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Remote Sensing
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Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection
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Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection

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

Deadline for manuscript submissions: 30 July 2025 | Viewed by 2289

Special Issue Editors

Dr. Tao Luo

E-Mail Website
Guest Editor
1. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
2. Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, China
Interests: clouds; boundary layer; aerosols; turbulence; satellite; atmospheric radiation; atmospheric pollution; dust; atmospheric modeling
Dr. Juhua Luo

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing 210008, China
Interests: wetland remote sensing; remote sensing of water environment and water ecology; vegetation remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jianfeng Sun

E-Mail Website
Guest Editor
1. School of Astronautics, Harbin Institute of Technology, Harbin 150006, China
2. National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, China
Interests: laser imaging technology; laser image processing; target recognition; spatial light information technology; Gm-APD; low, slow, and small target detection
Dr. Jianfeng Chen

E-Mail Website
Guest Editor
State Key Laboratory of Laser Interaction with Matter, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: atmospheric detection; incoherent wind lidar; laser technology; opto-mechanical design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In environmental component monitoring and target detection, multi-source remote sensing technologies have emerged as a powerful tool for comprehensive analysis and observation. This approach, known as multi-source remote sensing, enables a synergistic fusion of data from various sources, such as satellites, ground-based sensors, and aerial platforms, to enhance the accuracy and range of environmental component monitoring.

Multi-source remote sensing data are derived from various platforms, encompassing Lidar, satellites, airborne systems, unmanned aerial vehicles (UAVs), imaging sensors, target detection methodologies, and data assimilation techniques. Lidar delivers finely detailed vertical atmospheric profiles, while satellite observations offer expansive spatial coverage. Airborne platforms and UAVs facilitate precise and adaptable data collection, supplementing ground-level measurements. Laser 3D imaging enhances the fidelity of atmospheric reconstruction, whereas target detection technologies pinpoint specific atmospheric elements like pollutants or aerosols. Data assimilation methods amalgamate observations with models to refine the precision of atmospheric forecasts.

In addition, the comprehensive application of these diverse data sources enables high-precision comprehensive observations of factors that play a key role in the evolution of the climate environment, such as vegetation coverage, solar irradiance, water vapor flux, and atmospheric turbulence. The in-depth exploration of multi-source remote sensing technology can further strengthen the fine-grained monitoring of the dynamic change in environmental components, deepen human understanding and cognition of the complex interaction mechanism between environmental components and natural ecosystems, and thus provide a more solid data foundation and theoretical support for climate and environmental research. To help promote sustainable development and innovative breakthroughs in scientific research in related fields.

This Special Issue invites manuscripts introducing recent advances in “Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection”. All theoretical, numerical, and experimental papers are accepted. Topics include, but are not limited to, the following:

  • New atmospheric detection technologies and methods (such as Lidar, Radar, SAR, Airborne, Satellite, Spectrometer, UAV, etc.).
  • Multi-source remote sensing technology in climate change, environmental monitoring, pollution transport, ocean change, and ecosystems.
  • Effects of turbulence, solar irradiance, water vapor flux, and vegetation coverage on the interaction of environment and climate.
  • Novel spatiotemporal intelligence method of multi-source remote sensing data.
  • Novel/optimized algorithms in multi-platform sensors (Satellite, SAR, Lidar, Sonde, Radar, UAV, etc.).
  • Lidar and infrared technology in target detection, 3D imaging, and spatial localization.
  • Novel single photon technology and GM-APD in target detection and remote sensing.

Dr. Tao Luo
Dr. Juhua Luo
Dr. Jianfeng Sun
Dr. Jianfeng Chen
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

  • environmental component monitoring
  • atmospheric detection
  • target detection
  • lidar
  • floating platform
  • satellite
  • airborne
  • unmanned aerial vehicles (UAVs)
  • collaborative observation
  • data assimilation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

