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Book: Special section: bridging the rural-urban dichotomy in land use science

Vliet, Jasper van

(Journal of land use science ; volume 15, issues 4/6 (August-December 2020))

2020

Title variant	Bridging the rural-urban dichotomy in land use science
Author's details	[guest editors]: Jasper van Vliet [und weitere]
Series title	Journal of land use science ; volume 15, issues 4/6 (August-December 2020)
Collection
Language	English
Size	Seite 585-706, Diagramme, Karten
Publisher	Taylor & Francis
Publishing place	Abingdon
Publishing country	Great Britain
Document type	Book
HBZ-ID	HT020833675
Database	Catalogue ZB MED Nutrition, Environment, Agriculture

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Shelf mark	Loan status	Location
Z 7699 (15,4/6)	available for loan (reading room)	Lesesaal

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Article ; Online: Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China.

Niu, Lu / Zhang, Zhengfeng / Liang, Yingzi / van Vliet, Jasper

Environment international

2024 Volume 184, Page(s) 108455

Abstract: Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of ... ...

Abstract	Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban-rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM
MeSH term(s)	Humans ; Cities ; Air Pollutants/analysis ; Particulate Matter/analysis ; Air Pollution/analysis ; China ; Environmental Monitoring/methods
Chemical Substances	Air Pollutants ; Particulate Matter
Language	English
Publishing date	2024-01-21
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2024.108455
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Article ; Online: Solar park detection from publicly available satellite imagery

Plakman, Veerle / Rosier, Job / van Vliet, Jasper

GIScience & Remote Sensing. 2022 Dec. 31, v. 59, no. 1 p.462-481

2022

Abstract: The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land ... ...

Abstract	The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land covers, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery, which has the advantage of requiring relatively little training data and being easily extendable to large spatial extents and new areas. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm in Google Earth Engine. Thereafter, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2. These features are subsequently used to train and validate a range of RF models to select the most promising model setup. The training datasets consisted of subsampled presence/absence data, oversampled presence/absence data, and multiple land-cover categories. The best-performing model used an oversampled dataset trained on all 10- and 20- meter resolution spectral bands and the radar backscatter properties of one period. Independent test results show an overall classification accuracy of 99.97% (Kappa: 0.90). For this result, the producer accuracy was 85.86% for solar park objects and 99.999% for non-solar park objects. The user accuracy was 92.39% for solar park objects and 99.999% for non-solar park objects. These high classification accuracies indicate that our approach is suitable for transfer learning and is able to detect solar parks in new study areas.
Keywords	Internet ; algorithms ; data collection ; land cover ; models ; radar ; remote sensing ; standard deviation ; Photovoltaics ; object-based image classification ; random forest ; transfer learning ; land use
Language	English
Dates of publication	2022-1231
Size	p. 462-481.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2209042-3
ISSN	1943-7226 ; 1548-1603
ISSN (online)	1943-7226
ISSN	1548-1603
DOI	10.1080/15481603.2022.2036056
Database	NAL-Catalogue (AGRICOLA)

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Article: Global trends and local variations in land take per person

Li, Mengmeng / Verburg, Peter H. / van Vliet, Jasper

Landscape and urban planning. 2022 Feb., v. 218

2022

Abstract: Globally, urban areas are growing at a faster rate than their population, potentially reducing environmental sustainability due to undesirable land take in (semi)natural and agricultural lands. However, it is unclear to what extent this trend varies ... ...

Abstract	Globally, urban areas are growing at a faster rate than their population, potentially reducing environmental sustainability due to undesirable land take in (semi)natural and agricultural lands. However, it is unclear to what extent this trend varies locally, which may hamper the formulation and implementation of local-scale policies in the context of the global competition for land. Here, we attribute built-up land change to population dynamics and changes in land take per person, for >75,000 administrative regions worldwide, typically representing municipalities or counties. Results show that changes in land take per person, expressed as the area of built-up land per capita, relate to 38.3%, 49.6%, and 37.5% of the total increase in built-up land during the periods 1975–1990, 1990–2000, and 2000–2015, respectively, but with large local variations. Interestingly, we find that centres of large cities densify in all three periods, while their rural areas show an opposite development, suggesting an urban polarization effect. We also find densification in many regions in the Global South that already have a high population density, leading to potential trade-offs in terms of human wellbeing. Therefore, our work provides novel insights into the debate on sustainable urban development at a global scale.
Keywords	environmental sustainability ; landscapes ; population density ; population dynamics ; social welfare ; urban development
Language	English
Dates of publication	2022-02
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	742504-1
ISSN	1872-6062 ; 0169-2046
ISSN (online)	1872-6062
ISSN	0169-2046
DOI	10.1016/j.landurbplan.2021.104308
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Relocating built-up land for biodiversity conservation in an uncertain future.

