Special Issue "Spatiotemporal Computing for Sustainable Ecosystem"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 May 2017).

Special Issue Editors

Guest Editor
Prof. Yichun Xie

Department of Geography and Geology, Eastern Michigan University, Ypsilanti, Michigan 48197 USA
Website | E-Mail
Fax: +1-(734) 487-5394
Interests: geographic information science (GIScience); urban modeling; coupled human and environmental dynamics; spatial analysis
Guest Editor
Dr. Xinyue Ye

Department of Informatics, Urban Informatics & Spatial Computation Lab, New Jersey Institute of Technology, Newark, NJ 07102, USA
Website | E-Mail
Interests: GIS; spatial analysis; urban and regional modeling

Special Issue Information

Dear Colleagues,

With the ongoing trend of urbanization, it is estimated that more than half of the world’s population resides in urban areas, and this percentage is predicted to rise to 69.6 percent by 2050. The growth of urban areas and the impact of human activities on ecosystems have been leading issues of ecological interest. The impact of LULC (land use/land cover) change raises growing concerns about the processes and functions of ecosystems, in light of the new data and Big Data. Intensive and rapid urbanization generates human-induced LULC change, which exacerbates impacts on the climate system. Rapid urbanization and pervasive LULC change have significantly stimulated economic prosperity at an unprecedented spatial and temporal granularity level, altering the structure, pattern, and functionality of the ecosystems, which provide vital services supporting human society. This Special Issue will bring together scholars to share their research on challenges and solutions of Big Data and Spatiotemporal Computing for Sustainable Ecosystem.

Yichun Xie
Xinyue Ye
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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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

  • Big data
  • New data
  • Human-environmental interaction
  • Land use/land cover change
  • Smart city
  • Spatial-temporal analysis
  • Sustainable ecosystem

Published Papers (6 papers)

