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Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 34657

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Dr. Maria Kouli

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1. Department of Electronic Engineering, Hellenic Mediterranean University, 73133 Chania, Greece
2. Institute of Physics of the Earth’s Interior & Geohazards, UNESCO Chair on Solid Earth Physics and Geohazards Risk Reduction, Hellenic Mediterranean University Research Center, 73133 Chania, Greece
Interests: remote sensing applications; natural hazards; geophysics; geoinformatics
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Special Issue Information

Dear Colleagues,

In our rapidly changing world, it is of great significance to conduct an assessment of the susceptibility of urban and peri-urban areas to geo-environmental hazards such as landslides, collapses, floods, soil erosion, coastal erosion and earthquakes for hazard prevention and mitigation. Moreover, geohazards, especially landslides, but also erosion, karsts and active faults may pose severe constraints for the structural integrity of crucial infrastructures.

This Special Issue focuses on the potential of remote sensing (RS) and Earth observation (EO) to visualize and solve urban and peri-urban geo-environmental issues with the aim to protect the urban population which is becoming more and more vulnerable to disasters.

Remote sensing offers several state-of-the-art technologies (multispectral, hyperspectral, thermal, and radar), techniques (field and laboratory measurements, simulations, satellite, and UAVs), and image processing methods that can contribute on modeling and mapping the geo-environmental phenomena in space and time.

Consequently the many sources of remote sensing and Earth observation data are combined with auxiliary data (e.g. geophysical, hydrological, meteorological and soil data) usually in a GIS environment in order either to map, analyze or/and estimate the spatiotemporal distribution characteristics of geo-hazards.

Authors are encouraged to submit articles on, but not limited to, the following subjects:

Detection of geo-environmental changes – multitemporal remote sensing.
Multisensors onboard satellites or UAVs: multispectral, hyperspectral, radar and thermal.
GIS mapping, modeling and/or monitoring approaches in geo-environmental hazards.
Assessing the geo-environmental status and creating innovative solutions using integration between RS and GIS techniques.
Remote Sensing Indices (e.g. Built-Up Indices, Vegetation Indices, Thermal Indices).
Cultural heritage management and protection.
Site selection for infrastructures (e.g. pipelines).

Dr. Maria Kouli
Guest Editor

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

urban and peri-urban areas
geohazards
earthquakes
landslides
karsts
susceptibility mapping
cultural heritage protection
pipelines
assessment and mitigation

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Published Papers (10 papers)

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Editorial

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3 pages, 173 KiB

Open AccessEditorial

Editorial for the Special Issue “Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues”

by Maria Kouli

Remote Sens. 2023, 15(1), 63; https://doi.org/10.3390/rs15010063 - 23 Dec 2022

Cited by 2 | Viewed by 1738

Abstract

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

Research

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19 pages, 11963 KiB

Open AccessArticle

The 2021 Greece Central Crete M_L 5.8 Earthquake: An Example of Coalescent Fault Segments Reconstructed from InSAR and GNSS Data

by Nicola Angelo Famiglietti, Zeinab Golshadi, Filippos Vallianatos, Riccardo Caputo, Maria Kouli, Vassilis Sakkas, Simone Atzori, Raffaele Moschillo, Gianpaolo Cecere, Ciriaco D’Ambrosio and Annamaria Vicari

Remote Sens. 2022, 14(22), 5783; https://doi.org/10.3390/rs14225783 - 16 Nov 2022

Cited by 4 | Viewed by 2092

Abstract

The M_L 5.8 earthquake that hit the island of Crete on 27 September 2021 is analysed with InSAR (Interferometry from Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) data. The purpose of this work is to create a model with sufficient detail for the geophysical processes that take place in several kilometres below the earth’s surface and improve our ability to observe active tectonic processes using geodetic and seismic data. InSAR coseismic displacements maps show negative values along the LOS of ~18 cm for the ascending orbit and ~20 cm for the descending one. Similarly, the GNSS data of three permanent stations were used in PPK (Post Processing Kinematic) mode to (i) estimate the coseismic shifts, highlighting the same range of values as the InSAR, (ii) model the deformation of the ground associated with the main shock, and (iii) validate InSAR results by combining GNSS and InSAR data. This allowed us to constrain the geometric characteristics of the seismogenic fault and the slip distribution on it. Our model, which stands on a joint inversion of the InSAR and GNSS data, highlights a major rupture surface striking 214°, dipping 50° NW and extending at depth from 2.5 km down to 12 km. The kinematics is almost dip-slip normal (rake −106°), while a maximum slip of ~1.0 m occurred at a depth of ca. 6 km. The crucial though indirect role of inherited tectonic structures affecting the seismogenic crustal volume is also discussed suggesting their influence on the surrounding stress field and their capacity to dynamically merge distinct fault segments. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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18 pages, 8268 KiB

