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Urban Growth Monitoring and Modeling Using Historical Earth Observation Satellite Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 9034

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Special Issue Editors

Dr. Hiroyuki Miyazaki

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Guest Editor

Center for Spatial Information Science, University of Tokyo Institute of Industrial Science, University of Tokyo 4-6-1 Komaba Meguro Tokyo 153-8505, Japan
Interests: remote sensing; geospatial information; urban observation and monitoring; land cover and land use mapping; sustainable development; machine learning

Dr. Wataru Takeuchi

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Guest Editor

Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: atmosphere and high carbon reservoirs; agriculture; urban environment assessment; natural disaster
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban growth is a major issue in sustainable development, and a lack of control in urban development may fail in the insufficiency of urban infrastructure, disaster risk management, and managing public health. To achieve sustainable urban development, understanding the background history of urban areas is essential to measure the effects of policies, strategies, and investments. Moreover, history is useful to model urban growth for layout strategies and plans through a scenario-based analysis as baselines for the assessment of positive and negative effects of future growths.

Historical earth observation satellite data archives, such as the Landsat series, are useful for tracking urban growth over time, and processing the extensive amount of data has been challenging. However, machine learning techniques and strengthened computing resources have helped in breakthroughs through the automated processing of time-series data with many slices. Additionally, cloud-based computing infrastructures connected to satellite data archives and visualization interfaces, such as the Google Earth Engine, have sped up the application of historical earth observation satellite data. Time-series data sets on urban growth are useful inputs for the application of theories and models for urban growth projections, which could be the basis for urban planners and development agencies for formulating policies and strategies of urban development projects.

The aim of the present Special Issue is to cover the relevant topics, trends, and best practices in urban growth monitoring and modeling using historical earth observation satellite data. Moreover, the Special Issue’s scope covers applications of urban growth monitoring, modeling, and projections to sustainable urban development and introduces new methodologies in the field.

We would like to invite you to contribute to this Special Issue by submitting articles concerning your recent research, experimental work, reviews, and/or case studies related to the field of urban growth monitoring and modeling using historical earth observation satellite data. Contributions may be from, but not limited to, the following topics:

Remote sensing methods using historical earth observation satellite data, such as:

Time-series land cover and land use mapping;
Change detection of land cover and land use;
Modeling and predicting land use and land cover changes;
Mapping urban extents and human settlements;
Modeling and predicting urban growths.

Additionally, applications of the above, such as:

Disaster risk management;
Urban planning and development;
Transport infrastructure development;
Regional development;
Impact assessment of infrastructure development projects;
Socioeconomic monitoring and modeling.

Dr. Hiroyuki Miyazaki
Dr. Wataru Takeuchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

historical earth observation satellite data
satellite remote sensing
land cover and land use
urban growth
human settlement
change detection
time-series analysis
machine learning

Published Papers (4 papers)

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Research

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25 pages, 17056 KiB

Open AccessArticle

SIGNet: A Siamese Graph Convolutional Network for Multi-Class Urban Change Detection

by Yanpeng Zhou, Jinjie Wang, Jianli Ding, Bohua Liu, Nan Weng and Hongzhi Xiao

Remote Sens. 2023, 15(9), 2464; https://doi.org/10.3390/rs15092464 - 08 May 2023

Cited by 5 | Viewed by 2404

Abstract

Detecting changes in urban areas presents many challenges, including complex features, fast-changing rates, and human-induced interference. At present, most of the research on change detection has focused on traditional binary change detection (BCD), which becomes increasingly unsuitable for the diverse urban change detection tasks as cities grow. Previous change detection networks often rely on convolutional operations, which struggle to capture global contextual information and underutilize category semantic information. In this paper, we propose SIGNet, a Siamese graph convolutional network, to solve the above problems and improve the accuracy of urban multi-class change detection (MCD) tasks. After maximizing the fusion of change differences at different scales using joint pyramidal upsampling (JPU), SIGNet uses a graph convolution-based graph reasoning (GR) method to construct static connections of urban features in space and a graph cross-attention method to couple the dynamic connections of different types of features during the change process. Experimental results show that SIGNet achieves state-of-the-art accuracy on different MCD datasets when capturing contextual relationships between different regions and semantic correlations between different categories. There are currently few pixel-level datasets in the MCD domain. We introduce a new well-labeled dataset, CNAM-CD, which is a large MCD dataset containing 2508 pairs of high-resolution images. Full article

