Summary
The aim of WEQUAL project (WEb service centre for QUALity multidimensional design and tele-operated monitoring of Green Infrastructures) is the development of a system that is able to support a quick environmental monitoring of riparian areas subjected to the realization of new green infrastructures (GI). The Wequal’s idea is to organize a service center able to manage both the Web Platform and the whole data collection and analysis processes. Through a personal account, the final user (designer, technician, researcher) can get access to the service and requires the evaluation of alternatives GI projects. On the Web Platform, a set of algorithms runs in order to calculate, through automatic procedures, all the ecological criteria required to evaluate a quality environmental index that describes the eco-morphological value of the monitored riparian areas. For this aim, the WEQUI index was developed, which uses 15 indicators that are easy to monitor. In this paper, the approach for environmental data collection and the procedures to perform the automatic assessment of two of the ecological criteria are described. For the computation, the implemented algorithms use data including the vegetation indexes, Digital Terrain Model (DTM), Digital Surface Model (DSM) and a 3D point cloud classification. All the raw data are collected by UAVs (Unmanned Aircraft Vehicle) equipped with a 3D Lidar, multispectral camera and RGB camera. Interpreting all the raw data collected by these sensors, using a multi-attribute approach, the WEQUI index is assessed. The computed ecological index is then used to assess the riparian environmental quality at ex-ante and ex-post river stabilization works. This index, integrated with additional not-technical or not-ecological indicators such as investment required, maintenance costs or social acceptance, can be used in multicriteria analyses in order to evaluate the intervention from a wider point of view. The platform is expected to be attractive for GI designers and policy makers by providing a shared environment, which is able to integrate the method of detection and evaluation of complex indexes and a multidimensional evaluation supported by an expert guide.
Summary
This work aims to develop an automatic system capable of providing objective information about the bloom charge in an apple orchard in order to manage flower-thinning activities. The article presents and discusses the use of a mobile lab (ByeLab) equipped with several optical sensors to carry out a site-specific bloom charge assessment in apple trees. The data collected by the sensors were processed by a specific algorithm implemented in MatLab®. Investigations of the flower reflectance signature indicated that the Normalized Difference Vegetation Index (NDVI) is the most suitable parameter to distinguish leaves from flowers. Pure flowers produce NDVI values slightly negative or at least very near to 0. Despite the homogeneous behavior of the NDVI flower response, OptRx™ sensors, which provide an average assessment of an area, were not able to highlight a significant correlation between the number of flowers and the NDVI values. In the future, further studies will be conducted to assess if other techniques based on image analyses can provide better and more sensitive results regarding the bloom charge assessment. Such results could then be used as a reference in automating machines for thinning operations according to a site-specific approach.
The study concerns a mountain territory, bordering Liguria, Piemonte, Lombardia and Emilia, where a high power 151 MW wind farm, with 42 tower of 3.6 MW power, has been proposed. As a measure of environmental mitigation, the realization of a livestock system of a herd of sucker cows pasturing in the wind farm areas is proposed. This has implications for environmental maintenance, employment in a territory gradually losing its population, and for tourism. The study, having focused on those aspects that reduce landscape impact and carrying out an analysis of the individual areas to evaluate forage resources and the different pastoral indexes, identifies the maximum sustainable load of animals (335 UBA/ha) in the current conditions of neglect. So, some measures to improve and increase sustainable herds have been proposed and examined. The operations include: stone removal; light harrowing; overseeding; creation of fodder reserves for periods of shortage; and grazing will be managed by taking turns. Based on the results of two other studies, both previous tests carried out on site, encourage us to think that we will be able to increase the maximum sustainable seasonal load for the current situation by more than 50%.
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