ITS1.11/NP0.2 | Digitalisation processes of complex systems: Urban Geosciences and Geo-Health

ITS1.11/NP0.2

EDI
Digitalisation processes of complex systems: Urban Geosciences and Geo-Health
AGU and IUGG
Convener: Daniel Schertzer | Co-conveners: Son Ngo Thanh, Andrea ReimuthECSECS, Masatoshi Yamauchi, Klaus Fraedrich, Danlu CaiECSECS
Orals
| Wed, 26 Apr, 10:45–12:30 (CEST)
 
Room 0.94/95
Posters on site
| Attendance Wed, 26 Apr, 14:00–15:45 (CEST)
 
Hall X5
Posters virtual
| Attendance Wed, 26 Apr, 14:00–15:45 (CEST)
 
vHall ESSI/GI/NP
Orals |
Wed, 10:45
Wed, 14:00
Wed, 14:00
Far beyond the rocket science jargon, there has been a fast digitalisation of Urban Geosciences and Geo-Health. This is particularly illustrated by the almost immediate establishment of the Covid-19 database at the Johns Hopkins University Center for Systems Science and Engineering, which has enabled numerous studies of the environmental spread of the virus. Health threats are not limited to epidemics, as the recent spate of dramatic heatwaves, droughts, massive floods and resulting pollutions shows. It also includes ancient historical episodes like the demise of the ancient Maya culture or the abandoned settlements along the Silkroad. Geophysical databases, e.g. the EU Copernicus programme, are increasingly processing data relevant to Urban Geosciences and Geo-Health, especially at higher resolution.

However, there are scientific deadlocks: both Urban Geosciences and Geo-Health deal with complex systems that have strong interrelationships and common features. The Nobel Committee for Physics strongly emphasised, in awarding its 2021 prize, the fundamental roles of complexity and intermittency for geophysics and climate science, as well as the capacity of multiscale techniques to master them, notably multifractals.

In the line of the previous EGU sessions and great debates on Urban Geosciences and/or Geo-Health 2018, this ITS1 session welcomes data and/or theory driven studies dealing with Urban Geosciences and/or Geo-Health either at the methodological or original applications level.

Orals: Wed, 26 Apr | Room 0.94/95

Chairpersons: Daniel Schertzer, Andrea Reimuth, Klaus Fraedrich
Urban Geosciences and AI
10:45–10:55
|
EGU23-13357
|
ITS1.11/NP0.2
|
On-site presentation
Valerio Marsocci, Virginia Coletta, Roberta Ravanelli, Simone Scardapane, and Mattia Crespi

Keywords: Urban sustainability, Earth observation, 3D change detection, Deep Learning, Dataset

Nowadays, remote sensing products can provide useful and consistent information about urban areas and their morphological structures with different spatial and temporal resolutions, making it possible to perform long term spatiotemporal analyses of the historic development of the cities and in this way to monitor the evolution of their urbanization patterns, a goal strictly related to the United Nations (UN) Sustainable Development Goals (SDGs) concerning the sustainability of the cities (SDG 11 - Sustainable Cities and Communities).

In this context, Change Detection (CD) algorithms estimate the changes occurred at ground level and are employed in a wide range of applications, including the identification of urban changes. Most of the recently developed CD methodologies rely on deep learning architectures. Nevertheless, the CD algorithms currently available are mainly focused on generating two-dimensional (2D) change maps, where the planimetric extent of the areas affected by changes is identified without providing any information on the corresponding elevation (3D) variations. These algorithms can thus only identify planimetric changes such as appearing/disappearing buildings/trees, shrinking/expanding structures and are not able to satisfy the requirements of applications which need to detect and, most of all, to quantify the elevation variations occurred in the area of interest (AOI), such as the estimation of volumetric changes in urban areas.

It is therefore essential to develop CD algorithms capable of automatically generating an elevation (3D) CD map (a map containing the quantitative changes in elevation for the AOI) together with a standard 2D CD map, from the smallest possible amount of information. In this contribution, we will present the MultiTask Bitemporal Images Transformer (MTBIT) [1], a recently developed network, belonging to the family of vision Transformers and based on a semantic tokenizer, explicitly designed to solve the 2D and 3D CD tasks simultaneously from bitemporal optical images, and thus without the need to rely directly on elevation data during the inference phase. 

The MTBIT performances were evaluated in the urban area of Valladolid on the modified version of the 3DCD dataset [2], comparing this architecture with other networks designed to solve the 2D CD task. In particular, MTBIT reaches a metric accuracy equal to 6.46 m – the best performance among the compared architectures – with a limited number of parameters (13,1 M) [1].

The code and the 3DCD dataset are available at https://sites.google.com/uniroma1.it/3dchangedetection/home-page.

 

References

[1] Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., and Crespi, M.: Inferring 3D change detection from bitemporal optical images. ISPRS Journal of Photogrammetry and Remote Sensing. 2023 - in press.

[2] Coletta, V., Marsocci, V., and Ravanelli, R.: 3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 1349–1354, 2022.

How to cite: Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., and Crespi, M.: New trends in urban change detection: detecting 3D changes from bitemporal optical images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13357, https://doi.org/10.5194/egusphere-egu23-13357, 2023.

10:55–11:05
|
EGU23-16766
|
ITS1.11/NP0.2
|
ECS
|
On-site presentation
Rui Deng, Yanning Guan, Danlu Cai, Tao Yang, Klaus Fraedrich, Chunyan Zhang, Jiakui Tang, Zhouwei Liao, Zhishou Wei, and Shan Guo

To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised classification models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).

