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Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and...
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Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and Applications

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

Deadline for manuscript submissions: 10 December 2024 | Viewed by 7283

Special Issue Editors

Dr. Hao Zhang

E-Mail Website
Guest Editor
Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
Interests: radiometric calibration; atmospheric correction; remote sensing retrieval validation
Prof. Dr. L. Monika Moskal

E-Mail Website
Guest Editor
School of Environmental and Forest Sciences, College of the Environment, University of Washington (UW), Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial Analysis Laboratory, Washington, Box 352100, Seattle, WA 98195-2100, USA
Interests: ALT; TLS; MLS; lidar precision forestry; hyper-resolution (spatial, temporal, spectral) remote sensing; ecosystem services
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carl Philipp Lipo

E-Mail Website
Guest Editor
Department of Anthropology and the Enviornmental Studies Program, Binghamton University, 4400 Vestal Parkway, Binghamton, NY 13902, USA
Interests: remote sensing; near-surface geophysical methods; quantitative methods; agent-based modeling; evolutionary archaeology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multisource remote sensing data integration has attracted intensive attention for fully characterizing the features of surface covers, such as vegetation, soil, water, and human-made materials. Remote sensing data acquired on different platforms (e.g., satellites, airplane, unmanned aerial vehicles (UAV), ground-based observation towers) have greatly improved spatial, spectral, radiometric, and temporal performance in recent decades. For example, high-resolution satellites or constellations may be revisited every 2–3 days; hyperspectral satellites Gaofen5-02, ZY-02D, and ZY-02E from China and PRISMA from Italy could provide fine spectral imaging data covering 400 to 2500 nm; many researchers could also easily acquire abundant UAV data when needed. It is impossible for any single remote sensing data source to provide all the imaging merits in terms of temporal frequency, spatial resolution, spectral resolution, polarization, radiometric performance, angular availability, and spatial dimensional coverage. Therefore, it is important to combine hyperspectral imaging data, multispectral data, thermal data, and other data to fulfill the quantitative application aims. However, big challenges and uncertainties emerge when combining these multisource data through quantitative methodologies or techniques to achieve reasonable, explainable, consistent results. Public fusion products such as the harmonized Landsat and Sentinel-2 datasets, or comprehensive experiments from researchers using various imaging sensors, require integration methods and applications for these multisource data.

This Special issue aims at quantitative integration for multisource remote sensing data acquired on different platforms and by different sensors. Topics may cover scientific data acquisition, parameter retrieval, multiscale approaches, normalization methods, and applications related to multisource data. Comprehensive reviews of multisource remote sensing development, novel methodology of quantitative remote sensing, and other relative issues are also welcome. Topics of interest include:

  • New methods to retrieve the ground reflectance, temperature, and aerosol parameters;
  • Quantitative parameter retrieval for specific ground types;
  • Optical remote sensing data and Lidar integration;
  • Hyperspectral and multispectral data combination;
  • Multiscale approaches;
  • Multitemporal data integration and normalization;
  • Quantitative product applications and validation among multisource data.

Dr. Hao Zhang
Prof. Dr. L. Monika Moskal
Prof. Dr. Carl Philipp Lipo
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

  • multisource data
  • remote sensing data normalization
  • quantitative parameter retrievals
  • lidar/hyperspectral/high-resolution/thermal data combination
  • multiscaling
  • product validation

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Published Papers (4 papers)

