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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: closed (15 August 2023) | Viewed by 26527

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Special Issue Editors

Prof. Dr. Shaogang Lei

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Guest Editor

School of Environmental and Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China
Interests: remote sensing of ecology and environment; mine ecological restoration

Prof. Dr. Jun Li

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Guest Editor

Resource-Environmental Remote Sensing and Spatial Intelligence Lab, China University of Mining & Technology, Beijing 100083, China
Interests: remote sensing of ecology and environment; geospatial analysis; ecology and environment in mining areas; machine learning; spatiotemporal data mining

Special Issue Information

Dear Colleagues,

The environment significantly impacts the quality of human survival and development. In recent decades, human society and economy have developed rapidly. Population growth, urbanization, and nature resource exploration have placed tremendous pressure on the environment. A series of environmental problems such as ecological damage, environmental pollution, and land degradation have become increasingly serious, especially in ecologically fragile areas and areas with intensive human activities. The problem of ecological and environmental damage has become a key issue that countries around the world are concerned about. How to quickly and accurately monitor environmental changes and evaluate corresponding ecological restoration measures is crucially important for environmental protection and governance.

Remote sensing has huge advantages in real-time, rapid, and accurate collection of large-scale ground observation information and has become an important technical means for environmental monitoring and ecological restoration evaluation. However, single data or methods can hardly solve this comprehensive scientific problem. Various remote sensing technologies from space, sky, and ground form a three-dimensional monitoring method of the environment, generating multisource geospatial data. However, different platforms and different sensors have their unique advantages. Therefore, how to synergize multisource remote sensing data for environmental monitoring and ecological restoration evaluation has great research value and has become a research hotspot today. Finally, through the monitoring of the environment and the evaluation of ecological restoration, the aim is to seek methods and approaches to improve and enhance environmental quality.

This Special Issue aims at making scientific contributions to environmental monitoring and ecological restoration assessment. Papers addressing these topics are invited for this Special Issue, especially those with a practical focus on providing environmental monitoring and ecological restoration assessment solutions by integrating multisource remote sensing data.

Prof. Dr. Shaogang Lei
Prof. Dr. Jun Li
Guest Editors

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Keywords

environmental monitoring
environmental assessment
ecological restoration
land use and land cover
ecological index
remote sensing
GIS
multisource data
geospatial analysis
machine learning

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Published Papers (12 papers)

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Research

15 pages, 5036 KiB

Open AccessArticle

Spatial Pattern Characteristics and Factors for the Present Status of Rural Settlements in the Lijiang River Basin Based on ArcGIS

by Wenjun Zheng, Wentao Cao, Guifang Li, Sijia Zhu and Xianyan Zhang

Int. J. Environ. Res. Public Health 2023, 20(5), 4124; https://doi.org/10.3390/ijerph20054124 - 25 Feb 2023

Cited by 4 | Viewed by 1646

Abstract

In China, rural settlements have undergone significant changes in response to dramatic socioeconomic shifts. However, there has not been any report on rural settlements in the Lijiang River Basin. In this study, ArcGIS 10.2 (including hot spot analysis and kernel density estimation) and Fragstats 4.2 (such as the landscape pattern index) software were used to analyze the spatial pattern and causes of rural settlements in the Lijiang River Basin. The Lijiang River Basin is mainly dominated by micro- and small-sized rural settlements with small areas. Moreover, the results of a hot spot analysis showed that micro- and small-sized rural settlements were mainly located in the upper reaches, and medium- and large-sized rural settlements were mainly located in the middle and lower reaches. The kernel density estimation results showed that the distribution characteristics of the rural settlements in the upper, middle, and lower reaches were significantly different. The spatial forms of rural settlements were affected by physiographic factors such as elevation and slope, karst landforms, and river trunk channels as well as the national policy system, tourism economic development, town distribution, historical heritage, and minority culture. This study is the first to systematically elaborate on the rural settlement pattern and its internal logic from the perspective of the Lijiang River Basin, providing a basis for the optimization and construction of the rural settlement pattern. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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23 pages, 11128 KiB

Open AccessArticle

The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing

by Quansheng Li, Feiyue Li, Junting Guo, Li Guo, Shanshan Wang, Yaping Zhang, Mengyuan Li and Chengye Zhang

Int. J. Environ. Res. Public Health 2023, 20(4), 3759; https://doi.org/10.3390/ijerph20043759 - 20 Feb 2023