How to cite: Deng, R., Guan, Y., Cai, D., Yang, T., Fraedrich, K., Zhang, C., Tang, J., Liao, Z., Wei, Z., and Guo, S.: Supervised versus semi-supervised urban functional area prediction: uncertainty, robustness and sensitivity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16766, https://doi.org/10.5194/egusphere-egu23-16766, 2023.

11:05–11:15
|
EGU23-768
|
ITS1.11/NP0.2
|
ECS
|
Highlight
|
On-site presentation
Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Precipitation nowcasting (short-term forecasting ahead for 0 to 6 hours) is crucial for decision-making of weather-dependent industries in order to mitigate socio-economic impacts. Accurate and trustworthy precipitation nowcasting can serve as an early warning of massive floods, as well as a guide for water-related risk management. Although precipitation nowcasting is not a novel concept, it is challenging and complicated due to the extreme variability of precipitation. The traditional theory-driven numerical weather prediction (NWP) methods confront numerous obstacles, including an insufficient understanding of physical processes, enormous initial conditions impacts on predictions and requiring substantial computing resources. On the other hand, data-driven deep learning models establish a relationship between input and output data to predict future precipitation without taking into account the underlying physical processes. The framework of universal multifractal (UM) is also presented to describe the variability of precipitation nowcasting and compared to the radar observations. In this study, the convolutional long short-term memory (ConvLSTM) model is used to perform precipitation nowcasting over Metropolitan France. The study employs radar data collected every 5 minutes with a spatial resolution of 1km from Meteo-France. The preliminary results show that the structure of the field is reasonably forecast, as well as the somewhat moderate rain rates, but not the most intense ones. We discuss how to improve the methodology.

How to cite: Zhou, H., Schertzer, D., and Tchiguirinskaia, I.: Deep Learning and Universal Multifractal for Nowcasting Precipitation in Urban Geosciences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-768, https://doi.org/10.5194/egusphere-egu23-768, 2023.

Complex flows and floods
11:15–11:25
|
EGU23-6138
|
ITS1.11/NP0.2
|
solicited
|
Virtual presentation
|
Ali Ercan and M. Levent Kavvas

This presentation focuses on the governing equations of incompressible and compressible flow in fractional time and multi-fractional space as developed recently by the authors in DOI: 10.1038/s41598-022-20911-3. Mathematical differentiation has found many applications in real-life problems in the last two decades, before which it was mainly utilized by mathematicians and theoretical physicists. The proposed fractional governing equations for fluid flow may be interpreted as the general forms of the classical Navier–Stokes equations; as they reduce to the classical ones when integer values are replaced with their fractional powers in space and time. Due to their nonlocal structure, proposed governing equations in factional time/space can reflect the initial conditions for long times, and the boundary conditions for long distances. Results of numerical applications are presented for flow due to a wall suddenly set into motion. It is found that the proposed equations have the potential to model both sub-diffusive and super-diffusive flow cases.

How to cite: Ercan, A. and Kavvas, M. L.: Navier–Stokes equations in Fractional Time and Multi‑Fractional Space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6138, https://doi.org/10.5194/egusphere-egu23-6138, 2023.

11:25–11:35
|
EGU23-6276
|
ITS1.11/NP0.2
|
ECS
|
On-site presentation
Veronika Zwirglmaier and Matthias Garschagen

Limited knowledge on current and future processes as well as data scarcity pose a major challenge when it comes to the evaluation of adaptation strategies towards flooding. Current simulation approaches often lack the flexibility do deal with the inherit dynamics of future development (land use, urban growth, re-development of slum areas, infrastructure construction, etc.) in coastal cities and the resulting changes in flood hazard, exposure and vulnerability whilst facing a lack of sufficient data. Therefore, we developed a modelling approach which is able to integrate future dynamics in the three risk components, hazard, exposure and vulnerability under the uncertainties arising from lacking data as well as limited knowledge. We used Mumbai, India as a first case study to combine Urban Structure Types with Bayesian Networks (BN) and to assess pluvial flooding. BN structures are defined by process understanding supported by existing models, literature and expert evaluations. The quantification of the BNs is done by using urban structure types as proxies for relevant parameters/nodes where data is not available, like the distribution and capacity drainage infrastructure and its condition or the degree of imperviousness of certain areas. This is justified by the assumption that the appearance and the processes in urban structure types are similar. However, the probabilistic definition of nodes in a BN allows to account for the variability within an urban structure type class. As a first step, the approach was set up for the hazard component of risk. Here first results of the simulation of pluvial flooding are shown and validated against flood hotspots reported by the government of Mumbai. The simulation approach reproduced the flooding hotspots, however it has a great sensitivity towards certain parameters, especially towards the digital elevation model and the condition of the drainage infrastructure. In a next step BNs for multi-hazard evaluation and vulnerability assessment will be developed and linked, i.e. fluvial and coastal flooding as well as social vulnerability. The integration of different risk components and the flexibility of the approach help to assess the effect of individual and combinations of soft and hard adaptation measures on future flood risk.

How to cite: Zwirglmaier, V. and Garschagen, M.: Towards an integrated assessment of future flooding in dynamic and data-scarce urban environments by linking Urban Structure Types with Bayesian Network modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6276, https://doi.org/10.5194/egusphere-egu23-6276, 2023.