(This article belongs to the Special Issue Urban Growth Monitoring and Modeling Using Historical Earth Observation Satellite Data)

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26 pages, 9169 KiB

Open AccessArticle

Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020

by Jing Zhang, Jiaqiang Du, Shifeng Fang, Zhilu Sheng, Yangchengsi Zhang, Bingqing Sun, Jialin Mao and Lijuan Li

Remote Sens. 2023, 15(1), 268; https://doi.org/10.3390/rs15010268 - 02 Jan 2023

Cited by 3 | Viewed by 1918

Abstract

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km² in 2020 which was seven times more than that in 1986, at rates of 166 km²/a during 1986–2001, 365 km²/a during 2001–2010, and 1044 km²/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries. Full article

(This article belongs to the Special Issue Urban Growth Monitoring and Modeling Using Historical Earth Observation Satellite Data)

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24 pages, 7039 KiB

Open AccessArticle

Effects of Mining on Urban Environmental Change: A Case Study of Panzhihua

by Xiaoai Dai, Wenyu Li, Zhilong Liu, Chenbo Tong, Cheng Li, Jianwen Zeng, Yakang Ye, Weile Li, Yunfeng Shan, Jiayun Zhou, Junjun Zhang, Li Xu, Xiaoli Jiang, Huihua Ruan, Jinbiao Zhang and Wei Huang

Remote Sens. 2022, 14(23), 6004; https://doi.org/10.3390/rs14236004 - 26 Nov 2022

Cited by 3 | Viewed by 2018

Abstract

The environment supplies water, land, biological resources, and climate resources for people’s daily life and development, dramatically affecting the subsistence and development of human beings. Panzhihua City is a representative resource-based industrial city of southwestern China. The abundant mineral resources provide the material basis for the city’s development. However, while the overdevelopment of the past decades has provided the preconditions for its rapid economic growth, it has also inevitably had a huge impact on its environmental quality and land use structure. In this study, the landsat remote sensing images, terrain data, socio-economic data, and mining resources exploitation data of Panzhihua were used to extract the NDVI (Normalized Difference Vegetation Index), NDBSI (Normalized Difference Build and Soil Index) and LST (Land Surface Temperature) of the past 20 years at 5-year intervals. We normalized four indicators by Principal Component Analysis to derive a remote sensing ecological index of each factor and build the Remote Sensing-based Ecological Index evaluation model. This research quantified the changes in environmental quality in the past 20 years through the range method, showing that the environmental quality of Panzhihua City first declined and then increased slowly. This research also analyzed the influence of land use types, terrain, mining area, and socio-economy on the environmental quality of Panzhihua City by grey relational analysis and buffer analysis. It is found that with the influence of its unique topographical factors and economic aspects, the environmental quality of Panzhihua City changed to varying degrees. The results provide a reliable basis for the future environmental planning of Panzhihua City and a reference for the ecological restoration of mining areas with different mineral species accurately. Full article

(This article belongs to the Special Issue Urban Growth Monitoring and Modeling Using Historical Earth Observation Satellite Data)

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25 pages, 12860 KiB

Open AccessTechnical Note

Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

by Anna Uciechowska-Grakowicz, Oscar Herrera-Granados, Stanisław Biernat and Joanna Bac-Bronowicz

Remote Sens. 2023, 15(24), 5776; https://doi.org/10.3390/rs15245776 - 18 Dec 2023

Viewed by 974

Abstract

The cities of the future should not only be smart, but also smart green, for the well-being of their inhabitants, the biodiversity of their ecosystems and for greater resilience to climate change. In a smart green city, the location of urban green spaces should be based on an analysis of the ecosystem services they provide. Therefore, it is necessary to develop appropriate information technology tools that process data from different sources to support the decision-making process by analysing ecosystem services. This article presents the methodology used to develop an urban green space planning tool, including its main challenges and solutions. Based on the integration of data from ALS, CLMS, topographic data, and orthoimagery, an urban green cover model and a 3D tree model were generated to complement a smart-city model with comprehensive statistics. The applied computational algorithms allow for reports on canopy volume, CO

_{2}

reduction, air pollutants, the effect of greenery on average temperature, interception, precipitation absorption, and changes in biomass. Furthermore, the tool can be used to analyse potential opportunities to modify the location of urban green spaces and their impact on ecosystem services. It can also assist urban planners in their decision-making process. Full article

(This article belongs to the Special Issue Urban Growth Monitoring and Modeling Using Historical Earth Observation Satellite Data)

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