ALBERT

Description: Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.

Description: A methodology for an optical optimization of the intercept factor concerning a linear Fresnel reflector is described to increase the amount of solar irradiation that will be delivered in the absorber for Agua Prieta, Sonora; taking the FRESDEMO’s Fresnel field as the reference design. For the performed optimization, the intercept factor is determined as a function of the receiver’s height, establishing a simple criterion for the optimization. The FRESDEMO’s field description is determined and briefly discussed, next compared with the proposed optimization. The compound parabolic concentrator (CPC) Winston function for a circular absorber is modified to relocate the cusp of the reflector and the absorber. This modified CPC will redirect all the reflected rays that do not hit directly the absorber, as in the FRESDEMO field, so all of them are captured by the absorber. Through ray-tracing, the graphic flux distribution in the receiver aperture is conceived. This flux distribution is compared with the FRESDEMO field and with a PTC with a flat absorber, ensuring an adequate optimization regarding the intercept factor. The result of the opto-geometric optimization is compared between the FRESDEMO and the optimized field for a specified thermal process, addressing a considerable reduction in the length of the loops.

Description: In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is a problem faced by institutions and companies that want to replace time-consuming and exhausting human work with AI technology. In this work, we propose a method that combines deep learning with a human-in-the-loop strategy to achieve expert-level results at a low cost. We use a neural network to segment the images. In parallel, another network is used to measure uncertainty for predicted pixels. Finally, we combine these neural networks with a human-in-the-loop approach to produce correct predictions as if developed by human photointerpreters. Applying this methodology shows that we can increase the accuracy of land cover segmentation tasks while decreasing human intervention.

Description: The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.

Description: Tequila vinasses is a mixture made from up to six still distillation two-stage process residual effluents. First stage fractions: residual must (60%), heads (0.9%) and tails (20.0%); second stage fractions: non-evaporated (8.0%), heads (0.1%) and tails (1.0%); the result is a more complex effluent for its treatment or biorefining. The objectives of this study were to: (a) characterize the five still distillation volatile streams in the Tequila 100% Agave processing; compounds: methanol, ethanol, acetaldehyde, ethyl acetate, sec-butanol, n-propanol, iso-butanol, n-butanol, iso-amyl, n-amyl, and ethyl lactate were detected by gas chromatography; calculated chemical oxygen demand from chemical composition had very high values (53,760–1,239,220 mg/L); measurement of pH (3.24–4.80), color (38.6 UC Pt-Co max), turbidity (46.1 max), electrical conductivity (3.30–172.20 μS/cm), and solid content (0 mg/L) was also made; (b) report an energy analysis (2.02 × 109 KWh) and CO2 production (429 × 106 kg) in the Tequila industry during 2019; (c) up to date residues (365.2 × 106 kg agave bagasse, 1146.1 × 106 kg agave leaves and 3300.0 × 106 L agave vinasse) in 2019; (d) economic analysis, current tequila vinasses treatment price is 16.00 USD/m3 but could reach a considerable fraction value if is bio-refined, a break down component analysis reach for five volatile streams $51.23–$140.00 USD/m3.

Description: This work aims at enhancing the current methodologies used for generating as-built CAD models suitable for advanced numerical simulations. To this end, this paper proposes the use of a wearable mobile mapping system that allows one to improve the digitalization stage in terms of flexibility and time required. The noise showed by the resulting point cloud, based on the simultaneous location and mapping (SLAM) solution, demands a post-processing stage that introduces the use of a parameter-free noise reduction filter. This filter improves the quality of the point cloud, allowing for the adjustment of surfaces by means of parametric and non-parametric shapes. These shapes are created by using reverse engineering procedures. The results showed during this investigation highlight a novel application of this sensor: the creation of as-built CAD models for advanced numerical simulations. The results of this investigation are complemented by a valuable contribution with respect to the use of an advanced restoration solution, by means of textile reinforced mortar. To this end, the CAD model is used as the geometrical base for several numerical simulations by means of the finite element method. All this procedure is applied in a construction with structural problems.

Description: Empty fruit bunch, a significant by-product of the palm oil industry, represents a tremendous and hitherto neglected renewable energy resource for many countries in South East Asia and Sub-Saharan Africa. The design and simulation of a plant producing pure hydrogen through autothermal reforming (ATR) of palm empty fruit bunch (PEFB) was carried out based on successful laboratory experiments of the core process. The bio-oil feed to the ATR stage was represented in the experiments and in the simulation by a surrogate bio-oil mixture of 11 organic compounds shown to be main constituents of PEFB oil from previous work, and whose combined elemental composition and volatility was determined to be as close as possible to that of the real PEFB bio-oil. The experiments confirmed that H2 yields close to equilibrium predictions were achievable using an in-house synthetised Rh-Al2O3 catalyst in a packed bed reactor. Initial sensitivity analysis on the plant revealed that feed molar steam to carbon ratio should not exceed 3 for the optimal design of the ATR hydrogen production plant. An overall plant efficiency of 39.4% was obtained for the initial design, this value was improved to 67.5% by applying pinch analysis to enhance the integration of heat in the design. The proposed design renders CO2 savings of about 0.56 kg per kg of raw PEFB processed. The proposed design and accompanying experimental studies together make a strong case on the possibility of polygeneration of H2, heat, and power from an otherwise discarded agricultural waste.

Description: The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.

Description: The residential building stock represents one of the major players in energy use and greenhouse gas emissions; thus, it is fundamental to reduce the energy used. Simulation tools are becoming more and more accurate in compliance with the new requirements both at the single-building and at the district scale, although they are not affordable by non-specialist users such as policymakers. The research concerns the evaluation of the energy demand for space heating for a historical district that is representative of the Italian building stock. The work compares dynamic and specialist-oriented urban scale tools such as Energy Urban Resistance Capacitance Approach (EUReCA) and City Energy Analyst (CEA)) as well as a quasi-steady-state calculation method (Excel spreadsheet), which is more affordable for non-specialist users. The work was carried out to assess the possible deviation of the results between the dynamic and quasi-steady-state calculation methods, as well as to identify any limits and opportunities in the application of the latter procedure, which is currently the official national calculation tool for the implementation of Directive 2010/31/EU. The study shows how the quasi-steady-state method predicts a reliable building energy demand, in line with the results obtained by the two dynamic tools, when considering only geometry and infiltrations as input. However, the limits of the quasi-steady-state method emerge when introducing internal loads, significantly underestimating the energy demand compared to CEA and EUReCA simulations. The results underline the potential application of the quasi-steady-state method to predict energy demand, although dynamics tools are more reliable but far more complex. Major findings through two methods concern the impact of solar heat gains on the overall heating demand at both the single building and the district scale. The different results between the tools provided evidence of a gap in the use of the simplest tool and demonstrated the accuracy and reliability of the proposed approach with a lower computational effort.