11:35–11:45
|
EGU23-13059
|
ITS1.11/NP0.2
|
ECS
|
On-site presentation
Jiachang Tu, Andrea Reimuth, Antje Katzschner, Liang Emlyn Yang, and Matthias Garschagen

Understanding how the exposure and vulnerability to floods and other climate hazard varies between different groups of urban residents is an urgent prerequisite for guiding urban climate change adaptation policy and action. To be most effective, adaptation measures need to be designed specifically in relation the exposure and vulnerability profiles of different groups. Index approaches have since long been used to cluster households according to different levels of exposure and vulnerability. However, two main gaps remain in current research: First, while indices are typically based on either survey or statistical data, approaches transcending both levels of resolution through proxy variables are rare. Second, profiling is typically not linked to spatial categories such as urban morphology types.

The approach presented here contributes to bridging both gaps. We use original household survey data from Ho Chi Minh City to generate an exposure and vulnerability index for the city. We then test the validity of that index, which is based on detailed data, in comparison to an index which builds on rougher statistical data. In a third step, we test how well vulnerability and exposure profiles from the index map against urban morphology types which can be used in risk modeling in order to analyse in how far valid exposure and vulnerability profiles can be linked to such morphology types. Our results show an existing yet limited link between exposure and vulnerability profiles on the one side and urban morphology types on the other. The results are essential for advancing urban risk modeling in an integrated manner and rolling such modeling out to larger spatial areas.

How to cite: Tu, J., Reimuth, A., Katzschner, A., Yang, L. E., and Garschagen, M.: Profiling households through a combined vulnerability and flood exposure index in Ho Chi Minh City, Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13059, https://doi.org/10.5194/egusphere-egu23-13059, 2023.

Geo-Health and NBS
11:45–11:55
|
EGU23-2994
|
ITS1.11/NP0.2
|
ECS
|
Highlight
|
On-site presentation
Shi Yin, Junyi Hua, Chao Ren, Benoit Guénard, Runxi Wang, André Ibáñez Weemaels, Yuan Shi, Tsz-Cheung Lee, Hsiang-Yu Yuan, Marc Ka-chun Chong, and Linwei Tian

Dengue fever is a mosquito-borne disease caused by the dengue virus bringing huge health burdens in tropical regions. With global warming, rapid urbanization, and mosquito species introductions, the range of dengue fever is expected to expand to subtropical regions and increase potential health risks for local populations. To reduce dengue fever transmission, relevant risk map is one of the most effective tools for public health management. Though there is abundant literature about mapping the dengue fever risks in endemic regions, few studies in contrast have investigated dengue fever risks for non-endemic regions; hindering the development of preparedness planning.

In this study, the spatial hazard-exposure-vulnerability assessment framework proposed by the Intergovernmental Panel on Climate Change was applied in to detect the dengue fever risk in Hong Kong, which is a typical high-density city located within a subtropical region. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, a mosquito species common in Hong Kong and proxy for the potential dengue fever hazard, was predicted using MaxEnt models relying on the surveillance data and a list of variables related to urban morphology, landscape, land utilization, and local climate. Secondly, the bivariate local Moran’s I was measured to identify urban areas with both high dengue hazard and high human population exposure. Then, vulnerable groups among the human population were identified from the 2016 Hong Kong census data. Finally, dengue risks were assessed at the community scale by overlapping the results of hazard, exposure, and vulnerability analysis.

In the optimal MaxEnt model predicting the presence possibility of Aedes albopictus, the normalized difference vegetation index, frontal area index, and the aggregation index of public residential land ranked the top three among all predictors, with permutation importance of 31.8%, 22.8%, and 17% respectively. Three components were generated after principal component analysis on the vulnerable groups. Lastly, this approach allowed the identification of 17 high-risk spots within Hong Kong. In addition, the underlying factors behind each hot spot were investigated from the aspects of hazard, exposure, and vulnerability respectively, and specific suggestions for dengue prevention were proposed accordingly.

The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.

This study was funded by the Health and Medical Research Fund of the Food and Health Bureau (No. 20190672).

How to cite: Yin, S., Hua, J., Ren, C., Guénard, B., Wang, R., Weemaels, A. I., Shi, Y., Lee, T.-C., Yuan, H.-Y., Chong, M. K., and Tian, L.: Mapping dengue fever risk for a non-endemic high-density city in subtropical region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2994, https://doi.org/10.5194/egusphere-egu23-2994, 2023.

11:55–12:05
|
EGU23-13960
|
ITS1.11/NP0.2
|
On-site presentation
|
Massimiliano Alvioli, Daniel Fowler, and Samsung Lim

COVID-19 severely affected Italy from the beginning of the pandemic. The number of cases can be analysed within statistical methods, to understand its spread over time, in conjunction with factors as meteorological and environmental conditions, socio-economic conditions and urban settings [1]. The role of spatial aggregation of data is seldom investigated in detail, as the information available to the public is often limited to administrative boundaries.

We investigated the number of infections stratified by spatial location and time, analyzing each of the 107 provinces of Italy during the two infection waves in 2020. Infections were greater in urban areas such as Rome, Milan and Naples. We further investigated the role of urban areas by considering specific spatial aggregations that explicitly included indicators of human presence [2].

