Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing for Engineering and Sustainable Development Goals

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 22740

Share This Special Issue

Special Issue Editors

Dr. Minquan Wu

E-Mail Website
Guest Editor

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing of engineering projects; remote sensing for sustainable development goals; energy; infrastructure remote sensing; belt and road initiative; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jianping Pan

E-Mail Website
Guest Editor

Smart City College, Chongqing Jiaotong University, Chongqing 400074, China
Interests: remote sensing; change detention; deep learning; geological disasters

Dr. Yaohuan Huang

E-Mail Website
Guest Editor

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; GIS; water cycle; hydrological model; precipitation extremes; floods; droughts; spatial analysis; land use and land cover change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Remote Sensing for Engineering and Sustainable Development Goals.

The United Nations 2030 Sustainable Development Goals are one of the common goals (SDGs) of governments and all mankind. The construction of infrastructure such as roads and energy power stations plays a vital role in the realization of the Sustainable Development Goals. Spatial information technology plays a fundamental role in infrastructure construction. Studies examining how to use remote sensing and other spatial information technologies to help the realization of the United Nations 2030 Sustainable Development Goals and the tracking of the SDGS progress have received more and more attention. Spatial information technology helps engineering construction to be more sustainable. It also makes the management of government departments more intelligent and information-based when dealing with sustainable development goals. Spatial information products are also becoming an increasingly important data source for tracking the achievement of the Sustainable Development Goals.

This Special Issue studies the theory and application of remote sensing technology in the realization of sustainable development goals. Topics may include applications of remote sensing to sustainable development infrastructure, SDG tracking, and SDG achievement, with a special focus on carbon neutrality and energy. Articles may cover, but are not limited to, the following topics:

Identification and extraction of engineering projects;
Monitoring of eco-environmental impact of engineering projects;
Remote sensing tracking of SDG7 and other SDG progress;
Path to zero carbon goal;
AI extraction for clean energy projects;
Remote sensing mapping of greenhouse gases;
Remote sensing monitoring of terrestrial carbon sinks.

Dr. Minquan Wu
Prof. Dr. Jianping Pan
Dr. Yaohuan Huang
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. Remote Sensing 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 2700 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

SDGs
infrastructure
energy
belt and road initiative
zero carbon goal
deep learning
UAV
nighttime light

Published Papers (10 papers)

Download All Papers

Research

Jump to: Other

20 pages, 20617 KiB

Open AccessArticle

Automatic Detection Method for Loess Landslides Based on GEE and an Improved YOLOX Algorithm

by Zhengbo Yu, Ruichun Chang and Zhe Chen

Remote Sens. 2022, 14(18), 4599; https://doi.org/10.3390/rs14184599 - 14 Sep 2022

Cited by 12 | Viewed by 2162

Abstract

The Loess Plateau is an ecologically fragile area in China; furthermore, loess landslides are typical forms of geological disasters, which severely limit the sustainable development of the local societies and the economy. Studying the automatic detection of landslides can facilitate disaster prevention and mitigation in the Loess Plateau, and help realize the climate action goal (SDG 13) of the United Nations Sustainable Development Goals (SDGs). This paper takes typical loess areas in China as the research object, and establishes a historical loess landslide sample database based on Google Earth (GEE) image data, with a total of 1451 loess landslides. The automatic detection of loess landslides is implemented by improving the You Only Look Once X (YOLOX) algorithm. The results show that the average accuracy of landslide detection in this method is 95.43%, and the accuracy rate is 96.32%, which effectively combines the earth’s big data to realize the automatic detection of loess landslides. The research results provide technical support for the promotion of disaster prevention and mitigation in China’s loess regions, the realization of sustainable development goals, and the improvement of natural disaster prevention–resistance–reduction systems. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

24 pages, 16677 KiB

Open AccessArticle

Hyperspectral Remote Sensing Inversion and Monitoring of Organic Matter in Black Soil Based on Dynamic Fitness Inertia Weight Particle Swarm Optimization Neural Network

by Ruichun Chang, Zhe Chen, Daming Wang and Ke Guo

Remote Sens. 2022, 14(17), 4316; https://doi.org/10.3390/rs14174316 - 01 Sep 2022

