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Applications of Remote Sensing in Environmental and Ecological Sciences
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Applications of Remote Sensing in Environmental and Ecological Sciences

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 11377

Special Issue Editors

Dr. Yindi Zhao

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Guest Editor
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: remote sensing data processing and analysis; remote sensing application
Prof. Dr. Bo Wu

E-Mail Website
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Interests: remote sensing image processing and application; spatial statistical analysis
Dr. Pei Liu

E-Mail Website
Guest Editor
Hainan Academy of Ocean and Fisheries Sciences, Haikou 570100, China
Interests: remotely sensed data processing for urbanization and its environmental impacts analysis; land use/cover classification; landscape ecological research

Special Issue Information

Dear Colleagues,

This Special Issue focuses on remote sensing applications in environmental and ecological sciences. The environment and ecology are the essential bases of human survival and development. Environmental protection and ecological sustainable development are the global challenges facing humanity in the 21st century.

Remote sensing is the science of obtaining information from a distance. It has the capability to perform spatially continuous and highly frequent observations over a variety of scales and resolutions, which can be used for environment and ecology monitoring at global and regional levels. With the development of Earth observation technology and information processing technology, remote sensing technology has played an important role in environmental and ecological science research.

This Special Issue aims to highlight the contributions of remote sensing-related research in environmental and ecological sciences and to collect theoretical and practical innovations that promote bridging gaps between remote sensing and environmental and ecological sciences. We are open to manuscripts presenting the newest theories and methodologies of applying remote sensing in air, soil, and water quality, land use and land cover changes, ecosystem restoration, and urban liveability assessment, as well as the newest remote sensing acquisition systems and artificial intelligence technologies to deal with the heterogeneous data involved in environmental and ecological monitoring. Both state-of-the-art articles and comprehensive literature reviews are welcome for submission.

Dr. Yindi Zhao
Prof. Dr. Bo Wu
Dr. Pei Liu
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. Sustainability 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 2400 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

  • remote sensing
  • land cover and land use changes
  • environmental monitoring
  • ecosystem monitoring
  • ecological restoration monitoring
  • urban liveability assessment
  • heterogeneous data processing

Published Papers (7 papers)