We used the hhh4 endemic-epidemic model to study the spatial-temporal pattern of COVID-19 [3]. The model includes three components, representing autoregressive effects (transmission of disease within a single province), neighborhood effects (transmission between provinces) and endemic effects (sporadic events by unobserved sources of infection).  Covariates included daily temperature, humidity, employment rate and number of high-care hospital beds for each province. In addition, we considered specific covariates to account for urban indicators: population, population density, the proportion of urban area, average area of cities, and number of cities. Covariates were considered on both the autoregressive and neighbourhood components to determine the effect of transmission within and between provinces. To simulate the spread between provinces on the neighbourhood component, we considered the spatial adjacency between provinces, and considered decreasing importance with increasing distance.

Outputs from the model included the risk ratios (RRs) of the covariates, with resulting RR of 0.89 on the autoregressive component and RR of 0.83 on the neighbourhood component. An existing study found that higher temperatures were related to a decline in daily confirmed COVID-19 case counts with a corresponding RR of 0.80 [4].

We specifically looked at covariates related to urban settings, as an existing study showed positive correlation between population density and COVID-19 transmission rate [5]. Our results showed a RR of 1.23 (autoregressive component) and RR of 1.48 (neighbourhood component), suggesting that larger population density leads to more infections, and that movement of people across provinces could lead to a higher risk of COVID-19 cases.  Province area, average city area and number of cities were not statistically significant.

Eventually, we explicitly considered the role of urban settings by aggregating spatial-temporal data within individual urban areas [2], instead of administrative boundaries. As COVID-19 data itself were available at the province level, we distributed them to urban areas proportionally to the area occupied within each province; other data was actually aggregated within urban polygons. We argue that study of the spatial-temporal transmission of infection using urban areas may provide reliable results and help selecting characteristics in urban settings that may favour or prevent the spread of diseases.

[1] M. Agnoletti et al. DOI: 10.1016/j.landurbplan.2020

[2] M. Alvioli. DOI: 10.1016/j.landurbplan.2020.103906

[3] S. Meyer et al. DOI: 10.1214/14-AOAS743

[4] J. Liu et al. DOI: 10.1016/j.scitotenv.2020.138513

[5] K.T.L. Sy et al. DOI: 10.1371/journal.pone.024927

How to cite: Alvioli, M., Fowler, D., and Lim, S.: Spatial-temporal modeling of COVID-19 areas in Italy and the role of urban settings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13960, https://doi.org/10.5194/egusphere-egu23-13960, 2023.

12:05–12:15
|
EGU23-14283
|
ITS1.11/NP0.2
|
Highlight
|
On-site presentation
Understanding the effects land use and urban form on the urban heat island in Can Tho city, Vietnam
(withdrawn)
Antje Katzschner, Kieu Diem Nguyen, and Nigel K. Downes
12:15–12:25
|
EGU23-1989
|
ITS1.11/NP0.2
|
ECS
|
Highlight
|
On-site presentation
Giacomo Falchetta and Ahmed T. Hammad

Urban green space - the presence of vegetation-covered area within cities' boundaries - is an increasingly relevant indicator for evaluating sustainable cities. This is because besides providing a set of local services such as mitigating the urban heat island effect and reducing the impact of extreme precipitation events, urban green space has been widely associated with increasing well-being of urban dwellers. Here we present a global analysis of recent trends in urban green space based on modelling of multi-spectral satellite imagery data to reproduce street-based vegetation presence indicators. We estimate local to continental trends over the 2016-2022 period and estimate global scale (considering a large set of the most populated cities) urban green space change statistical trends. We examine heterogeneities in the direction and magnitude of trends across cities and regions, while also analysing within-city inequalities. Our analysis provides an updated picture of urban green space across world cities and an open-source and open data-driven, spatially cross-validated approach to assess changes in near-real-time.

How to cite: Falchetta, G. and Hammad, A. T.: Tracking global urban green space trends, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1989, https://doi.org/10.5194/egusphere-egu23-1989, 2023.

12:25–12:30

Posters on site: Wed, 26 Apr, 14:00–15:45 | Hall X5

Chairpersons: Danlu Cai, Son Ngo Thanh, Masatoshi Yamauchi
Urban Geoscience, NBS and Remote Sensing
X5.423
|
EGU23-820
|
ITS1.11/NP0.2
|
ECS
Yangzi Qiu, Ioulia Tchiguirinskaia, and Daniel Schertzer

During the lockdown of the COVID-19 pandemic, most countries imposed mobility restrictions such as physical distancing from others, “self-isolation”, and/or “quarantine”. Although these “isolation” measures had been effective in hindering the spread of the virus effectively, people’s mental health can be strongly affected by these “isolation” measures. Some researchers found that the mental health problem indeed increased during the lockdown. In order to reduce the negative effects of isolation on mental health, some studies suggested that people should be brought closer to nature spaces during the lockdown. In this regard, policymakers are paying more attention to Nature-based Solutions (NBS) (e.g. green roofs, gardens, and urban parks), which can potentially improve people's physical and mental health via interactions between people and nature.

In order to enhance the connectivity of the landscape to improve the ecosystem services and reduce health risks with the help of NBS in a post-COVID world, it is significant to consider the heterogeneity of the spatial distribution of green spaces. A number of studies have found that estimates of green space areas are scale-dependent, it is therefore important to investigate the intrinsic complexity of the heterogeneity of the green spaces across a range of scales. This could be achieved with the help of the universal multifractal (UM) framework (Schertzer and Lovejoy, 1987), a stochastic approach widely used to quantify the variability of geophysical fields across a range of scales. This study aims to improve the landscape connectivity of the green spaces in Paris across scales with the help of the UM cascade model.

