69 research outputs found
Estimating Solar Energy Production in Urban Areas for Electric Vehicles
Cities have a high potential for solar energy from PVs installed on buildings\u27 rooftops. There is an increased demand for solar energy in cities to reduce the negative effect of climate change. The thesis investigates solar energy potential in urban areas. It tries to determine how to detect and identify available rooftop areas, how to calculate suitable ones after excluding the effects of the shade, and the estimated energy generated from PVs. Geographic Information Sciences (GIS) and Remote Sensing (RS) are used in solar city planning. The goal of this research is to assess available and suitable rooftops areas using different GIS and RS techniques for installing PVs and estimating solar energy production for a sample of six compounds in New Cairo, and explore how to map urban areas on the city scale.
In this research, the study area is the new Cairo city which has a high potential for harvesting solar energy, buildings in each compound have the same height, which does not cast shade on other buildings affecting PV efficiency. When applying GIS and RS techniques in New Cairo city, it is found that environmental factors - such as bare soil - affect the accuracy of the result, which reached 67% on the city scale. Researching more minor scales, such as compounds, required Very High Resolution (VHR) satellite images with a spatial resolution of up to 0.5 meter. The RS techniques applied in this research included supervised classification, and feature extraction, on Pleiades-1b VHR. On the compound scale, the accuracy assessment for the samples ranged between 74.6% and 96.875%.
Estimating the PV energy production requires solar data; which was collected using a weather station and a pyrometer at the American University in Cairo, which is typical of the neighboring compounds in the new Cairo region. It took three years to collect the solar incidence data. The Hay- Devis, Klucher, and Reindl (HDKR) model is then employed to extrapolate the solar radiation measured on horizontal surfaces β =0°, to that on tilted surfaces with inclination angles β =10°, 20°, 30° and 45°. The calculated (with help of GIS and Solar radiation models) net rooftop area available for capturing solar radiation was determined for sample New Cairo compounds . The available rooftop areas were subject to the restriction that all the PVs would be coplanar, none of the PVs would protrude outside the rooftop boundaries, and no shading of PVs would occur at any time of the year; moreover typical other rooftop occupied areas, and actual dimensions of typical roof top PVs were taken into consideration. From those calculations, both the realistic total annual Electrical energy produced by the PVs and their daily monthly energy produced are deduced. The former is relevant if the PVs are tied to a grid, whereas the other is more relevant if it is not; optimization is different for both. Results were extended to estimate the total number of cars that may be driven off PV converted solar radiation per home, for different scenarios
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture
radar (TSX SC) images from November 2019 to March 2020,
covering the Multidisciplinary drifting Observatory for the
Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and
relatively high spatial resolution of TSX SC data and is a
useful basic dataset for future MOSAiC studies on physical
sea ice processes and ocean and climate modeling. Sea ice is
classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with
different degrees of deformation. We establish the per-class
incidence angle (IA) dependencies of TSX SC intensities
and gray-level co-occurrence matrix (GLCM) textures and
use a classifier that corrects for the class-specific decreasing
backscatter with increasing IAs, with both HH intensities and
textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while
maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier
are adjusted by Markov random field contextual smoothing
to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 %
and good correspondence to geometric ice surface roughness
derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive
logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of
classes representing ice openings (leads and young ice) show
prominent increases in middle to late November 2019 and
March 2020, corresponding well to ice-opening time series
derived from in situ data in this study and those derived from
satellite synthetic aperture radar (SAR) and optical data in
other MOSAiC studies
Land Surface Monitoring Based on Satellite Imagery
This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought
Modelling peatland water table depth using remotely sensed satellite data
Peatlands are carbon-rich wetland ecosystems and represent the largest terrestrial carbon store.
Although they are natural carbon sinks, damage, drainage and extraction over past decades have turned
peatlands into a global carbon source. To tackle this nearly irreversible loss, peatland conservation and
restoration projects on global and national levels have been increasing in numbers. High water table
depth (WTD) is a highly important factor that influences peatland condition, resilience and ability to
accumulate carbon. Given the extent of peatlands, a regular physical collection of data in situ, looking
forward, would be impractical and difficult to accomplish, and the development of a remote sensing
methods for peatland WTD monitoring would be highly beneficial.
