Upgrade of foss date plug-in: Implementation of a new radargrammetric DSM generation capability

Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome "La Sapienza", and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery

Graphenic materials for biomedical applications

Graphene-based nanomaterials have been intensively studied for their properties, modifications, and application potential. Biomedical applications are one of the main directions of research in this field. This review summarizes the research results which were obtained in the last two years (2017-2019), especially those related to drug/gene/protein delivery systems and materials with antimicrobial properties. Due to the large number of studies in the area of carbon nanomaterials, attention here is focused only on 2D structures, i.e. graphene, graphene oxide, and reduced graphene oxide.Web of Science912art. no. 175

Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors

La tesis doctoral titulada “Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors” propone el desarrollo de un algoritmo de detección de área quemada global para sensores ópticos de resolución espacial moderada. El trabajo ha sido financiado y desarrollado bajo los proyectos Fire Disturbance (FireCCI) del programa Climate Change Initiative (CCI) de la European Space Agency (ESA) y el Copernicus Climate Change Service (C3S) de la European Commission (EC). El autor de este trabajo también ha recibido financiación del Ministerio de Ciencia, Innovación y Universidades, a través de una beca FPU. Cuando se propuso esta tesis solo había un único producto global de área quemada que ofrecía una serie temporal larga y consistente. Se trataba del producto MCD64A1 de la National Aeronautics and Space Administration (NASA) que se generaba operacionalmente y que proveía información de área quemada a nivel global a 500 m desde noviembre del 2000. Por la parte europea solo había dos productos, el FireCCI41 y el GIO_GL1_BA, pero se trataba de productos que o bien ofrecían una serie temporal demasiado reducida (FireCCI41) o bien una serie con baja fiabilidad. En cualquier caso, los tres productos, incluido el MCD64A1, presentaban limitaciones que les hacían estar lejos de cumplir los requerimientos establecidos por los usuarios en términos de errores de comisión y omisión. Es en este contexto donde se plantea esta tesis que pretende avanzar en el conocimiento de los algoritmos de área quemada globales y la generación de productos globales que cumplan o se acerquen de forma más significativa a las expectativas de los usuarios. Para este propósito, se ha utilizado información proveniente de sensores que no se habían utilizado hasta el momento para generar productos de área quemada globales. Esta información incluye las bandas de alta resolución a 250 m del Moderate Resolution Imaging Spectroradiometer (MODIS), las bandas del Ocean and Land Colour Instrument (OLCI) y del SYNERGY, así como fuegos activos de MODIS y del Visible Infrared Imaging Radiometer Suite (VIIRS). En este último caso, ha sido la primera vez que se utilizan globalmente para generar este tipo de productos. Así, se han desarrollado cuatro algoritmos y se han generado sus respectivos productos de área quemada a escala global. Cada uno de ellos ha jugado un papel complementario al resto, ya sea a modo de versión mejorada o como adaptación de un mismo algoritmo a distintos sensores. Todos los productos derivados han sido validados globalmente y se han llevado a cabo comparaciones exhaustivas con otros productos existentes. Además, para confirmar la estabilidad de los patrones espacio temporales, los productos se han aplicado para dar respuesta a distintas preguntas científicas relacionadas con las anomalías en las tendencias del área quemada en distintas partes del mundo. Para explicar todo este proceso la tesis se ha estructurado en ocho capítulos: introducción, seis publicaciones en revistas internacionales y unas conclusiones

Earth-observation-based estimation and forecasting of particulate matter impact on solar energy in Egypt

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined