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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 33344

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Special Issue Editors

Prof. Dr. Hong Tang

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of natural disasters; environmental remote sensing and digital city; image processing and pattern recognition; deep learning

Dr. Jing Wei

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Guest Editor

Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; artificial intelligence; big data; air pollution; aerosol; particulate matter; trace gas; cloud
Special Issues, Collections and Topics in MDPI journals

Dr. Naisen Yang

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Guest Editor

Environment Research Institute, Shandong University, Qingdao 266237, China
Interests: remote sensing of environment; environmental big data; deep learning of remote sensing

Special Issue Information

Dear Colleagues,

Remote sensing provides an effective way to monitor the atomesphere, land-cover, and water on the Earth’s surface, and allows for the measurement and extracraction of multiscale spatiotemporal information from big data by integrating artificial intelligence, geographical models, and spatial data analyzing techniques. It has been widely utilized to maintain sustainable development, for instance, through environment change monitoring, air pollution modeling, and quick response to disaster and mitigation, urbanization and long-term human well-being.

This Special Issue entitled “Application of Remote Sensing for Sustainable Development” will contain significant results regarding how to achieve sustainable development in terms of different geographical scales ranging from global to local regions by applying remote sensing as an important techinique. Topics of this issue consist of but not limited to:

Remote sensing applications in environmental assessments;
Air quality remote sensing;
Urban remote sensing;
Remote sensing of natural disasters;
Time-serial remote sensing analysis for sustainable environment monitoring;
Remote sensing image processing and machine learning for sustainable environment.

Prof. Dr. Hong Tang
Dr. Jing Wei
Dr. Naisen Yang
Guest Editors

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Keywords

atmosphere pollution
PM2.5 concentration
climate change
greenhouse gas
public health
assessment of biodiversity and ecosystem services
carbon cycle
forest fire
biomass birning
change of land use
urban sustainability assessment
dynamic monitering of urban growth
wetland
flood
quick response to natural disasters
assessment of disaster
recovering
risk management
remote sensing data analysis and deep learning

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Published Papers (11 papers)

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Research

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19 pages, 14943 KiB

Open AccessArticle

Study of Ecosystem Degradation Dynamics in the Peruvian Highlands: Landsat Time-Series Trend Analysis (1985–2022) with ARVI for Different Vegetation Cover Types

by Deyvis Cano, Samuel Pizarro, Carlos Cacciuttolo, Richard Peñaloza, Raúl Yaranga and Marcelo Luciano Gandini

Sustainability 2023, 15(21), 15472; https://doi.org/10.3390/su152115472 - 31 Oct 2023

Cited by 2 | Viewed by 3604

Abstract

The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation (p < 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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15 pages, 3639 KiB

Open AccessArticle

An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution

by Suhaimee Buya, Sasiporn Usanavasin, Hideomi Gokon and Jessada Karnjana

Sustainability 2023, 15(13), 10024; https://doi.org/10.3390/su151310024 - 25 Jun 2023

Cited by 7 | Viewed by 2813

Abstract

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R²) and root mean square error (RMSE), and the results indicate high accuracy for training (R²: 0.95, RMSE: 5.58 μg/m³), validation (R²: 0.78, RMSE: 11.18 μg/m³), and testing (R²: 0.71, RMSE: 8.79 μg/m³) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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16 pages, 5071 KiB

Open AccessArticle

A Study on the Impact of Roads on Grassland Degradation in Shangri-La City

by Zilin Zhou, Feng Cheng, Jinliang Wang and Bangjin Yi

Sustainability 2023, 15(10), 7747; https://doi.org/10.3390/su15107747 - 9 May 2023

Viewed by 1625

Abstract

Shangri-La is located in the eastern part of the Qinghai-Tibet Plateau, which has a fragile ecology. The plateau grassland has suffered from irreversible degradation under the influence of human activities. To address this issue, the Sentinel-2A data obtained is used in this study to calculate the RVI and build an inversion model of grassland degradation grade with GDI data, which was used to obtain the area and proportion of grassland degradation. Landscape indexes were then calculated for different degradation grades of grassland to examine the correlation between roads and degraded grassland in spatial distance and the spatial distribution characteristics of different degradation grades of grassland. The results show that the grassland area in Shangri-La was 2207.94 km², of which the heavily degraded area reaches 824.03 km², exceeding the undegraded grassland area by 172.62 km², indicating that the grassland degradation is severe. From south to north, the proportion of heavily degraded and moderately degraded grassland in townships gradually decreased, while the proportion of lightly degraded and undegraded grassland gradually increased. The townships with high percentages of degraded grassland were predominantly located in the southern area, where there was a dense road network and well-developed transport networks, particularly along National Highway 214, which is the main road in Shangri-La. Conversely, townships with low percentages are generally located in the north with dispersed roads and sparse transport lines. The study’s outcomes are significant in providing a better understanding of the current status of grassland degradation and promoting the sustainable utilization of grassland resources in Shangri-La. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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18 pages, 4906 KiB

