The Land Surface Temperature (LST) is among the Essential Climatic Variables defined by the World Meteorological Organization as it enables the monitoring of climate change and in extension can provide an insight about its causes and effects in urban areas. By taking advantage of all the Landsat satellites with thermal sensor, starting from Landsat 4 up to the most recent Landsat 8, we can produce extensive LST timeseries spanning from 1982 to present on global scale. The RSLab has developed in 2017 an application that calculates Landsat LST on-the-fly with configurable emissivity sources. It is built upon Google cloud with the use of Google Earth Engine. It requires no data input, process power from the users or installation of third-party applications, making it very lightweight and fast. It uses a single channel algorithm LST calculation for the whole Landsat archive. USGS Landsat Collection 2 surface temperature product was made available in December 2020 after the initial provisional product which covered only the United States. With the new product available we are able to further validate the RSLab LST within cities and compare it against the USGS one. Radiometers mounted on towers in the center of Heraklion and Basel are used to gather in-situ data for the LSTs assessment. The web application is accessible through: rslab.gr/downloads_LandsatLST.html
Resilience has become an important necessity for cities, particularly in the face of climate change. Mitigation and adaptation actions that enhance the resilience of cities need to be based on a sound understanding and quantification of the drivers of urban transformation and settlement structures, human and urban vulnerability, and of local and global climate change. Copernicus, as the means for the establishment of a European capacity for Earth Observation (EO), is based on continuously evolving Core Services. A major challenge for the EO community is the innovative exploitation of the Copernicus products in dealing with urban sustainability towards increasing urban resilience. Due to the multidimensional nature of urban resilience, to meet this challenge, information from more than one Copernicus Core Services, namely the Land Monitoring Service (CLMS), the Atmosphere Monitoring Service (CAMS), the Climate Change Service (C3S) and the Emergency Management Service (EMS), is needed. Furthermore, to address urban resilience, the urban planning community needs spatially disaggregated environmental information at local (neighbourhood) scale. Such information, for all parameters needed, is not yet directly available from the Copernicus Core Services mentioned above, while several elements - data and products - from contemporary satellite missions consist valuable tools for retrieving urban environmental parameters at local scale. The H2020-Space project CURE (Copernicus for Urban Resilience in Europe) is a joint effort of 10 partners from 9 countries that synergistically exploits the above Copernicus Core Services to develop an umbrella cross-cutting application for urban resilience, consisting of individual cross-cutting applications for climate change adaptation/mitigation, energy and economy, as well as healthy cities and social environments, at several European cities. These cross-cutting applications cope with the required scale and granularity by also integrating or exploiting third-party data, in-situ observations and modelling. CURE uses DIAS (Data and Information Access Services) to develop a system capable of supporting operational applications and downstream services across Europe. The CURE system hosts the developed cross-cutting applications, enabling its incorporation into operational services in the future. CURE is expected to increase the value of Copernicus Core Services for future emerging applications in the domain of urban resilience, exploiting also the improved data quality, coverage and revisit times of the future satellite missions. Thus, CURE will lead to more efficient routine urban planning activities with obvious socioeconomic impact, as well as to more efficient resilience planning activities related to climate change mitigation and adaptation, resulting in improved thermal comfort and air quality, as well as in enhanced energy efficiency. Specific CURE outcomes could be integrated into the operational Copernicus service portfolio. The added value and benefit expected to emerge from CURE is related to transformed urban governance and quality of life, because it is expected to provide improved and integrated information to city administrators, hence effectively supporting strategies for resilience planning at local and city scales, towards the implementation of the Sustainable Development Goals and the New Urban Agenda for Europe.
Detailed mapping of urban surfaces is one of the most challenging tasks in remote sensing due to the three-dimensional structure of cities, spatial diversity, and material spectral variability. Satellite urban applications demand better spatial, spectral, and temporal resolution, although there are strict technical constraints among them. Therefore, the development of sophisticated methods that exploit both high spectral and spatial data sources becomes necessary. A hierarchical multiple endmember spectral mixture analysis (MESMA) approach is developed and applied on Sentinel-2 imagery for the detailed quantification of the urban land cover, taking advantage of Worldview-2 high spatial resolution. The case study is the urban and peri-urban area of Heraklion, Greece. The area to point regression kriging (ATPRK) method is applied to downscale Sentinel-2 bands from 10 and 20 m to 2 m (WorldView-2 spatial resolution) and create a spectral library (SL) of urban materials, which contain 180 separate spectra. The urban SL is then used in the developed hierarchical MESMA approach to estimate the abundances of 11 urban land cover classes based on the original Sentinel-2 image. The estimated land cover fractions validate against a very high-resolution (1 m) land cover map of the area. It is proved that the complexity of the urban land cover can be efficiently investigated by the proposed methodology. Error analysis shows good accuracy of the results in all estimated class fractions. Moreover, the good validation results lead to the conclusion that ATPRK fusion algorithm between Sentinel-2 and WorldView-2 bands produced reliable urban material spectra, capable for advanced spectral analysis on Sentinel-2 imagery. The developed methodology is easily transferable to other cities since it is based exclusively on earth observation data and is suitable for multiple urban applications related to urban climate, urban sprawl, and urban regeneration.
