Remote Sensing

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17 pages, 10509 KiB

Open AccessArticle

Radar Interferometry as a Monitoring Tool for an Active Mining Area Using Sentinel-1 C-Band Data, Case Study of Riotinto Mine

by Joaquin Escayo, Ignacio Marzan, David Martí, Fernando Tornos, Angelo Farci, Martin Schimmel, Ramon Carbonell and José Fernández

Remote Sens. 2022, 14(13), 3061; https://doi.org/10.3390/rs14133061 - 25 Jun 2022

Cited by 3 | Viewed by 2639

Abstract

Soil instability is a major hazard facing the mining industry in its role of supplying the indispensable mineral resources that our societal challenges require. Aiming to demonstrate the monitoring potential of radar satellites in the mining sector, we analyze the deformation field in [...] Read more.

Soil instability is a major hazard facing the mining industry in its role of supplying the indispensable mineral resources that our societal challenges require. Aiming to demonstrate the monitoring potential of radar satellites in the mining sector, we analyze the deformation field in the Riotinto mine, Spain. We propose a new method for combining ascending and descending results into a common dataset that provides better resolution. We project the LOS measurements resulting from both geometries to a common reference system without applying any type of geometric restriction. As a projection system, we use the vertical direction in flat areas and the slope in steep topographies. We then identify and remove outliers and artifacts from the joint dataset to finally obtain a deformation map that combines the two acquisition perspectives. The results in the Atalaya pit are consistent with GNSS measurements. The movements observed in the rock dumps were unknown before this study. We demonstrate the great potential of the Sentinel-1 satellite as a complementary tool for monitoring systems in mining environments and we call for its use to be standardized to guarantee a safe and sustainable supply of mineral resources necessary for a just technological transition. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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23 pages, 79766 KiB

Open AccessArticle

A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter

by Weiqi Wang, Xiong You, Lingyu Chen, Jiangpeng Tian, Fen Tang and Lantian Zhang

Remote Sens. 2022, 14(6), 1468; https://doi.org/10.3390/rs14061468 - 18 Mar 2022

Cited by 21 | Viewed by 4086

Abstract

Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of [...] Read more.

Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance–Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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29 pages, 9239 KiB

Open AccessArticle

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

by Andrea De Giorgi, David Solarna, Gabriele Moser, Deodato Tapete, Francesca Cigna, Giorgio Boni, Roberto Rudari, Sebastiano Bruno Serpico, Anna Rita Pisani, Antonio Montuori and Simona Zoffoli

Remote Sens. 2021, 13(17), 3509; https://doi.org/10.3390/rs13173509 - 4 Sep 2021

Cited by 3 | Viewed by 2263

Abstract

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been [...] Read more.

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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19 pages, 13100 KiB

Open AccessArticle

Multidimensional Assessment of Food Provisioning Ecosystem Services Using Remote Sensing and Agricultural Statistics

by Donghui Shi, Yishao Shi, Qiusheng Wu and Ruibo Fang

Remote Sens. 2020, 12(23), 3955; https://doi.org/10.3390/rs12233955 - 3 Dec 2020

Cited by 7 | Viewed by 3158

Abstract

With the increasing global population, human demands for natural resources continue to grow. There is a critical need for the sustainable use and development of natural resources. In this context, ecosystem services have attracted more and more attention, and ecosystem services assessment has [...] Read more.

With the increasing global population, human demands for natural resources continue to grow. There is a critical need for the sustainable use and development of natural resources. In this context, ecosystem services have attracted more and more attention, and ecosystem services assessment has proven to be useful for guiding research, policy formulation, and management implementation. In this paper, we attempted to assess ecosystem services more comprehensively from various perspectives. We used food provisioning ecosystem services in Minnesota as a case study and proposed two new concepts for assessing ecosystem services: efficiency and trend. We designed a multidimensional assessment framework, analyzed the total output, efficiency, and trend temporally based on both area and space with Exploratory Spatial Data Analysis (ESDA). We also identified major influencing factors based on remote sensing images in Google Earth Engine and explored the quantitative influence on each assessment dimension. We found that: (1) Food provisioning ecosystem service in Minnesota has generally been improving from 1998 to 2018. (2) We identified food provisioning ecosystem services in Minnesota as superior zones, mixed zones, and inferior zones with a ‘sandwich geo-configuration’. (3) The total output tends to be stable while the efficiency is disturbed by some natural disasters. Simultaneously, the trend index has been improving with slight fluctuations. (4) Agricultural disaster financial support has a stronger impact on stabilizing the total output of food provisioning than the other two dimensions. (5) Soil moisture, diurnal temperature difference, and crop growth are the three main influencing aspects of food provisioning ecosystem services, and the order of the influential density is: the Perpendicular Drought Index (PDI), Normalized Difference Vegetation Index (NDVI), Rainfall (RF), Daytime Temperature (DT), and Diurnal Temperature Difference (DIF). Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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25 pages, 5488 KiB

