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Theory, Model, Technology, and Application of Remote Sensing in Sustainability

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 14344

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Prof. Dr. Chao Chen

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Guest Editor

School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: remote sensing; environment
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Prof. Dr. Jinsong Chen

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Guest Editor

Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen 518055, China
Interests: remote sensing of environment

Dr. Juhua Luo

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Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing 210008, China
Interests: remote sensing of aquaculture; land cover and land use change; wetland remote sensing; remote sensing of water environment and water ecology
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Dr. Lei Fang

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Guest Editor

Environment Research Institute, Shandong University, Qingdao 266237, China
Interests: forest remote sensng; climate change; wildland fire
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Special Issue Information

Dear Colleagues,

Ongoing climate change has induced a series of crises and challenges that are greatly threatening human well-being and natural ecosystem health. The warming, drought, and extreme climatic events can exert strong impacts on food production, human health, environmental safety, and social development. Increasing evidence also suggests that climate changes are responsible for declined biodiversity, intensified disturbance regimes, degraded ecosystem resilience, and decreased ecosystem services. Numerous 'climate-related events' such as floods, storms, landslides, extreme temperatures (like heat waves or freezes), and wildfires adversely impact human society and terrestrial ecosystems remarkably. For aquatic ecosystems, issues such as saltwater intrusion, sea-level rise, damage from coastal storms, wetland degradation, and lake eutrophication are also widely reported globally. Therefore, a timely and continuously monitoring of land-surface changes and internal shifts within ecosystems is of great significance to maintaining the sustainable development of human beings.

Benefitting from the well-known advantages and newly achieved progress, remote sensing is becoming the primary pathway to obtain ancillary information aiming to aid decision-making in the fields of natural resource management, natural hazards mitigation, environmental protection, and the success of sustainable development goals. State-of-the-art sensor technology, communication technology, and computer technology have promoted the development of remote sensing technology, while numerous satellite-based, airborne-based, UAV-based, and ground-based sensors have acquired massive data on earth observation. At present, how to accurately perceive the dynamic information from the massive remote sensing data to support the sustainable development of human society is a challenging subject.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Current and future of the Earth Observation System (EOS) and missions
Innovation Earth observation sensors, platform, concepts, and techniques
Theory, model, technology, and application of Earth observation
Artificial intelligence in Earth observation
Long-time spatial and temporal analysis of surface, environment and ecological parameters using remote sensing technology
Rapid parameters estimation and mapping using the cloud-computing platforms (e.g., Google Earth Engine, PIE-Engine, Amazon Web Services, and Microsoft Azure)
Processing of remote sensing images (including but not limited to quality improvement, registration, and fusion)
Health monitoring of marine, terrestrial, and aquatic ecosystems
Scientific management of space resources and restoration of ecological environment
Other related topics

We look forward to receiving your contributions.

Prof. Dr. Chao Chen
Prof. Dr. Jinsong Chen
Prof. Dr. Juhua Luo
Prof. Dr. Lei Fang
Guest Editors

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Keywords

Natural resources
ecological environment
land
forest
wetland
coastal zone
ecological
biodiversity
sustainability
remote sensing big data
multi-source remote sensing
remote sensing cloud platform

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Published Papers (8 papers)

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Research

16 pages, 11227 KiB

Open AccessArticle

Study on Accuracy Evaluation of MCD19A2 and Spatiotemporal Distribution of AOD in Arid Zones of Central Asia

by Zhengnan Zhu, Zhe Zhang, Fangqing Liu, Zewei Chen, Yuxin Ren and Qingfu Guo

Sustainability 2023, 15(18), 13959; https://doi.org/10.3390/su151813959 - 20 Sep 2023

Viewed by 1225

Abstract

The Central Asian arid zone is the largest non-territorial arid zone in the world, so it is particularly important to understand the optical properties of aerosols in this region. In this paper, we validate the MCD19A2 atmospheric aerosol optical depth (AOD) remote sensing data by using ground-based data and measured data. To explore the spatial and temporal changes in aerosols in the Central Asian arid zone as well as the interannual variations and seasonal variations, we characterize the spatial and temporal distributions of the AOD over 20 years. Finally, we analyze the spatial and temporal variations of the AOD in the Central Asian arid zone by using three methods, namely, the Theil–Sen median trend analysis combined with the Mann–Kendall test, coefficient of variation, and Hurst index; analyze the characteristics of the spatial and temporal variations of the AOD in the Central Asian arid zone; and explore the relationships among the AOD, wind speed, and NDVI. This study reveals the characteristics of the long-term changes in the aerosol optical properties in the Central Asian arid zone and provides a scientific basis for estimating the factors affecting climate change. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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17 pages, 5460 KiB