Besides new economical, managerial and social challenges associated with growing cities, the modifications caused in the energy budget of the urban surface intensifies the existing urban heat island (UHI). UHI can vary temporally and spatially according to meteorological conditions, landscape and urban typologies. Urban cover and form, as well as anthropogenic activities, pose an important effect on the city’s thermal behaviour that influence UHI and therefore the quality of life of the citizens. In this study, we focus on quantifying the air temperature spatiotemporal patterns across the urban and peri-urban area of Heraklion, Greece at a grid of 100 m x 100 m cells. We use point air temperature observations from the Wireless Sensors Network of Heraklion and interpolate spatially by means of sophisticated geostatistical modelling parameterized with satellite derived predictors. Regression kriging interpolation technique is implemented over the study area, using different predictors to minimize the uncertainty in air temperature estimation. We deal for multicollinearity between predictors and spatio-temporal correlations between measurements. A maximum magnitude of UHI ~ 4 oC has been observed between 04:00-05:00 (UTC+3). Cross-validations indicate a mean MAE ~0.86 oC in the estimated air temperature maps.
Nektarios Chrysoulakis, Mattia Marconcini, Jean-Philippe Gastellu-Etchegorry, C.S.B. Grimmond, Christian Feigenwinter, Fredrik Lindberg, Fabio Del Frate, Judith Klostermann, Zina Mitraka, Thomas Esch, Lucas Landier, Andy Gabey, Eberhard Parlow, Frans Olofson
H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. This will lead to the development of tools and strategies to mitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from the UEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisit times and increase the value of EO data for scientific work and future emerging applications. These observations can reveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budget fluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity for space-borne observations to enable the development of operational services in the field of urban environmental monitoring and energy efficiency in cities.
Atmospheric correction is the process to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects (Scattering and Absorption). The process determines the optical characteristics of the atmosphere and then applies it in order to correct the atmospheric effects on satellite images. Two main categories of atmospheric correction methods can be identified, the ones that rely on radiative transfer modeling and the image-based ones. In this study, four methods are compared, three physically-based (6S, FLAASH, Sen2Cor) and one image-based (DOS) for their effectiveness on atmospheric correction of Sentinel-2 high resolution optical imagery. A Sentinel-2 image, acquired on a clear day over Heraklion, Greece was used. Ancillary information on the aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used for the physically based methods. In line with similar studies using Landsat images, the physically based methods perform better than the image-based ones also for the Sentinel-2 imagery. Nevertheless, their high computational demand and the need for ancillary atmospheric information makes them difficult to apply. Different atmospheric correction methods showed different results for specific land cover types, suggesting that the selection of the suitable method is also application dependent.
KEYWORDS: Data modeling, Vegetation, Atmospheric modeling, Floods, Visualization, Visual process modeling, RGB color model, Web services, Satellites, Meteorology
Wildfires in forests and forested areas in South Europe, North America, Central Asia and Australia are a diachronic threat with crucial ecological, economic and social impacts. Last decade the frequency, the magnitude and the intensity of fires have increased even more because of the climate change. An efficient response to such disasters requires an effective planning, with an early detection system of the ignition area and an accurate prediction of fire propagation to support the rapid response mechanisms. For this reason, information systems able to predict and visualize the behavior of fires, are valuable tools for fire fighting. Such systems, able also to perform simulations that evaluate the fire development scenarios, based on weather conditions, become valuable Decision Support Tools for fire mitigation planning. A Web-based Information System (WIS) developed in the framework of the FLIRE (Floods and fire risk assessment and management) project, a LIFE+ co-funded by the European Commission research, is presented in this study. The FLIRE WIS use forest fuel maps which have been developed by using generalized fuel maps, satellite data and in-situ observations. Furthermore, it leverages data from meteorological stations and weather forecast from numerical models to feed the fire propagation model with the necessary for the simulations inputs and to visualize the model’s results for user defined time periods and steps. The user has real-time access to FLIRE WIS via any web browser from any platform (PC, Laptop, Tablet, Smartphone).
