Multi-Temporal Data Processing for Detection of urban growth / sprawl#
Author: Jan-Josef Hartke & Maren Heinemann
Idea:#
“Urban sprawl has many detrimental environmental, economic, and social consequences. Sprawl leads to higher greenhouse-gas emissions and poses an increasing threat to the long-term availability of many vital ecosystem services. Therefore, urban sprawl is in stark contradiction to the principles of sustainable land use and to the need for asustainability transformation” (Behnisch et al., 2022).
“Urban sprawl [has] induced irreversible land cover conversions. […] Researchers propose that composite indexes can capture the complexity of urban sprawl by incorporating detailed input data” (Chettry, 2023).
Project idea:#
Automated detection & visualization of the expansion of urban area (growth / sprawl) for a region of interest based on satellite imagery and composite indexes. Potentially focus on one specific region only.
Data:#
Satellite imagery (Sentinel-2; 10m resolution)
(OSM data (ohsome-API) -> problem: completeness of data)
Global Human Settlement Layer (GHSL)
Method / Implementation:#
Download satellite imagery (Sentinel-2) from different years (sentinelsat - python library)
Preprocessing (clearing clouds, creating mosaic of single pictures, clip to AoI, …)
Calculate indices (Normalized Difference Builit-up Index; Modified Normalized Urban Area Composite Index?) -> City area
Dervive City area differences
Create maps and animate as GIF
(Website for 5 ECTS)
FOSSGIS-Tools:#
GDAL / OGR
GRASS GIS
QGIS
Python
Literature:#
Behnisch, Martin; Krüger, Tobias; Jaeger, Jochen A. G. (2022): Rapid rise in urban sprawl: Global hotspots and trends since 1990. In: PLOS Sustainability and Transformation 1 (11), e0000034. DOI: 10.1371/journal.pstr.0000034.
Chettry, Vishal (2023): A Critical Review of Urban Sprawl Studies. In: Journal of Geovisualization and Spatial Analysis. DOI: 10.1007/s41651-023-00158-w.
El Garouani A, Mulla DJ, El Garouani S, Knight J (2017) Analysis of urban growth and sprawl from remote sensing data: case of Fez, Morocco. Int J Sustain Built Environ 6:160–169. https://doi.org/10.1016/j.ijsbe.2017.02.003
Gautam, Vivek Kumar; Murugan, Palani; Annadurai, Mylswamy (2017): A New Three Band Index for Identifying Urban Areas using Satellite Images. In: Mangalore Institute of Technology & Engineering, Moodbidri, INDIA.
Guan D, He X, He C, Cheng L, Qu S (2020) Does the urban sprawl matter in Yangtze River Economic Belt, China? An integrated analysis with urban sprawl index and one scenario analysis model. Cities 99:102611. https://doi.org/10.1016/j.cities.2020.102611
Jamil, Abdlhamed; Al-Shareef, Abdulmohsen; Al-Thubaiti, Amer (2020): Classifications of Satellite Imagery for Identifying Urban Area Structures. In: Advances in Remote Sensing 9, S. 12–32.
Karanam, Hari Krishna (2018): STUDY OF NORMALIZED DIFFERENCE BUILT-UP (NDBI) INDEX IN AUTOMATICALLY MAPPING URBAN AREAS FROM LANDSAT TM IMAGERY. In: International Journal of Scientific Research and Review 7 (1).
Li, Feng; Liu, Xiaoyang; Liao, Shunbao; Jia, Peng (2021): The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas. In: Remote Sensing 13, S. 2350. DOI: 10.3390/rs13122350.
Lynch, Philip; Blesius, Leonhard; Hines, Ellen (2020): Classification of Urban Area Using Multispectral Indices for Urban Planning. In: Remote Sensing 12 (15), S. 2503. DOI: 10.3390/rs12152503.
Matci, D. (2023): Development of a new index for mapping urban areas in Türkiye using Sentinel-2 images. In: Advances in Space Research 72 (11), S. 4677-4691. https://doi.org/10.1016/j.asr.2023.08.058
https://joint-research-centre.ec.europa.eu/index_en?prefLang=de https://www.nature.com/scitable/knowledge/library/the-characteristics-causes-and-consequences-of-sprawling-103014747/ https://www.treehugger.com/urban-sprawl-definition-causes-and-solutions-5186856 https://www.destatis.de/DE/Service/Statistik-Visualisiert/flaechenatlas.html