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

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 34584

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Special Issue Editors

Prof. Dr. Guoxiang Liu

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Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China
Interests: time-series InSAR; high-resolaution optical remote sensing; digital elevation modelling; monitoring and inversing of ground deformation; monitoring and analyzing of geological hazards
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Dr. Saeid Pirasteh

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Department of Geography and Environmental Management, Faculty of Environment, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: natural hazards; remote sensing; climate change; GIS; water; disaster management; geology; geo-environmental issues; geography and environmental management
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Prof. Dr. Jonathan Li

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Guest Editor

Geospatial Sensing and Data Intelligence Lab, Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: LiDAR remote sensing; point cloud understanding; deep learning; 3D vision; HD maps for smart cities and autonomous vehicles
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Special Issue Information

Dear Colleagues,

This Special Issue will actively contribute emerging techniques and knowledge to global geospatial information and hazards management incorporated with earth observation systems for environmental changes with innovation, research, development, problem-solving, and embedded products toward a smart and geo-intelligent world. This Special Issue will present integrated technologies for disaster risk reduction and environmental changes with solutions management implementation in various parts of the world. We encourage papers from various aspects of Global Geospatial Information Technology, and future trends in geomatics to support the global geospatial disasters reduction and environmental sustainability vision with contributing to building a better world. Therefore, the subject area will cover various disciplines including digital transformation and data sharing; artificial intelligence and machine learning; computer vision for 3D real-world reconstruction; LiDAR technologies and mobile sensing; Building Information Management System (BIMS); mapping; 3D modelling; big data analytics; deep learning; artificial intelligence with related digital environments and earth; geoalgorithms; and any other related subjects such as natural hazards, disaster management, and earth observation systems for environmental changes and geohazards. Furthermore, this Special Issue will respond to current problem solving regarding the growing need for a smart and intelligent world that requires big data for analysis, integration, and dissemination, using innovative solutions.

The sections that best match the scope of the research topic:

Environmental and earth sciences, geomatics and big data analytics, geosensing and mobile sensing, digital transformation and sharing data, artificial intelligence and machine learning, smart cities and smart environments, natural hazards and disaster management, geoalgorithm development, visualization, and real-time and related topics that can work for digital earth and intelligent world development.

Dr. Saeid Pirasteh
Prof. Guoxiang Liu
Prof. Jonathan Li
Guest Editor

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Keywords

geospatial
hazards management
remote sensing
geospatial environmental ecosystem
artificial intelligent
computer vision and machine learning
digital transformation and sharing data
smart environment

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Published Papers (7 papers)

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Research

26 pages, 12010 KiB

Open AccessArticle

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

by Omid Rahmati, Omid Ghorbanzadeh, Teimur Teimurian, Farnoush Mohammadi, John P. Tiefenbacher, Fatemeh Falah, Saied Pirasteh, Phuong-Thao Thi Ngo and Dieu Tien Bui

Remote Sens. 2019, 11(24), 2995; https://doi.org/10.3390/rs11242995 - 13 Dec 2019

Cited by 53 | Viewed by 5679

Abstract

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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24 pages, 7053 KiB

Open AccessArticle

Modeling Population Density Using a New Index Derived from Multi-Sensor Image Data

by Peng Luo, Xianfeng Zhang, Junyi Cheng and Quan Sun

Remote Sens. 2019, 11(22), 2620; https://doi.org/10.3390/rs11222620 - 8 Nov 2019

Cited by 15 | Viewed by 4820

Abstract

The detailed information about the spatial distribution of the population is crucial for analyzing economic growth, environmental change, and natural disaster damage. Using the nighttime light (NTL) imagery for population estimation has been a topic of interest in recent decades. However, the effectiveness of NTL data in population estimation has been impeded by some limitations such as the blooming effect and underestimation in rural regions. To overcome these limitations, we combine the NPP-VIIRS day/night band (DNB) data with normalized difference vegetation index (NDVI) and land surface temperature (LST) data derived from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite, to create a new vegetation temperature light population index (VTLPI). A statistical model is developed to predict 250m grid-level population density based on the proposed VTLPI and the least square regression approach. After that, a case study is implemented using the data of Sichuan Province, China in 2015, and the results indicates that the VTLPI-estimated population density outperformed the results from other two methods based on nighttime light imagery or human settlement index, and the three publicized population products, LandScan, WorldPop, and GPW. When using the census data as reference, the mean relative error and median absolute relative error on a township level are 0.29 and 0.12, respectively, and the root-mean-square error is 212 persons/km². The results show that our VTLPI-based model can achieve a better estimation of population density in rural areas and urban suburbs and characterize more spatial variations at 250m grid level both in both urban and rural areas. The resultant population density offers better population exposure data for assessing natural disaster risk and loss as well as other related applications. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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16 pages, 13106 KiB

