linear regression and were good enough to predict these attributes with great accuracy. Second, the measured diameter distribution at each plot was fitted to a simple and a double Weibull distribution and different methods for its estimation were tested. Neither parameter prediction method nor percentile prediction method were able to account for the diameter distribution. Finally, volume and top height growths were estimated comparing 2011 LiDAR flight with 2002 LiDAR flight. Even though the LiDAR technology was not the same and there was just one forest inventory with sample plots, the models properly explain the growth. Besides, growth at each pixel is significantly related to its average slope, orientation and altitude.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Metodología para ejecución y control de calidad para ortofotos, mediante combinación de sensor digital con LIDAR en zonas rurales

La utilización de una cámara fotogramétrica digital redunda en el aumento demostrable de calidad radiométrica debido a la mejor relación señal/ruido y a los 12 bits de resolución radiométrica por cada pixel de la imagen. Simultáneamente se consigue un notable ahorro de tiempo y coste gracias a la eliminación de las fases de revelado y escaneado de la película y al aumento de las horas de vuelo por día. De otra parte, el sistema láser aerotransportado (LIDAR - Light Detection and Ranging) es un sistema con un elevado rendimiento y rentabilidad para la captura de datos de elevaciones para generar un modelo digital del terreno (MDT) y también de los objetos sobre el terreno, permitiendo así alcanzar alta precisión y densidad de información. Tanto el sistema LIDAR como el sistema de cámara fotogramétrica digital se combinan con otras técnicas bien conocidas: el sistema de posicionamiento global (GPS - Global Positioning System) y la orientación de la unidad de medida inercial (IMU - Inertial Measure Units), que permiten reducir o eliminar el apoyo de campo y realizar la orientación directa de los sensores utilizando datos de efemérides precisas de los satélites. Combinando estas tecnologías, se va a proponer y poner en práctica una metodología para generación automática de ortofotos en países de América del Sur. Analizando la precisión de dichas ortofotos comparándolas con fuente de mayor exactitud y con las especificaciones técnicas del Plan Nacional de Ortofotografía Aérea (PNOA) se determinará la viabilidad de que dicha metodología se pueda aplicar a zonas rurales. ABSTRACT Using a digital photogrammetric camera results in a demonstrable increase of the radiometric quality due to a better improved signal/noise ratio and the radiometric resolution of 12 bits per pixel of the image. Simultaneously a significant saving of time and money is achieved thanks to the elimination of the developing and film scanning stages, as well as to the increase of flying hours per day. On the other hand, airborne laser system Light Detection and Ranging (LIDAR) is a system with high performance and yield for the acquisition of elevation data in order to generate a digital terrain model (DTM), as well as objects on the ground which allows to achieve high accuracy and data density. Both the LIDAR and the digital photogrammetric camera system are combined with other well known techniques: global positioning system (GPS) and inertial measurement unit (IMU) orientation, which are currently in a mature evolutionary stage, which allow to reduce and/or remove field support and perform a direct guidance of sensors using specific historic data from the satellites. By combining these technologies, a methodology for automatic generation of orthophotos in South American countries will be proposed and implemented. Analyzing the accuracy of these orthophotos comparing them with more accurate sources and technical specifications of the National Aerial Orthophoto (PNOA), the viability of whether this methodology should be applied to rural areas, will be determined.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Estimación de la superficie foliar en viña mediante el uso de un escáner láser (LIDAR)

El present treball, pretén presentar una nova metodologia per a l'estimació de la superfície foliar en vinya mitjançant l'ús d'un sensor làser terrestre (LIDAR). Per a fer això, prèviament va tindre lloc una recollida de dades del camp en una parcel·la de vinya a Raïmat. Posteriorment, en gabinet es va realitzar un anàlisi de les dades per tal d'estimar a partir de diferents paràmetres, la superfície foliar existent en cadascún dels trams on van tindre lloc les mesures. Finalment, un cop obtinguts els resultats a partir del sensor terrestre, es van comparar amb els proporcionats per un sensor remot.

Innovative LIDAR 3D dynamic measurement system to estimate fruit-tree leaf area

In this work, a LIDAR-based 3D Dynamic Measurement System is presented and evaluated for the geometric characterization of tree crops. Using this measurement system, trees were scanned from two opposing sides to obtain two three-dimensional point clouds. After registration of the point clouds, a simple and easily obtainable parameter is the number of impacts received by the scanned vegetation. The work in this study is based on the hypothesis of the existence of a linear relationship between the number of impacts of the LIDAR sensor laser beam on the vegetation and the tree leaf area. Tests performed under laboratory conditions using an ornamental tree and, subsequently, in a pear tree orchard demonstrate the correct operation of the measurement system presented in this paper. The results from both the laboratory and field tests confirm the initial hypothesis and the 3D Dynamic Measurement System is validated in field operation. This opens the door to new lines of research centred on the geometric characterization of tree crops in the field of agriculture and, more specifically, in precision fruit growing.

Comparison of balloon-carried atmospheric motion sensors with Doppler lidar turbulence measurements

Magnetic sensors have been added to a standard weather balloon radiosonde package to detect motion in turbulent air. These measure the terrestrial magnetic field and return data over the standard uhf radio telemetry. Variability in the magnetic sensor data is caused by motion of the instrument package. A series of radiosonde ascents carrying these sensors has been made near a Doppler lidar measuring atmospheric properties. Lidar-retrieved quantities include vertical velocity (w) profile and its standard deviation (w). w determined over 1 h is compared with the radiosonde motion variability at the same heights. Vertical motion in the radiosonde is found to be robustly increased when w>0.75 m s−1 and is linearly proportional to w. ©2009 American Institute of Physics

Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.