Urban 1

Urban greenspaces are crucial for urban sustainability and resilience to environmental vulnerabilities. Yet in many cities in Africa, green spaces are marginalized in urban plans and their distribution uncertain.  This paper analyzed the spatio-temporal change in green cover, examined its composition and extent and highlighted possible distributional inequities associated with greenspaces in Kumasi, Ghana. Spatio-temporal dynamics were analyzed using GIS and remote sensing techniques whereas distributional equity at the submetro level was assessed with multiple approaches including GINI sustainability index.

Greenspace area decline in the last five years (2009-2014) was 3.5 times greater than that of the first 15 years since 1986. Shannon entropy for built-up sprawl for 1986 and 2014 were respectively 0.8 and 0.99 with high built-up density proportion of of at least 0.75 (1986) and 0.62 (2014) respectively spreading to 4 km and 8 km from the city center. Mean per capita green space was 25 m² and ranged between 6 m² in Tafo and 52 m² in Oforikrom. Housing quality index, a surrogate of socioeconomic standards of living, had a somewhat weak relationship with per capita greenspace area for 2009 (r² = 0.50, p=0.049) and 2014 (r² = 0.53, p=0.0398) at the submetro level. A GINI coefficient of 0.26 was estimated for each of vegetation and tree cover distribution in Kumasi. At least 8 green spaces were identified with home gardens accounting for roughly 50% of vegetation cover in the city.

These findings are essential to government officials, traditional leaders, urban planners, and private individuals as we seek solutions to current and potential future urban vulnerabilities to climate change and other environmental setbacks. Ample availability and strategic planning for greenspaces can ensure equity in access and minimize the impact of climate related vulnerabilities such floods, extreme heat, diseases, and food insecurity.  

The recently launched 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). Temperatures in cities are predicted to rise even more in the future, resulting in increased energy demand for cooling systems in low and mid-latitude cities, modifying UEB. A positive feedback cycle occurs in many urban areas, where higher temperatures result in more energy being used for cooling, which in turn adds to heat emissions and increases temperatures further during periods with increased heat wave risk. It is expected that without mitigation measures, energy demand will continue to increase during the warmest months. 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. A dense network of conventional meteorological stations is used in each case study city: London, Basel and Heraklion. EO data is initially analysed to map urban surface morphology and cover, whilst a new approach has been developed to define Local Climate Zones (LCZ). Using the LCZ as a framework, advanced EO-based methods are used to estimate UEB fluxes: a sophisticated radiative transfer model (Discrete Anisotropic Radiative Transfer) is employed to simulate the net all-wave radiation; the computation of the storage term is based on the Element Surface Temperature Method, supported by the auxiliary datasets; and the estimation of the turbulent heat fluxes is based on the Aerodynamic Resistance Method, supported by standard meteorological measurements. In-situ flux measurements (Eddy Covariance, scintilometry) and bottom-up approaches (inventories, building energy models) are used to evaluate URBANFLUXES outcomes, whereas uncertainties are specified and analysed. 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. It is therefore expected to prepare the ground for further innovative exploitation of European space data in scientific activities (climate variability studies at local and regional scales) and future and emerging applications (sustainable urban planning, mitigation technologies) to benefit climate change mitigation/adaptation and civil protection. 

Urbanization is a global phenomenon driven by multiple complex variables and with widespread impact over the health of the ecosystems. The fast pace at which urban development frequently takes place, calls for dynamic and flexible monitoring tools that can provide accurate and meaningful data in a timely manner to all stakeholders.

Earth Observation (EO) data can provide the necessary spatial and temporal resolution to address the challenges shaped by the rapid mutation of a patchwork of different land use and land cover classes within the urban and peri-urban space. Impervious Surface Area (ISA) is an objective metric and indicator of the spatial extent and intensity of urban development. Rooftops, roads, sidewalks, and compacted soil constitute examples of impervious surfaces. These can be responsible for important changes in the surface runoff, water quality indicators and overall ecological impacts over urbanized river catchments. Furthermore, ISA patterns can be correlated with relevant population and socio-economic variables.

As such, mapping and understanding the connectivity of these surfaces is a relevant task for the conservation of ecosystems and the management of the territory. In addition, emerging patterns leading to a reduction of the population residing in the city centers and the abandonment of former development loci are important aspects of the territorial dynamics of several European countries that must be monitored closely in the coming years.

The lack of high spatial and temporal resolution (1 year interval or less) ISA estimates for much of the world, including parts of Europe, motivated this work. A regional ISA estimate was generated for a test area consisting of a Portuguese watershed (Mondego river, covering an area with 6670 km²) through the data fusion of SAR (Sentinel-1A) and optical (Landsat-8 OLI) data. A single OLI scene was fused with a collocated VV/VH multi-date (6 consecutive acquisitions) corrected and rescaled Sentinel-1A product through Principal Component Analysis (PCA). A Regression Tree Model (RTM) was created using the resulting EO products and a representative high resolution ground truth sample digitized from satellite and aerial imagery (over 133 km² of calibration data and an additional 10 km² for validation purposes). The results clearly show that the introduction of Sentinel-1A data improves the model (Correlation Coefficient of 96.2% and Mean Absolute Error of 2.9%) while reducing the number of rules in the regression tree model (and thus conserving computing power) and the need for ancillary data.  Preliminary results also suggest the data used for ISA estimates can also be employed seamlessly as the input of Decision Tree Models used for the classification of discrete land use classes and a detailed characterization of development types.