Open AccessArticle

Analysis of Urban Ecological Quality Spatial Patterns and Influencing Factors Based on Remote Sensing Ecological Indices and Multi-Scale Geographically Weighted Regression

by Pan Yang, Xinxin Zhang and Lizhong Hua

Sustainability 2023, 15(9), 7216; https://doi.org/10.3390/su15097216 - 26 Apr 2023

Cited by 4 | Viewed by 1854

Abstract

With the acceleration of urbanization, problems such as urban ecological environment quality have become increasingly prominent. How to scientifically analyze and evaluate the spatial pattern of urban ecological environment changes and influential variables is a prerequisite for achieving green development and ecological priority new in urban planning. Our study was conducted on Pingtan Island, located in Fujian Province, China. First, we selected Landsat 8 OLI images in 2013, 2017, and 2021. Second, we extracted the remote sensing ecological index (RSEI) from these images and created RSEI maps to assess the spatial-temporal variations and spatial autocorrelation of the ecological environment condition in Pingtan Island. Third, the proportion of land-use types, road, and population density were selected as independent variable factors, RSEI as the dependent variable, least squares regression (OLS), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) were used to establish global and local regression models. According to the regression coefficients of the model and its spatial distribution, the spatial heterogeneity between the ecological environment and the influencing factors was assessed. The results indicated that: (1) the mean value of the RSEI increased from 0.422 to 0.504 during 2013–2021, indicating that the overall ecological environment improved. (2) Based on the global Moran’s I value, the distribution of ecological environment quality was positively correlated. The local Moran’s I cluster map showed that the high-high cluster gradually extended to the northwest high-altitude region. Low-low clustering gradually extended to the more populous areas in the southeast. (3) The

R_{a d j}^{2}

of the MGWR model was 0.866, which was better than the results of the OLS model and GWR model, indicating that MGWR had obvious advantages in revealing the spatial heterogeneity between the ecological environment and the influencing factors. Importantly, the results indicate that population density, road density, and the proportion of cropland land and impervious surface in land-use types have varying degrees of negative effects on the urban ecological environment, with the impervious surface being more severe, followed by population density, while forest land in land-use types shows significant positive effects. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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14 pages, 5220 KiB

Open AccessArticle

Rural Building Extraction Based on Joint U-Net and the Generalized Chinese Restaurant Franchise from Remote Sensing Images

by Zixiong Wang, Shaodan Li and Zimeng Zhu

Sustainability 2023, 15(5), 4685; https://doi.org/10.3390/su15054685 - 6 Mar 2023

Cited by 1 | Viewed by 1551

Abstract

The extraction of rural buildings from remote sensing images plays a critical role in the development of rural areas. However, automatic building extraction has a challenge because of the diverse types of buildings and complex backgrounds. In this paper, we proposed a two-layer clustering framework named gCRF_U-Net for the extraction of rural buildings. Before the building extraction, the potential built-up areas are firstly detected, which are taken as a constraint for building extraction. Then, the U-Net network is employed to obtain the prior probability of the potential buildings. After this, the calculated probability and the satellite image are put into the generalized Chinese restaurant franchise (gCRF) model to cluster for buildings and non-buildings. In addition, it is worth noting that the hierarchical spatial relationship in the images is clarified for the building extraction. According to the compared experiments on the satellite images and public building datasets, the results show that the proposed method has a better performance, compared with other methods based on the same unified hierarchical models, in terms of quantitative and qualitative evaluation. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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20 pages, 4157 KiB

Open AccessArticle

Characterizing Informal Settlement Dynamics Using Google Earth Engine and Intensity Analysis in Durban Metropolitan Area, South Africa: Linking Pattern to Process

by Dadirai Matarira, Onisimo Mutanga, Maheshvari Naidu, Terence Darlington Mushore and Marco Vizzari