Open AccessArticle

Monitoring Ecological Changes on a Rapidly Urbanizing Island Using a Remote Sensing-Based Ecological Index Produced Time Series

by Lili Lin, Zhenbang Hao, Christopher J. Post and Elena A. Mikhailova

Remote Sens. 2022, 14(22), 5773; https://doi.org/10.3390/rs14225773 - 16 Nov 2022

Cited by 16 | Viewed by 2804

Abstract

Island ecosystems are susceptible to the considerable impacts of increasing human activities, landscape reconstruction, and urban expansion, resulting in changes in the ecological environment and urban ecological security issues. Remote sensing techniques can achieve the near-real-time ecological environment monitoring of these rapidly changing areas. The remote sensing-based ecological index (RSEI), as a comprehensive remote sensing ecological environment index, was adopted to dynamically monitor urban ecological quality (EQ) over time in this study, combined with the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm. Annual composite images were generated using Landsat 5, Landsat 7, and Landsat 8 imagery to extract four metrics (Greenness, Moisture, Heat, and Dryness) to calculate RSEI from 1991 to 2021. The ecological quality in the study area was evaluated using a five-level classification (poor, inferior, medium, good, and excellent), and the changes in EQ on a pixel basis were identified by the LandTrendr algorithm. The results showed that (1) the average value of the RSEI ranged from 0.47 to 0.57 over 31 years, indicating that EQ was maintained at the medium level; (2) the distribution of different EQ levels had visible patterns, and an area of 47.87 km² was affected by a poor EQ at least once in 31 years; (3) 38.22 km² of this area experienced EQ poor disturbance once, and 3.05 km² of the area had poor disturbance twice. Urban expansion, forest degradation, and policy are the main factors causing the reduction of the RSEI. The results demonstrate that combining time series of RSEI and LandTrendr can effectively monitor the changes of EQ, which is helpful to identify the spatial–temporal variation patterns of EQ and provide valuable information for policymakers and protection. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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23 pages, 6730 KiB

Open AccessArticle

Integrating Gaussian Mixture Dual-Clustering and DBSCAN for Exploring Heterogeneous Characteristics of Urban Spatial Agglomeration Areas

by Tong Xiao, Yiliang Wan, Rui Jin, Jianxin Qin and Tao Wu

Remote Sens. 2022, 14(22), 5689; https://doi.org/10.3390/rs14225689 - 10 Nov 2022

Cited by 7 | Viewed by 1839

Abstract

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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20 pages, 3939 KiB

Open AccessArticle

Analysis of Spatiotemporal Variation and Drivers of Ecological Quality in Fuzhou Based on RSEI

by Jianwei Geng, Kunyong Yu, Zhen Xie, Gejin Zhao, Jingwen Ai, Liuqing Yang, Honghui Yang and Jian Liu

Remote Sens. 2022, 14(19), 4900; https://doi.org/10.3390/rs14194900 - 30 Sep 2022

Cited by 53 | Viewed by 3730

Abstract

Background: High-speed urbanization has brought about a number of ecological and environmental problems, as well as the use of remote sensing to monitor the urban ecological environment and explore the main factors affecting its changes. It is important to promote the sustainable development of cities. Methods: In this study, we quantify the ecological quality of the study area from 2000 to 2020 based on the remote sensing ecological index (RSEI) and analyze its drivers through Geodetector and geographically weighted regression. Results: The RSEI of Fuzhou City from 2000 to 2020 showed an increasing followed by a decreasing trend, with obvious spatial autocorrelation. The main driving factors causing the spatial divergence of the RSEI were elevation (q = 0.48–0.63), slope (0.42–0.59), and GDP (0.3–0.42), and the driving effect and range of each factor changed with time. Conclusion: In this paper, we explore changes in the ecological environment in Fuzhou City over the past 20 years, as well as the scope and magnitude of the drivers, providing an important reference basis to improve the ecological environment quality of the city. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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20 pages, 18182 KiB

Open AccessArticle

Satellite-Observed Thermal Anomalies and Deformation Patterns Associated to the 2021, Central Crete Seismic Sequence

by Sofia Peleli, Maria Kouli and Filippos Vallianatos

Remote Sens. 2022, 14(14), 3413; https://doi.org/10.3390/rs14143413 - 16 Jul 2022