Cited by 8 | Viewed by 1689

Abstract

Long-term degradation of black soil has led to reductions in soil fertility and ecological service functions, which have seriously threatened national food security and regional ecological security. This study is motivated by the UN’s Sustainable Development Goal (SDG) 2—Zero Hunger, specifically, SDG 2.4 Sustainable Food Production Systems. The aim was to monitor the soil organic matter (SOM) content of black soil and its dynamics via hyperspectral remote sensing inversion. This is of great significance to the effective utilization and sustainable development of black soil resources. Taking the typical black soil area of Northeast China as an example, the hyperspectral data of ground features were compared with SOM contents measured in soil samples to correlate SOM with spectral features. Based on their quantitative relationship, a dynamic fitness inertia weighted particle swarm optimization (DPSO) algorithm is proposed, which balances the global and local search abilities of a particle swarm optimization algorithm. The DPSO algorithm is applied to the parameter adjustment of an artificial neural network (BPNN), which is used instead of a traditional error back propagation algorithm, to build a DPSO-BPNN model. Then a global optimal analytical expression of hyperspectral inversion is obtained to improve the generalization ability and stability of the remote sensing quantitative inversion model. The results show that DPSO-BPNN model is more stable and accurate than existing models, such as multiple stepwise regression, partial least squares, and BP neural network models (adjust complex coefficient of determination = 0.89, root mean square error = 1.58, relative recent deviation = 2.93). The results of DPSO-BPNN inversion are basically consistent with the trend in SOM contents measured during surface geochemical exploration. As such, this study provides a basis for hyperspectral remote sensing inversion and monitoring of the SOM contents in black soil. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

18 pages, 23556 KiB

Open AccessArticle

Remote Sensing Monitoring of Ecological-Economic Impacts in the Belt and Road Initiatives Mining Project: A Case Study in Sino Iron and Taldybulak Levoberezhny

by Yue Jiang, Wenpeng Lin, Mingquan Wu, Ke Liu, Xumiao Yu and Jun Gao

Remote Sens. 2022, 14(14), 3308; https://doi.org/10.3390/rs14143308 - 09 Jul 2022

Cited by 4 | Viewed by 2284

Abstract

Under the Belt and Road Initiatives, China’s overseas cooperation in constructing mining projects has developed rapidly. The development and utilization of mining resources are essential requirements for socio-economic development. At the same time, the ecological impacts of the exploitation and utilization of mining resources have increasingly aroused the widespread concern of the international community. This paper uses Landsat images, high-resolution images, and nighttime light (NTL) data to remotely monitor Sino Iron in Australia and Taldybulak Levoberezhny in Kyrgyzstan in different development periods to provide a reference for the rational development of mineral resources and environmental management. The results show that the Chinese enterprises have achieved good results in the ecological protection of the mining area during the construction period. The development of the mine has caused minor damage to the surrounding environment and has not destroyed the local natural ecological pattern. The different NTL indices show an overall rising trend, indicating that the construction of mines has dramatically promoted the socio-economic development of countries along the Belt and Road in both time and space. Therefore, relevant departments should practice green development in overseas projects, establish a scientific mine governance system, and promote a win-win economic growth and environmental governance situation. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

22 pages, 6542 KiB

Open AccessArticle

Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project

by Zhe Chen, Ruichun Chang, Huadong Guo, Xiangjun Pei, Wenbo Zhao, Zhengbo Yu and Lu Zou

Remote Sens. 2022, 14(13), 3036; https://doi.org/10.3390/rs14133036 - 24 Jun 2022

Cited by 10 | Viewed by 2162

Abstract

As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

21 pages, 8289 KiB

Open AccessArticle

Spatial Sustainable Development Assessment Using Fusing Multisource Data from the Perspective of Production-Living-Ecological Space Division: A Case of Greater Bay Area, China

by Ku Gao, Xiaomei Yang, Zhihua Wang, Huifang Zhang, Chong Huang and Xiaowei Zeng

Remote Sens. 2022, 14(12), 2772; https://doi.org/10.3390/rs14122772 - 09 Jun 2022

Cited by 12 | Viewed by 2134

Abstract

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

22 pages, 10582 KiB

Open AccessArticle

Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

by Yong Bo, Xueke Li, Kai Liu, Shudong Wang, Hongyan Zhang, Xiaojie Gao and Xiaoyuan Zhang

Remote Sens. 2022, 14(11), 2564; https://doi.org/10.3390/rs14112564 - 27 May 2022

Cited by 19 | Viewed by 2718

Abstract

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m² per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO₂) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

26 pages, 10321 KiB

Open AccessArticle

Estimation of Photovoltaic Energy in China Based on Global Land High-Resolution Cloud Climatology

by Shuyan Zhang, Yong Ma, Fu Chen, Erping Shang, Wutao Yao, Jianbo Liu and An Long

Remote Sens. 2022, 14(9), 2084; https://doi.org/10.3390/rs14092084 - 26 Apr 2022

Cited by 4 | Viewed by 1968

Abstract

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Figure 1

22 pages, 6718 KiB

Open AccessArticle

A Self-Attentive Hybrid Coding Network for 3D Change Detection in High-Resolution Optical Stereo Images