Yue, Wenze / Zhou, Qiushi / Li, Mengmeng / van Vliet, Jasper

Journal of environmental management

2023 Volume 345, Page(s) 118706

Abstract: Land use changes associated with habitat loss, fragmentation, and degradation exert profoundly detrimental impacts on biodiversity conservation. Urban development is one of the prevailing anthropogenic disturbances to wildlife habitat, because these ... ...

Abstract	Land use changes associated with habitat loss, fragmentation, and degradation exert profoundly detrimental impacts on biodiversity conservation. Urban development is one of the prevailing anthropogenic disturbances to wildlife habitat, because these developments are often considered permanent and irreversible. As a result, the potential benefits of built-up land relocation for biodiversity conservation have remained largely unexplored in environmental management practices. Here, we analyze recent built-up land relocation in Shanghai and explore how such restoration programs can affect future land change trajectories with regards to biodiversity conservation. Results show that 187.78 km
MeSH term(s)	Animals ; Conservation of Natural Resources/methods ; China ; Biodiversity ; Ecosystem ; Mammals ; Reptiles
Language	English
Publishing date	2023-08-01
Publishing country	England
Document type	Journal Article
ZDB-ID	184882-3
ISSN	1095-8630 ; 0301-4797
ISSN (online)	1095-8630
ISSN	0301-4797
DOI	10.1016/j.jenvman.2023.118706
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Article: Trade-offs between prosperity and urban land per capita in major world cities

Bakker, Vita / Verburg, Peter H. / van Vliet, Jasper

Geography and sustainability. 2021 June, v. 2, no. 2

2021

Abstract: Globally, urban land expands at a faster rate than the corresponding urban population, which comes at a cost of agricultural and natural land. Wealth has been identified as an underlying driver of this trend, but it is unclear whether more prosperous ... ...

Abstract	Globally, urban land expands at a faster rate than the corresponding urban population, which comes at a cost of agricultural and natural land. Wealth has been identified as an underlying driver of this trend, but it is unclear whether more prosperous cities inevitably have a greater urban land consumption. Here, we map urban prosperity indicators to their relevant Sustainable Development Goals (SDGs) for 64 major world cities and relate these to the corresponding urban land consumption (defined here as built-up land per capita). Results indicate a moderately-weak but significant correlation between overall prosperity and urban land consumption (Spearman's correlation, ρ = 0.47, p < 0.001), suggesting a trade-off between both. In addition, we find a regional clustering, with for example cities with relatively low prosperity and low urban land consumption in Africa, and cities with high prosperity and low-to-medium urban land consumption in Europe. The moderately-weak correlation in combination with these regional differences suggests that the observed trade-off is avertable and that other drivers moderate this relation. Consequently, cities can increase their prosperity without additional environmental consequences entailing land take and the conversion of natural and agricultural land.
Keywords	agricultural land ; geography ; sustainable development ; urban population ; Africa ; Europe
Language	English
Dates of publication	2021-06
Size	p. 134-138.
Publishing place	Elsevier B.V.
Document type	Article
ISSN	2666-6839
DOI	10.1016/j.geosus.2021.05.004
Database	NAL-Catalogue (AGRICOLA)

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Article: Urbanisation as driver of food system transformation and opportunities for rural livelihoods.

de Bruin, Sophie / Dengerink, Just / van Vliet, Jasper

Food security

2021 Volume 13, Issue 4, Page(s) 781–798

Abstract: Urbanisation is changing food systems globally, and in particular in sub-Saharan Africa and South Asia. This transformation can affect rural livelihoods in multiple ways. Evidence on what enabling conditions are needed to materialise the opportunities ... ...

Abstract	Urbanisation is changing food systems globally, and in particular in sub-Saharan Africa and South Asia. This transformation can affect rural livelihoods in multiple ways. Evidence on what enabling conditions are needed to materialise the opportunities and limit risks is scattered. Here we review scientific literature to elaborate on how urbanisation affects food systems, and on the enabling conditions that subsequently shape opportunities for rural livelihoods. We find that urbanisation leads to a rising and changing food demand, both direct and indirect land use changes, and often to more complex market linkages. Evidence shows that a wide range of enabling conditions can contribute to the materialisation of opportunities for rural livelihoods in this context. Reviewed evidence suggests that the connectivity to urban centres is pivotal, as it provides access to finance, inputs, information, services, and off-farm employment. As a result, physical and communication infrastructure, the spatial pattern of urbanisation, and social networks connecting farmers to markets are identified as important enabling factors for the improvement of rural livelihood outcomes. Our findings suggest that coordinated and inclusive efforts are needed at different scales to make sure rural livelihoods benefit from urbanisation and food system transformation.
Language	English
Publishing date	2021-06-28
Publishing country	Netherlands
Document type	Journal Article ; Review
ZDB-ID	2486755-X
ISSN	1876-4525 ; 1876-4517
ISSN (online)	1876-4525
ISSN	1876-4517
DOI	10.1007/s12571-021-01182-8
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Book ; Online: Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning

Kurchaba, Solomiia / van Vliet, Jasper / Verbeek, Fons J. / Veenman, Cor J.