To achieve the aim of this study, we first quantified the heterogeneous spatial distributions of green spaces of the selected areas by using the fractal dimension. Then, a distance analysis is performed for non-green spaces to green spaces, and a series of NBS scenarios are created based on integrating potential NBS into the current landscape by using the UM cascade model. Finally, the spatial distributions of the NBS combined with the original green spaces are quantified by the fractal dimension and distance analysis. The results indicate that NBS can effectively improve the connectivity of the landscape and has the potential to reduce the physical and mental risks caused by COVID-19. More specifically, this study proposes a scale-independent approach for enhancing the multiscale connectivity of the NBS network in urban areas and provides quantitative suggestions for cities in a post-COVID world.

How to cite: Qiu, Y., Tchiguirinskaia, I., and Schertzer, D.: Enhancing landscape connectivity by Nature-based Solutions for cities in a post-COVID world: a case study in Paris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-820, https://doi.org/10.5194/egusphere-egu23-820, 2023.

X5.424
|
EGU23-14511
|
ITS1.11/NP0.2
Andrea Reimuth, Duong Huu Nong, and Son Thanh Ngo

Expansion of built-up areas has consumed large areas of natural ecological patches in many cities around the world, affecting environmental and living quality of urban residents.  Managing urban landscape and urban trees has gained a special attention in Vietnam in recent years. The green space development plan for Hanoi to 2030 and a vision to 2050 has targeted to reach 62% of green spaces. However, there is lack of detailed green space development plan at the district and commune/ward levels. This study aims to assess urban green space area and quality in Hanoi at the commune/ward levels using remote sensing and population data. The study uses combined remote sensing data from Google Earth, Sentinel-2, and Normalized Difference Vegetation Index (NDVI) to analyze urban green quality and space for Hanoi. The urban green space will be combined with population data at commune/ward level to estimate urban tree cover per person. The research results can contribute to improve the credibility and scientifically of green space construction so that urban planning can adapt and serve the city and its residents and achieve green development.

How to cite: Reimuth, A., Nong, D. H., and Ngo, S. T.: Assessing urban green space area and quality using remote sensing and population data: A case study of Hanoi urban districts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14511, https://doi.org/10.5194/egusphere-egu23-14511, 2023.

X5.425
|
EGU23-14618
|
ITS1.11/NP0.2
|
ECS
Sitian Zhu, Auguste Gires, Cedo Maksimovic, Ioulia Tchiguirinskaia, and Daniel Schertzer

The cooling impact of green roofs is highlighted in the context of urbanisation and urban heat island (UHI) effect. And it is usually described and quantified by evapotranspiration (ET) processes. Understanding ET process is the key to optimize cooling effect. ET estimation can be achieved either directly (weighing lysimeters) or indirectly (e.g., Penman-Monteith equation). Micro-meteorological approaches have been developed in recent years. Among which scintillometer can evaluate ET by its measurement parameter Cn2 (which corresponds to the fluctuations of air refractive index n ) in combination with surface energy balance (SEB) and Monin-Obukhov similarity theory (MOST) . Hence, Cn2 improvement in Cn2 data would result in better ET estimation. But it is often overlooked and very little research has focused on it. In this project, the research area lies on the top of the Carnot and Bienvenüe buildings in Ecole des Ponts Paristech. Covering an area of ​​1 ha, it is a wavy and vegetated large green roof, known as the Blue Green Wave (BGW). Data from a large aperture scintillometer (LAS) with 10-minute time step during December 2019 and January 2020 on BGW is used in this study. Three estimates of Cn2(Cn2_UCn2, Cn2_PUCn2 and Cn2_Var) were analysed with structure function and universal multifractal model (UM). Such framework has been widely use to characterize geophysical fields extremely variable across wide range of space-time scales. There are two relevant parameters in an UM model, the mean codimension of intermittency C1≥0and multifractality index 0≤α≤2. α=0, indicates monofractal; α=2, indicates log-normal model. Data in UM framework is analysed by Trace Moment (TM) method and Double Trace Moment (DTM) method. All of estimates demonstrated scale invariance, which could be used for upscaling and downscaling. Cn2_Var performed well even during measurement malfunction, but UM analysis showed it was contradictory to the hypothesis of lognormality. It implies the way it calculates Cn2_Var need some revisions and an assessment of the scintillometer could be achieved by analysing Cn2. This research provides a complete grasp of the properties of Cn2 and sets the stage for its future application in precise ET estimates.

 

How to cite: Zhu, S., Gires, A., Maksimovic, C., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of Cn2 scitillometer data and consequences for evapotranspiration estimates in urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14618, https://doi.org/10.5194/egusphere-egu23-14618, 2023.

X5.426
|
EGU23-9983
|
ITS1.11/NP0.2
Sara Akodad and Pierre Lassalle

The improved ability of imaging sensors to capture very high resolution (VHR) remote sensing images has been boosted by recent enhancement in the data processing algorithms. This improvement raises the potential of providing precise scene understanding for many applications. For instance, the task of semantic segmentation is an important one in the context of 3D building modelling. In this work, the main objective is to show the potential of using images of a stereo pair as inputs to a neural network trained for semantic segmentation into different urban classes. Actually, instead of increasing artificially the amount of training data, the use of stereo pair images can be seen as a realistic data augmentation, where the model will be trained to see the same object from different acquisition angles. From an experimental point of view, the results show that the method achieves better performances and gives a greater ability to generalize compared to the use of a single view. 