Cited by 2 | Viewed by 2058

Abstract

Understanding the synergistic effect between topography and vegetation in the underground coal mine is of great significance for the ecological restoration and sustainable development of mining areas. This paper took advantage of unmanned aerial vehicle (UAV) remote sensing to obtain high-precision topographic factors (i.e., digital elevation model (DEM), slope, and aspect) in the Shangwan Coal Mine. Then, a normalized difference vegetation index (NDVI) was calculated utilizing Landsat images from 2017 to 2021, and the NDVI with the same spatial resolution as the slope and aspect was acquired by down-sampling. Finally, the synergistic effect of topography and vegetation in the underground mining area was revealed by dividing the topography obtained using high-precision data into 21 types. The results show that: (1) the vegetation cover was dominated by “slightly low-VC”, “medium-VC”, and “slightly high-VC” in the study area, and there was a positive correlation between the slope and NDVI when the slope was more than 5°. (2) When the slope was slight, the aspect had less influence on the vegetation growth. When the slope was larger, the influence of the aspect increased in the study area. (3) “Rapidly steep–semi-sunny slope” was the most suitable combination for the vegetation growth in the study area. This paper revealed the relationship between the topography and vegetation. In addition, it provided a scientific and effective foundation for decision-making of ecological restoration in the underground coal mine. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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19 pages, 5283 KiB

Open AccessArticle

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

by Shiqi Huang, Yucheng Zhang and Ouya Zhang

Int. J. Environ. Res. Public Health 2023, 20(4), 3030; https://doi.org/10.3390/ijerph20043030 - 9 Feb 2023

Cited by 2 | Viewed by 1468

Abstract

Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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21 pages, 7069 KiB

Open AccessArticle

Fine-Scale Monitoring of Industrial Land and Its Intra-Structure Using Remote Sensing Images and POIs in the Hangzhou Bay Urban Agglomeration, China

by Lingyan Huang, Shanshan Xiang and Jianzhuang Zheng

Int. J. Environ. Res. Public Health 2023, 20(1), 226; https://doi.org/10.3390/ijerph20010226 - 23 Dec 2022

Cited by 2 | Viewed by 1997

Abstract

China has experienced rapid industrial land growth over last three decades, which has brought about diverse social and environmental issues. Hence, it is extremely significant to monitor industrial land and intra-structure dynamics for industrial land management and industry transformation, but it is still a challenging task to effectively distinguish the internal structure of industrial land at a fine scale. In this study, we proposed a new framework for sensing the industrial land and intra-structure across the urban agglomeration around Hangzhou Bay (UAHB) during 2010–2015 through data on points of interest (POIs) and Google Earth (GE) images. The industrial intra-structure was identified via an analysis of industrial POI text information by employing natural language processing and four different machine learning algorithms, and the industrial parcels were photo-interpreted based on Google Earth. Moreover, the spatial pattern of the industrial land and intra-structure was characterized using kernel density estimation. The classification results showed that among the four models, the support vector machine (SVM) achieved the best predictive ability with an overall accuracy of 84.5%. It was found that the UAHB contains a huge amount of industrial land: the total area of industrial land rose from 112,766.9 ha in 2010 to 132,124.2 ha in 2015. Scores of industrial clusters have occurred in the urban-rural fringes and the coastal zone. The intra-structure was mostly traditional labor-intensive industry, and each city had formed own industrial characteristics. New industries such as the electronic information industry are highly encouraged to build in the core city of Hangzhou and the subcore city of Ningbo. Furthermore, the industrial renewal projects were also found particularly in the core area of each city in the UAHB. The integration of POIs and GE images enabled us to map industrial land use at high spatial resolution on a large scale. Our findings can provide a detailed industrial spatial layout and enable us to better understand the process of urban industrial dynamics, thus highlighting the implications for sustainable industrial land management and policy making at the urban-agglomeration level. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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14 pages, 1816 KiB

Open AccessArticle

Mid-Infrared Emissivity Retrieval from Nighttime Sentinel-3 SLSTR Images Combining Split-Window Algorithms and the Radiance Transfer Method

by Xin Ye, Huazhong Ren, Pengxin Wang, Zhongqiu Sun and Jian Zhu

Int. J. Environ. Res. Public Health 2023, 20(1), 37; https://doi.org/10.3390/ijerph20010037 - 20 Dec 2022

