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Advances to GIS for Sensing of Earth and Human Interaction

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 22592

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Special Issue Editors

Dr. Tao Pei

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Guest Editor

Institute of Geographic Sciences and Natural Resources Research, the Chinese Academy of Sciences, Beijing 100101, China
Interests: big geodata mining; geostatistics; urban computation

Prof. Dr. Piotr A. Werner

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Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland
Interests: application of information and communication technologies; information society development; GIS and remote sensing; methodology and applications of geoinformation systems; analysis, modeling, and simulation of spatial data; monitoring and land use changes; urbanization issues; natural hazard issues

Prof. Dr. Ling Yin

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Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: spatiotemporal data analysis; geographically computational epidemiology; GIS for transportation
Special Issues, Collections and Topics in MDPI journals

Dr. Xi Gong

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Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM 87131, USA
Interests: big data science and analytics; health and environment; spatially integrated social science; GIS-based modeling

Dr. Johannes H. Uhl

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University of Colorado Population Center (CUPC), Institute of Behavioral Science & Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO 80309, USA
Interests: urban analysis; long-term land change, built environment, time series analysis in remote sensing; integrative approaches using remotely sensed data and other geospatial datasets

Special Issue Information

Dear Colleagues,

Advances in GIS for Sensing of Earth and Human Interactions

Earth and human interaction is one of the core issues in geography. Remotely sensed data and associated analytical methods are essential for detecting changes in the Earth’s surface. Although changes derived from remote sensing data can be used as a proxy to infer the intensity of human activity, the observation of human activities from remote sensing is indirect. With the development of location-aware technologies, alternative data sources measuring human activities have emerged, and social sensing techniques have been developed to reveal human behavior patterns. The combination of remote sensing data and human behavior data provides a new perspective on coupled human-natural systems. For this reason, we have launched this special issue on sensing the interactions between Earth and humans with both types of data. The topics of this issue include but are not limited to:

Multisource big data processing (e.g., social media data, cellular signaling data, vehicle trajectories, and other VGIs);
Urban function identification with remote sensed data and human behavior data;
Simulation models of human–Earth system interactions;
Urban environment evaluation with multisource data;
Urban renewal detection based on remote sensing and VGI data;
Human activity and its environmental effects;
Evolution of urban systems and populated places based on alternative (non-remote sensing) data.

Dr. Tao Pei
Prof. Dr. Piotr A. Werner
Prof. Dr. Ling Yin
Dr. Xi Gong
Dr. Johannes H. Uhl
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

Coupled human-natural systems
Social sensing
Human-environmental interactions
Human behavior
Geospatial big data
Urban analysis

Published Papers (9 papers)

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Research

18 pages, 19435 KiB

Open AccessArticle

Quantifying Ecological Landscape Quality of Urban Street by Open Street View Images: A Case Study of Xiamen Island, China

by Dongxin Wen, Maochou Liu and Zhaowu Yu

Remote Sens. 2022, 14(14), 3360; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143360 - 12 Jul 2022

Cited by 9 | Viewed by 2383

Abstract

With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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23 pages, 21961 KiB

Open AccessArticle

Changes in Land Relief in Urbanised Areas Using Laser Scanning and Archival Data on the Example of Łódź (Poland)

by Marcin Jaskulski, Iwona Jażdżewska and Aleksander Szmidt

Remote Sens. 2022, 14(13), 2961; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132961 - 21 Jun 2022

Viewed by 1297

Abstract

The authors undertook to examine and analyse the changes in the relief in Łódź city centre over a period of over one hundred years. Archival cartographic resources containing morphometric information and contemporary laser scanning data (LIDAR) are used to analyse changes. This required appropriate transformation of these data to generate a differential relief map. Information on the geographical environment (waters, relief) is linked to the spatial development of the city. The analyses revealed several characteristic types of changes occurring in the area, which are presented in the form of case studies. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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16 pages, 11590 KiB