Open AccessEditor’s ChoiceArticle

Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake

by Mirko Piersanti, Massimo Materassi, Roberto Battiston, Vincenzo Carbone, Antonio Cicone, Giulia D’Angelo, Piero Diego and Pietro Ubertini

Remote Sens. 2020, 12(20), 3299; https://doi.org/10.3390/rs12203299 - 11 Oct 2020

Cited by 43 | Viewed by 4132

Abstract

The short-term prediction of earthquakes is an essential issue connected with human life protection and related social and economic matters. Recent papers have provided some evidence of the link between the lithosphere, lower atmosphere, and ionosphere, even though with marginal statistical evidence. The [...] Read more.

The short-term prediction of earthquakes is an essential issue connected with human life protection and related social and economic matters. Recent papers have provided some evidence of the link between the lithosphere, lower atmosphere, and ionosphere, even though with marginal statistical evidence. The basic coupling is hypothesized as being via the atmospheric gravity wave (AGW)/acoustic wave (AW) channel. In this paper we analyze a scenario of the low latitude earthquake (Mw = 6.9) which occurred in Indonesia on 5 August 2018, through a multi-instrumental approach, using ground and satellites high quality data. As a result, we derive a new analytical lithospheric–atmospheric–ionospheric–magnetospheric coupling model with the aim to provide quantitative indicators to interpret the observations around 6 h before and at the moment of the earthquake occurrence. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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22 pages, 13660 KiB

Open AccessArticle

Regression Analysis of Subsidence in the Como Basin (Northern Italy): New Insights on Natural and Anthropic Drivers from InSAR Data

by Nicoletta Nappo, Maria Francesca Ferrario, Franz Livio and Alessandro Maria Michetti

Remote Sens. 2020, 12(18), 2931; https://doi.org/10.3390/rs12182931 - 10 Sep 2020

Cited by 8 | Viewed by 2568

Abstract

Natural and anthropogenic subsidence such as that in the Como urban area (northern Italy) can cause significant damage to structures and infrastructure, and expose the city’s lakefront to an increasing risk of inundation from Lake Como. This phenomenon affecting the Como basin has [...] Read more.

Natural and anthropogenic subsidence such as that in the Como urban area (northern Italy) can cause significant damage to structures and infrastructure, and expose the city’s lakefront to an increasing risk of inundation from Lake Como. This phenomenon affecting the Como basin has been studied by several researchers, and the major drivers of subsidence are known. However, the availability of historical InSAR data allowed us to reconsider the relationship between subsidence predisposing factors (i.e., the thicknesses of reworked and compressible layers, overburden stress, and the piezometric level) and ground surface displacements with higher precision over the entire basin. Benefiting from the deep knowledge of the hydromechanical setting of the Como basin and the availability of InSAR measurements from 1992 to 2010, in this paper we model subsidence-related movements using linear and nonlinear regression methods in order to determine the combination of natural and anthropic factors that have caused subsidence in the Como basin over the past decades. The results of this study highlight peculiar patterns of subsidence that suggest the influence of two further causes, namely tectonic control of the sedimentary architecture and diversion of local streams, which have never been considered before. This analysis aims to assess the spatial distribution of subsidence through InSAR analysis in order to enhance the knowledge and understanding of the phenomenon in the Como urban area. The interferometric data could be used to better plan urban risk management strategies. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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21 pages, 6303 KiB