The land surface albedo is among the most important parameters controlling the atmospheric radiation fluxes and the surface–atmosphere interactions. In the present study, surface albedo parameters and aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, onboard NASA’s Terra and Aqua satellites, were analyzed and processed for the estimation of the shortwave surface albedo over Europe, Northern Africa and the Middle East, at 1 km × 1 km spatial resolution and on an 8–day average basis, for the period 2001–2012. The surface albedo was computed as a linear combination of black-sky and white-sky albedos. This methodology allows the computation of surface albedo for different values of AOT and solar zenith angle (SZA). MODIS Level 3 AOT data were used in the computations, while the surface albedo was calculated as an average of albedo values, using different SZAs on a pixel basis. The final albedo product was analyzed in terms of spatial and seasonal characteristics, and inter– annual trends, during the period examined. A strong dependency of the albedo on land cover type was found, as it was expected. The results also revealed substantial spatiotemporal variability of the surface albedo in the area examined, highlighting the great potential of satellite remote sensing in supporting climate change related studies, at both local and regional scales.
Knowledge of the air and land surface temperature and their temporal and spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions and to monitoring environmental changes due to urbanization. We present a number of air and land surface temperature products that have been produced, archived, evaluated, and analyzed for 10 European cities within the framework of the European Space Agency-funded "Urban Heat Islands and Urban Thermography" project. We evaluate in what way these products are suited to explore the urban thermal dynamics and how products with different temporal and spatial resolution can provide a complementary view, both for thermal patterns as well as heat waves. Level of confidence was evaluated through quantitative, qualitative, and user-based analyses.
Research indicates that aerosol optical thickness (AOT) values and particulate matter (PM10) measurements can be used as indicators of atmospheric pollution. The problem of relating AOT with suspended particulate matter near the ground is still an open question. While satellite images can provide reliable and synoptic measurements from space, comparisons with monitoring surface level air pollution continues to be a challenge since satellite measurements are column integrated quantities. In this study, in-situ spectroradiometric measurements were taken during satellite overpass using field spectrometers to obtain the reflectance values of the calibration targets used. Sun photometer measurements were taken with the Microtops hand-held sun photometer to measure AOT. Meteorological data was collected from nearby meteorological stations and PM10 measurements were collected from local mobile air pollution stations. Following, the darkest pixel method of atmospheric correction was applied to a series of Landsat satellite images. The reflectance values of the atmospherically-corrected image were used in the radiative transfer equation to solve for AOT. Thematic maps were generated in order to develop air quality indices. The image-derived AOT values were examined for a positive correlation with PM10 measurements. It appears there exists a significant correlation between AOT and PM10 measurements.
Pseudo-invariant targets are often used for atmospheric correction, as their reflectance values are stable across time. Sand is often used as a pseudo-invariant target, although there is conflicting research about its effectiveness as a pseudo invariant target. This study will examine the effectiveness of volcanic sand as a pseudo-invariant target. The study area is a 250x250 meter area of volcanic beach sand near Limassol, Cyprus. In-situ spectroradiometric measurements were taken using field spectrometers to obtain the reflectance values of volcanic sand over wet and dry conditions. The varying saturation levels of the sand due to rainfall, humidity and high temperatures was considered. A series of Landsat-5 TM and Landsat-7 ETM+ satellite imagery were atmospherically corrected using the darkest pixel method in order to assess the effectiveness of the volcanic sand as a pseudo-invariant target. The mean in-situ in-band reflectance values as found from the ground measurements were compared with the at-satellite reflectance values following atmospheric correction. It was found that precipitation conditions such as rainfall affected the reflectance values of sand. The study found that wet sand had a significantly lower reflectance value compared to dry sand. Further, salinization also affected the reflectance value of volcanic sand. Therefore, precipitation conditions need to be considered when using sand as a non-variant target for atmospheric correction.