Open AccessArticle

Urban Sprawl in Poland (2016–2021): Drivers, Wildcards, and Spatial Externalities

by Piotr A. Werner, Veranika Kaleyeva and Mariusz Porczek

Remote Sens. 2022, 14(12), 2804; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122804 - 10 Jun 2022

Cited by 3 | Viewed by 1596

Abstract

The COVID-19 lockdown in 2020–2021 and the refugee crisis in 2021–2022 were two new and unexpected social and political events in Poland in recent years. These “wildcards” will certainly have major effects on individuals and cities, both directly and indirectly, through the influence of “externalities.” The paper examines trends in the spatial development of Polish cities during the last five years (2016–2021), focusing on residential suburbanization and urban sprawl. The study aims to reveal the elements that determine the spatial scale of suburbanization, as well as “wildcards” that may have an indirect impact on the process but are difficult to quantify and include in spatial analysis. The use of location quotient (LQ) metrics, as well as a subset of the Global Human Settlement Layer in the spatial analysis allow for comparisons of locations with intensified urbanization throughout different periods, serving a task that is comparable to feature standardization from a time and space viewpoint. The analysis provides evidence of growing suburbanization surrounding major Polish cities from 2016 to 2021, while also exposing distinct elements of spatial development during a period that was marked by social and political stress (2021). Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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22 pages, 2895 KiB

Open AccessArticle

Optimization Framework for Spatiotemporal Analysis Units Based on Floating Car Data

by Haifu Cui, Liang Wu and Zhenming He

Remote Sens. 2022, 14(10), 2376; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102376 - 14 May 2022

Cited by 2 | Viewed by 1247

Abstract

Spatiotemporal scale is a basic component of geographical problems because the size of spatiotemporal units may have a significant impact on the aggregation of spatial data and the corresponding analysis results. However, there is no clear standard for measuring the representativeness of conclusions when geographical data with different temporal and spatial units are used in geographical calculations. Therefore, a spatiotemporal analysis unit optimization framework is proposed to evaluate candidate analysis units using the distribution patterns of spatiotemporal data. The framework relies on Pareto optimality to select the spatiotemporal analysis unit, thereby overcoming the subjectivity and randomness of traditional unit setting methods and mitigating the influence of the modifiable areal unit problem (MAUP) to a certain extent. The framework is used to analyze floating car trajectory data, and the spatiotemporal analysis unit is optimized by using a combination of global spatial autocorrelation coefficients and the coefficients of variation of local spatial autocorrelation. Moreover, based on urban hotspot calculations, the effectiveness of the framework is further verified. The proposed optimization framework for spatiotemporal analysis units based on multiple criteria can provide suitable spatiotemporal analysis scales for studies of geographical phenomena. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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17 pages, 3872 KiB

Open AccessArticle

Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China

by Sheng Huang, Yunyan Du, Jiawei Yi, Fuyuan Liang, Jiale Qian, Nan Wang and Wenna Tu

Remote Sens. 2022, 14(5), 1269; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051269 - 05 Mar 2022

Cited by 8 | Viewed by 2180

Abstract

Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China’s coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent’s location request data to advance our understanding of dweller’s collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans’ location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R². The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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17 pages, 6603 KiB

Open AccessCommunication

The Effects of Climate and Bioclimate on COVID-19 Cases in Poland

by Piotr A. Werner, Oleh Skrynyk, Mariusz Porczek, Urszula Szczepankowska-Bednarek, Robert Olszewski and Małgorzta Kęsik-Brodacka

Remote Sens. 2021, 13(23), 4946; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234946 - 05 Dec 2021

Cited by 7 | Viewed by 2392

Abstract

The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim of the study is to identify the effects of changes in air temperature on the number of COVID-19 infections in Poland. The hypothesis under consideration concerns an increase in the number of COVID-19 cases as temperature decreases. The spatial heterogeneity of the relationship under study during the first year and a half of the COVID-19 pandemic in Polish counties is thus revealed. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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17 pages, 45481 KiB

Open AccessArticle

Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

by Guoqing Zhi, Bin Meng, Juan Wang, Siyu Chen, Bin Tian, Huimin Ji, Tong Yang, Bingqing Wang and Jian Liu