The segmentation process is performed using an encoder-decoder network architecture, namely the U-net network which includes an EfficientNet for the encoder part and a RefineNet for the decoder stage. The model is trained on Pleiades images involving different sources of ground truth (OpenStreetMap, IGN databases and in-house LCLU AI4GEO hierarchical labelled data). Additionally to the spectral information, height information is also considered to enhance the segmentation accuracy. This latter information is obtained using digital surface model (DSM). Indeed, classes identifying urban areas (building class for example) can be more easily discerned according to their height information. 

Furthermore, since Pleiades images are used as inputs of the proposed model, some geometrical issues need to be handled. To remove this complexity, a simulated imaging geometry of a perfect instrument is designed with no optical distortion and no high attitude perturbations. Resulting geometry is commonly called perfect sensor geometry. Since then, to avoid problems of geometric offsets between different data sources (satellite images in perfect geometry), terrain geometry of DSM/DTM and various ground truth databases, several tools have been developed to allow conversion between different geometries. The ortho-rectification is a commonly used geometrical correction that aims at presenting images as if they had been captured from the vertical. Therefore, this correction requires the availability of a HR digital terrain model (DTM) and may result on some distortions. In particular, some area may be occluded and others may arise a spreading effect of buildings.

 To address this issue, and preserve the native image information of the perfect sensor geometry, one key contribution of this work is to map the DSM data and ground truth image into the perfect sensor geometry. By doing this geometric processing and object positioning during the training process, better overlays between different data sources (stereo pair images, DSM model and ground truth data) are ensured and geometrical distortions and offsets can be avoided. In addition, the inferences can be done directly on the perfect sensor geometry without having to go through terrain geometries which requires high resolved DSM/DTM models.

How to cite: Akodad, S. and Lassalle, P.: Automatic Land Cover Segmentation from Perfect Sensor Stereo Images with Height Information, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9983, https://doi.org/10.5194/egusphere-egu23-9983, 2023.

X5.427
|
EGU23-14807
|
ITS1.11/NP0.2
Francesco Dessì, Maria Teresa Melis, Stefania Da Pelo, and Antonio Funedda

The Einstein Telescope (ET) is a proposed underground infrastructure to host a third-generation, gravitational-wave observatory. There are currently two candidate sites to host it: one of this is located in Sardinia region (Italy), in a favourable geological context, the other one in the Meuse-Rhine Euregion. Site-characterization studies are under way towards the site selection, which is expected for 2024. The scope work of this research is to evaluate the surface deformation of this site by integration of remote sensing techniques with geological and geophysical data. In this framework the PSI (Persistent Scattered Interferometry) technique with SAR data is the proposed approach for the analysis of a long time-series imagery. Although recent crustal movements in the study area are supposed to be very small (≃ - 0.5 mm/years from 2014 as measured by EUREF Permanent Network https://epnd.sgo-penc.hu), ESA Sentinel-1 data from Copernicus program, represents an effective tool to update this knowledge and monitor the phenomenon. A first analysis has been performed in the study area using the Snap2Stamps methodology. During the first assessment of this research, this methodology has been tested to a dataset of 94 images from Sentinel-1. The radar data (SLC, Single Looking complex) acquired from January 2021 to July 2022 for both descending and ascending orbits on an area of 250 sq km has been managed. The applied methodology requires a long-time for the processing in order to derive vertical velocities, and we considered the opportunity to use a cloud service. So, we exploited the possibility offered by SNAPPING service provided by Terradue (https://www.terradue.com/portal/), a cloud on-demand computing service for Sentinel-1 Multi-Temporal DInSAR processing, based on integrated SNAP and StaMPS chain. In this service, the dataset can be improved, considering a longer time of acquisition, exploiting the complete Sentinel revisiting time, starting from 2014.

The first results of this analysis have been calibrated with the existing GNSS measures provided by EUREF using the data of the Nuoro station. The ground vertical displacement calculations, composing data from both acquisition orbits, confirm the existing evaluations and extend the current information to the whole study area. Moreover, it will be possible to consider also future acquisition with a continuous monitoring process.

These results can be considered an important value for the proposed Italian site and the ET infrastructure realization.

 

How to cite: Dessì, F., Melis, M. T., Da Pelo, S., and Funedda, A.: Monitoring land deformation through PSI technique for Einstein Telescope site characterization of Sos Enattos (Sardinia, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14807, https://doi.org/10.5194/egusphere-egu23-14807, 2023.

