Special Issue "Spatio-Temporal Environmental Monitoring and Social Sensing"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: 30 December 2019.

Special Issue Editor

Dr. Hone-Jay Chu
E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan
Interests: We are interested to generate space–time insights from remote sensing, big data, and open data for environmental management and social sensing. Our current research covers four key themes: 1. Environmental Resilience; 2. Human Mobility; 3. Health; 4. Location Intelligence.

Special Issue Information

Dear Colleagues,

Environmental science reflects the interaction of human activities and the natural environment. Mapping spatial and temporal patterns of urban cities and understanding the causes and consequences of such details are critical tasks in the study of global change. As a result of the rapid development of information and data technology, environmental monitoring and social sensing in cities for land cover (use) change, air/water pollution, land subsidence, public health, crime, traffic information, and crowd movement have become important means of analyzing smart city and sustainable earth issues. This Special Issue of the International Journal of Environmental Research and Public Health offers an opportunity to publish high-quality multi-disciplinary environmental monitoring and social sensing research.

We will welcome papers related to environmental monitoring and social sensing. For this Special Issue, we invite submissions that provide spatio-temporal information on these environmental and human system dynamics, including GIS, GPS, remote sensing, UAV, social sensing, IoT, and big data. Implications for AI, data mining, and models of environmental monitoring and social sensing may also be addressed.

Dr. Hone-Jay Chu
Guest Editor

Manuscript Submission Information

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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. International Journal of Environmental Research and Public Health 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 1800 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

  • Environmental monitoring
  • Social sensing
  • Remote sensing
  • Land cover and land use
  • Global change
  • Water
  • Air quality
  • GIS
  • Spatial interaction
  • Temporal activity pattern

Published Papers (9 papers)

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Research

Open AccessArticle
Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network
Int. J. Environ. Res. Public Health 2019, 16(20), 3788; https://doi.org/10.3390/ijerph16203788 - 09 Oct 2019
Abstract
The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, [...] Read more.
The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Determinants of Urban Expansion and Spatial Heterogeneity in China
Int. J. Environ. Res. Public Health 2019, 16(19), 3706; https://doi.org/10.3390/ijerph16193706 - 01 Oct 2019
Abstract
China is the world’s largest developing country and its regions vary considerably. However, spatial heterogeneity in determinants of urban expansion in prefecture-level cities have not been identified. The present study explored the spatiotemporal characteristics of Chinese urban expansion and adopted a geographically weighted [...] Read more.
China is the world’s largest developing country and its regions vary considerably. However, spatial heterogeneity in determinants of urban expansion in prefecture-level cities have not been identified. The present study explored the spatiotemporal characteristics of Chinese urban expansion and adopted a geographically weighted regression (GWR) method to determine this spatial heterogeneity. The results indicated that China experienced massive urban expansion during 1990–2015, with urban areas growing from 4.88 × 104 km2 to 1.06 × 105 km2, 46.42% of which was distributed in the eastern region. The results of the GWR model revealed the spatial heterogeneity in the determinants of urban expansion. Marketization was vital for urban expansion and had a stronger impact in the developed eastern and southern regions than in the less-developed northern and western regions. Globalization and decentralization bi-directionally affected urban expansion. The constraining effects of physical factors were limited and stronger in the developing northern region than in the developed southern region. Identifying the varying determinants of urban expansion is essential for policy-making in various regions. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Exploring Spatiotemporal Pattern of Grassland Cover in Western China from 1661 to 1996
Int. J. Environ. Res. Public Health 2019, 16(17), 3160; https://doi.org/10.3390/ijerph16173160 - 29 Aug 2019
Cited by 1
Abstract
Historical grassland cover change is vital for global and regional environmental change modeling; however, in China, estimates of this are rare, and therefore, we propose a method to reconstruct grassland cover over the past 300 years. By synthesizing remote sensing-derived Chinese land use [...] Read more.
Historical grassland cover change is vital for global and regional environmental change modeling; however, in China, estimates of this are rare, and therefore, we propose a method to reconstruct grassland cover over the past 300 years. By synthesizing remote sensing-derived Chinese land use and land cover change (LULCC) data (1980–2015) and potential natural vegetation data simulated by the relationship between vegetation and environment, we first determined the potential extent of natural grassland vegetation (PENG) in the absence of human activities. Then we reconstructed grassland cover across western China between 1661 and 1996 at 10 km resolution by overlaying the Chinese historical cropland dataset (CHCD) over the PENG. As this land cover type has been significantly influenced by anthropogenic factors, the data show that the proportion of grassland in western China continuously decreased from 304.84 × 106 ha in 1661 to 277.69 × 106 ha in 1996. This reduction can be divided into four phases, comprising a rapid decrease between 1661 and 1724, a slow decrease between 1724 and 1873, a sharp decrease between 1873 and 1980, and a gradual increase since 1980. These reductions correspond to annual loss rates of 7.32 × 104 ha, 2.90 × 104 ha, 17.04 × 104 ha, and −2.37 × 104 ha, respectively. The data reconstructed here show that the decrease in grassland area between 1661 and 1724 was mainly limited to the Gan-Ning region (Gansu and Ningxia) and was driven by the early agricultural development policies of the Qing Dynasty. Grassland was extensively cultivated in northeastern China (Heilongjiang, Jilin, and Liaoning) and in the Xinjiang region between 1724 and 1980, a process which resulted from an exponential increase in immigrants to these provinces. The reconstruction results enable provide crucial data that can be used for modeling long-term climate change and carbon emissions. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City
Int. J. Environ. Res. Public Health 2019, 16(13), 2318; https://doi.org/10.3390/ijerph16132318 - 30 Jun 2019
Cited by 1
Abstract
Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban [...] Read more.
Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Exploring Multidimensional Spatiotemporal Point Patterns Based on an Improved Affinity Propagation Algorithm
Int. J. Environ. Res. Public Health 2019, 16(11), 1988; https://doi.org/10.3390/ijerph16111988 - 04 Jun 2019
Abstract
Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP [...] Read more.
Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data
Int. J. Environ. Res. Public Health 2019, 16(11), 1950; https://doi.org/10.3390/ijerph16111950 - 01 Jun 2019
Abstract
With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal [...] Read more.
With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City (R2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic (R2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
The Response of Net Primary Production to Climate Change: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone in China
Int. J. Environ. Res. Public Health 2019, 16(9), 1497; https://doi.org/10.3390/ijerph16091497 - 27 Apr 2019
Cited by 2
Abstract
The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been [...] Read more.
The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%). Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
Int. J. Environ. Res. Public Health 2019, 16(7), 1300; https://doi.org/10.3390/ijerph16071300 - 11 Apr 2019
Cited by 1
Abstract
This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O3 concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O3 concentrations from 2000 [...] Read more.
This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O3 concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O3 concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R2 of 0.74 and a 10-fold cross-validated R2 of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O3 concentration variation in Asian areas. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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Open AccessArticle
Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland
Int. J. Environ. Res. Public Health 2019, 16(7), 1109; https://doi.org/10.3390/ijerph16071109 - 28 Mar 2019
Abstract
Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can [...] Read more.
Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill. Full article
(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)
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