Remote Sens. 2021, 13(20), 4086; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204086 - 13 Oct 2021

Cited by 5 | Viewed by 2323

Abstract

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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16 pages, 4860 KiB

Open AccessArticle

Impact of Urban Expansion on Rain Island Effect in Jinan City, North China

by Yanjun Zhao, Jun Xia, Zongxue Xu, Lei Zou, Yunfeng Qiao and Peng Li

Remote Sens. 2021, 13(15), 2989; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152989 - 29 Jul 2021

Cited by 21 | Viewed by 2784

Abstract

Rapid urbanization leads to changes in urban micro meteorology, such as the urban heat island effect and rain island effect, which eventually brings about urban waterlogging and other problems. In this study, the data of precipitation, temperatures and impervious surfaces with long series and high resolution are used to study the rain island effect in Jinan City, China. MK-Sen’s slope estimator, Pettitt test and Pearson correlation analysis are used to quantitatively analyze the impact of urban expansion on extreme climate indices. The results show that Jinan City has experienced rapid urbanization since the 1978 economic reform, and the impervious surface areas have increased from 311.68 km² (3.04%) in 1978 to 2389.50 km² (23.33%) in 2017. Urban expansion has a significant impact on temperature, with large variations in extreme temperature indices over the intensive construction area relative to the sparse construction area. The extreme temperature indices have a significant correlation with impervious surfaces. Jinan City shows a certain degree of rain island effect, which seems to be spatially correlated with the urban heat island effect. The frequency of short-duration precipitation events significantly increases and the intensity of precipitation events generally increases. The magnitude and frequency of extreme precipitation indices in the intensive construction area significantly increase when compared to that in the sparse construction area, and they have a significant correlation with impervious surfaces. There is a tendency that Jinan City’s rainfall center moves towards to the intensive construction area. Full article

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34 pages, 21929 KiB

Open AccessArticle

Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector

by Yue Wang, Haijia Wen, Deliang Sun and Yuechen Li

Remote Sens. 2021, 13(13), 2625; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132625 - 04 Jul 2021

Cited by 41 | Viewed by 4361

Abstract

This study aims to evaluate risk and discover the distribution law for landslides, so as to enrich landslide prevention theory and method. It first selected Fengjie County in the Three Gorges Reservoir Area as the study area. The work involved developing a landslide risk map using hazard and vulnerability maps utilizing landslide dataset from 2001 to 2016. The landslide dataset was built from historical records, satellite images and extensive field surveys. Firstly, under four primary conditioning factors (i.e., topographic factors, geological factors, meteorological and hydrological factors and vegetation factors), 19 dominant factors were selected from 25 secondary conditioning factors based on the GeoDetector to form an evaluation factor library for the LSM. Subsequently, the random forest model (RF) was used to analyze landslide susceptibility. Then, the landslide hazard map was generated based on the landslide susceptibility mapping (LSM) for the study region. Thereafter, landslide vulnerability assessment was conducted using key elements (economic, material, community) and the weights were provided based on expert judgment. Finally, when risk equals vulnerability multiplied by hazard, the region was categorized as very low, low, medium, high and very high risk level. The results showed that most landslides distribute on both sides of the reservoir bank and the primary and secondary tributaries in the study area, which showed a spatial distribution pattern of more north than south. Elevation, lithology and groundwater type are the main factors affecting landslides. Fengjie County landslide risk level is mostly low (accounting for 73.71% of the study area), but a small part is high and very high risk level (accounting for 2.5%). The overall risk level shows the spatial distribution characteristics of high risk in the central and eastern urban areas and low risk in the southern and northern high-altitude areas. Secondly, it is necessary to strictly control the key risk areas, and carry out prevention and control zoning management according to local conditions. The study is conducted for a specific region but can be extended to other areas around the investigated area. The developed landslide risk map can be considered by relevant government officials for the smooth implementation of management at the regional scale. Full article

(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)

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