Geo-Health and Climate
X5.428
|
EGU23-11346
|
ITS1.11/NP0.2
|
ECS
Linu Elizabeth Danielkutty, Raphaëlle Krummeich, Armelle Couillet, Iris Charalambidou, and Emmanuel Eliot

Malaria is a widespread, mosquito-borne, potentially lethal infectious disease that affects humans and other animals. Its prevention and treatment have been targeted in science and medicine for hundreds of years. During the 20th century it was widespread in the Middle East, including Cyprus, and was one of the most important health hazards worldwide. In 1967, the World Health Organisation (WHO) declared Cyprus as malaria-free, an impressive feat considering malaria had plagued the island since the Roman period (Demetrios, 2009). We conducted a critical analysis of anti-malarial campaigns on urban and rural districts in Cyprus, under British colonial rule (1878 – 1960), in the context of malarial disease knowledge in health surveillance and care policy. Under the HIGH-PASM (High-resolution palaeoclimate records and social vulnerability for the last Millennium) project, we present a methodology for constructing database tools relative to heterogeneously distributed historical sources. The aim of this research is to study the impact of the anti-malarial works on the Cypriot landscape as the social-political situation and the methods implemented did not follow the stringent protocols that exist today. Main issues are the complexity regarding British, Ottoman and French social and political roles, ground truth data extracted from historical sources - that need critical analysis, and the complex phenomena under scope. Primary sources are annual medical reports, written by British medical officers, published from 1913 to 1953. The main focus of these actions linked geography to healthcare issues by eliminating the newly identified malaria-vector by directly influencing the mosquito’s habitat, thus indirectly affecting the Cyprus landscape. The assertion, verification and evaluation of the before mentioned actions requires the medical reports to be contextually placed alongside secondary sources (for example correspondences, journal articles, conference proceedings, etc), which were produced or disseminated during this time period by different actors or groups of actors. We aim to apply methodologies used in digital humanities and conceptual modelling within geosciences to verify and understand spatial-temporal information that may be found within archival references. Raw data are extracted from a small corpus to produce meta-data (data sense ((Hui, 2015)) using existing cultural heritage vocabularies (CIDOC CRM base and extensions) relative to different fields and objects to model spatial-temporal events and align these data with authority databases using W3C (World Wide Web Consortium) semantic web standards (technologies). Given the British colonial role in the governance of the island, there is a lack of empirical evidence on the choices of techniques or actions employed. Thus, this meta-data conceptual modelling and raw data collection, as a data management approach, offers a syntactic, semantic and pragmatic understanding of archival sources. This methodology ultimately aims to study the impact on the landscape of the anti-malarial campaigns by bridging gaps in existing literature by the digitisation of physical reports and digitalisation of a healthcare system from the late 19th to 20th century.

How to cite: Danielkutty, L. E., Krummeich, R., Couillet, A., Charalambidou, I., and Eliot, E.: Constructing an event centred modelling process to produce spatial and temporal data for the critical study of the impact of British colonial rule (1878 – 1960) on the Cypriot landscape through their anti-malarial campaigns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11346, https://doi.org/10.5194/egusphere-egu23-11346, 2023.

X5.429
|
EGU23-11851
|
ITS1.11/NP0.2
|
ECS
Darren Beriro, Yolande Macklin, Jane Thrasher, David Griggs, and Angela Haslam

The remediation of brownfield is vital to sustainable place-making and levelling up across the country. It provides an improved local environment that can unlock regeneration and the social, economic and ecological revitalisation of communities. However the total benefits of remediation are not fully understood or utilised in decision making. As a result, sites can remain derelict for years and opportunities to optimise value from public and private investment are missed.

Jacobs and BGS undertook research for the Environment Agency in England to evaluate the feasibility of developing a tool, which included:

  • A virtual workshop using MURAL to enable digital interaction and collaboration to refine scope, define data requirements and map project stakeholders;
  • Primary benefit and user requirements research, including looking at the potential impact of a tool through the development of a Theory of Change model and focussed interviews with key stakeholders to understand user requirements.
  • Review of academic and grey literature;
  • Accelerated design sprint to frame the problem/opportunity, explore technology agonistic solutions for the tool and develop into a storyboard.
  • Develop a low fidelity prototype as a blueprint of how a tool might look.

The outcome of the work indicated there is both a need and demand for such a tool. It was also demonstrated to be technically feasible through the literature review and design sprint. Such a tool would have an extremely positive impact on the perceptions of brownfield, shifting it from a constraint to an opportunity. The presentation will provide a summary of the methods, an overview of the results and a demonstration of a prototype digital tool. Our disucssion will focus on the opportunities presented by using systems thinking combined with design thinking to influence the approach taken to planning and redeveloping brownfield sites.  

How to cite: Beriro, D., Macklin, Y., Thrasher, J., Griggs, D., and Haslam, A.: A feasibility study for a novel remediation and sustainable growth digital tool for the Environment Agency, England, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11851, https://doi.org/10.5194/egusphere-egu23-11851, 2023.

X5.430
|
EGU23-841
|
ITS1.11/NP0.2
|
ECS
|
Adarsh Jojo Thomas, Jürgen Kurths, and Daniel Schertzer
Precipitation is a complex process that is extremely variable over a wide range of space-time scales. More specifically, it is strongly intermittent: the heaviest precipitation are increasingly concentrated on sparser and sparser fractions of the space-time domain. At the same time, precipitation is a key variable of urban geosciences. 
Multifractals have been developed to analyse and simulate across scales this multiscale intermittency, while the climate networks can detect and characterise event synchronisation. In contrast to multifractal analysis, climate networks are usually performed at a given scale, defined by the resolution of the data. In this communication, we present how to overcome this dichotomy and propose multiscale climate networks in the hope of reaching scales relevant to urban geosciences.
Specifically, we study theoretically and/or numerically the scale dependance of different centrality measures of climate networks determined at different scales by coarse graining the precipitation data, as is done for multifractal analysis. Among the preliminary results, we show how to modify some of the parameters of the climate networks to force scale invariance of their structure.