Sustainability 2023, 15(3), 2724; https://doi.org/10.3390/su15032724 - 2 Feb 2023

Cited by 4 | Viewed by 4397

Abstract

The growing population in informal settlements expedites alterations in land use and land cover (LULC) over time. Understanding the patterns and processes of landscape transitions associated with informal settlement dynamics in rapidly urbanizing cities is critical for better understanding of consequences, especially in environmentally vulnerable areas. The study sought to map and systematically analyze informal settlement growth patterns, dynamics and processes, as well as associated LULC transitions in Durban Metropolitan area, from 2015 to 2021. The study applied an object-based image classification on PlanetScope imagery within the Google Earth Engine (GEE) platform. Further, intensity analysis approach was utilized to quantitatively investigate inter-category transitions at category and transition levels. Thus far, no study of land conversion to and from informal settlement areas in South Africa has exploited both GEE and intensity analysis approaches. The results suggest spatial growth of informal settlements with a total net gain of 3%. Intensity analysis results at category level revealed that informal settlements were actively losing and gaining land area within the period, with yearly gain and loss intensity of 72% and 54%, correspondingly, compared to the uniform intensity of 26%. While the growth of informal settlements avoided water bodies over the studied period, there was an observed systematic process of transition between informal settlements and other urban land. Government policy initiatives toward upgrading informal housing could be attributed to the transitions between informal and other urban settlements. This study illustrates the efficacy of intensity analysis in enhancing comprehension of the patterns and processes in land changes, which aids decision making for suitable urban land upgrading plans in the Durban Metropolitan area. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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17 pages, 4507 KiB

Open AccessArticle

Contrastive Analysis and Accuracy Assessment of Three Global 30 m Land Cover Maps Circa 2020 in Arid Land

by Qiang Bie, Ying Shi, Xinzhang Li and Yueju Wang

Sustainability 2023, 15(1), 741; https://doi.org/10.3390/su15010741 - 31 Dec 2022

Cited by 8 | Viewed by 2021

Abstract

Fine-resolution land cover (LC) products are critical for studies of urban planning, global climate change, the Earth’s energy balance, and the geochemical cycle as fundamental geospatial data products. It is important and urgent to evaluate the performance of the updated global land cover maps. In this study, three widely used LC maps with 30 m spatial resolution (FROM-GLC30-2020, GLC_FCS30, and GlobeLand30) published around 2020 were evaluated in terms of their degree of consistency and accuracy metrics. First, we compared their similarities and difference in the area ratio and spatial patterns over different land cover types. Second, the sample and response protocol was proposed and validation samples were collected. Based on this, the overall accuracy, producer’s accuracy, and user’s accuracy were analyzed. The results revealed that: (1) the consistent areas of the three maps accounted for 65.96% of the total area and that two maps exceeded 75% of it. (2) The dominant land cover types, bare land and grassland, were the most consistent land cover types across the three products. In contrast, the spatial inconsistency of the wetland, shrubland, and built-up areas were relatively high, with the disagreement mainly occurring in the heterogeneous regions. (3) The overall accuracy of the GLC_FCS30 map was the highest with a value of 87.07%, which was followed by GlobeLand30 (85.69%) and FROM-GLC30 (83.49%). Overall, all three of the LC maps were found to be consistent and have a good performance in classification in the arid regions, but their ability to accurately classify specific types varied. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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23 pages, 21966 KiB

Open AccessArticle

Identifying Ecological Security Patterns Based on Ecosystem Service Supply and Demand Using Remote Sensing Products (Case Study: The Fujian Delta Urban Agglomeration, China)

by Xiaonan Niu, Huan Ni, Qun Ma, Shangxiao Wang and Leli Zong

Sustainability 2023, 15(1), 578; https://doi.org/10.3390/su15010578 - 29 Dec 2022

