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Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 15044

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Special Issue Editors

Dr. Xiangqin Wei

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of vegetation; remote sensing of ecological environment; agriculture remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xingfa Gu

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing technology and application; information extraction and engineering; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Thomas Gathungu Ngigi

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Guest Editor

Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000, Kenya
Interests: digital image processing; remote sensing of land covers; geographical information systems

Special Issue Information

Dear Colleagues,

The "Silk Road Economic Belt" and the "21st Century Maritime Silk Road" (referred to as the "Belt and Road") which is proposed by Chinese President Xi Jinping in September and October 2013 have become a great practice in developing a community with a shared future for humankind. The “Belt and Road” has a wide range of regions, complex ecosystems, and frequent changes in the ecological environment. Remote sensing provides the only effective means for quickly monitoring a large area. Quantitative, accurate and scientific evaluation of the ecological environment, construction planning and green development have huge potential in supporting the realization of sustainable development goals of various countries.

Most of the countries along the “Belt and Road” are developing countries, and they are faced with the sustainable development needs of the utilization of natural resources and responding to natural disasters in the process of their construction. At present, it is difficult for most countries along the “Belt and Road” to use their own independent space observation capabilities to monitor and evaluate the ecological environment, such as vegetation, water resources, and atmosphere. Therefore, it is urgent to use remote sensing data resources and technologies, jointly with the "Belt and Road" countries to carry out the sustainable development of remote sensing technology and application research around the application topics of common concern, such as land water resources and ecological environment. This Special Issue of Sensors focuses on “Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development”. Potential topics include but are not limited to the following:

Advances in Remote Sensing Data Processing；
Land cover remote sensing;
Vegetation and ecological remote sensing;
Atmospheric remote sensing;
Remote sensing of water and carbon cycle;
Remote sensing applications;
The "Belt and Road" and sustainable development goals;
Remote sensing supports sustainable development evaluation index system;
Remote sensing assessment about the “Belt and Road” region of the comprehensive status and trends.

Dr. Xiangqin Wei
Prof. Dr. Xingfa Gu
Dr. Thomas Gathungu Ngigi
Guest Editors

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Keywords

advances in remote sensing data processing
land cover remote sensing
vegetation and ecological remote sensing
atmospheric remote sensing
remote sensing of water and carbon cycle
remote sensing applications
the “Belt and Road” and sustainable development goals
remote sensing supports sustainable development evaluation index system
remote sensing assessment about the “Belt and Road” region of the comprehensive status and trends

Published Papers (10 papers)

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Research

14 pages, 3570 KiB

Open AccessArticle

A Multi-Stage Progressive Network with Feature Transmission and Fusion for Marine Snow Removal

by Lixin Liu, Yuyang Liao and Bo He

Sensors 2024, 24(2), 356; https://doi.org/10.3390/s24020356 - 7 Jan 2024

Viewed by 746

Abstract

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network’s performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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17 pages, 40916 KiB

Open AccessArticle

Assessment of Forest Ecosystem Variations in the Lancang–Mekong Region by Remote Sensing from 2010 to 2020

by Jing Zhao, Jing Li, Qinhuo Liu, Yadong Dong, Li Li and Hu Zhang

Sensors 2023, 23(22), 9038; https://doi.org/10.3390/s23229038 - 8 Nov 2023

Viewed by 837

Abstract

Five countries in the Lancang–Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang–Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang–Mekong region, which is crucial for implementing effective conservation strategies. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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20 pages, 15597 KiB

Open AccessArticle

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016–2020

by Li Li, Xiaozhou Xin, Jing Zhao, Aixia Yang, Shanlong Wu, Hailong Zhang and Shanshan Yu

Sensors 2023, 23(20), 8452; https://doi.org/10.3390/s23208452 - 13 Oct 2023

Cited by 2 | Viewed by 1343

Abstract

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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18 pages, 10090 KiB

Open AccessArticle

Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples

by Zhouwei Zhang, Xiaofei Mi, Jian Yang, Xiangqin Wei, Yan Liu, Jian Yan, Peizhuo Liu, Xingfa Gu and Tao Yu

Sensors 2023, 23(18), 8010; https://doi.org/10.3390/s23188010 - 21 Sep 2023

Cited by 3 | Viewed by 1387

Abstract

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical–quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical–quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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16 pages, 9045 KiB

Open AccessArticle

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region

by Xiangqin Wei, Tianhai Cheng, Jian Yang, Shijiao Qiao, Li Li, Haidong Yu, Xiaofei Mi, Yan Liu, Hong Guo, Jiaguo Li, Yuan Sun, Chunmei Wang and Xingfa Gu

Sensors 2023, 23(18), 7752; https://doi.org/10.3390/s23187752 - 8 Sep 2023

Cited by 1 | Viewed by 905

Abstract

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the “Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road”. This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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16 pages, 15900 KiB

Open AccessArticle

Quality Analysis and Correction of Sea Surface Temperature Data from China HY-1C Satellite in Southeast Asia Seas

by Weifu Sun, Chalermrat Sangmanee, Yuanchi Jiang, Yi Ma, Jiang Li and Yujia Zhao

