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Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 7548

Special Issue Editors

Dr. Marco Polcari

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, 00143 Rome, Italy
Interests: SAR interferometry; multitemporal InSAR analysis; offset tracking; multiaperture interferometry; natural and anthropogenic deformation phenomena; data integration
Special Issues, Collections and Topics in MDPI journals
Dr. Letizia Anderlini

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Bologna, Via Franceschini 31, 40128 Bologna, Italy
Interests: time series analysis; GNSS; natural and anthropogenic crustal deformation; seismic cycle; data modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Montuori

E-Mail Website
Guest Editor
Agenzia Spaziale Italiana (ASI), Earth Observation Unit, Via del Politecnico snc, 00133 Rome, Italy
Interests: spaceborne remote sensing; SAR; multitemporal analysis; electromagnetic modeling; polarimetry; natural hazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The second volume of the Special Issue “Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena” is now open for submissions. (https://www.mdpi.com/journal/remotesensing/special_issues/Natural_Anthropogenic_RS). This volume aims to collect contributions on the use of Synthetic Aperture Radar (SAR), Global Navigation Satellite Systems (GNSS) and optical data (e.g., data from multi-spectral and hyperspectral missions) to study natural and anthropogenic phenomena characterizing the Earth’s surface.

Thanks to the increasing number of space missions equipped with SAR and optical sensors and GNSS networks, EO data can be utilized to better understand several phenomena and improve our knowledge of the Earth’s dynamic processes.

Therefore, we welcome studies on seismic or volcanic processes, crop production, subsoil exploitation activities, urban or coastal subsidence and landslides and avalanches, as well as papers focusing on the testing and demonstration of novel analytical methods including, but not limited to, data fusion approches, artificial intelligence (AI), machine/deep learning (ML/DL) and neural networks, with analysis of their performance to improve the processing and post-processing of satellite data, with reference to the combined use of multi-mission products.

Moreover, we will consider contributions focusing on phenomena in the framework of climate change. Long-term shifts in temperatures and weather patterns are significantly modifying our planet and thus affecting its inner and surface structure, putting its inhabitants at risk. Thus, contributions supporting both hazard assessment and risk mitigation are welcome, including papers considering wildfire detection, floods, sea level rise, glacier monitoring, plastic pollution and oil spills, coastal erosion and drying rivers and gas emission monitoring.

Dr. Marco Polcari
Dr. Letizia Anderlini
Dr. Antonio Montuori
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

  • SAR
  • GNSS
  • optical data
  • InSAR
  • data integration
  • data processing techniques
  • natural and anthropogenic phenomena
  • artificial intelligence (AI)
  • climate change

Published Papers (6 papers)