Cited by 4 | Viewed by 2451

Abstract

As the global population increases and cities expand, increasing social needs and ecosystem degradation generally coexist, especially in China’s urban agglomerations. Identifying ecological security patterns (ESPs) for urban agglomerations serves as an effective way to sustain regional ecological security and promote harmonious ecological conservation and economic development. Focusing on the Fujian Delta Urban Agglomeration (FDUA) as an example, this study aims to present a framework for linking the supply and demand of ecosystem services (ESs) to identify ESPs in 2020. First, the ecological sources are delimited by coupling the supply and demand of four critical ESs (carbon storage, water provision, grain production, and outdoor recreation). Afterward, the resistance coefficient is modified using nighttime light intensity data and the ecological risk index, the second of which combines the effects of the soil erosion sensitivity index, the geological disaster risk index, and the land desertification risk index. Then, ecological corridors are determined by employing the minimum cumulative resistance method. With the integration of ecological sources and corridors, the ESPs of the FDUA can be identified. The results show a distinct supply–demand mismatch for ESs, with supply exhibiting an upward gradient from coastal cities to inland mountain cities and demand showing the opposite trend. The ESPs consist of 8359 km² of ecological sources that are predominantly forests, 171 ecological corridors with a total length of 789.04 km, 34 pinch points, 26 barriers, and 48 break points. This paper presents a realizable approach for constructing ESPs for urban agglomerations, which will help decision makers optimize ecological sources and ecological protection policies. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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15 pages, 8364 KiB

Open AccessArticle

Ecological Environment Evaluation Based on Remote Sensing Ecological Index: A Case Study in East China over the Past 20 Years

by Shangxiao Wang, Ming Zhang and Xi Xi

Sustainability 2022, 14(23), 15771; https://doi.org/10.3390/su142315771 - 27 Nov 2022

Cited by 8 | Viewed by 2491

Abstract

East China is one of the most active regions in terms of economic and social development, and with the accelerated urbanization process, environmental problems are becoming increasingly prominent. The objective, quantitative, and timely evaluation of spatial and temporal changes in ecological quality is of great significance for environmental protection and decision making. The remote sensing ecological index (RSEI) is an objective, fast, and easy ecological quality monitoring and evaluation technique which has been widely used in the field of ecological research, but it often involves problems of cloud occlusion and stitching difficulties when used to conduct large-scale and long-term monitoring. In this paper, based on the Google Earth Engine (GEE) platform, an RSEI was constructed using MODIS data products to evaluate the spatial and temporal changes in ecological quality in East China over the past 20 years. The study shows the following: (1) The mean RSEI values in 2000, 2005, 2010, 2015, and 2020 were 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, with the mean values first decreasing and then showing a stable increasing trend. In Shanghai and Jiangsu, the mean RSEI values show a fluctuating characteristic of “falling and then rising”, and large respective decreases of 32.4% and 25.8% throughout the monitoring period. The RSEI values in Fujian Province showed a relatively stable upward trend during the study period (19% increase). (2) The RSEI spatially correlated clustering maps of the local indicators showed that the regions with a high degree of clustering are mainly located in Quzhou City, Zhejiang Province, Ningde City, Fujian Province, and northern Anhui Province (Bozhou and Huabei). With the promotion of ecological civilization and the enhancement of environmental protection awareness, the vegetation cover has significantly increased, which has led to the rise in RSEI values. The low values are mainly distributed in densely populated areas with more human activity, such as the central-eastern part of Jiangsu Province, central Anhui Province, Shanghai, and northern Zhejiang Province. With the development of cities, impervious surfaces occupy more and more ecological land, which eventually affects the regional RSEI values. (3) This research provides a promising method for the evaluation of spatial and temporal changes in ecological environment quality based on an RSEI and GEE. The image processing, based on GEE cloud computing, can help overcome the problems of missing remote sensing data, chromatic aberrations, and spatial and temporal inconsistency, which could greatly improve the efficiency of image processing and extend the application of the remote sensing ecological index to large-scale, long-term ecological monitoring. The research results can provide a reference for improving the applicability and accuracy of remote sensing ecological indices and provide a theoretical basis for ecological conservation and land management in the context of rapid urbanization. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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16 pages, 9021 KiB

Open AccessArticle

Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network

by Shaodan Li, Shiyu Fu and Dongbo Zheng

Sustainability 2022, 14(3), 1272; https://doi.org/10.3390/su14031272 - 24 Jan 2022

Cited by 7 | Viewed by 1974

Abstract

A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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Review

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22 pages, 2801 KiB

Open AccessReview

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

by Zhujun Gu and Maimai Zeng

Sustainability 2024, 16(1), 274; https://doi.org/10.3390/su16010274 - 28 Dec 2023

Cited by 6 | Viewed by 5915

Abstract

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Sustainable Development)

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