Open AccessArticle

A New Weighting Method by Considering the Physical Characteristics of Atmospheric Turbulence and Decorrelation Noise in SBAS-InSAR

by Meng Duan, Bing Xu, Zhiwei Li, Wenhao Wu, Yunmeng Cao, Jihong Liu, Guanya Wang and Jingxin Hou

Remote Sens. 2020, 12(16), 2557; https://doi.org/10.3390/rs12162557 - 9 Aug 2020

Cited by 14 | Viewed by 3360

Abstract

Time series of ground subsidence can not only be used to describe motion produced by various anthropocentric and natural process but also to better understand the processes and mechanisms of geohazards and to formulate effective protective measures. For high-accuracy measurement of small baseline [...] Read more.

Time series of ground subsidence can not only be used to describe motion produced by various anthropocentric and natural process but also to better understand the processes and mechanisms of geohazards and to formulate effective protective measures. For high-accuracy measurement of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), atmospheric turbulence and decorrelation noise are regarded as random variables and cannot be accurately estimated by a deterministic model when large spatio-temporal variability presents itself. Various weighting methods have been proposed and improved continuously to reduce the effects of these two parts and provide uncertainty information of the estimated parameters, simultaneously. Network-based variance-covariance estimation (NVCE) and graph theory (GT) are the two main weighting methods which were developed on the basis of previous algorithms. However, the NVCE weighting method only focuses on the influence of atmospheric turbulence and neglects the decorrelation noise. The GT method weights each interferogram in a time series by using the Laplace transformation. Although simple to implement, it is not reasonable to have an equal weight for each pixel in the same interferogram. To avoid these limitations, this study presents a new weighting method by considering the physical characteristics of atmospheric turbulence and decorrelation noise in SBAS-InSAR images. The effectiveness of the proposed method was tested and validated by using a set of simulated experiments and a case study on a Hawaiian island. According to the GPS-derived displacements, the average RMSE of the results from the new weighting method was 1.66 cm, indicating about an 8% improvement compared with 1.79, 1.80 and 1.80 cm from the unweighted method, the NVCE method and the GT method, respectively. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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23 pages, 14807 KiB

Open AccessArticle

Multi-Parametric Climatological Analysis Reveals the Involvement of Fluids in the Preparation Phase of the 2008 Ms 8.0 Wenchuan and 2013 Ms 7.0 Lushan Earthquakes

by Qinqin Liu, Angelo De Santis, Alessandro Piscini, Gianfranco Cianchini, Guido Ventura and Xuhui Shen

Remote Sens. 2020, 12(10), 1663; https://doi.org/10.3390/rs12101663 - 22 May 2020

Cited by 11 | Viewed by 2408

Abstract

A multi-parametric approach was applied to climatological data before the Ms 8.0 2008 Wenchuan and Ms 7.0 2013 Lushan earthquakes (EQs) in order to detect anomalous changes associated to the preparing phase of those large seismic events. A climatological analysis for seismic Precursor [...] Read more.

A multi-parametric approach was applied to climatological data before the Ms 8.0 2008 Wenchuan and Ms 7.0 2013 Lushan earthquakes (EQs) in order to detect anomalous changes associated to the preparing phase of those large seismic events. A climatological analysis for seismic Precursor Identification (CAPRI) algorithm was used for the detection of anomalies in the time series of four parameters (aerosol optical depth, AOD; skin temperature, SKT; surface latent heat flux, SLHF and total column water vapour, TCWV). Our results show a chain of processes occurred within two months before the EQs: AOD anomalous response is the earliest, followed by SKT, TCWV and SLHF in the EQs. A close spatial relation between the seismogenic Longmenshan fault (LMSF) zone and the extent of the detected anomalies indicates that some changes occurred within the faults before the EQs. The similarity of time sequence of the anomalies between the four parameters may be related to the same process: we interpret the observed anomalies as the consequence of the upraising of gases from a fluid-rich middle/upper crust along pre-existing seismogenic faults, and of their release into the atmosphere. Our multi-parametric analytical approach is able to capture phenomena related to the preparation phase of strong EQs. Full article

(This article belongs to the Special Issue Space-Borne Earth Observation Data for Monitoring Natural and Anthropogenic Phenomena)

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