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Research

21 pages, 3799 KiB  
Article
Concept of a Satellite Cross-Calibration Radiometer for In-Orbit Calibration of Commercial Optical Satellites
by Medhavy Thankappan, Jon Christopherson, Simon Cantrell, Robert Ryan, Mary Pagnutti, Courtney Bright, Denis Naughton, Kathryn Ruslander, Lan-Wei Wang, David Hudson, Jerad Shaw, Shankar Nag Ramaseri Chandra and Cody Anderson
Remote Sens. 2024, 16(8), 1333; https://doi.org/10.3390/rs16081333 - 10 Apr 2024
Viewed by 587
Abstract
The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, [...] Read more.
The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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19 pages, 18794 KiB  
Article
Slope-Scale Evolution Categorization of Deep-Seated Slope Deformation Phenomena with Sentinel-1 Data
by Davide Cardone, Martina Cignetti, Davide Notti, Danilo Godone, Daniele Giordan, Fabiana Calò, Simona Verde, Diego Reale, Eugenio Sansosti and Gianfranco Fornaro
Remote Sens. 2023, 15(23), 5440; https://doi.org/10.3390/rs15235440 - 21 Nov 2023
Viewed by 930
Abstract
Deep-seated gravitational slope deformations (DsGSDs) are slope-scale phenomena which are widespread in mountainous regions. Despite interacting with human infrastructures and settlements, only a few cases are monitored with ground-based systems. Remote sensing technologies have recently become a consolidated instrument for monitoring and studying [...] Read more.
Deep-seated gravitational slope deformations (DsGSDs) are slope-scale phenomena which are widespread in mountainous regions. Despite interacting with human infrastructures and settlements, only a few cases are monitored with ground-based systems. Remote sensing technologies have recently become a consolidated instrument for monitoring and studying such widespread and slow processes. This paper proposes a three-step novel methodology to analyze the morpho-structural domain of DsGSDs by exploiting the advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) technique through (i) the analysis of A-DInSAR measurement point density and distribution defining a coverage threshold; (ii) the assessment of the actual ground deformation with respect to the orientation of phenomena based on slope, aspect, and C-index; and (iii) ground deformation mapping with previously ranked velocity interpolation. The methodology was tested on two differently oriented phenomena: the mainly north–south-oriented Croix de Fana and the mainly east–west-oriented Valtournenche DsGSD, located in the Aosta Valley Region, northern Italy. The results show a variation in the kinematic behavior between the morpho-structural domains, while also considering any other superimposed surficial deformations. This work provides the lines for the implementation of a rapid and low-cost tool based on the use of A-DInSAR measurements which are suitable for assessing the impact of any type of DsGSD on the anthropic facilities and infrastructures in mountainous areas. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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16 pages, 54240 KiB  
Article
Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil
by Helen Cristina Dias, Daniel Hölbling and Carlos Henrique Grohmann
Remote Sens. 2023, 15(21), 5137; https://doi.org/10.3390/rs15215137 - 27 Oct 2023
Cited by 3 | Viewed by 1191
Abstract
Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the decades, a variety of mapping approaches [...] Read more.
Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the decades, a variety of mapping approaches have been employed for the detection of landslides using Earth observation (EO) data. Object-based image analysis (OBIA) is a widely recognized method for mapping landslides and other morphological features. In Brazil, despite the high frequency of landslides, methods for inventory construction are poorly developed. The aim of this study is to semi-automatically recognize shallow landslides in Itaóca (Brazil) and evaluate the transferability of the approach within different areas in Brazil. RapidEye satellite images (5 m) and the derived normalized difference vegetation index (NDVI), as well as a digital elevation model (DEM) (12.5 m) and morphological data, were integrated into the classification. The results show that the method is suitable for the recognition of this type of hazard in Brazil. The overall accuracy was 89%. The main challenges were the identification of small landslides and the exact delineation of scars. The findings validate the applicability of the approach in Brazil, although additional adjustments to the primary rule set might lead to better results. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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29 pages, 55580 KiB  
Article
Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry
by Yi Zhang, Yuanxi Li, Xingmin Meng, Wangcai Liu, Aijie Wang, Yiwen Liang, Xiaojun Su, Runqiang Zeng and Xu Chen
Remote Sens. 2023, 15(20), 4951; https://doi.org/10.3390/rs15204951 - 13 Oct 2023
Viewed by 1059
Abstract
Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot [...] Read more.
Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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25 pages, 9198 KiB  
Article
A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)
by Michelle Rygus, Alessandro Novellino, Ekbal Hussain, Fifik Syafiudin, Heri Andreas and Claudia Meisina
Remote Sens. 2023, 15(15), 3776; https://doi.org/10.3390/rs15153776 - 29 Jul 2023
Cited by 5 | Viewed by 1671
Abstract
Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract [...] Read more.
Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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22 pages, 8972 KiB  
Article
Landslide Susceptibility Zoning in Yunnan Province Based on SBAS-InSAR Technology and a Random Forest Model
by Meiyu Liu, Bing Xu, Zhiwei Li, Wenxiang Mao, Yan Zhu, Jingxin Hou and Weizheng Liu
Remote Sens. 2023, 15(11), 2864; https://doi.org/10.3390/rs15112864 - 31 May 2023
Cited by 4 | Viewed by 1542
Abstract
Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province [...] Read more.
Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province. Full article
(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena: A Look towards Climate Change and Advanced Processing Methods)
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