Sensors 2023, 23(18), 7692; https://doi.org/10.3390/s23187692 - 6 Sep 2023

Viewed by 708

Abstract

China’s marine satellite infrared radiometer SST remote sensing observations began relatively late. Thus, it is essential to evaluate and correct the SST observation data of the Ocean Color and Temperature Scanner (COCTS) onboard the China HY-1C satellite in the Southeast Asia seas. We conducted a quality assessment and correction work on the SST of the China COCTS/HY-1C in Southeast Asian seas based on multisource satellite SST data and temperature data measured by Argo buoys. The accuracy evaluation results of the COCTS SST indicated that the bias, Std, and RMSE of the daytime SST data for HY-1C were −0.73 °C, 1.38 °C, and 1.56 °C, respectively, while the bias, Std, and RMSE of the nighttime SST data were −0.95 °C, 1.57 °C, and 1.83 °C, respectively. The COCTS SST accuracy was significantly lower than that of other infrared radiometers. The effect of the COCTS SST zonal correction was most significant, with the Std and RMSE approaching 1 °C. After correction, the RMSE of the daytime SST and nighttime SST data decreased by 32.52% and 42.04%, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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16 pages, 8701 KiB

Open AccessArticle

A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission

by Weijia Cao, Xiaoran Leng, Tao Yu, Xingfa Gu and Qiyue Liu

Sensors 2023, 23(17), 7600; https://doi.org/10.3390/s23177600 - 1 Sep 2023

Cited by 4 | Viewed by 998

Abstract

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with “Belt and Road” countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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13 pages, 8001 KiB

Open AccessArticle

Assessment of Land Cover Status and Change in the World and “the Belt and Road” Region from 2016 to 2020

by Aixia Yang, Bo Zhong, Longfei Hu, Ao Kai, Li Li, Fei Zhao and Junjun Wu

Sensors 2023, 23(16), 7158; https://doi.org/10.3390/s23167158 - 14 Aug 2023

Cited by 2 | Viewed by 907

Abstract

The assessment of land cover and changes will help to understand the temporal and spatial pattern of land cover in the world and the Belt and Road (B&R) region, and provide reference information for global sustainable development and the Belt and Road construction. In this paper, the 1 km global land cover classification maps of 2016 and 2020 with a high accuracy of 88% are mapped using the Moderate Resolution Imaging Spectroradiometer (MODIS) time series surface reflectance products. Based on the maps, the land cover status of the world and the Belt and Road region, the land cover change from 2016 to 2020, and the mutual transformation characteristics between various types, are analyzed. The research results indicate that from 2016 to 2020, the global change rates of cropland, forest, grassland, and impervious surface are 0.25%, 0.22%, 0.08% and 3.41%, respectively. In the Belt and Road region, the change rates of cropland, forest, grassland, and impervious surface are 0.42%, 0.60%, −0.55% and 2.98% respectively. The assessment results will help to clarify the spatial pattern of land cover change in the five years from 2016 to 2020, so as to provide valuable scientific information for the global realization of sustainable development goals and the construction of the B&R. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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20 pages, 8598 KiB

Open AccessArticle

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information

by Min Gao, Xingfa Gu, Yan Liu, Yulin Zhan, Xiangqin Wei, Haidong Yu, Man Liang, Chenyang Weng and Yaozong Ding

Sensors 2022, 22(21), 8524; https://doi.org/10.3390/s22218524 - 5 Nov 2022

Cited by 1 | Viewed by 4686

Abstract

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert’s edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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12 pages, 1777 KiB

Open AccessArticle

Fast Positioning Model and Systematic Error Calibration of Chang’E-3 Obstacle Avoidance Lidar for Soft Landing

by Donghong Wang, Xingfeng Chen, Jun Liu, Zongqi Liu, Fengjie Zheng, Limin Zhao, Jiaguo Li and Xiaofei Mi

Sensors 2022, 22(19), 7366; https://doi.org/10.3390/s22197366 - 28 Sep 2022

Cited by 2 | Viewed by 1227

Abstract

Chang’E-3 is China’s first soft landing mission on an extraterrestrial celestial body. The laser Three-Dimensional Imaging (TDI) sensor is one of the key payloads of the Chang’E-3 lander. Its main task is to provide accurate 3D lunar surface information of the target landing area in real time for the selection of safe landing sites. Here, a simplified positioning model was constructed, to meet the accuracy and processing timeline requirements of the TDI sensor of Chang’E-3. By analyzing the influence of TDI intrinsic parameters, a permanent outdoor calibration field based on flat plates was specially designed and constructed, and a robust solution of the geometric calibration adjustment was realized by introducing virtual observation equations for unknowns. The geometric calibration and its absolute and relative positioning accuracy verification were carried out using multi-measurement and multi-angle imaging data. The results show that the error of TDI intrinsic parameters will produce a false obstacle with a maximum height of about 1.4 m on the plane, which will cause the obstacle avoidance system of Chang’E-3 to fail to find a suitable landing area or find a false flat area. Furthermore, the intrinsic parameters of the TDI have good stability and the accuracy of the reconstructed three-dimensional surface can reach about 4 cm after error calibration, which provides a reliable terrain guarantee for the autonomous obstacle avoidance of the Chang’E-3 lander. Full article

(This article belongs to the Special Issue Remote Sensing Technology Supporting the "Belt and Road" Sustainable Development)

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