by Jianping Pan, Xin Li, Zhuoyan Cai, Bowen Sun and Wei Cui

Remote Sens. 2022, 14(9), 2046; https://doi.org/10.3390/rs14092046 - 25 Apr 2022

Cited by 5 | Viewed by 2425

Abstract

Real-time monitoring of urban building development provides a basis for urban planning and management. Remote sensing change detection is a key technology for achieving this goal. Intelligent change detection based on deep learning of remote sensing images is a current focus of research. However, most methods only use unimodal remote sensing data and ignore vertical features, leading to incomplete characterization, poor detection of small targets, and false detections and omissions. To solve these problems, we propose a multi-path self-attentive hybrid coding network model (MAHNet) that fuses high-resolution remote sensing images and digital surface models (DSMs) for 3D change detection of urban buildings. We use stereo images from the Gaofen-7 (GF-7) stereo mapping satellite as the data source. In the encoding stage, we propose a multi-path hybrid encoder, which is a structure that can efficiently perform multi-dimensional feature mining of multimodal data. In the deep feature fusion link, a dual self-attentive fusion structure is designed that can improve the deep feature fusion and characterization of multimodal data. In the decoding stage, a dense skip-connection decoder is designed that can fuse multi-scale features flexibly and reduce spatial information losses in small-change regions in the down-sampling process, while enhancing feature utilization and propagation efficiency. Experimental results show that MAHNet achieves accurate pixel-level change detection in complex urban scenes with an overall accuracy of 97.44% and F1-score of 92.59%, thereby outperforming other methods of change detection. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

21 pages, 2191 KiB

Open AccessArticle

Global Identification of Unelectrified Built-Up Areas by Remote Sensing

by Xumiao Gao, Mingquan Wu, Zheng Niu and Fang Chen

Remote Sens. 2022, 14(8), 1941; https://doi.org/10.3390/rs14081941 - 17 Apr 2022

Cited by 4 | Viewed by 2371

Abstract

Access to electricity (the proportion of the population with access to electricity) is a key indica for of the United Nations’ Sustainable Development Goal 7 (SDG7), which aims to provide affordable, reliable, sustainable, and modern energy services for all. Accurate and timely global data on access to electricity in all countries is important for the achievement of SDG7. Current survey-based access to electricity datasets suffers from short time spans, slow updates, high acquisition costs, and a lack of location data. Accordingly, a new method for identifying the electrification status of built-up areas based on the remote sensing of nighttime light is proposed in this study. More specifically, the method overlays global built-up area data with night-time light remote sensing data to determine whether built-up areas are electrified based on a threshold night-time light value. By using our approach, electrified and unelectrified built-up areas were extracted at 500 m resolution on a global scale for the years 2014 and 2020. The acquired results show a significant reduction in an unelectrified built-up area between 2014 and 2020, from 51,301.14 km² to 22,192.52 km², or from 3.05% to 1.32% of the total built-up area. Compared to 2014, 117 countries or territories had improved access to electricity, and 18 increased their proportion of unelectrified built-up area by >0.1%. The identification accuracy was evaluated by using a random sample of 10,106 points. The accuracies in 2014 and 2020 were 97.29% and 98.9%, respectively, with an average of 98.1%. The outcomes of this method are in high agreement with the spatial distribution of access to electricity data reported by the World Bank. This study is the first to investigate the global electrification of built-up areas by using remote sensing. It makes an important supplement to global data on access to electricity, which can aid in the achievement of SDG7. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures

Graphical abstract

Other

Jump to: Research

12 pages, 3646 KiB

Open AccessTechnical Note

Monitoring Extractive Activity-Induced Surface Subsidence in Highland and Alpine Opencast Coal Mining Areas with Multi-Source Data

by Shuqing Wang, Zechao Bai, Yuepeng Lv and Wei Zhou

Remote Sens. 2022, 14(14), 3442; https://doi.org/10.3390/rs14143442 - 18 Jul 2022

Cited by 6 | Viewed by 1528

Abstract

Mining developments in alpine coal mining areas result in slow or rapid ground subsidence, which can lead to melting and collapse of permafrost. This paper integrated unmanned aerial vehicle (UAV) images and satellite-based SAR interferometry images to monitor intensive surface mining subsidence during reclamation. Digital Surface Model (DSM) acquired from UAV images was first used to evaluate the changes of the reclamation scheme on the microtopography carried out by slope and the Digital Elevation Model (DEM) of difference (DoD). The monitoring results showed that the slope had been reduced from over 30 degrees to under 15 degrees after the terrain had been reshaped. The DoD map revealed the distribution of main extraction areas and landfill areas. To further monitor the surface subsidence after local terrain adjustment, the Permanent Scatterer Interferometry (PS-InSAR) method was used to reveal the surface subsidence characteristics of the mine site before and after reclamation. The maximum cumulative subsidence ranged from −772.3 to 1183 mm based on 21 Sentinel-1A images in three years. Within a year of terrain reshaping, uplift and subsidence still occurred at hills and pit side slopes, following the nearly equal subsidence rate. The experimental results showed that the slope reshaping and vegetation recovery had a limited impact on the reduction of the ground subsidence in a short period. Therefore, on this basis, a combination of UAV and PS-InSAR methods can be used to continue monitoring time series subsidence in alpine mines. Full article

(This article belongs to the Special Issue Remote Sensing for Engineering and Sustainable Development Goals)

► Show Figures