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Highlights: [•] Tree height quantification was conducted in an olive orchard from UAV imagery. [•] A tree-level digital surface model was derived from VHR imagery. [•] The 3D photo-reconstruction methods derived accurate tree crown shapes. [•] Tree heights were estimated with R 2 =0.83 and RMSE=35cm (11.5% R-RMSE). [ABSTRACT FROM AUTHOR]

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. [Copyright &y& Elsevier]

This article reports a case of a congenital unilateral forelimb defect in a two-month-old male puppy with clinical and radiographic findings. Congenital deformities are structural or functional congenital defects arising from errors during development, and they can affect an isolated portion of the body, the complete body or parts of several systems of the body. The congenital absence of a portion of the proximal epiphysis of the radius is an extremely rare condition in human and animal species. Although similar congenital limb deformities have been infrequently reported in dogs, the present case is, to the best of the authors' knowledge, the first report of this type of partial forelimb paraxial radial hemimelia in a puppy, and it represents an addition to the scant literature on this topic. [ABSTRACT FROM AUTHOR]

Ecological focus areas are one of three greening measures that were introduced into the European Common Agricultural Policy by the reform in 2014, with the aim of enhancing the ecological function of agricultural landscapes. However, there are concerns that they will provide little or no additional ecological benefit (enhanced biodiversity and ecosystem services) as those that are declared may already exist and/or any new areas will be implemented on the basis of farm management burdens rather than ecological criteria, such as those which are the easiest or least costly to implement. To implement ecological focus areas to achieve greater benefits requires taking account of numerous spatial and management parameters, scientific understanding of ecosystem services, and the needs and behaviour individual and communities of species. Such an approach is not readily practical or feasible for many farm and land managers. This paper describes the development of an indicator framework which aims to distil this complex scientific information to aid decision making with regard to the implementation of ecological focus areas to enhance and increase benefits for ecosystem services and biodiversity. It involved collating scientific evidence from over 350 papers, reports and guides and then structuring this evidence to form the indicator framework. 230 impacts were identified for 20 land uses and landscape features, and these are characterised using 138 parameters and attributes, containing 708 descriptive classes. The framework aims to help land managers identify the potential benefits and burdens of different options for the specific spatial and management context of their farm, and thus select those with greatest benefits and least burden for their circumstances. Ecological focus areas are part of the first evolution of greening measures, so there is scope to improve them to make their implementation more ecological and more focused. Tools, such as the indicator framework presented herein, have the potential to support this process by educating and raising awareness of potential impacts, facilitating the transfer of scientific knowledge, and resulting in a more ecological aware industry. [ABSTRACT FROM AUTHOR]