3D model validation to estimate intercepted radiation using high spatial resolution imagery in row-tree canopies

En este trabajo se llevó a cabo la validación del modelo 3D de transferencia radiativa FLIGHT para la estimación de la fracción de radiación fotosintéticamente activa interceptada (fIPAR) en cubiertas heterogéneas. El modelo permite simular cubiertas de tipo discontinuo evaluando la relación entre la energía reflejada y absorbida en función de distintos parámetros como la estructura de la plantación, geometría de visión o las propiedades espectrales del suelo y la vegetación. El estudio fue llevado a cabo en cultivos de melocotón y naranjo, pertenecientes a fincas comerciales situadas en las provincias de Córdoba y Sevilla. De cada plantación, se tomaron imágenes multiespectrales de alta resolución mediante un vehículo aéreo no tripulado (UAV) en zonas de estudio con un amplio rango de heterogeneidad estructural, donde se realizaron medidas ópticas foliares, estructurales y de interceptación de radiación. El sensor utilizado para la toma de imágenes fue una cámara multiespectral de 6 bandas y 10 nm FWHM, obteniendo los datos de radiación interceptada para validación de fIPAR mediante ceptómetro en el momento del vuelo del UAV. Los errores obtenidos en la estimación de fIPAR usando el modelo FLIGHT fueron de 10% RMSE, permitiendo parametrizar la relación NDVI vs fIPARA study was conducted to evaluate the 3D radiative transfer model FLIGHT to estimate fraction of Intercepted Photosyntetically Active Radiation (fIPAR) in heterogeneous canopies. The FLIGHT 3D canopy model enables simulation of the effects of different input parameters on fIPAR, such as the orchard architecture, planting grid, solar geometry and background artifacts. The study was conducted over two commercial peach and orange orchards located in Cordoba and Seville, where study areas showing a gradient in heterogeneous structure were selected. High resolution multispectral imagery was acquired by an unmanned aerial vehicle (UAV). The multispectral sensor used in this study was a 6-band multispectral camera with 10nm FWHM bands, using a ceptometer for ground truth data of intercepted radiation. Estimates for radiation interception using a modeling approach yielded errors bellow 10% RMS

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection

Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation

This work provides a description of the research conducted to assess methods for the discrimination between irrigated and rainfed open-tree canopies using advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite imagery and discrete anisotropic radiative transfer (DART) radiative transfer 3D simulation model. Summer and winter ASTER images were acquired over a study area in southern Spain during a 6-year period. A total of 1076 olive orchards were monitored in this area, gathering the field location, field area, tree density, and whether the field was drip irrigated or rainfed. Surface temperature images from ASTER were estimated using the temperature and emissivity separation (TES) method. A panchromatic ortho-rectified imagery dataset collected over the entire area at 0.5 m resolution was used to estimate orchard vegetation cover for each field. Results for summer ASTER thermal images showed differences between irrigated and rainfed orchards of up to 2 K for fields with the same percentage cover, decreasing the differences in ASTER winter images. An approach based on a cumulative index using temperature and the normalized difference vegetation index (NDVI) information for the 6-year ASTER time-series was capable of detecting differences between irrigated and rainfed open-canopy orchards, obtaining 80% success on field-to-field assessments. The method considered that irrigated orchards with equal vegetation cover would yield lower temperature and NDVI than rainfed orchards; an overall accuracy of 75% and a kappa (k) of 0.34 was obtained with a supervised classification method using visible, near infrared and temperature information for the 6-year ASTER imagery series. These experimental ASTER results were confirmed with DART radiative transfer 3D model used to simulate the influence of vegetation cover, leaf area index (LAI) and background temperature on the irrigated and rainfed orchard temperature at the ASTER pixel size

Primary and secondary effects of climate variability on net ecosystem carbon exchange in an evergreen Eucalyptus forest

To understand the dynamics of ecosystem carbon cycling more than 10 years of eddy covariance data, measured over an evergreen, temperate, wet sclerophyll forest, were analysed and related to climate drivers on time scales ranging from hours to years. On