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Research

18 pages, 5462 KiB  
Article
Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)
by Yong Yan, Ying Wang, Cheng Yu and Zhimin Zhang
Sustainability 2023, 15(13), 10298; https://doi.org/10.3390/su151310298 - 29 Jun 2023
Cited by 5 | Viewed by 1363
Abstract
Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water [...] Read more.
Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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24 pages, 6985 KiB  
Article
Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City
by Mingyan Ni, Yindi Zhao, Caihong Ma, Xiaolin Hou and Yanmei Xie
Sustainability 2023, 15(12), 9729; https://doi.org/10.3390/su15129729 - 18 Jun 2023
Cited by 1 | Viewed by 1156
Abstract
It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting [...] Read more.
It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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20 pages, 5059 KiB  
Article
Spatiotemporal Dynamics of Aboveground Biomass and Its Influencing Factors in Xinjiang’s Desert Grasslands
by Gongxin Wang, Changqing Jing, Ping Dong, Baoya Qin and Yang Cheng
Sustainability 2022, 14(22), 14884; https://doi.org/10.3390/su142214884 - 10 Nov 2022
Cited by 4 | Viewed by 1331
Abstract
Grassland biomass is a significant parameter for measuring grassland productivity and the ability to sequester carbon. Estimating desert grassland biomass using the best remote sensing inversion model is essential for understanding grassland carbon stocks in arid and semi-arid regions. The present study constructed [...] Read more.
Grassland biomass is a significant parameter for measuring grassland productivity and the ability to sequester carbon. Estimating desert grassland biomass using the best remote sensing inversion model is essential for understanding grassland carbon stocks in arid and semi-arid regions. The present study constructed an optimal inversion model of desert grassland biomass based on actual biomass measurement data and various remote-sensing product data. This model was used to analyze the spatiotemporal variation in desert grassland biomass and climate factor correlation in Xinjiang from 2000 to 2019. The results showed that (1) among the established inversion models of desert grasslands aboveground biomass (AGB), the exponential function model with the normalized differential vegetation index (NDVI) as the independent variable was the best. Furthermore, (2) the NDVI of desert grasslands in Xinjiang showed a highly significant increasing trend from 2000 to 2019 with a spatially concentrated distribution in the north and a more dispersed distribution in the south. In addition, (3) the average AGB value was 52.35 g·m−2 in Xinjiang from 2000 to 2019 and showed a spatial distribution with low values in the southeast and high values in the northwest. Moreover, (4) the low fluctuation in the coefficient of desert grassland variation accounted for 65.26% of overall AGB fluctuation (<0.10) from 2000 to 2019. Desert grassland AGB in most areas (88.65%) showed a significant increase over the last 20 years. Lastly, (5) the correlation between desert grassland precipitation and AGB was stronger than that between temperature and AGB from 2000 to 2019. This study provides a scientific basis and technical support for grassland livestock management and carbon storage assessments in Xinjiang. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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19 pages, 5519 KiB  
Article
Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China
by Meng Luo, Shengwei Zhang, Lei Huang, Zhiqiang Liu, Lin Yang, Ruishen Li and Xi Lin
Sustainability 2022, 14(20), 13232; https://doi.org/10.3390/su142013232 - 14 Oct 2022
Cited by 9 | Viewed by 1910
Abstract
The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To [...] Read more.
The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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19 pages, 5709 KiB  
Article
Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region
by Caihong Ma, Xin Sui, Yi Zeng, Jin Yang, Yanmei Xie, Tianzhu Li and Pengyu Zhang
Sustainability 2022, 14(18), 11228; https://doi.org/10.3390/su141811228 - 07 Sep 2022
Cited by 5 | Viewed by 1311
Abstract
The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In [...] Read more.
The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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12 pages, 7676 KiB  
Article
Emergency Monitoring of a Tailings Pond Leakage Accident Based on the GEE Platform
by Hengqian Zhao, Zihan Yang, Hongwei Zhang, Jianwei Meng, Qian Jin and Shikang Ming
Sustainability 2022, 14(14), 8558; https://doi.org/10.3390/su14148558 - 13 Jul 2022
Cited by 5 | Viewed by 1503
Abstract
The utilization of mineral resources plays an important role in supporting and promoting economic development and social progress. As a necessary facility for the development and utilization of mineral resources, tailings ponds will cause a series of safety and environmental problems once accidents [...] Read more.
The utilization of mineral resources plays an important role in supporting and promoting economic development and social progress. As a necessary facility for the development and utilization of mineral resources, tailings ponds will cause a series of safety and environmental problems once accidents occur. Based on the Sentinel-2 images obtained from the GEE (Google Earth Engine) platform, this paper carried out emergency monitoring of the Yichun Luming Mining tailings pond leakage accident on 28 March 2020, through the spectral changes in monitoring points in the downstream rivers of the tailings pond, the changes in the images before and after the accident, and the analysis of long-time series various indexes. The results revealed that the pollution was quickly treated in a short time, and the river spectrum returned to normal on April 13. The pollution spread for approximately 300 km downstream to the Yijimi River and the Hulan River, and was finally intercepted at the Lanxi Old Bridge 67 km away from the Songhua River, so that more serious pollution was avoided. This accident had a direct impact on the surrounding six counties. The decrease in NDVI reflects that the accident has a certain degree of influence on the vegetation around the tailings pond, while the change in NDTI reflects that some remedial measures have been taken for the tailings pond after the accident. This study demonstrates the advantages of the GEE platform for the emergency monitoring of accidents, which can provide a reference for the emergency monitoring of similar accidents. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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18 pages, 7508 KiB  
Article
Temporal and Spatial Changes in Vegetation Ecological Quality and Driving Mechanism in Kökyar Project Area from 2000 to 2021
by Ziyi Wang, Tingting Bai, Dong Xu, Juan Kang, Jian Shi, He Fang, Cong Nie, Zhijun Zhang, Peiwen Yan and Dingning Wang
Sustainability 2022, 14(13), 7668; https://doi.org/10.3390/su14137668 - 23 Jun 2022
Cited by 12 | Viewed by 1723
Abstract
The “Kökyar Greening Project” in the suburb of Aksu, Xinjiang, is a model of large-area artificial afforestation in an environment of drought and water scarcity. As an important part of the “3-North Shelter Forest Program”, it plays an important role in promoting the [...] Read more.
The “Kökyar Greening Project” in the suburb of Aksu, Xinjiang, is a model of large-area artificial afforestation in an environment of drought and water scarcity. As an important part of the “3-North Shelter Forest Program”, it plays an important role in promoting the economic development and the environmentally friendly construction of Aksu and even of the whole Xinjiang region. Based on multisource remote-sensing data and meteorological observation data, this study explored the temporal and spatial changes in the vegetation parameters (FVC, NPP, and VEQI) and the ecological parameters (RSEI and LULC) in the Kökyar Project Area from 2000 to 2021. Based on the Theil–Sen median and TSS-RESTREND, this study investigated the path of mutual influence among the FVC, NPP, VEQI, and RSEI, as well as their responses to climate change and human activities. The results show that: (1) from 2000 to 2021, the FVC, NPP, VEQI, and RSEI in the Kökyar Project Area showed a significant upward trend and showed the distribution characteristics of “high in the south and low in the north”. (2) Over the past 22 years, the RSEI has shown a significant increase with the FVC, NPP and VEQI (p < 0.001), indicating that the “Kökyar Greening Project” has achieved significant ecological benefits. (3) The changes in the vegetation parameters and RSEI in the Kökyar Project Area were dominated by human activities. (4) The Kökyar Project Area has caused great changes to the ecosystem pattern of the region, and the vegetation parameters and RSEI in the Kökyar Project Area have increased, mainly in the form of cropland and grassland expansion over the past 22 years. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Environmental and Ecological Sciences)
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