How to cite: Thomas, A. J., Kurths, J., and Schertzer, D.: Multifractals, Climate Networks and the extreme variability of precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-841, https://doi.org/10.5194/egusphere-egu23-841, 2023.

X5.431
|
EGU23-9216
|
ITS1.11/NP0.2
|
ECS
In-network Sewage Surveillance Allow Source Apportionment for Public Health Pathogens of Concern
(withdrawn)
Hila Korach-Rechtman, Gali Fux, and Jacob Moran Gilad
X5.432
|
EGU23-8235
|
ITS1.11/NP0.2
|
Highlight
Paola Coratza, Alessandro Ghinoi, Lucia Palandri, Elena Righi, Cristiana Rizzi, Mauro Soldati, and Vittoria Vandelli

A significant number of papers focusing on the relationships between COVID-19 diffusion and geographic factors is available in literature. The same applies to the use of geographic techniques (e.g., spatial tools and mapping) for the study of the pandemic. Although the literature on these topics is already abundant, a detailed and comprehensive review is still lacking.
In this context, the purpose of this paper is to fill the existing gap by presenting a literature review of geographical studies dealing with the COVID-19 pandemic. The review is aimed at: i) understanding the role of geographic/territorial determinants (e.g., geographic location of confirmed cases, climatic and environmental characteristics, urbanization) in the spread of COVID-19; ii) identifying common approaches, materials, and methods used in the study of the COVID-19 outbreak from a geographical perspective; iii) recognising possible research gaps to address future in-depth analyses.
To achieve these goals a literature review was made concerning the application of geographical approaches for the study of one or more geographical factors/variables, as well as socioeconomic factors in relation to the outbreak and diffusion of the COVID-19 pandemic. The main academic literature databases were inquired. More than 80 papers were reviewed and categorized according to different criteria, e.g., considered variables, investigated period, spatial and temporal resolution and applied methodologies.
This research is part of an interdisciplinary project (“DISCOV19”) funded by the University of Modena and Reggio Emilia and aiming at identifying the main vulnerability and risk factors related to COVID-19 outbreak and at formulating prevention and management schemes with a focus on the Province of Modena (Northern Italy). The investigation crosses different disciplines: i) public health epidemiology, investigating the contagion modalities and health and socio-demographic predisposing factors; ii) economic-statistical methodology, pointing out the structural characteristics of the networks that convey the contagion and the main social, technological and management vulnerabilities with respect to COVID-19 spread; iii) geography and geomorphology, for thematic mapping and spatial analysis of COVID-19 outbreak and understanding the role of environmental and physical-geographical factors on COVID-19 incidence. The review here presented fits into this context being one of the first outputs of the project implementation.

How to cite: Coratza, P., Ghinoi, A., Palandri, L., Righi, E., Rizzi, C., Soldati, M., and Vandelli, V.: The influence of geographical factors on COVID-19 outbreak: A literature review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8235, https://doi.org/10.5194/egusphere-egu23-8235, 2023.

Posters virtual: Wed, 26 Apr, 14:00–15:45 | vHall ESSI/GI/NP

Chairperson: Masatoshi Yamauchi
vEGN.2
|
EGU23-16718
|
ITS1.11/NP0.2
|
ECS
|
Highlight
|
|
Siyi Huang, Lijun Yu, Danlu Cai, Jianfeng Zhu, Ze Liu, Zongke Zhang, Yueping Nie, and Klaus Fraedrich

Urbanization induced changes have attracted widespread attention. Key challenges arise from the inherent uncertainties in attribution models diagnosing the driving mechanisms and the interrelationships of the attributes given by the complexity of interactions within a city. Here, we investigate urbanization dynamics from nighttime light signals before analyzing their driving mechanisms from 2014 to 2020 on both provincial and regional scale and a flat versus mountainous urbanization comparison. Model uncertainties are discussed comparing the contribution results from Geodetector and the Gini importance from Random Forest analyses. The method is applied to Shaanxi Province, where flat urban land is located mainly in its center and mountainous urban land is situated in the North and South. The following results are noted: i) Employing the Geodetector based maximum contribution method for urban region extraction of night time light reveals a notable accuracy improvement in flat urban land compared with the closest area method. ii) Geographical factors attain high contribution for mountainous urban land of Shannan, while for flat urbanization land dynamics, economic factors and community factors prevail. iii) The most obvious driving mechanisms are economic factors which, associated with local urban development strategies, show highest contribution values in 2014 (2018) over the flat (mountainous) urban land of Guanzhong Plain (Northern Shaanxi Plateau or Shanbei region) linked with an early (late) development. iv) Population factors achieve high contribution values in the initially low populated urban land of the northern mountainous land initiating huge migration. v) The contributions resulting from Geodetector are in agreement with the Gini importance from Random Forest in agriculture, geographical and population factors (R2 > 0.5) but not in economy, community and climatic factors (R2 < 0.5). The dynamics of driving mechanisms for urbanization provides insights in connecting urban geographical expansion with multi-factors and thus to assist municipal governments and city stakeholders to design a city with geographical, climatic and social-economic changes and interactions in mind.

How to cite: Huang, S., Yu, L., Cai, D., Zhu, J., Liu, Z., Zhang, Z., Nie, Y., and Fraedrich, K.: Driving mechanisms of urbanization: Evidence from Geographical, Climatic, Social-economic and Nighttime Light data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16718, https://doi.org/10.5194/egusphere-egu23-16718, 2023.