Cited by 2 | Viewed by 1838

Abstract

Land surface emissivity is a key parameter that affects energy exchange and represents the spectral characteristics of land cover. Large-scale mid-infrared (MIR) emissivity can be efficiently obtained using remote sensing technology, but current methods mainly rely on prior knowledge and multi-temporal or multi-angle remote sensing images, and additional errors may be introduced due to the uncertainty of external data such as atmospheric profiles and the inconsistency of multiple source data in spatial resolution, observation time, and other information. In this paper, a new practical method was proposed which can retrieve MIR emissivity with only a single image input by combining the radiance properties of TIR and MIR channels and the spatial information of remote sensing images based on the Sentinel-3 Sea and land surface temperature radiometer (SLSTR) data. Two split-window (SW) algorithms that use TIR channels only and MIR and TIR channels to retrieve land surface temperature (LST) were developed separately, and the initial values of MIR emissivity were obtained from the known LST and TIR emissivity. Under the assumption that the atmospheric conditions in the local area are constant, the radiance transfer equations for adjacent pixels are iterated to optimize the initial values to obtain stable estimation results. The experimental results based on the simulation dataset and real SLSTR images showed that the proposed method can achieve accurate MIR emissivity results. In future work, factors such as angular effects, solar radiance, and the influence of atmospheric water vapor will be further considered to improve performance. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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13 pages, 4879 KiB

Open AccessArticle

3D Co-Seismic Surface Displacements Measured by DInSAR and MAI of the 2017 Sarpol Zahab Earthquake, Mw7.3

by Randa Ali, Xiyong Wu, Qiang Chen, Basheer A. Elubid, Dafalla S. Dafalla, Muhammad Kamran and Abdelmottaleb A. Aldoud

Int. J. Environ. Res. Public Health 2022, 19(16), 9831; https://doi.org/10.3390/ijerph19169831 - 10 Aug 2022

Viewed by 1842

Abstract

On 12 November 2017, an earthquake occurred in Sarpol Zahab city, located on the Iraq/Iran boundary, with a moment magnitude (Mw) of 7.3. Advanced Land Observing Satellite 2 (ALOS-2) L-band (23.6 cm wavelength) and C-band Sentinel-1A data (ascending and descending) were used to detect the co-seismic displacements maps caused by this earthquake. The ALOS-2 data was utilized to reconstruct the 3D co-seismic displacements maps, as well as estimate the fault-dip and slip distribution along the rupture. The results showed the maximum surface displacement in the north, east, and up directions to be 100, 50, and 100 cm, respectively. The best-fit faulting geometry had a strike of 337.5° and a dip of 11.2° toward the northeast, at a depth of 8 km. The predicted geodetic moment was 1.15 1020 Nm, which corresponds to a magnitude of Mw 7.31. There were two significant slip sources: one in the shallower depth range of 8.5–10 km, with a peak slip of 5 m, and another in the depth range of 10.5–20 km, with a peak slip of 5.3 m. Both controlled the principal deformation signals in geodetic images. The slip was concentrated, along with a strike distance of 20 to 40 km, at a depth of 10 to 20 km. The earthquake was caused by the Zagros Mountains Front Fault (ZMFF), based on the results of 3D co-seismic deformation, inferred slip, preliminary investigation, and interpretation of the mainshock, as well as aftershock distributions. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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15 pages, 17022 KiB

Open AccessArticle

Monitoring of Vegetation Disturbance and Restoration at the Dumping Sites of the Baorixile Open-Pit Mine Based on the LandTrendr Algorithm

by Junting Guo, Quansheng Li, Huizhen Xie, Jun Li, Linwei Qiao, Chengye Zhang, Guozhu Yang and Fei Wang

Int. J. Environ. Res. Public Health 2022, 19(15), 9066; https://doi.org/10.3390/ijerph19159066 - 25 Jul 2022

Cited by 11 | Viewed by 2179

Abstract

Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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22 pages, 7424 KiB

Open AccessArticle

Land Use and Climate Change Altered the Ecological Quality in the Luanhe River Basin

by Yongbin Zhang, Tanglei Song, Jihao Fan, Weidong Man, Mingyue Liu, Yongqiang Zhao, Hao Zheng, Yahui Liu, Chunyu Li, Jingru Song, Xiaowu Yang and Junmin Du

Int. J. Environ. Res. Public Health 2022, 19(13), 7719; https://doi.org/10.3390/ijerph19137719 - 23 Jun 2022

Cited by 5 | Viewed by 2086

Abstract

Monitoring and assessing ecological quality (EQ) can help to understand the status and dynamics of the local ecosystem. Moreover, land use and climate change increase uncertainty in the ecosystem. The Luanhe River Basin (LHRB) is critical to the ecological security of the Beijing–Tianjin–Hebei region. To support ecosystem protection in the LHRB, we evaluated the EQ from 2001 to 2020 based on the Remote Sensing Ecological Index (RSEI) with the Google Earth Engine (GEE). Then, we introduced the coefficient of variation, Theil–Sen analysis, and Mann–Kendall test to quantify the variation and trend of the EQ. The results showed that the EQ in LHRB was relatively good, with 61.08% of the basin rated as ‘good’ or ‘excellent’. The spatial distribution of EQ was low in the north and high in the middle, with strong improvement in the north and serious degradation in the south. The average EQ ranged from 0.58 to 0.64, showing a significant increasing trend. Furthermore, we found that the expansion of construction land has caused degradation of the EQ, whereas climate change likely improved the EQ in the upper and middle reaches of the LHRB. The results could help in understanding the state and trend of the eco-environment in the LHRB and support decision-making in land-use management and climate change. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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15 pages, 4535 KiB