This paper presents a comparison of the darkest pixel (DP) and empirical line (EL) atmospheric correction methods in
order to examine their effectiveness to retrieve aerosol optical thickness (AOT) using the radiative transfer (RT)
equations. Research has found that the DP and the EL methods are the two simplest and most effective methods of
atmospheric correction; however, which of the two atmospheric correction methods is more effective in deriving
accurate AOT values remains an open question. The accuracy of the DP and EL atmospheric correction methods were
examined using pseudo-invariant targets in the urban area of Limassol in Cyprus, by using reflectance values before and
after atmospheric correction. Eleven Landsat 5 and Landsat 7 satellite images were atmospherically corrected using both
the DP and EL methods. The reflectance values following the DP and EL method of atmospheric correction were used in
the radiative transfer equation to derive the AOT values. Following, an accuracy assessment was conducted comparing
the in-situ AOT values as measured from sun photometers with the AOT values derived from the RT equations in order
to determine the effectiveness of the DP and EL methods for retrieving AOT. The study found that the EL atmospheric
correction method provided more accurate AOT values than the DP method.
The problem of atmospheric intervention has received considerable attention from researchers in remote sensing who have developed a range of methods, either simple or sophisticated. The sophisticated methods require auxiliary information about the state of the atmosphere which is obtained either from standard databases or from simultaneous in-situ field measurements or by iterative techniques. It has been found that the darkest pixel atmospheric correction (DP) is one of the most effective atmospheric correction methods especially for visible spectral bands. The DP is the simplest and fully image-based correction method. The integrated use of the DP basic theory and the radiative transfer equation is implemented in this study. Indeed, this leads to the development of the proposed 'image-based atmospheric correction algorithm.' The proposed algorithm retrieves the aerosol optical thickness (AOT) only for areas with urban and maritime aerosols. The effectiveness of this algorithm is assessed by comparing the AOT values retrieved from the proposed 'image-based atmospheric correction algorithm' after applied to Landsat TM/ETM+ images with those measured in-situ both from MICROTOPS II hand-held sun photometer and the CIMEL sun photometer (AERONET). It has been found that the AOT values retrieved from the proposed algorithm were very close with those measured from the CIMEL sun photometer for the Limassol area in Cyprus.
It has been shown by Hadjimitsis et al. (2009) that the use of suitable non-variant targets in conjunction with the
application of the empirical line method can remove atmospheric effects from satellite images effectively. The method is
based on the selection of a number of suitable generic non-variant targets, on the basis that they are large, distinctive in
shape, and occur in many geographical areas. The need to further test such method by suggesting more suitable nonvariant
targets is one of the main aims of this study. Indeed, six targets have been already identified in the Lemesos
District area in Cyprus, near the harbour and tested. In-situ spectro-radiometric measurements using the SVC HR-1024
field spectro-radiometer have been made on November 2009 and from February 2010 to April 2010. Some of the in-situ
measurements were coincided with the Landsat TM/ETM+ overpass and the removal of atmospheric effects was very
effective. The above targets have been scanned using a 3D terrestrial laser scanner (Leica ScanStation C10) so as to
investigate the non-variability and uniformity of the proposed targets (through the laser scanner intensity values).
Atmospheric correction is still considered as the most important part of pre-processing of satellite remotely sensed
images. The accuracy assessment of the existing atmospheric correction must be monitored on a systematic basis since
the user must be aware about the effectiveness of each algorithm intended for specific application. Indeed this study
integrates the following measurements coincided with the satellite overpass (ASTER and Landsat TM/ETM+) in order to
assess the accuracy of the most widely used atmospheric correction algorithms (such as darkest pixel, atmospheric
modelling, ATCOR, 6S code etc.): spectroradiometric measurements of suitable calibration targets using GER1500 or
SVC HR-1024 field spectro-radiometers, MICROTOPS hand held sun-photometers, LIDAR backscattering system,
CIMEL sun photometer (Cyprus University of Technology recently joined with AERONET).
The change detection and analysis, as remote sensing application, is based on the multi-temporal and/or multi-sensors
approaches. However, the accuracy of such change detection activities can be limited by several factors. A key variable
that may reduce the accuracy of change detection is the misregistration error between the used images. By accounting for
the spatial variation in geometric and misregistration errors there is the potential to reduce their effects during change
detection, increasing the accuracy of land cover change mapping. The effect of misregistration on land cover mapping
and change detection could be more accurately predicted and ultimately removed if this spatial variation in error was
modeled. In this study, the estimation of the effect of misregistration on ASTER derived land cover types was attempt.