The accessibility to satellite data along with advancements in sensors, both in variety - optical,
microwave, thermal, and their resolutions - spatial, spectral, and temporal, has greatly increased in the
last decade. Combined with advances in image processing using cloud computing and machine learning,
it has made it easier to access and process remotely sensed data. Synthetic aperture radar (SAR), with
its ability to provide data regardless of the weather, has emerged as an important source of data for
environmental applications.
This project aimed to advance the usage of remotely sensed SAR data to predict peatland water
table depth. First, a unique high resolution laboratory study was completed confirming SAR backscatter
sensitivity to changes in peatland soil moisture and water table depth. This was followed by a case study
for the Forsinard Flows area, where Sentinel-1 SAR data were used to build and test three models of
different complexity for WTD prediction. The random forest model was found to be the most suited
with an overall good temporal fit, highest correlation scores and lowest RMSE values. The model was
later tested on a wider Peatland ACTION dataset, reaching an even higher score, affirming its
applicability to peatlands in various conditions (near natural, degraded and undergoing restoration). In
the final section of the thesis, up to twenty year-long time series of remote sensing data were analysed
to investigate trends and change points in peatland restoration areas. The trends found using lower
resolution satellite data from MODIS gave mixed results and would only be indicative of very abrupt
changes, such as tree felling. The trends from the modelled WTD series based on Sentinel-1 data were
indicative of positive trajectories towards higher WTD, following restoration.
The results from this thesis suggest that remotely sensed data can be informative about changes
in the WTD and overall peatland condition, can be used to look at seasonal change, and can be indicative
of restoration progress and response to droughts. Recent studies have shown a close link between
greenhouse gasses and peatland WTD, therefore, if methods of predicting WTD based on remotely
sensed data are developed further, they ultimately could be used as a proxy for greenhouse gas emission
reporting
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Remote Sensing of Savannas and Woodlands
Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome
EVOLUTION OF THE SUBCONTINENTAL LITHOSPHERE DURING MESOZOIC TETHYAN RIFTING: CONSTRAINTS FROM THE EXTERNAL LIGURIAN MANTLE SECTION (NORTHERN APENNINE, ITALY)
Our study is focussed on mantle bodies from the External Ligurian ophiolites, within the Monte Gavi and Monte Sant'Agostino areas. Here, two distinct pyroxenite-bearing mantle sections were recognized, mainly based on their plagioclase-facies evolution. The Monte Gavi mantle section is nearly undeformed and records reactive melt infiltration under plagioclase-facies conditions. This process involved both peridotites (clinopyroxene-poor lherzolites) and enclosed spinel pyroxenite layers, and occurred at 0.7–0.8 GPa. In the Monte Gavi peridotites and pyroxenites, the spinel-facies clinopyroxene was replaced by Ca-rich plagioclase and new orthopyroxene, typically associated with secondary clinopyroxene. The reactive melt migration caused increase of TiO2 contents in relict clinopyroxene and spinel, with the latter also recording a Cr2O3 increase. In the Monte Gavi peridotites and pyroxenites, geothermometers based on slowly diffusing elements (REE and Y) record high temperature conditions (1200-1250 °C) related to the melt infiltration event, followed by subsolidus cooling until ca. 900°C. The Monte Sant'Agostino mantle section is characterized by widespread ductile shearing with no evidence of melt infiltration. The deformation recorded by the Monte Sant'Agostino peridotites (clinopyroxene-rich lherzolites) occurred at 750–800 °C and 0.3–0.6 GPa, leading to protomylonitic to ultramylonitic textures with extreme grain size reduction (10–50 μm). Compared to the peridotites, the enclosed pyroxenite layers gave higher temperature-pressure estimates for the plagioclase-facies re-equilibration (870–930 °C and 0.8–0.9 GPa). We propose that the earlier plagioclase crystallization in the pyroxenites enhanced strain localization and formation of mylonite shear zones in the entire mantle section. We subdivide the subcontinental mantle section from the External Ligurian ophiolites into three distinct domains, developed in response to the rifting evolution that ultimately formed a Middle Jurassic ocean-continent transition: (1) a spinel tectonite domain, characterized by subsolidus static formation of plagioclase, i.e. the Suvero mantle section (Hidas et al., 2020), (2) a plagioclase mylonite domain experiencing melt-absent deformation and (3) a nearly undeformed domain that underwent reactive melt infiltration under plagioclase-facies conditions, exemplified by the the Monte Sant'Agostino and the Monte Gavi mantle sections, respectively. We relate mantle domains (1) and (2) to a rifting-driven uplift in the late Triassic accommodated by large-scale shear zones consisting of anhydrous plagioclase mylonites.