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25 pages, 11838 KiB  
Article
Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints
by Panli Cai, Jingxian Guo, Runkui Li, Zhen Xiao, Haiyu Fu, Tongze Guo, Xiaoping Zhang, Yashuai Li and Xianfeng Song
Remote Sens. 2024, 16(2), 263; https://doi.org/10.3390/rs16020263 - 9 Jan 2024
Cited by 4 | Viewed by 1827
Abstract
Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties [...] Read more.
Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas. Full article
(This article belongs to the Special Issue Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and Applications)
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26 pages, 28212 KiB  
Article
Spatio-Temporal Evolution of Urban Expansion along Suburban Railway Lines in Megacities Based on Multi-Source Data: A Case Study of Beijing, China
by Hongya Tang, Xin Yan, Tianshu Liu and Jie Zheng
Remote Sens. 2023, 15(19), 4684; https://doi.org/10.3390/rs15194684 - 25 Sep 2023
Cited by 1 | Viewed by 1369
Abstract
Suburban railways in megacities exert a pivotal role in propelling urbanization and shaping urban agglomeration. However, previous study endeavors have overlooked the transformations occurring in urban expansion along suburban railways, with a particular dearth of attention on the spatio-temporal evolution of landscape ecology [...] Read more.
Suburban railways in megacities exert a pivotal role in propelling urbanization and shaping urban agglomeration. However, previous study endeavors have overlooked the transformations occurring in urban expansion along suburban railways, with a particular dearth of attention on the spatio-temporal evolution of landscape ecology and urban function. Therefore, this study employs the megacity of Beijing as an example. It utilizes remote sensing and point-of-interest (POI) data spanning from 2008 to 2022 to construct an indicator system from two essential dimensions: urban form and function. We explored the spatio-temporal characteristics of alterations in urban expansion within the gradient buffer zone adjacent to the suburban railway network in Beijing. The results showed that: (1) The rates of urban expansion were highest in 2008–2013 and lowest in 2013–2018; moreover, suburban railways had the greatest impact on the built-up area within 2–4 km along the route, and the impact gradually decreased beyond 4 km. (2) The direction of urban expansion shifted northward in the direction of latitude and eastward in the direction of longitude from 2008 to 2022, with the shift in latitude being more distinct. (3) The number of urban functions gradually increased from 2008 to 2018, but the number of medical services suddenly increased and the number of other urban functions decreased from 2018 to 2022; in addition, urban functions other than scenic spots were mainly distributed in the main urban areas, with very few clusters distributed near stations. (4) The landscape shape index became more irregular and fragmented from the center along the route to the edge of the buffer zone from 2008 to 2013, and the degree of fragmentation was highest in the 2–4 km buffer zone. In summary, this paper analyzes the spatio-temporal characteristics of urban expansion along suburban railways through four indexes, namely expansion rate, expansion direction, urban function, and landscape shape, and the results of this study are of great significance to the development and planning of suburban railways in megacities. Full article
(This article belongs to the Special Issue Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and Applications)
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34 pages, 11080 KiB  
Article
Evaluation of Temporal Stability in Radiometric Calibration Network Sites Using Multi-Source Satellite Data and Continuous In Situ Measurements
by Enchuan Qiao, Chao Ma, Hao Zhang, Zhenzhen Cui and Chenglong Zhang
Remote Sens. 2023, 15(10), 2639; https://doi.org/10.3390/rs15102639 - 18 May 2023
Cited by 5 | Viewed by 1594
Abstract
The radiometric calibration network (RadCalNet) comprises four pseudo-invariant calibration sites (PICS): Gobabeb, Baotou, Railroad Valley Playa, and La Crau. Due to its site stability characteristics, it is widely used for sensor performance monitoring and radiometric calibration, which require high spatiotemporal stability. However, some [...] Read more.
The radiometric calibration network (RadCalNet) comprises four pseudo-invariant calibration sites (PICS): Gobabeb, Baotou, Railroad Valley Playa, and La Crau. Due to its site stability characteristics, it is widely used for sensor performance monitoring and radiometric calibration, which require high spatiotemporal stability. However, some studies have found that PICS are not invariable. Previous studies used top-of-atmosphere (TOA) data without verifying site data, which could affect the accuracy of their results. In this study, we analyzed the short- and long-term radiometric trends of RadCalNet sites using bottom-of-atmosphere (BOA) data, and verified the trends revealed by the TOA data from Landsat 7, 8, and Sentinel-2. Besides the commonly used methods (e.g., nonparametric Mann–Kendall and sequential Mann–Kendall tests), a more robust Sen’s slope method was used to estimate the magnitude of the change. We found that (1) the trends based on TOA reflectance contrasted with those based on BOA reflectance in certain cases, e.g., the reflectance trends in the red band of BOA data for La Crau in summer and autumn and Baotou were not significant, while the TOA data showed a significant downward trend; (2) the temporal trends showed statistically significant and abrupt changes in all PICS, e.g., the SWIR2 band of La Crau in winter and spring changed by 1.803% per year, and the SWIR1 band of Railroad Valley Playa changed by >0.282% per year, indicating that the real changes in sensor performance are hard to detect using these sites; (3) spatial homogeneity was verified using the coefficient of variation (CV) and Getis statistic (Gi*) for each PICS (CV < 3% and Gi* > 0). Overall, the RadCalNet remains a highly reliable tool for vicarious calibration; however, the temporal stability should be noted for radiometric performance monitoring of sensors. Full article
(This article belongs to the Special Issue Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and Applications)
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17 pages, 22292 KiB  
Technical Note
Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images
by Liyao Song, Haiwei Li, Song Liu, Junyu Chen, Jiancun Fan, Quan Wang and Jocelyn Chanussot
Remote Sens. 2024, 16(1), 180; https://doi.org/10.3390/rs16010180 - 31 Dec 2023
Cited by 1 | Viewed by 1182
Abstract
Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted [...] Read more.
Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods. Full article
(This article belongs to the Special Issue Quantitative Integration for Multi-source Remote Sensing Data: Theory, Methods, and Applications)
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