The proposed methodology is based on the comparison of the regression correlation coefficients between two images
derived either from one single band or from two bands. To check the level of correlation, a procedure of modifying the
geometric position of one single band or of two different bands, using the same resolution or different resolutions was
applied. In order to obtain this artificial degradation, a transformation on three directions: on x axis, on y axis and on
both x and y axis of one image comparing with itself or with another was applied. The study was performed for different
scales, different land cover types and different complexity to evaluate the most influencing factors. This approach
allowed quantification of the inappropriate image georeferencing, as well as the quantitative estimation of the size of
distortion of the final results, in case of comparison of images from different dates and different sensors.
The 1:50.000 topographic maps present a nominal horizontal accuracy of 20 meters and a nominal vertical accuracy of 10 meters with 90% confidence. The data were in most cases extracted with photogrammetric techniques from aerial stereo-photographs during the 80's. The usual update rate for these maps ranges from ten to twenty years. The Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) offers along-track stereoscopic viewing capability. Its viewing geometry is suitable for DEM generation even without the use of ground control points. Recent studies have proved that in this case the vertical accuracy of DEM is about 20m with 95% confidence. The horizontal geolocation accuracy appears to be limited by the spacecraft position accuracy which is considered to be better than 50 m. Other studies have shown that the use of GCP's resulted in a plannimetric accuracy of 15 m and in a near pixel size vertical accuracy. The Shuttle Radar Topography Mission (SRTM), used an Interferometric Synthetic Aperture Radar (IFSAR) instrument to produce a near-global digital elevation map of the Earth's land surface with 16 m absolute vertical height accuracy at 30 meter postings. An SRTM 3-arc-second product (90m resolution) is available for the entire world. In this paper we examine the possibility of updating the 1:50.000 topographic maps using ASTER and SRTM DEMs. The area of study is the broader area of Athens, Greece. Presupposing, that the horizontal and vertical accuracy of the ASTER and SRTM DEM is similar to the relative accuracies of the DEM from digitized contours, optical comparison of the DEMs and statistical analysis (difference, correlation) can immediately prove if there is any need for update to the topographic maps. A DEM from digitized contours from the 1:50.000 topographic maps was created and compared with ASTER and SRTM derived DEMs. Almost three hundreds points of known elevation have been used to estimate the accuracy of these three DEMs. The resulted accuracy of the SRTM and ASTER derived DEMs was satisfactory, therefore they are considered as suitable for updating 1:50.000 topographic maps.
Digital Elevation Models (DEMs) and land cover products are primary inputs for hydrologic models of surface runoff that affects infiltration, erosion, and evapotranspiration. DEM and land cover play important role in determining the runoff characteristics of specific catchment areas. Recently, at local level, a number of data sources have been used to derive land cover products for high resolution studies. These studies have been carried out for a number of different applications, including estimation of biomass and vegetation mapping. A hydrologic land cover classification includes information not only about vegetation species, but also about the land surface and what classes are important hydrologically. This kind of classification must therefore incorporate information on elevation, slope, aspect, surface roughness, as well as vegetation species derived from satellite added-value products. The main problems when generating hydrologic land cover maps is the lack of accurate DEMs and the confusion of spectral responses from different features. In this study, a Terra/ASTER image acquired over the region of Heraklion, Crete, Greece was used. ASTER stereo imagery is used for DEM production because it gives a strong advantage in terms of radiometric variations versus the multi-date stereo-data acquisition with across-track stereo, which can then compensate for the weaker stereo geometry. GCPs (Ground Control Points) derived from differential GPS measurements were also used for absolute DEM production. A hydrologic land cover classification scheme was developed by combining ASTER multispectral imagery, ASTER DEM products and the spectral signatures derived from field observations at predefined training sites.
KEYWORDS: Satellites, Geographic information systems, Roads, Data modeling, Environmental monitoring, Flame detectors, Detection and tracking algorithms, Vegetation, Spatial resolution, RGB color model
In this study, the design and implementation of a GIS based tool for the support of technological risk management is presented. This Decision Support Tool is based on the detection and space-time monitoring of plumes caused by technological accidents, by integrating moderate and high-resolution satellite imagery and in-situ spatial data. The Advanced Very High Resolution (AVHRR) on board the NOAA satellites has been used for the detection of fire as well as for the detection and monitoring of plumes. The detection algorithms have been presented in previous studies for past accidents in Europe. AVHRR images, were adjusted to the broader area of Athens in order to develop a major technological accident scenario based on real plumes. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board the EOS AM-1 satellite has been used for the extraction of topographic information, land cover and spatial distribution of vegetation, as well as for the depiction of urban areas, road network and major industrial installations. The scenario was used to present the functionality of the developed GIS tool for the support of decision making during the crisis, as well as for the assessment of the accident's impact to natural and human environment.
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