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Research

Open AccessArticle
Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation
ISPRS Int. J. Geo-Inf. 2017, 6(7), 226; https://doi.org/10.3390/ijgi6070226
Received: 30 May 2017 / Revised: 12 July 2017 / Accepted: 17 July 2017 / Published: 21 July 2017
Cited by 4 | PDF Full-text (4412 KB) | HTML Full-text | XML Full-text
Abstract
Demand for agricultural food production is projected to increase dramatically in the coming decades, putting at risk our clean water supply and prospects for sustainable development. Fragmentation-free land allocation (FF-LA) aims to improve returns on ecosystem services by determining both space partitioning of [...] Read more.
Demand for agricultural food production is projected to increase dramatically in the coming decades, putting at risk our clean water supply and prospects for sustainable development. Fragmentation-free land allocation (FF-LA) aims to improve returns on ecosystem services by determining both space partitioning of a study area and choice of land-use/land-cover management practice (LMP) for each partition under a budget constraint. In the context of large-scale industrialized food production, fragmentation (e.g., tiny LMP patches) discourages the use of modern farm equipment (e.g., 10- to 20-m-wide combine harvesters) and must be avoided in the allocation. FF-LA is a computationally challenging NP-hard problem. We introduce three frameworks for land allocation planning, namely collaborative geodesign, spatial optimization and a hybrid model of the two, to help stakeholders resolve the dilemma between increasing food production capacity and improving water quality. A detailed case study is carried out at the Seven Mile Creek watershed in the midwestern US. The results show the challenges of generating near-optimal solutions through collaborative geodesign, and the potential benefits of spatial optimization in assisting the decision-making process. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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Open AccessArticle
The Effects of Rural Settlement Evolution on the Surrounding Land Ecosystem Service Values: A Case Study in the Eco-Fragile Areas, China
ISPRS Int. J. Geo-Inf. 2017, 6(2), 49; https://doi.org/10.3390/ijgi6020049
Received: 18 November 2016 / Revised: 7 February 2017 / Accepted: 14 February 2017 / Published: 17 February 2017
Cited by 1 | PDF Full-text (1749 KB) | HTML Full-text | XML Full-text
Abstract
General declines in ecosystem service values (ESV) are acknowledged worldwide; however, rather few studies have quantitatively analyzed the interrelationship between changing rural settlements and values of ecosystem services. This study used the county of Tongyu in West Jilin Province, China, as a case [...] Read more.
General declines in ecosystem service values (ESV) are acknowledged worldwide; however, rather few studies have quantitatively analyzed the interrelationship between changing rural settlements and values of ecosystem services. This study used the county of Tongyu in West Jilin Province, China, as a case study to analyze how changing rural settlements impact the values of ecosystem services on surrounding land in the eco-fragile areas during 1997–2010. Quantitative analytical techniques mainly include the buffer analysis and an ecosystem services valuation. The results show that as the area of rural settlements increased in 1997–2010, the structure of land ecosystems had changed significantly during this time period, causing a change in ESV that was observed with a decline by 1.87 billion yuan and above 20%. The degradation of grasslands, wetlands, and water areas, as well as the farmland reclamation, were the main drivers of the decreases in ESV. The effects of the increased rural settlements on the distribution and variation of ESV were larger than the decreased rural settlements, especially the new rural settlements whose effect was largest, and the effect of changing rural settlements on the values of ecosystem services on the surrounding land was significant in proximity to these settlements. In conclusion, the effects of rural settlement evolution on the natural environment were obvious in the eco-fragile areas. Thus the encroachment of rural settlements still requires enhanced supervision in land management practices, and the scale and spatial distribution of rural settlements should be befittingly allocated in the eco-fragile areas to reduce the disturbance to the ecosystem. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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Open AccessArticle
A NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data: A Case Study of Public Security Video Surveillance
ISPRS Int. J. Geo-Inf. 2017, 6(1), 21; https://doi.org/10.3390/ijgi6010021
Received: 16 October 2016 / Revised: 9 January 2017 / Accepted: 16 January 2017 / Published: 19 January 2017
Cited by 8 | PDF Full-text (2415 KB) | HTML Full-text | XML Full-text
Abstract
With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The [...] Read more.
With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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Open AccessArticle
Pattern of Spatial Distribution and Temporal Variation of Atmospheric Pollutants during 2013 in Shenzhen, China
ISPRS Int. J. Geo-Inf. 2017, 6(1), 2; https://doi.org/10.3390/ijgi6010002
Received: 2 November 2016 / Revised: 8 December 2016 / Accepted: 19 December 2016 / Published: 23 December 2016
Cited by 9 | PDF Full-text (7777 KB) | HTML Full-text | XML Full-text
Abstract
Air pollution caused by atmospheric particulate and gaseous pollutants has drawn broad public concern globally. In this paper, the spatial-temporal distributions of major air pollutants in Shenzhen from March 2013 to February 2014 are discussed. In this study, ground-site monitoring data from 19 [...] Read more.
