Fire and Smoke Remote Sensing and Modeling Uncertainties: Case Studies in Northern Sub‐Saharan Africa

Significant uncertainties are incurred in deriving various quantities related to biomass burning from satellite measurements at different scales, and, in general, the coarser the resolution of observation the larger the uncertainty. WRF‐Chem model simulations of smoke over the northern sub‐Saharan African (NSSA) region for January–February 2010, using fire energetics and emissions research version 1.0 (FEERv1) aerosol emissions derived from MODIS measurements of fire radiative power (FRP) and aerosol optical depth (AOD), resulted in a severe model underestimation of AOD compared with satellite retrievals. Such uncertainties are attributable to three major factors: limitations in the spatial and temporal resolutions of the satellite observations used to quantify emissions, modeling parameters and assumptions, and the unique geographic characteristics of NSSA. It is recommended that field campaigns involving synergistic coordination of ground‐based, airborne, and satellite measurements with modeling be conducted in major and complex biomass burning regions such as the NSSA, and that significant improvements in the spatial and temporal resolutions of observation systems needed to reduce uncertainties in biomass burning characterization be seriously considered in future satellite missions

Fire and Smoke Remote Sensing and Modeling Uncertainties: Case Studies in Northern Sub‐Saharan Africa

Significant uncertainties are incurred in deriving various quantities related to biomass burning from satellite measurements at different scales, and, in general, the coarser the resolution of observation the larger the uncertainty. WRF‐Chem model simulations of smoke over the northern sub‐Saharan African (NSSA) region for January–February 2010, using fire energetics and emissions research version 1.0 (FEERv1) aerosol emissions derived from MODIS measurements of fire radiative power (FRP) and aerosol optical depth (AOD), resulted in a severe model underestimation of AOD compared with satellite retrievals. Such uncertainties are attributable to three major factors: limitations in the spatial and temporal resolutions of the satellite observations used to quantify emissions, modeling parameters and assumptions, and the unique geographic characteristics of NSSA. It is recommended that field campaigns involving synergistic coordination of ground‐based, airborne, and satellite measurements with modeling be conducted in major and complex biomass burning regions such as the NSSA, and that significant improvements in the spatial and temporal resolutions of observation systems needed to reduce uncertainties in biomass burning characterization be seriously considered in future satellite missions

Atmospheric remote sensing and radiopropagation: from numerical modeling to spaceborne and terrestrial applications

The remote sensing of electromagnetic wave properties is probably the most viable and fascinating way to observe and study physical media, comprising our planet and its atmosphere, at the same time ensuring a proper continuity in the observations. Applications are manifold and the scientific community has been importantly studying and investing on new technologies, which would let us widen our knowledge of what surrounds us. This thesis aims at showing some novel techniques and corresponding applications in the field of the atmospheric remote sensing and radio-propagation, at both microwave and optical wavelengths. The novel Sun-tracking microwave radiometry technique is shown. The antenna noise temperature of a ground-based microwave radiometer is measured by alternately pointing toward-the-Sun and off-the-Sun while tracking it along its diurnal ecliptic. During clear sky the brightness temperature of the Sun disk emission at K and Ka frequency bands and in the under-explored millimeter-wave V and W bands can be estimated by adopting different techniques. Parametric prediction models for retrieving all-weather atmospheric extinction from ground-based microwave radiometers are tested and their accuracy evaluated. Moreover, a characterization of suspended clouds in terms of atmospheric path attenuation is presented, by exploiting a stochastic approach used to model the time evolution of the cloud contribution. A model chain for the prediction of the tropospheric channel for the downlink of interplanetary missions operating above Ku band is proposed. On top of a detailed description of the approach, the chapter presents the validation results and examples of the model-chain online operation. Online operation has already been tested within a feasibility study applied to the BepiColombo mission to Mercury operated by the European Space Agency (ESA) and by exploiting the Hayabusa-2 mission Ka-band data by the Japan Aerospace Exploration Agency (JAXA), thanks to the ESA cross-support service. A preliminary (and successful) validation of the model-chain has been carried out by comparing the simulated signal-to-noise ratio with the one received from Hayabusa-2. At the next ITU World Radiocommunication Conference 2019, Agenda Item 1.13 will address the identification and the possible additional allocation of radio-frequency spectrum to serve the future development of systems supporting the fifth generation of cellular mobile communications (5G). The potential impact of International Mobile Telecommunications (IMT) deployments is shown in terms of received radio frequency interference by ESA’s telecommunication links. Received interference can derive from several radio-propagation mechanisms, which strongly depend on atmospheric conditions, radio frequency, link availability, distance and path topography; at any time a single mechanism, or more than one may be present. Results are shown in terms of required separation distances, i.e. the minimum distance between the earth station and the IMT station ensuring that the protection criteria for the earth station are met

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Flaring and pollution detection in the Niger Delta using Remote Sensing

Merged with duplicate record 10026.1/6553 on 28.02.2017 by CS (TIS)Abstract Through the Global Gas Flaring Reduction (GGFR) initiative a substantial amount of effort and international attention has been focused on the reduction of gas flaring since 2002 (Elvidge et al., 2009). Nigeria is rated as the second country in the world for gas flaring, after Russia. In an attempt to reduce and eliminate gas flaring the federal government of Nigeria has implemented a number of gas flaring reduction projects, but poor governmental regulatory policies have been mostly unsuccessful in phasing it out. This study examines the effects of pollution from gas flaring using multiple satellite based sensors (Landsat 5 TM and Landsat 7 ETM+) with a focus on vegetation health in the Niger Delta. Over 131 flaring sites in all 9 states (Abia, Akwa Ibom, Bayelsa, Cross Rivers, Delta, Edo, Imo, Ondo and Rivers) of the Niger Delta region have been identified, out of which 11 sites in Rivers State were examined using a case study approach. Land Surface Temperature data were derived using a novel procedure drawing in visible band information to mask out clouds and identify appropriate emissivity values for different land cover types. In 2503 out of 3001 Landsat subscenes analysed, Land Surface Temperature was elevated by at least 1 ℃ within 450 m of the flare. The results from fieldwork, carried out at the Eleme Refinery II Petroleum Company and Onne Flow Station, are compared to the Landsat 5 TM and Landsat 7 ETM+ data. Results indicate that Landsat data can detect gas flares and their associated pollution on vegetation health with acceptable accuracy for both Land Surface Temperature (range: 0.120 to 1.907 K) and Normalized Differential Vegetation Index (sd ± 0.004). Available environmental factors such as size of facility, height of stack, and time were considered. Finally, the assessment of the impact of pollution on a time series analysis (1984 to 2013) of vegetation health shows a decrease in NDVI annually within 120 m from the flare and that the spatio-temporal variability of NDVI for each site is influenced by local factors. This research demonstrated that only 5 % of the variability in δLST and only 12 % of the variability in δNDVI, with distance from the flare stack, could be accounted for by the available variables considered in this study. This suggests that other missing factors (the gas flaring volume and vegetation speciation) play a significant role in the variability in δLST and δNDVI respectively