21 pages, 50903 KiB  
Article
Observation of Urban Atmospheric Environment in High Latitude Regions of China—A Case Study of Harbin
by Bowen Zhang, Guangqiang Fan, Tianshu Zhang, Xiang Jin and Wenqing Liu
Remote Sens. 2025, 17(6), 1003; https://doi.org/10.3390/rs17061003 - 13 Mar 2025
Viewed by 356
Abstract
Temperature and humidity profile lidar is one of the important means of urban atmospheric environment monitoring, which can capture atmospheric elements such as lidar ratio, color ratio, depolarization ratio, Ångström exponent, and temperature and humidity profile with research values. This study was based [...] Read more.
Temperature and humidity profile lidar is one of the important means of urban atmospheric environment monitoring, which can capture atmospheric elements such as lidar ratio, color ratio, depolarization ratio, Ångström exponent, and temperature and humidity profile with research values. This study was based on the observation results of temperature and humidity profile lidar in Harbin and discusses the changes in the urban atmospheric environment under different conditions. The interaction processes between water vapor, temperature, and particulate matter, including aggregation, diffusion, phase transition, and transport, were explored under the main factor of anthropogenic pollution. This article analyzes the mutual influence of these atmospheric parameters in different environments, highlighting the important impact of temperature and humidity on the formation and diffusion of pollutants during pollution events. It supplements more data on urban atmospheric environment monitoring in the region and provides more data support for urban environmental governance. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection)
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24 pages, 8594 KiB  
Article
Identification and Analysis of Wind Shear Within the Transiting Frontal System at Xining International Airport Using Lidar
by Shijun Zhao, Chao Yang, Yulong Shan and Fei Zhu
Remote Sens. 2025, 17(5), 732; https://doi.org/10.3390/rs17050732 - 20 Feb 2025
Viewed by 502
Abstract
The principal factor contributing to the restricted accuracy of wind shear detection resides in the rapid oscillations of the wind field and the intricate characteristics of wind shear. The abrupt alterations in wind speed and direction over a short span pose a formidable [...] Read more.
The principal factor contributing to the restricted accuracy of wind shear detection resides in the rapid oscillations of the wind field and the intricate characteristics of wind shear. The abrupt alterations in wind speed and direction over a short span pose a formidable challenge for conventional detection techniques to precisely capture and expeditiously analyze this phenomenon. In this study, three algorithms were employed to analyze wind shear within the frontal system at Xining International Airport on 5 April 2023, and the same analytical approach was applied to three additional paradigmatic cases. Initially, the slope characteristics of the Lidar signal were utilized to ascertain the existence and intensity of wind shear by assessing the rate of variation of the wind field parameters along a specific trajectory. Secondly, the S-factor algorithm was applied to detect wind shear. This algorithm revolves around particular mathematical relationships and statistical measures within the wind field data. By taking into account multiple variables and their mutual interactions, an S-factor value was computed to signify the strength of wind shear. Furthermore, an enhanced F-factor algorithm was found upon scrutinizing the identical wind field data, as they all detected a substantial intensification in wind shear intensity prior to and after the issuance of the voice report. This evinces that despite the differences in sensitivity, all three algorithms are able to capture the general trend of wind shear fluctuations during the passage of the frontal system. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection)
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21 pages, 6473 KiB  
Article
Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform
by Di Liu, Jianfeng Sun, Wei Lu, Sining Li and Xin Zhou
Remote Sens. 2025, 17(4), 622; https://doi.org/10.3390/rs17040622 - 11 Feb 2025
Viewed by 624
Abstract
Array Geiger-mode avalanche photodiode (GM-APD) Light Detection and Ranging (LiDAR) has the advantages of high sensitivity and long imaging range. However, due to its operating principle, GM-APD LiDAR requires processing based on multiple-laser-pulse data to complete the target reconstruction. Therefore, the influence of [...] Read more.
Array Geiger-mode avalanche photodiode (GM-APD) Light Detection and Ranging (LiDAR) has the advantages of high sensitivity and long imaging range. However, due to its operating principle, GM-APD LiDAR requires processing based on multiple-laser-pulse data to complete the target reconstruction. Therefore, the influence of the device’s movement or scanning motion during GM-APD LiDAR imaging cannot be ignored. To solve this problem, we designed a reconstruction method based on coordinate system transformation and the Position and Orientation System (POS). The position, attitude, and scanning angles provided by POS and angular encoders are used to reduce or eliminate the dynamic effects in multiple-laser-pulse detection. Then, an optimization equation is constructed based on the negative-binomial distribution detection model of GM-APD. The spatial distribution of photons in the scene is ultimately computed. This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. Moreover, with sufficient data acquisition, this method can achieve super-resolution reconstruction. Finally, numerical simulations and imaging experiments verify the effectiveness of the proposed method. For a 1.95 km building scene with SBR ~0.137, the 2 × 2-fold super-resolution reconstruction results obtained by this method reduce the distance error by an order of magnitude compared to traditional methods. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection)
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19 pages, 6549 KiB  
Article
Research on the Tunable Optical Alignment Technology of Lidar Under Complex Working Conditions
by Jianfeng Chen, Jie Ji, Chenbo Xie and Yingjian Wang
Remote Sens. 2025, 17(3), 532; https://doi.org/10.3390/rs17030532 - 5 Feb 2025
Viewed by 570
Abstract
Lidar technology is pivotal for detecting and monitoring the atmospheric environment. However, maintaining optical path stability in complex environments poses significant challenges, especially regarding adaptability and cost efficiency. This study proposes a tunable optical alignment method that is applied to the Rotating Rayleigh [...] Read more.
Lidar technology is pivotal for detecting and monitoring the atmospheric environment. However, maintaining optical path stability in complex environments poses significant challenges, especially regarding adaptability and cost efficiency. This study proposes a tunable optical alignment method that is applied to the Rotating Rayleigh Doppler Wind Lidar (RRDWL) to enable precise detection of mid-to-upper atmospheric wind fields. Building on the conventional echo signal strength method, this approach calibrates the signal strength using cloud information and the signal-to-noise ratio (SNR), enabling stratified and tunable optical alignment. Experimental results indicate that the optimized RRDWL achieves a maximum detection height increase from 42 km to nearly 51 km. Additionally, the average horizontal wind speed error at 30 km decreases from 11.3 m/s to 4.4 m/s, with a minimum error of approximately 1 m/s. These findings confirm that the proposed method enhances the effectiveness and reliability of the Lidar system under complex operational and diverse weather conditions. Furthermore, it improves detection performance and provides robust support for applications in related fields. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing for Environmental Component Monitoring and Target Detection)
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