Open AccessArticle

Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques

by Dong Luo, Douglas G. Goodin and Marcellus M. Caldas

Remote Sens. 2019, 11(21), 2548; https://doi.org/10.3390/rs11212548 - 30 Oct 2019

Cited by 8 | Viewed by 3469

Abstract

Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type’s sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year’s Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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18 pages, 19522 KiB

Open AccessArticle

Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar

by Hongguo Jia, Hao Zhang, Luyao Liu and Guoxiang Liu

Remote Sens. 2019, 11(19), 2273; https://doi.org/10.3390/rs11192273 - 29 Sep 2019

Cited by 19 | Viewed by 4246

Abstract

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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16 pages, 7170 KiB

Open AccessArticle

3D Coseismic Deformation Field and Source Parameters of the 2017 Iran-Iraq Mw7.3 Earthquake Inferred from DInSAR and MAI Measurements

by Zhiheng Wang, Rui Zhang and Yuxin Liu

Remote Sens. 2019, 11(19), 2248; https://doi.org/10.3390/rs11192248 - 27 Sep 2019

Cited by 5 | Viewed by 2923

Abstract

The coseismic slip on the main fault related to the 2017 Iran-Iraq Mw7.3 earthquake has been investigated by previous studies using DInSAR (differential interferometric synthetic aperture radar) ground deformation measurements. However, DInSAR observation is not sensitive to the ground deformation in the along-track (AT) direction. Therefore, only the one-dimensional (1D) DInSAR coseismic deformation field measurements, derived in the LOS (line-of-sight) direction of radar, was applied in source parameters estimation. To further improve the accuracy of the fault slip inversion, the 3D (three-dimensional) coseismic deformation fields were reconstructed in the first place, by a combined use of the DInSAR and MAI (multiple aperture InSAR) measurements. Subsequently, the LOS and 3D deformation data sets were used as the constraint respectively, to perform a two-step inversion scheme to find an optimal geometry and slip distribution on the main fault. The comparative analysis indicated that the 3D coseismic deformation data sets improved the inversion-accuracy by 30%. Besides, the fault invention results revealed a deep dislocation on a NNW trending fault (the strike is 352.63°) extending about 60 km, along the fault dips 14.76° to the ENE. The estimated seismic moment is 8.44 × 10¹⁹ Nm (Mw7.3), which is close to the solution provided by USGS (United States Geological Survey). The slip distributed at the depth between 12 and 18 km, and the peak slip of 6.53 m appears at the depth of 14.5 km left near the epicenter. Considering the geological structure in the earthquake region and fault source-parameters, it comes to a preliminary conclusion that the ZMFF (the Zagros Mountain Front fault) should be responsible for the earthquake. In general, this paper demonstrated the superiority of using the 3D coseismic deformation fields on source parameters estimation. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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40 pages, 7318 KiB

Open AccessArticle

Investigating Fold-River Interactions for Major Rivers Using a Scheme of Remotely Sensed Characteristics of River and Fold Geomorphology

by Kevin P. Woodbridge, Saied Pirasteh and Daniel R. Parsons

Remote Sens. 2019, 11(17), 2037; https://doi.org/10.3390/rs11172037 - 29 Aug 2019

Cited by 5 | Viewed by 4376

Abstract

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m³s⁻¹). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: (1) Channel width at location of fold axis, w, (2) Channel-belt width at location of fold axis, cbw, (3) Floodplain width at location of fold axis, fpw, (4) Channel sinuosity, Sc, (5) Braiding index, BI, (6) General river course direction, RCD, (7) Distance from fold core to location of river crossing, C-RC, (8) Distance from fold core to river basin margin, C-BM, (9) Width of geological structure at location of river crossing, Wgs, (10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, (11) Channel water surface slope at location of fold axis, s, (12) Average channel migration rate, Rm, (13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for almost all major rivers using widely available satellite imagery and fine scale geological maps. This use of remote sensing allows a large number of major rivers to be investigated relatively easily, including those in remote or inaccessible areas, without recourse to expensive fieldwork. The last three geomorphological characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m³s⁻¹ and 230 m³s⁻¹, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. This scheme should be used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions will be achieved. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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34 pages, 55388 KiB

Open AccessArticle

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

by Yichi Zhang, Pan Yue, Guike Zhang, Tao Guan, Mingming Lv and Denghua Zhong

Remote Sens. 2019, 11(11), 1311; https://doi.org/10.3390/rs11111311 - 1 Jun 2019

Cited by 32 | Viewed by 8054

Abstract

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation. Full article

(This article belongs to the Special Issue Global Geospatial Information and Hazards Management for Smart Environments)

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