Hidas K., Borghini G., Tommasi A., Zanetti A. & Rampone E. 2021. Interplay between melt infiltration and deformation in the deep lithospheric mantle (External Liguride ophiolite, North Italy). Lithos 380-381, 105855
Synergistic optical and microwave remote sensing approaches for soil moisture mapping at high resolution
Aplicat embargament des de la data de defensa fins al dia 1 d'octubre de 2022Soil moisture is an essential climate variable that plays a crucial role linking the Earth’s water, energy, and carbon cycles. It is responsible for the water exchange between the Earth’s surface and the atmosphere, and provides key information about soil evaporation, plant transpiration, and the allocation of precipitation into runoff, surface flow and infiltration. Therefore, an accurate estimation of soil moisture is needed to enhance our current climate and meteorological forecasting skills, and to improve our current understanding of the hydrological cycle and its extremes (e.g., droughts and floods). L-band Microwave passive and active sensors have been used during the last decades to estimate soil moisture, since there is a strong relationship between this variable and the soil dielectric properties.
Currently, there are two operational L-band missions specifically devoted to globally measure soil moisture: the ESA’s Soil Moisture and the Ocean Salinity (SMOS), launched in November 2009; and the NASA’s Soil Moisture Active Passive (SMAP), launched in January 2015. The spatial resolution of the SMOS and SMAP radiometers, in the order of tens of kilometers (~40 km), is adequate for global applications. However, to fulfill the needs of a growing number of applications at local or regional scale, higher spatial detail (< 1 km) is required. To bridge this gap and improve the spatial resolution of the soil moisture maps, a variety of spatial enhancement or spatial (sub-pixel) disaggregation approaches have been proposed.
This Ph.D. Thesis focuses on the study of the Earth’s surface soil moisture from remotely sensed observations. This work includes the implementation of several soil moisture retrieval techniques and the development, implementation, validation and comparison of different spatial enhancement or downscaling techniques, applied at local, regional, and continental scale. To meet these objectives, synergies between several active/passive microwave sensors (SMOS, SMAP and Sentinel-1) and optical/thermal sensors (MODIS) have been explored. The results are presented as follows:
- Spatially consistent downscaling approach for SMOS using an adaptive moving window
A passive microwave/optical downscaling algorithm for SMOS is proposed to obtain fine-scale soil moisture maps (1 km) from the native resolution (~40 km) of the instrument. This algorithm introduces the concept of a shape-adaptive window as a central improvement of the disaggregation technique presented by Piles et al. (2014), allowing its application at continental scales.
- Assessment of multi-scale SMOS and SMAP soil moisture products across the Iberian Peninsula
The temporal and spatial characteristics of SMOS and SMAP soil moisture products at coarse- and fine-scales are assessed in order to learn about their distinct features and the rationale behind them, tracing back to the physical assumptions they are based upon.
