This study presents a global explanatory analysis of the interplay between the severity of flood losses and human presence in floodplain areas. In particular, we relate economic losses and fatalities ...caused by floods during 1990–2000, with changes in human population and built‐up areas in floodplains during 2000–2015 by exploiting global archives. We found that population and built‐up areas in floodplains increased in the period 2000–2015 for the majority of the analyzed countries, albeit frequent flood losses in the previous period 1990–2000. In some countries, however, population in floodplains decreased in the period 2000–2015, following more severe floods losses that occurred in the period 1975–2000. Our analysis shows that (i) in low‐income countries, population in floodplains increased after a period of high flood fatalities; while (ii) in upper‐middle and high‐income countries, built‐up areas increased after a period of frequent economic losses. In this study, we also provide a general framework to advance knowledge of human‐flood interactions and support the development of sustainable policies and measures for flood risk management and disaster risk reduction.
Key Points
We analyzed the interplay between the severity of flood losses and human presence in floodplains using freely available global data sets
Despite the frequent flood losses in the period 1990–2000, human presence and built‐up areas in the floodplains increased between 2000 and 2015
In low‐income countries, population in floodplains increased after a period of high flood fatalities
Timely and accurate extraction of urban built-up areas is crucial to addressing environmental problems related to fast changes in urban land cover, which is fundamental for optimizing land use ...patterns and supporting global sustainable development. Nighttime light (NTL) from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) offer a new data source for extracting urban information. However, this kind of data suffer from drawbacks of blooming effects. To address this problem, in this study, the Enhanced Nighttime Light Urban Index (ENUI) approach, which involves the combination of NPP-VIIRS NTL with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), is proposed and tested. This approach was used to rapidly monitor the urban built-up areas in the Guangdong-Hong Kong-Macao Greater Bay Area in 2012, 2015, and 2018. The average overall accuracy and Map-level Image Classification Efficacy (MICE) for the extraction results are 93.56% and 0.77, respectively, while those of the Local-Optimized Thresholding (LOT) are 86.48% and 0.54, respectively; meanwhile, the average F-score values, user's accuracy and producer's accuracy for urban areas using the proposed approach increased by 9.98%, 10.90% and 8.67%, respectively, compared with the LOT. These findings suggest that this approach has a higher extraction accuracy than the LOT; this is primarily ascribed to the integration of NTL data with the NDVI, NDWI, and NDBI, which increases the variability of nighttime light in the urban core area and adequately alleviates the blooming effects of nighttime light brightness in water bodies and vegetated areas. The proposed approach shows great potentials to accurately and effectively monitor multi-temporal urban information and address environmental issues using NPP-VIIRS NTL data in global urban agglomerations.
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•NPP-VIIRS NTL, NDVI, NDWI and NDBI are utilized to improve built-up-areas monitor.•Our approach reduces blooming effects of NTL brightness in water and vegetated areas.•The resulting average overall accuracy and MICE are 93.56% and 0.77, respectively.•The resulting average urban F-score value increased by 9.98% compared to LOT.
This study aims to demonstrate the relationships between residential development and the spatio-temporal dynamics of land use patterns in the case of the southern coast of Turkey (Mersin). This coast ...has witnessed extensive tourism development that destroyed fertile agricultural lands and natural vegetation since the early 1980 s. We analyzed the impacts of this development using several class-level pattern metrics. These include the percentage of landscape (PL), largest patch index (LPI), edge density (ED), patch density (PD), fractal dimension (FRAC) and shape (SHAPE). The eastern part of the study area consists of an alluvial plain, while the western part has undulated terrain with dense vegetation and limestone outcrops. The region has urban character on the coast with many multistory apartment blocks used for residential purposes. However, inland has a rural character with agricultural areas and interspersed single-family units. The results showed that the numbers and density of built-up patches on the alluvial plain tend to decrease as the patches aggregate and thus appear in simpler forms (i.e., closer to simple Euclidean forms). The small and interspersed patches are characteristic of rugged terrain with limestone outcrops, where topography and the spatial distribution of protected areas prevent the formation of large, aggregated patches. We discussed the drivers of the observed changes and provided recommendations for analyzing trends in similar coastal landscapes. We also highlighted the importance of spatio-temporal information due to its potential to increase the efficacy of decision-making processes for development planning.
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•Coastal landscapes are subject to change due to urban land use•Landscape pattern indices are indicators for change trends•Aggregation of built-up patches decreases patch density and increases largest patch index•Increase in patch shape complexity has a potential to degrade natural ecosystems•Time series of pattern indices may serve as indicators for policy making
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. ...The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.
To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments ...of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.
•We provide a framework for localized accuracy assessment of built-up surface layers.•We assess the suitability of agreement measures for localized accuracy estimation.•We examine the sensitivity of localized accuracy estimates to the spatial support.•We apply our framework to the Global Human Settlement Layer in 1975 and 2014.•Peri-urban densification processes may drive localized accuracy increase over time.
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•This study compares urban sprawl between cities with and without greenbelts.•Greenbelts have been largely effective at reducing urban sprawl.•The effect was somewhat stronger in ...cities of larger population sizes.•The main mechanism was a reduction of land uptake per person, i.e., densification.•We recommend greenbelts for de-sprawling strategies toward more compact green cities.
As Europe takes continuous steps towards urbanization, many cities in this continent are affected by the negative repercussions caused by urban sprawl. Among the efforts adopted to overcome urban sprawl and its adverse impacts is the greenbelt policy which is highly popular in several European countries. However, the actual effectiveness of this urban growth management strategy has been disputed. Using a sample of 60 European cities, 30 of which have greenbelts, this study compares (1) changes in urban sprawl in a 9-year time period (2006–2015), and (2) the level of sprawl between the cities with and without greenbelt in 2006 and 2015 separately, to investigate the performance of the greenbelts, applying the metrics of Weighted Urban Proliferation (WUP) and Weighted Sprawl per Capita (WSPC). The results show that (1) greenbelts have been largely effective at slowing down urban sprawl; and in most cases, they have helped reduce sprawl; (2) while urban sprawl decreased also in some cities without greenbelt, the average relative decrease in sprawl was much stronger in cities with greenbelts; (3) greenbelts were somewhat more beneficial in limiting urban sprawl in cities with larger population sizes; (4) the effectiveness of greenbelts was mainly due to the reduction of land uptake per person, i.e., through densification of the built-up areas. These findings are useful to inform future de-sprawling strategies in urban and regional planning as well as the formulation of new scenarios and of targets and limits to urban sprawl in support of more sustainable forms of urban development.
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