Furthermore, in this work, we describe the SmartBasins project, under which a set of regional, but replicable EO datasets, including the aforementioned ISA flagship product, is under development. The relational outputs generated for the test area and based on the core ISA product are used to describe the platform and objectives of the project. The resulting products and visualizations will be made available, free of cost in a cross-platform environment, to the existing regional EO-communities and new users, through the development of tiered data outputs, adequate to different skill-levels and requirements. In the near future, Sentinel data (S1 and S2) will be at the core of the high temporal resolution land use and land cover products of the SmartBasins initiative. These shall be integrated with other critical data sources to support education, research, and the monitoring of environmental, social and economic variables at the watershed level from a holistic perspective.

Land Surface Temperature (LST) is a valuable source of for studying land surface processes and interactions with the atmosphere. Detailed, frequent and accurate LST mapping is essential for supporting various applications related to the urban climate and the urban heat island. ESA’s Sentinel-3 mission will soon provide frequent thermal measurement over large areas with the Sea and Land Surface Temperature Radiometer (SLSTR), which has a large swath. Its low spatial resolution though (1 km), makes it inappropriate for studies that need to assess the intra-urban variations of temperature.

In this study, we propose a method for downscaling the Sentinel-3 thermal measurements, using the surface cover information from Sentinel-2. Sentinel-2 provides frequent measurements in the visible and near infrared domain, which can be used to characterize the surface cover, and monitor its changes through time. The developed method employs hierarchical Bayesian models with spatial random effects for improving the spatial resolution of the Sentinel-3 thermal measurements. These models are estimated using integrated nested Laplace approximation, which is much faster than Markov Chain Monte Carlo. Downscaled thermal measurements combined with high resolution emissivity products (Mitraka et al. 2012) provide high resolution LST.

The proposed method is applied to both synthetic and Sentinel data. Two case studies are presented: the first in the Mediterranean area and the second in Phoenix, Arizona. The performance of the Bayesian methodology is evaluated against Tikhonov regularization, which was proposed by Zhukov et al. (1999). The initial results are promising and show the potential of the method to be used for time series LST maps estimation, which is essential for assessing the intra-urban thermal behaviour of cities. 

References:

Z. Mitraka, N. Chrysoulakis, Y. Kamarianakis, P. Partsinevelos, and A. Tsouchlaraki, “Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery,” Remote Sens. Environ., vol. 117, pp. 125–134, Feb. 2012.

B. Zhukov, D. Oertel, F. Lanzl, and G. Reinhackel, “Unmixing-based multisensor multiresolution image fusion,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1212–1226, May 1999.

Urban heat island (UHI) is becoming an extremely important phenomenon for inhabitants of cities and towns due to the growing threat of more frequent and longer lasting heat waves as a result of climate change. Multi-day periods of hot weather are particularly dangerous to health and comfort in urban areas because of increased heat storage and reduced cooling of the air at nights. Urban surfaces heat up very quickly during cloudless and calm summer days. Surface UHI (SUHI) is regarded as a measure of the overheating. SUHI may be estimated based on land surface temperature (LST) occurring in urban areas and their rural surroundings. Thermal infrared satellite remote sensing enables monitoring LST patterns which vary over time.

The aim of the study is to assess the dynamics of changes in urban surface thermal characteristics over the area of Krakow in 2002-2011. Krakow is the second most populous Polish city (about 0.76 mln inhabitants). Only days with maximum air temperature exceeding 30°C were considered in the study. Air temperature values recorded at the weather station located in vicinity of the city were used to select the days. For the selected days cloud-free satellite data were requested. The ENVISAT/AATSR LST product delivered by the ESA DUE Globtemperature Project (Ghent, 2012) and the LANDSAT/TM LST retrieved by the authors with the use of single-channel algorithm according to Jimenez-Munoz and Sobrino (2003) were applied to the analyses. Created LST maps are of different spatial and temporal resolution depending on the satellite data source. The 1-km LST maps are derived from the ENVISAT/AATSR data acquired over Poland two times a day, in late-morning and late-evening hours. The 120-m LST maps are derived from the LANDSAT/TM data acquired over Poland every 16 days in late-morning hours.

The LST maps were further processed in order to determine statistical characteristics such as areal mean and deviation values (in the whole area and land cover zones). The analyses of LST patterns variability over the years 2002-2011 were performed on the basis of the processing results. The multi-year changes in magnitude and extent of SUHI in Krakow were evaluated.

The presented study is an example of urban climate research conducted with the use of satellite remote sensing data to assess risk arising from synergy of heat waves and urban heat island for cities and towns all over the world.

References:

Ghent, D. (2012). Land Surface Temperature Validation and Algorithm Verification (Report to European Space Agency).

Jimenez-Munoz, J. C., and Sobrino, J. A. (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108 (D22), 4688, doi: 10.1029/2003JD003480.