Remote Sensing

24 pages, 16031 KiB

Open AccessArticle

Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity

by Jialang Xu, Chunbo Luo, Xinyue Chen, Shicai Wei and Yang Luo

Remote Sens. 2021, 13(15), 3053; https://doi.org/10.3390/rs13153053 - 03 Aug 2021

Cited by 22 | Viewed by 4003

Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in [...] Read more.

Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities. Full article

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23 pages, 5931 KiB

Open AccessArticle

Anatomy of a Paroxysmal Lava Fountain at Etna Volcano: The Case of the 12 March 2021, Episode

by Sonia Calvari, Alessandro Bonaccorso and Gaetana Ganci

Remote Sens. 2021, 13(15), 3052; https://doi.org/10.3390/rs13153052 - 03 Aug 2021

Cited by 23 | Viewed by 3521

Abstract

On 13 December 2020, Etna volcano entered a new eruptive phase, giving rise to a number of paroxysmal episodes involving increased Strombolian activity from the summit craters, lava fountains feeding several-km high eruptive columns and ash plumes, as well as lava flows. As [...] Read more.

On 13 December 2020, Etna volcano entered a new eruptive phase, giving rise to a number of paroxysmal episodes involving increased Strombolian activity from the summit craters, lava fountains feeding several-km high eruptive columns and ash plumes, as well as lava flows. As of 2 August 2021, 57 such episodes have occurred in 2021, all of them from the New Southeast Crater (NSEC). Each paroxysmal episode lasted a few hours and was sometimes preceded (but more often followed) by lava flow output from the crater rim lasting a few hours. In this paper, we use remote sensing data from the ground and satellite, integrated with ground deformation data recorded by a high precision borehole strainmeter to characterize the 12 March 2021 eruptive episode, which was one of the most powerful (and best recorded) among that occurred since 13 December 2020. We describe the formation and growth of the lava fountains, and the way they feed the eruptive column and the ash plume, using data gathered from the INGV visible and thermal camera monitoring network, compared with satellite images. We show the growth of the lava flow field associated with the explosive phase obtained from a fixed thermal monitoring camera. We estimate the erupted volume of pyroclasts from the heights of the lava fountains measured by the cameras, and the erupted lava flow volume from the satellite-derived radiant heat flux. We compare all erupted volumes (pyroclasts plus lava flows) with the total erupted volume inferred from the volcano deflation recorded by the borehole strainmeter, obtaining a total erupted volume of ~3 × 10⁶ m³ of magma constrained by the strainmeter. This volume comprises ~1.6 × 10⁶ m³ of pyroclasts erupted during the lava fountain and 2.4 × 10⁶ m³ of lava flow, with ~30% of the erupted pyroclasts being remobilized as rootless lava to feed the lava flows. The episode lasted 130 min and resulted in an eruption rate of ~385 m³ s⁻¹ and caused the formation of an ash plume rising from the margins of the lava fountain that rose up to 12.6 km a.s.l. in ~1 h. The maximum elevation of the ash plume was well constrained by an empirical formula that can be used for prompt hazard assessment. Full article

(This article belongs to the Special Issue Volcanic Processes Monitoring and Hazard Assessment Using Integration of Remote Sensing and Ground-Based Techniques)

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12 pages, 2675 KiB

Open AccessCase Report

Study of the Spatiotemporal Characteristics of the Equatorial Ionization Anomaly Using Shipborne Multi-GNSS Data: A Case Analysis (120–150°E, Western Pacific Ocean, 2014–2015)

by Xiaowen Luo, Di Wang, Jinling Wang, Ziyin Wu, Jinyao Gao, Tao Zhang, Chunguo Yang, Xiaoming Qin and Xiaolun Chen

Remote Sens. 2021, 13(15), 3051; https://doi.org/10.3390/rs13153051 - 03 Aug 2021

Cited by 1 | Viewed by 2180

Abstract

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron [...] Read more.

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron Content (TEC) during two research cruises across the equator in the Western Pacific Ocean in 2014 (31 October–8 November) and 2015 (29 March–6 April). The purpose of the study was to use sufficient spatial–temporal resolution data to conduct a detailed analysis of the diurnal variation of the equatorial ionization anomaly in different seasons. The two-year data collected were used to draw the following conclusions. During the test in 2014, the EIA phenomenon in the Northern and Southern Hemispheres was relatively obvious. The maximum values occurred at local time (LT) 15:00 (~136TECu) and LT22:00 (~107TECu) in the Northern Hemisphere and at LT14:00 (100TECu) and LT22:00 (80TECu) in the Southern Hemisphere. During the test in 2015, the EIA in the Southern Hemisphere reached its maximum level at LT14:00 (~115TECu) and LT20:00 (~85TECu). However, the EIA phenomenon in the Northern Hemisphere was weakened, and a maximum value occurred only at LT 15:00 (~85TECu). The intensity contrast was reversed. The EIA phenomenon manifests a strong hemisphere asymmetry in this region. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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11 pages, 3753 KiB

Open AccessTechnical Note

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

by Martin Štroner, Rudolf Urban, Martin Lidmila, Vilém Kolář and Tomáš Křemen

Remote Sens. 2021, 13(15), 3050; https://doi.org/10.3390/rs13153050 - 03 Aug 2021

Cited by 23 | Viewed by 2859

Abstract

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, [...] Read more.