Open AccessArticle

Analysis on the Rationality of Urban Green Space Distribution in Hangzhou City Based on GF-1 Data

by Danying Zhang, Haijian Liu and Zhifeng Yu

Sustainability 2023, 15(15), 12027; https://doi.org/10.3390/su151512027 - 5 Aug 2023

Cited by 2 | Viewed by 1418

Abstract

With its ecological, economic, and social benefits, urban green space (UGS) plays an important role in urban planning. Accordingly, it is also an important indicator in the evaluation of urban liveability. However, the extraction and statistical analysis of UGS are difficult because urban land use involves complex types and UGS exhibits fragmented distribution and common vegetation extraction models such as the NDVI model and pixel bipartite model. In addition, there are few studies that analyze UGS in Hangzhou with a pixel decomposition model. Therefore, applying the mixed pixel decomposition model with GF-1 data, the following three objectives were set in this study: (1) analyzing the temporal changes of UGS in Shangcheng District, Hangzhou from 2018 to 2020; (2) analyzing the spatial distribution characteristics of UGS in the six main urban areas of Hangzhou in 2020; (3) analyzing the rationality and influencing factors of UGS distribution in Hangzhou. In Shangcheng District, the overall UGS area increased from 2018 to 2020 due to the increase in forest area rather than grassland area. As for the main built-up area in Hangzhou, medium and high coverage of UGS were primarily observed, with an overall high level of greening and a relatively uniform vegetation cover. Only a few areas showed very low UGS coverage. Some differences were observed among administrative regions under the influence of topography, but the overall coverage is high. At the same time, the distribution of UGS in Hangzhou is closely related to policy guidance, the needs of urban residents, and the requirements of economic development. This research not only can provide a new way to analyze UGS features in Hangzhou but also provides scientific guidance for governments in urban planning. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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15 pages, 6351 KiB

Open AccessArticle

Study on Accuracy Evaluation of MODIS AOD Products and Spatio-Temporal Distribution Characteristics of AOD in Hangzhou

by Xiaohong Yuan, Yuji Xia, Jinqi He, Mengjia Cheng, Bing Qi, Zhifeng Yu and Ben Wang

Sustainability 2023, 15(13), 10171; https://doi.org/10.3390/su151310171 - 27 Jun 2023

Cited by 3 | Viewed by 1209

Abstract

In recent years, although the quality of the atmospheric environment has improved in some regions, monitoring air quality remains crucial. Aerosol optical thickness (aerosol optical depth, AOD) reflects the attenuation effect of atmospheric aerosol on light, which is an important parameter in aerosol research and plays an important role in the study of the atmosphere. Based on the CE-318 photometer data of 2015, 2016, and 2020 in Hangzhou and the MODIS satellite AOD data of 2015, 2016, and 2020, this paper first obtains the correlation between the MODIS AOD data and the ground-measured data by linear fitting. Then, the spatial and temporal distribution of the MODIS AOD data from 2012 to 2020 is analyzed. The analysis results indicate that the average correlation coefficient between the MODIS AOD data and ground-measured data is 0.77, and the average relative error is 30.53%. Thus, the MODIS AOD data can be used as an important basis for atmospheric research in Hangzhou. Based on this, the conclusions are as follows: (1) the AOD value in Hangzhou has been decreasing in recent years, from 0.43 in 2012 to 0.28 in 2020, with an average annual decrease of 0.017. (2) The AOD value in Hangzhou is large in spring and summer, and small in autumn and winter, with an average AOD value of 0.45 in spring, 0.39 in summer, 0.30 in autumn, and 0.33 in winter. (3) The AOD value in Hangzhou is large in the east and small in the west, and the AOD values in the Hangzhou urban area, Xiaoshan, and Yuhang are higher. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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16 pages, 5251 KiB

Open AccessArticle

Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery

by Hongxia Zheng, Xiao Li, Jianhua Wan, Mingming Xu, Shanwei Liu and Muhammad Yasir

Sustainability 2023, 15(9), 7199; https://doi.org/10.3390/su15097199 - 26 Apr 2023

Cited by 4 | Viewed by 1670

Abstract

Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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12 pages, 9408 KiB

Open AccessArticle

Spatiotemporal Variation in and Responses of the NDVI to Climate in Western Ordos and Eastern Alxa

by Hui Zhang, Jinting Guo, Xiaotian Li, Yajie Liu and Tiejuan Wang

Sustainability 2023, 15(5), 4375; https://doi.org/10.3390/su15054375 - 1 Mar 2023