Air pollution caused by atmospheric particulate and gaseous pollutants has drawn broad public concern globally. In this paper, the spatial-temporal distributions of major air pollutants in Shenzhen from March 2013 to February 2014 are discussed. In this study, ground-site monitoring data from 19 monitoring sites was used and spatial interpolation and spatial autocorrelation methods were applied to analyze both spatial and temporal characteristics of air pollutants in Shenzhen City. During the study period, the daily average concentrations of Particulate Matter (PM10 and PM2.5) ranged from 16–189 μg/m3 and 10–136 μg/m3, respectively, with 13 and 44 over-limit days, indicating that particulate matter was the primary air pollutant in Shenzhen. The highest PM occupation in the polluted air was observed in winter, indicating that fine particulate pollution was most serious in winter. Meanwhile, seasonal agglomeration patterns for six kinds of air pollutants showed that Guangming, Baoan, Nanshan, and the northern part of Longgang were the most polluted areas and PMs were their primary air pollutants. In addition, wind scale and rainfall played an important role in dissipating air pollutant in Shenzhen. The wind direction impacted the air pollution level in Shenzhen in multiple ways: the highest concentrations for all air pollutants all occurred on days with a northeast wind; the second highest ones appeared on the days with no wind. The concentrations on days with north-related winds are higher on average than those of days with south-related winds. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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Open AccessArticle
Analysis of Attraction Features of Tourism Destinations in a Mega-City Based on Check-in Data Mining—A Case Study of Shenzhen, China
ISPRS Int. J. Geo-Inf. 2016, 5(11), 210; https://doi.org/10.3390/ijgi5110210
Received: 21 September 2016 / Revised: 4 November 2016 / Accepted: 7 November 2016 / Published: 10 November 2016
Cited by 10 | PDF Full-text (4614 KB) | HTML Full-text | XML Full-text
Abstract
Location-based service information, provided by social networks, provides new data sources and perspectives to research tourism activities, especially in highly populated mega-cities. Based on three years (2012–2014) of approximately 340,000 check-in records collected from Sina micro-blog at 86 tourist attractions in Shenzhen, a [...] Read more.
Location-based service information, provided by social networks, provides new data sources and perspectives to research tourism activities, especially in highly populated mega-cities. Based on three years (2012–2014) of approximately 340,000 check-in records collected from Sina micro-blog at 86 tourist attractions in Shenzhen, a first-tier city in southern China, we conducted a comprehensive study of the attraction features involving different aspects, such as tourist source, duration of stay, check-in activity index, and attraction correlation degree. The results showed that (1) theme parks established in the early 1990s were the most popular tourist attractions in Shenzhen, but a negative trend was detected in the check-in population; (2) compared with check-in times from surrounding activities and the kernel density of tourists, most destinations in Shenzhen showed a lack of attraction, failing to make the most of their geographic accessibility; and (3) the homogeneity and inconvenient traffic conditions of major tourist destinations leading to the construction of a tourism tour chain has become a challenge. The results of this study demonstrate the potential of big-data mining and provide valuable insights into tourism market design and management in mega-cities. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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Open AccessArticle
Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China
ISPRS Int. J. Geo-Inf. 2016, 5(9), 158; https://doi.org/10.3390/ijgi5090158
Received: 15 May 2016 / Revised: 29 July 2016 / Accepted: 19 August 2016 / Published: 2 September 2016
Cited by 8 | PDF Full-text (5101 KB) | HTML Full-text | XML Full-text
Abstract
Land degradation is a major threat to the sustainability of human habitation, and it is essential to assess it quantitatively. Assessment of the human-induced aspect is especially important for planning appropriate prevention measures. This paper used the Three-North Shelter Forest Program region as [...] Read more.
Land degradation is a major threat to the sustainability of human habitation, and it is essential to assess it quantitatively. Assessment of the human-induced aspect is especially important for planning appropriate prevention measures. This paper used the Three-North Shelter Forest Program region as the study area, and assessed the land degradation dynamic using a time series of summed normalized difference vegetation index (NDVI) based on a trend analysis of the Theil-Sen slope and Mann-Kendall test. The human-induced land degradation was separated from degradation driven by climate using the meteorological dataset through the residual trend (RESTREND) method for the period 1982–2006. The results showed that (1) the NDVI in the study area mainly exhibited an increasing trend, approximately 13.00% of the study area experienced significantly positive NDVI trends and 6.20% showed decline. Furthermore, (2) the correlation between the summed NDVI and precipitation was higher than the correlation between NDVI and temperature, suggesting that precipitation was the most essential factor that impacted NDVI dynamic in the study area; (3) The significant trends of vegetation by anthropogenic disturbances were detected, which were significant positive and negative trends of 11.93% and 6.19%, respectively. All of these findings enrich our knowledge of human activities that impact land degradation in arid or semi-arid regions and provide a scientific basis for the management of ecological restoration programs. Full article
(This article belongs to the Special Issue Spatiotemporal Computing for Sustainable Ecosystem)
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