Cited by 14 | Viewed by 3432

Abstract

Nowadays, there has been a growing interest in understanding earthquake forerunners, i.e., anomalous variations that are possibly associated with the complex process of earthquake evolution. In this context, the Robust Satellite Technique was coupled with 10 years (2012–2021) of daily night-time MODIS-Land Surface Temperature remote sensing data to detect thermal anomalies likely related to the 27 September 2021, strong onshore earthquake of magnitude Mw6.0 occurring near the Arkalochori village in Central Crete, Greece. Eight intense (signal-to-noise ratio > 3) and infrequent, quite extensive, and temporally persistent thermal signal transients were detected and characterized as pre-seismic anomalies, while one thermal signal transient was identified as a co-seismic effect on the day of the main tectonic event. The thermal anomalies dataset was combined with tectonic parameters of Central Crete, such as active faults and fault density, seismogenic zones and ground displacement maps produced using Sentinel-1 satellite imagery and the Interferometric Synthetic Aperture Radar technique. Regarding the thermal anomaly of 27 September, its greatest portion was observed over the footwall part of the fault where a significant subsidence up to 20 cm exists. We suggest that the thermal anomalies are possibly connected with gas release which happens due to stress changes and is controlled by the existence of tectonic lines and the density of the faults, even if alternative explanations could not be excluded. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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20 pages, 34884 KiB

Open AccessArticle

Understanding Landslide Susceptibility in Northern Chilean Patagonia: A Basin-Scale Study Using Machine Learning and Field Data

by Elizabet Lizama, Bastian Morales, Marcelo Somos-Valenzuela, Ningsheng Chen and Mei Liu

Remote Sens. 2022, 14(4), 907; https://doi.org/10.3390/rs14040907 - 14 Feb 2022

Cited by 13 | Viewed by 3280

Abstract

The interaction of geological processes and climate changes has resulted in growing landslide activity that has impacted communities and ecosystems in northern Chilean Patagonia. On 17 December 2017, a catastrophic flood of 7 × 10⁶ m³ almost destroyed Villa Santa Lucía and approximately 3 km of the southern highway (Route 7), the only land route in Chilean Patagonia that connects this vast region from north to south, exposing the vulnerability of the population and critical infrastructure to these natural hazards. The 2017 flood produced a paradigm shift on the analysis scale to understand the danger to which communities and their infrastructure are exposed. Thus, in this study, we sought to evaluate the susceptibility of landslides in the Yelcho and Rio Frio basins, whose intersection represents the origin of this great flood. For this, we used two approaches, (1) geospatial data in combination with machine learning methods using different training configurations and (2) a qualitative analysis of the landscape considering the geological and geomorphological conditions through fieldwork. For statistical modeling, we used an inventory of landslides that occurred between 2008 and 2017 and a total of 17 predictive variables, which are geoenvironmental, climatological and environmental triggers derived from volcanic and seismic activity. Our results indicate that soil moisture significantly impacted spatial susceptibility, followed by lithology, drainage density and seismic activity. Additionally, we observed that the inclusion of climatic predictors and environmental triggers increased the average performance score of the models by up to 3–5%. Based on our results, we believe that the wide distribution of volcanic–sedimentary rocks hydrothermally altered with zeolites in the western mountains of the Yelcho and Rio Frio basin are highly susceptible to generating large-scale landslides. Therefore, the town of Villa Santa Lucia and the “Carretera Austral” (Route 7) are susceptible to new landslides coming mainly from the western slope. This requires the timely implementation of measures to mitigate the impact on the population and critical infrastructure. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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30 pages, 8692 KiB

Open AccessArticle

A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)

by Husam A. H. Al-Najjar, Biswajeet Pradhan, Raju Sarkar, Ghassan Beydoun and Abdullah Alamri

Remote Sens. 2021, 13(19), 4011; https://doi.org/10.3390/rs13194011 - 7 Oct 2021

Cited by 34 | Viewed by 4316

Abstract

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory/data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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22 pages, 11345 KiB

Open AccessArticle

Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

by Husam A. H. Al-Najjar, Biswajeet Pradhan, Bahareh Kalantar, Maher Ibrahim Sameen, M. Santosh and Abdullah Alamri

Remote Sens. 2021, 13(16), 3281; https://doi.org/10.3390/rs13163281 - 19 Aug 2021

Cited by 36 | Viewed by 4690

Abstract

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement). Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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Other

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17 pages, 1226 KiB

Open AccessTechnical Note

Application of Copernicus Data for Climate-Relevant Urban Planning Using the Example of Water, Heat, and Vegetation

by Michael Max Bühler, Christoph Sebald, Diana Rechid, Eberhard Baier, Alexander Michalski, Benno Rothstein, Konrad Nübel, Martin Metzner, Volker Schwieger, Jan-Albrecht Harrs, Daniela Jacob, Lothar Köhler, Gunnar in het Panhuis, Raymundo C. Rodríguez Tejeda, Michael Herrmann and Gerd Buziek

Remote Sens. 2021, 13(18), 3634; https://doi.org/10.3390/rs13183634 - 11 Sep 2021

Cited by 10 | Viewed by 4340

Abstract

Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future. Full article

(This article belongs to the Special Issue Application of Satellite Remote Sensing in Solving Urban Geo-Environmental Issues)

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