Cited by 7 | Viewed by 1926

Abstract

Vegetation is an important component of the terrestrial ecosystem, and studying the rules of vegetation change and its driving factors is helpful to strengthen the ecological protection and sustainable development of regional vegetation. This study analyzes the changes in Normalized Difference Vegetation Index (NDVI) and its response to climate factors in the five regions of western Ordos and eastern Alxa in China between 2000 and 2020. The MODIS NDVI and meteorological data from 2000 to 2020 was used and the ordinary least squares, trend analysis, and correlation analysis methods were analyzed. The NDVI in this region shows spatial differentiation and is high in the east and low in the west. The overall NDVI has shown a significant increasing trend (p < 0.01), and the slope value of the rate of change also shows that the NDVI in 98.17% of the area is increasing. On a temporal scale, NDVI had a significant positive correlation with precipitation (p < 0.01), but no significant correlation with temperature changes. On a spatial scale, NDVI was positively correlated with precipitation, which accounted for 95.57% of spatial changes, of which a significant positive correlation accounted for 34.99% (p < 0.05). Meanwhile, the temperature and NDVI were negatively correlated but not significantly. A positive correlation accounted for 45.95% of the change, but the insignificant negative correlation accounted for 54.05%. Therefore, comprehensive analysis showed that precipitation played a leading role in the NDVI in the study area. The results are helpful to study the driving mechanism of vegetation growth and provide reference for vegetation protection in regions of western Ordos and eastern Alxa of Inner Mongolia, China. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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15 pages, 4205 KiB

Open AccessArticle

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

by Haixia Feng, Zhouhao Wu, Xin Li, Huacai Xian, Qiang Jia, Xingyu Wang and Maoxin Zhu

Sustainability 2023, 15(5), 4064; https://doi.org/10.3390/su15054064 - 23 Feb 2023

Cited by 2 | Viewed by 1867

Abstract

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO₂ had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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24 pages, 5993 KiB

Open AccessArticle

Aerosol Characterization of Northern China and Yangtze River Delta Based on Multi-Satellite Data: Spatiotemporal Variations and Policy Implications

by Kuifeng Luan, Zhaoxiang Cao, Song Hu, Zhenge Qiu, Zhenhua Wang, Wei Shen and Zhonghua Hong

Sustainability 2023, 15(3), 2029; https://doi.org/10.3390/su15032029 - 20 Jan 2023

Cited by 3 | Viewed by 2053

Abstract

Horizontal and vertical distributions of aerosol properties in the Taklimakan Desert (TD), North central region of China (NCR),North China Plain(NCP), and Yangtze River Delta (YRD) were investigated by statistical analysis using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) L3 data from 2007 to 2020, to identify the similarities and differences in atmospheric aerosols in different regions, and evaluate the impact of pollution control policies developed in China in 2013 on aerosol properties in the study area. The aerosol optical depth (AOD) distribution had substantial seasonal and spatial distribution characteristics. AOD had high annual averages in TD (0.38), NCP (0.49), and YRD (0.52). However, these rates showed a decline post-implementation of the long-term pollution control policies; AOD values declined by 5%, 13.8%, 15.5%, and 23.7% in TD, NCR, NCP, and YRD respectively when comparing 2014–2018 to 2007–2013, and by 7.8%, 11.5%, 16%, and 10.4% when comparing 2019–2020 to 2014–2018. The aerosol extinction coefficient showed a clear regional pattern and a tendency to decrease gradually as height increased. Dust and polluted dust were responsible for the changes in AOD and extinction coefficients between TD and NCR and NCP and YRD, respectively. In TD, with change of longitude, dust aerosol first increased and then decreased gradually, peaking in the middle. Similarly in NCP, polluted dust aerosol first increased and then decreased, with a maximum value in the middle. The elevated smoke aerosols of NCP and YRD were significantly higher than those observed in TD and NCR. The high aerosol extinction coefficient values (>0.1 km⁻¹) were mainly distributed below 4 km, and the relatively weak aerosol extinction coefficients (>0.001 km⁻¹) were mainly distributed between 5–8 km, indicating that the high-altitude long-range transport of TD and NCR dust aerosols affects NCP and YRD. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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19 pages, 7001 KiB

Open AccessArticle

Real-Time Identification of Cyanobacteria Blooms in Lakeshore Zone Using Camera and Semantic Segmentation: A Case Study of Lake Chaohu (Eastern China)

by Zhiyong Wang, Chongchang Wang, Yuchen Liu, Jindi Wang and Yinguo Qiu

Sustainability 2023, 15(2), 1215; https://doi.org/10.3390/su15021215 - 9 Jan 2023

Cited by 4 | Viewed by 1755

Abstract

The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone. Full article

(This article belongs to the Special Issue Theory, Model, Technology, and Application of Remote Sensing in Sustainability)

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