- Impact of incidence angle diversity on soil moisture retrievals at coarse and fine scales
An incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of radiative transfer effective parameters single scattering albedo and soil roughness has been carried out, highlighting the importance of such parameterization to accurately estimate soil moisture at coarse-resolution. Then, these parameterizations are used to examine the potential application of a physically-based active-passive downscaling approach to upcoming microwave missions, namely CIMR, ROSE-L and Sentinel-1 Next Generation. Soil moisture maps obtained for the Iberian Peninsula at the three different angles, and at coarse and fine scales are inter-compared using in situ measurements and model data as benchmarks.La humedad del suelo es una variable climática esencial que juega un papel crucial en la relación de los ciclos del agua, la energía y el carbono de la Tierra. Es responsable del intercambio de agua entre la superficie de la Tierra y la atmósfera, y proporciona información crucial sobre la evaporación del suelo, la transpiración de las plantas y la distribución de la precipitación en escorrentía, flujo superficial e infiltración. Por lo tanto, es necesaria una estimación precisa de la humedad del suelo para mejorar las predicciones climáticas y meteorológicas, y comprender mejor el ciclo hidrológico y sus extremos (v.g., sequías e inundaciones). Los sensores pasivos y activos en banda L se han usado durante las últimas décadas para estimar la humedad del suelo debido a la relación directa que existe entre esta variable y las propiedades dieléctricas del suelo.
Actualmente, hay dos misiones operativas en banda L específicamente dedicadas a medir la
humedad del suelo a escala global: la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA,
lanzada en noviembre de 2009; y la misión Soil Moisture Active Passive (SMAP) de la NASA,
lanzada en enero de 2015. La resolución espacial de los radiómetros SMOS y SMAP, del orden de unas decenas de kilómetros (~40 km), es adecuada para aplicaciones a escala global. Sin embargo, para satisfacer las necesidades de un número creciente de aplicaciones a escala local o regional, se requiere más detalle espacial (<1 km). Para solventar esta limitación y mejorar la resolución espacial de los mapas de humedad, se han propuesto diferentes técnicas de mejora o desagregación espacial.
Esta Tesis se centra en el estudio de la humedad de la superficie terrestre a partir de datos
obtenidos a través de teledetección. Este trabajo incluye la implementación de distintos
algoritmos de recuperación de la humedad del suelo y el desarrollo, implementación, validación y comparación de distintas técnicas de desagregación, aplicadas a escala local, regional y continental. Para cumplir estos objetivos, se han explorado sinergias entre diferentes sensores de microondas activos/pasivos (SMOS, SMAP y Sentinel-1) y sensores ópticos/térmicos. Los resultados se presentan de la siguiente manera:
- Técnica de desagregación espacialmente consistente, basada en una ventana móvil
adaptativa, aplicada a los datos SMOS
Se propone un algoritmo de desagregación del píxel basado en datos obtenidos de medidas
radiométricas de microondas en banda L y datos ópticos, para mejorar la resolución espacial de
los mapas de humedad del suelo desde la resolución nativa del instrumento (~40 km) hasta
resoluciones de 1 km. El algoritmo introduce el concepto de una ventana de contorno
adaptativo, como mejora principal sobre la técnica de desagregación presentada en Piles et al. (2014), permitiendo su implementación a escala continental.
- Análisis multiescalar de productos de humedad del suelo SMAP y SMOS sobre la
Península Ibérica Se han evaluado las características temporales y espaciales de distintos productos de humedad del suelo SMOS y SMAP, a baja y a alta resolución, para conocer sus características distintivas y comprender las razones de sus diferencias. Para ello, ha sido necesario rastrear los supuestos físicos en los que se basan.
- Impacto del ángulo de incidencia en la recuperación de la humedad del suelo a baja y a
alta resolución
Se ha llevado a cabo una calibración adaptada al ángulo de incidencia (32.5°, 42.5° y 52.5°)
de los parámetros efectivos, albedo de dispersión simple y rugosidad del suelo, descritos en el modelo de transferencia radiativa � − �, incidiendo en la importancia de esta parametrización para estimar la humedad del suelo de forma precisa a baja resolución. El resultado de las mismas se ha utilizado para estudiar la potencial aplicación de un algoritmo activo/pasivo de desagregación basado en la física para las próximas misiones de microondas, llamadas CIMR, ROSE-L y Sentinel-1 Next Generation. Los mapas de humedad recuperados a los tres ángulos de incidencia, tanto a baja como a alta resolución, se han obtenido para la Península Ibérica y se han comparado entre ellos usando como referencia mediciones de humedad in situ.Postprint (published version
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