Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates.
Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from bui...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249044&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850240612894244864 |
|---|---|
| author | Franz Schug David Frantz Sebastian van der Linden Patrick Hostert |
| author_facet | Franz Schug David Frantz Sebastian van der Linden Patrick Hostert |
| author_sort | Franz Schug |
| collection | DOAJ |
| description | Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates. |
| format | Article |
| id | doaj-art-cc3926e975a04c67ab8508bef9ec9975 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cc3926e975a04c67ab8508bef9ec99752025-08-20T02:00:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024904410.1371/journal.pone.0249044Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates.Franz SchugDavid FrantzSebastian van der LindenPatrick HostertGridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249044&type=printable |
| spellingShingle | Franz Schug David Frantz Sebastian van der Linden Patrick Hostert Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLoS ONE |
| title | Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. |
| title_full | Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. |
| title_fullStr | Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. |
| title_full_unstemmed | Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. |
| title_short | Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. |
| title_sort | gridded population mapping for germany based on building density height and type from earth observation data using census disaggregation and bottom up estimates |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0249044&type=printable |
| work_keys_str_mv | AT franzschug griddedpopulationmappingforgermanybasedonbuildingdensityheightandtypefromearthobservationdatausingcensusdisaggregationandbottomupestimates AT davidfrantz griddedpopulationmappingforgermanybasedonbuildingdensityheightandtypefromearthobservationdatausingcensusdisaggregationandbottomupestimates AT sebastianvanderlinden griddedpopulationmappingforgermanybasedonbuildingdensityheightandtypefromearthobservationdatausingcensusdisaggregationandbottomupestimates AT patrickhostert griddedpopulationmappingforgermanybasedonbuildingdensityheightandtypefromearthobservationdatausingcensusdisaggregationandbottomupestimates |