Open AccessArticle

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

by Sichen Wang, Xi Mu, Peng Jiang, Yanfeng Huo, Li Zhu, Zhiqiang Zhu and Yanlan Wu

Int. J. Environ. Res. Public Health 2022, 19(12), 7186; https://doi.org/10.3390/ijerph19127186 - 11 Jun 2022

Cited by 7 | Viewed by 2004

Abstract

Ozone (O₃), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O₃ is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O₃ across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O₃ column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R² and RMSE of our model were 0.94 and 10.64 μg m⁻³, respectively. Based on the O₃ distribution over eastern China derived from the model, we found that people in this region suffered from excessive O₃ exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O₃ > 100 μg m⁻³ for more than 150 days in 2020. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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21 pages, 9529 KiB

Open AccessArticle

A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

by Chengye Zhang, Huiyu Zheng, Jun Li, Tingting Qin, Junting Guo and Menghao Du

Int. J. Environ. Res. Public Health 2022, 19(9), 5176; https://doi.org/10.3390/ijerph19095176 - 24 Apr 2022

Cited by 8 | Viewed by 1860

Abstract

Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities (W_mine) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities (V-W_mine) to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the W_mine and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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21 pages, 14582 KiB

Open AccessArticle

Land Subsidence in Qingdao, China, from 2017 to 2020 Based on PS-InSAR

by Mengwei Li, Xuedong Zhang, Zechao Bai, Haoyun Xie and Bo Chen

Int. J. Environ. Res. Public Health 2022, 19(8), 4913; https://doi.org/10.3390/ijerph19084913 - 18 Apr 2022

Cited by 9 | Viewed by 2683

Abstract

Land subsidence is a global geological disaster that seriously affects the safety of surface and underground buildings/structures and even leads to loss of life and property. The large-scale and continuous long-time coverage of Interferometric Synthetic Aperture Radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Based on the 108 SAR data of Sentinel-1 from April 2017 to December 2020, this study used Persistent Scatterer InSAR (PS-InSAR) technology to monitor the land subsidence in Qingdao. In addition, detailed analysis and discussion of land subsidence combined with the local land types and subway construction were carried out. From the entire area to the local scale, the deformation analysis was carried out in the two dimensions of time and space. The results reveal that the rate of surface deformation in Qingdao from 2017 to 2020 was mainly −34.48 to 5.77 mm/a and that the cumulative deformation was mainly −126.10 to 30.18 mm. The subsidence areas were mainly distributed in coastal areas (along the coasts of Jiaozhou Bay and the Yellow Sea) and inland areas (northeast Laixi City and central Pingdu City). In addition, it was found that obvious land subsidence occurred near the Health Center Station of Metro Line 8, a logistics company in Qingdao, and near several high-rise residential areas and business office buildings. It is necessary for the relevant departments to take timely action to prevent and mitigate subsidence-related disasters in these areas. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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17 pages, 46201 KiB

Open AccessArticle

Spatial Distribution and Migration Characteristics of Heavy Metals in Grassland Open-Pit Coal Mine Dump Soil Interface

by Zhen Cai, Shaogang Lei, Yibo Zhao, Chuangang Gong, Weizhong Wang and Changchun Du

Int. J. Environ. Res. Public Health 2022, 19(8), 4441; https://doi.org/10.3390/ijerph19084441 - 7 Apr 2022

Cited by 12 | Viewed by 3028

Abstract

The open-pit coal mine dump in the study area contains many low-concentration heavy metal pollutants, which may cause pollution to the soil interface. Firstly, statistical analysis and geostatistical spatial interpolation methods described heavy metal pollution’s spatial distribution. The mine dump heavy metal pollution distribution is strongly random due to disorderly piles, but it is closely related to slope soil erosion. Furthermore, the soil deposition area is where pollutants accumulate. For example, all heavy metal elements converge at the bottom of the dump. Usually, the pollution in the lower part is higher than that in the upper part; the pollution in the lower step is higher than the upper step; the pollution in the soil deposition locations such as flat plate and slope bottom is higher than the soil erosion locations such as slope tip and middle slope. Finally, the hyperspectral remote sensing method described heavy metals pollution’s migration characteristics, that the pollutants could affect the soil interface by at least 1 km. This study provides a basis for preventing and controlling critical parts of mine dump heavy metal pollution and pollution path control. Full article

(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)

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