Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: further improvements by ortho-image analysis and application to Turin (Italy)

The ongoing rush of the UE member states to the 2020 overall targets on the national renewable energy share (see Directive 2009/28/EC), is propelling the large exploitation of the solar resource for the electricity production. However, the incentives to the large employment of PV solar modules and the relative perspective profits, are often cause of massive ground-mounted installations. These kind of installations are obviously the preferred solution by the investors for their high economic yields, but their social impact should be also considered. Over the Piedmont Region for instance, the large proliferation of PV farms is jeopardizing wide agricultural terrains and turistic areas, therefore the policy of the actual administration is to encourage the use of integrated systems in place of massive installations. For these reasons, an effort to demonstrate that the distributed residential generation can play a primary role in the market is mandatory. In our previous work “Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: application to Piedmont Region (Italy)”, we already proposed a basic methodology for the evaluation of the roof-top PV system potential. However, despite the total roof surface has been computed on a given cartographical dataset, the real roof surface available for PV installations has been evaluated through the assumption of representative roofing typologies and empirical coefficients found via visual inspection of satellite images. In order to overcome this arbitrariness and refine our methodology, in the present paper we present a brand new algorithm to compute the available roof surface, based on the systematical analysis and processing of aerial georeferenced images (ortho-images). The algorithm, fully developed in MATLAB®, accounts for shadow, roof surface available (bright and not), roof features (i.e. chimneys or walls) and azimuthal angle of the eventual installation. Here we apply the algorithm to the whole city of Turin, and process more than 60,000 buildings. The results achieved are finally compared with our previous work and the updated PV potential assessment is consequently discussed

Improving the Physical Processes and Model Integration Functionality of an Energy Balance Model for Snow and Glacier Melt

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available dat

Aerial Semantic Mapping for Precision Agriculture using Multispectral Imagery

Nowadays constant technological evolution cover several necessities and daily tasks in our society. In particular, drones usage, given its wide vision to capture the terrain surface images, allows to collect large amounts of information with high efficiency, performance and accuracy. This master dissertation’s main purpose is the analysis, classification and respective mapping of different terrain types and characteristics, using multispectral imagery. Solar radiation flow reflected on the surface is captured by the used multispectral camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared and RedEdge). It is possible to analyse the various spectrum indices from the collected imagery, according to the fusion of different combinations between coloured bands (e.g. NDVI, ENDVI, RDVI. . . ). This project engages a ROS (Robot Operating System) framework development, capable of correcting different captured imagery and, hence, calculating the implemented spectral indices. Several parametrizations of terrain analysis were carried throughout the project, and this information was represented in semantic maps by layers (e.g. vegetation, water, soil, rocks). The obtained experimental results were validated in the scope of several projects incorporated in PDR2020, with success rates between 70% and 90%. This framework can have multiple technical applications, not only in Precision Agriculture, but also in vehicles autonomous navigation and multi-robot cooperation

Statistical learning approach for wind resource assessment

Wind resource assessment is fundamental when selecting a site for wind energy projects. Wind is influenced by several environmental factors and understanding its spatial variability is key in determining the economic viability of a site. Numerical wind flow models, which solve physical equations that govern air flows, are the industry standard for wind resource assessment. These methods have been proven over the years to be able to estimate the wind resource with a relatively high accuracy. However, measuring stations, which provide the starting data for every wind estimation, are often located at some distance from each other, in some cases tens of kilometres or more. This adds an unavoidable amount of uncertainty to the estimations, which can be difficult and time consuming to calculate with numerical wind flow models. For this reason, even though there are ways of computing the overall error of the estimations, methods based on physics fail to provide planners with detailed spatial representations of the uncertainty pattern. In this paper we introduce a statistical method for estimating the wind resource, based on statistical learning. In particular, we present an approach based on ensembles of regression trees, to estimate the wind speed and direction distributions continuously over the United Kingdom (UK), and provide planners with a detailed account of the spatial pattern of the wind map uncertainty

Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India

The subsidence in coal mines induced by surface and subsurface fires leading to roof collapse, infrastructure loss, and loss of lives is a prominent concern. In the study, satellite imagery from thermal and microwave remote sensing data is used to deduce the effect of coal fires on subsidence in the Jharia Coalfields, India. The Thermal Infrared data acquired from the Landsat-8 (band 10) is used to derive the temperature anomaly maps. Persistent Scatterer Interferometry analysis was performed on sixty Sentinel-1, C-band images, the results are corrected for atmospheric error using Generic Atmospheric Correction Online Service for InSAR (GACOS) atmospheric modelling data and decomposed into vertical displacement values to quantify subsidence. A zone-wise analysis of the hazard patterns in the coalfields was carried out. Coal fire maps, subsidence velocity maps, and land cover maps were integrated to investigate the impact of the hazards on the mines and their surroundings. Maximum subsidence of approximately 20 cm/yr. and temperature anomaly of up to 25 °C has been observed. The findings exhibit a strong positive correlation between the subsidence velocity and temperature anomaly in the study area. Kusunda, Keshalpur, and Bararee collieries are identified as the most critically affected zones. The subsidence phenomenon in some collieries is extending towards the settlements and transportation networks and needs urgent intervention. © 2021 The Author