Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Osgouei, Paria Ettehadi; Sertel, Elif; Kabadayı, M. Erdem

doi:10.1038/s41598-022-11396-1

Cited by 24 publications

(16 citation statements)

References 40 publications

(41 reference statements)

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“…We generated a new LULC dataset for two different geographical locations with rich class varieties using VHR satellite images acquired by the WV-3 satellite. We used original WV-3 images and classified LULC maps as the reference data prepared in our recent study [6]. Initially, the preprocessing of satellite images was performed to generate datasets that were used for conducting the Deep Learning (DL) experiment, namely the Aksu and Kestel Dataset.…”

Section: Dataset Generationmentioning

confidence: 99%

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Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

et al. 2022

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Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.

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Section: Dataset Generationmentioning

confidence: 99%

“…These images provide the opportunity to study at large scales with high spatial details for a variety of applications such as LULC mapping, urbanization, location-based services, and navigation. One of the challenges while handling VHR data is the strong spatial correlation and high complexity that VHR image pixels contain [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

et al. 2022

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“…From a structural perspective, these policies should also reconnect semi-dense and sparse settlements with the high-quality agro-forest matrix typical of Mediterranean landscapes into a balanced mosaic mixing urban and rural functions. Based on the proposed logical approach, future studies can implement a comparative analysis of long-term landscape dynamics along an urban hierarchy (e.g., large cities to medium-sized towns) to unveil the effectiveness of spatial planning and anti-sprawl policies at different spatial scales and in largely variable socioeconomic contexts 25 .…”

Section: Discussionmentioning

confidence: 99%

“…In such contexts, the design of empirical models and operational frameworks to quantify and understand the long-term evolution of landscape systems in metropolitan regions is a challenging task [22][23][24][25] . Every approach should ensure theoretical parsimony and consistency with the state of knowledge [26][27][28] .…”

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confidence: 99%

Investigating metropolitan change through mathematical morphology and a dynamic factor analysis of structural and functional land-use indicators

Nickayin

Egidi

Cudlín

et al. 2023

Sci Rep

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We presented an operational rationale grounded on complex system thinking to quantify structural and functional landscape transformations along three stages representative of post-war metropolitan development in Rome, Italy (urbanisation with population/settlement densification, 1949–1974; suburbanisation with medium-density settlement expansion, 1974–1999; counter-urbanisation with settlement sprawl, 1999–2016). A mathematical morphology approach assessing the geometric form of land patches and a multi-way factor analysis (MFA) of landscape metrics were used to investigate the joint evolution of urban form and land-use functions over time. The empirical results of the MFA delineated the multivariate relationship between nine land-use classes (with distinctive socioeconomic functions) and seven morphological types (reflecting different landscape structures) according to four observation times (1949, 1974, 1999, 2016). Taken as an intrinsic attribute of complex landscape systems experiencing intense transformations, an estimation of the ‘rapidity-of-change’ in the form-functions relationship at a given development stage was derived from MFA outcomes separately for urbanisation, suburbanisation, and counter-urbanisation. A simplified form-functions relationship, reflecting the spatial polarisation in compact settlements and rural (low-density) landscapes, was observed with compact urbanisation. By stimulating urban sprawl into fringe farmland, suburbanisation resulted in patchy and heterogeneous rural landscapes. Counter-urbanization was associated with the fragmentation of built-up settlements leading to a chaotic mosaic of land structures that mixes urban and rural traits. Rapidity-of-change in form-function relationships was greater during suburbanisation than urbanisation and counter-urbanisation. It reflects the intrinsic pressure of economic growth in contemporary cities.

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“…About three-quarters of the Earth's land surface have been altered within the last millennium as a result of human activities and natural processes [1][2][3], which also brings a variety of ecological and environmental problems [4][5][6]. Changes in land use and land cover affect directly the Earth's energy balance and the biogeochemical cycle, and also have an impact on hydrological processes and water cycles [7], climate change (precipitation and temperature) [8], carbon cycles [9], biodiversity [10], and forest degradation [11].…”

Section: Introductionmentioning

confidence: 99%

Revealing the Land Use Volatility Process in Northern Southeast Asia

Rao

Zhang

2022

Land

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Frequent land use change has generally been considered as a consequence of human activities. Here, we revealed the land use volatility process in northern Southeast Asia (including parts of Myanmar, Thailand, Laos, Vietnam, and China) from 2000 to 2018 with LandTrendr in the Google Earth Engine (GEE) platform based on the Normalized Burning Index (NBR). The result showed that land use volatility with similar degrees had very obvious aggregation characteristics in time and space in the study area, and the time for the occurrence of land use volatility in adjacent areas was often relatively close. This trend will become more obvious with the intensity of land use volatility. At the same time, land use volatility also has obvious spillover effects, and strong land use volatility will drive changes in the surrounding land. If combined with the land use/cover types, which are closely related to human activities that could have more severe land use volatility, and with the increase of the volatility intensity, the proportion of the land use type with strong land use volatility will gradually increase. Revealing the land use volatility process has a possibility to deepen the understanding of land use change and to help formulate land use policy.

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Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Cited by 24 publications

References 40 publications

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Investigating metropolitan change through mathematical morphology and a dynamic factor analysis of structural and functional land-use indicators

Revealing the Land Use Volatility Process in Northern Southeast Asia

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