2023

Abstract: Starting from 2021, more demanding $\text{NO}_\text{x}$ emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it ... ...

Abstract	Starting from 2021, more demanding $\text{NO}_\text{x}$ emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to prioritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship $\text{NO}_\text{2}$ estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI/S5P images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a method for the automated selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data. It is based on a proposed regression model predicting the amount of $\text{NO}_\text{2}$ that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced $\text{NO}_\text{2}$ are integrated over observations of the ship in time and are used as a measure of the inspection worthiness of a ship. To assure the robustness of the results, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.
Keywords	Computer Science - Machine Learning ; Physics - Atmospheric and Oceanic Physics
Subject code	551
Publishing date	2023-02-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting the liveability of Dutch cities with aerial images and semantic intermediate concepts

Levering, Alex / Marcos, Diego / van Vliet, Jasper / Tuia, Devis

Remote Sensing of Environment

2023 Volume 287

Abstract: In order to provide urban residents with suitable living conditions, it is essential to keep track of the liveability of neighbourhoods. This is traditionally done through surveys and by predictive modelling. However, surveying on a large scale is ... ...

Abstract	In order to provide urban residents with suitable living conditions, it is essential to keep track of the liveability of neighbourhoods. This is traditionally done through surveys and by predictive modelling. However, surveying on a large scale is expensive and hard to repeat. Recent research has shown that deep learning models trained on remote sensing images may be used to predict liveability. In this paper we study how well a model can predict liveability from aerial images by first predicting a set of intermediate domain scores. Our results suggest that our semantic bottleneck model performs equally well to a model that is trained only to predict liveability. Secondly, our model extrapolates well to unseen regions (R2 between 0.45 and 0.75, Kendall's τ between 0.39 and 0.57), even to regions with an urban developmental context that is different from areas seen during training. Our results also suggest that domains which are directly visible within the aerial image patches (physical environment, buildings) are easier to generalize than domains which can only be predicted through proxies (population, safety, amenities). We also test our model's perception of different neighbourhood typologies, from which we conclude that our model is able to predict the liveability of neighbourhood typologies though with a varying accuracy. Overall, our results suggest that remote sensing can be used to extrapolate liveability surveys and their related domains to new and unseen regions within the same cultural and policy context.
Keywords	Aerial imagery ; Deep learning ; Liveability ; Urban studies
Subject code	006
Language	English
Publishing country	nl
Document type	Article ; Online
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Fusing Earth observation and socioeconomic data to increase the transferability of large-scale urban land use classification

Rosier, Job F. / Taubenböck, Hannes / Verburg, Peter H. / van Vliet, Jasper

Remote sensing of environment. 2022 Sept. 01, v. 278

2022

Abstract: Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required ... ...

Abstract	Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required resources limits their capacity to map urban land use at larger scales. Here, we use a combination of open-source satellite imagery, constituting of data from Sentinel-1 and Sentinel-2, and socioeconomic data, constituting of points-of-interest and spatial metrics from road networks to classify urban land-use at a national scale, using a deep learning approach. A related challenge for large-scale mapping is the availability of ground truth data. Therefore, we focus our analysis on the transferability of our classification approach, using ground truth labels from a nationwide land-use dataset for the Netherlands. By dividing the country into four regions, we tested whether a combination of satellite data and socioeconomic data increases the transferability of the classification approach, compared to using satellite data only. The results indicate that socioeconomic data increases the overall accuracy of the classification for the Netherlands by 3 percentage points. In a transfer learning approach we find that adding socioeconomic data increases the accuracy between 3 and 5 percentage points when trained on three regions and tested on the independent fourth one. In the case of training and testing on one region and testing on another, the increase in overall accuracy increased up to 9 percentage points. In addition, we find that our deep learning approach consistently outperforms a random forest model, used here as benchmark, in all of the abovementioned experiments. Overall, we find that socioeconomic data increases the accuracy of urban land use classification, but variations between experiments are large.
Keywords	algorithms ; data collection ; environment ; land use ; remote sensing ; urban development ; Netherlands
Language	English
Dates of publication	2022-0901
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
DOI	10.1016/j.rse.2022.113076
Database	NAL-Catalogue (AGRICOLA)

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