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice. Full article

(This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications)

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18 pages, 3144 KiB

Open AccessTechnical Note

Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

by Malgorzata Stramska, Marta Konik, Paulina Aniskiewicz, Jaromir Jakacki and Miroslaw Darecki

Remote Sens. 2021, 13(15), 3049; https://doi.org/10.3390/rs13153049 - 03 Aug 2021

Cited by 3 | Viewed by 2312

Abstract

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data [...] Read more.

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data. Full article

(This article belongs to the Special Issue Baltic Sea Remote Sensing)

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19 pages, 14310 KiB

Open AccessArticle

A Robust SAR Speckle Tracking Workflow for Measuring and Interpreting the 3D Surface Displacement of Landslides

by Davide Donati, Bernhard Rabus, Jeanine Engelbrecht, Doug Stead, John Clague and Mirko Francioni

Remote Sens. 2021, 13(15), 3048; https://doi.org/10.3390/rs13153048 - 03 Aug 2021

Cited by 7 | Viewed by 2734

Abstract

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive [...] Read more.

We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations. Full article

(This article belongs to the Special Issue Remote Sensing for Rock Slope and Rockfall Analysis)

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28 pages, 10499 KiB

Open AccessArticle

Effect of Error in SO₂ Slant Column Density on the Accuracy of SO₂ Transport Flow Rate Estimates Based on GEMS Synthetic Radiances

by Junsung Park, Wonei Choi, Hyung-Min Lee, Rokjin J. Park, Seong-Yeon Kim, Jeong-Ah Yu, Dong-Won Lee and Hanlim Lee

Remote Sens. 2021, 13(15), 3047; https://doi.org/10.3390/rs13153047 - 03 Aug 2021

Cited by 3 | Viewed by 2045

Abstract

This study investigates the uncertainties associated with estimates of the long-range transport SO₂ (LRT-SO₂) flow rate calculated hourly using Geostationary Environment Monitoring Spectrometer (GEMS) synthetic radiances. These radiances were simulated over the Korean Peninsula and the surrounding regions using inputs [...] Read more.

This study investigates the uncertainties associated with estimates of the long-range transport SO₂ (LRT-SO₂) flow rate calculated hourly using Geostationary Environment Monitoring Spectrometer (GEMS) synthetic radiances. These radiances were simulated over the Korean Peninsula and the surrounding regions using inputs from the GEOS-Chem model for January, April, July, and October 2016. The LRT-SO₂ calculation method, which requires SO₂ vertical column densities, wind data, and planetary boundary layer information, was used to quantify the effects of the SO₂ slant column density (SCD) retrieval error and uncertainties in wind data on the accuracy of the LRT-SO₂ estimates. The effects were estimated for simulations of three anthropogenic and three volcanic SO₂ transport events. When there were no errors in the GEMS SO₂ SCD and wind data, the average true LRT-SO₂ flow rates (standard deviation) and those calculated for these events were 1.17 (

\pm

0.44) and 1.21 (

\pm

0.44) Mg/h, respectively. However, the averages of the true LRT-SO₂ flow rates and those calculated for the three anthropogenic (volcanic) SO₂ events were 0.61 (1.17) and 0.64 (1.20) Mg/h, respectively, in the presence of GEMS SO₂ SCD errors. In the presence of both errors in the GEMS SO₂ SCD and wind data, the averages of the true LRT-SO₂ flow rates and those calculated for the three anthropogenic (volcanic) SO₂ events were 0.61 (1.17) and 0.61 (1.04) Mg/h, respectively. This corresponds to differences of 2.1% to 23.1% between the simulated and true mean LRT-SO₂ flow rates. The mean correlation coefficient (R), intercept, and slope between the true and simulated LRT-SO₂ flow rates were 0.51, 0.43, and 0.45 for the six simulated events, respectively. This study demonstrates that SO₂ SCD accuracy is an important factor in improving estimates of LRT-SO₂ flow rates. Full article

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23 pages, 7132 KiB

Open AccessArticle

Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites

by Xingxing Li, Hongmin Zhang, Keke Zhang, Yongqiang Yuan, Wei Zhang and Yujie Qin

Remote Sens. 2021, 13(15), 3046; https://doi.org/10.3390/rs13153046 - 03 Aug 2021

Cited by 6 | Viewed by 3129

Abstract

Earth rotation parameters (ERP) are one of the key parameters in realization of the International Terrestrial Reference Frames (ITRF). At present, the International Laser Ranging Service (ILRS) generates the satellite laser ranging (SLR)-based ERP products only using SLR observations to Laser Geodynamics Satellite [...] Read more.

Earth rotation parameters (ERP) are one of the key parameters in realization of the International Terrestrial Reference Frames (ITRF). At present, the International Laser Ranging Service (ILRS) generates the satellite laser ranging (SLR)-based ERP products only using SLR observations to Laser Geodynamics Satellite (LAGEOS) and Etalon satellites. Apart from these geodetic satellites, many low Earth orbit (LEO) satellites of Earth observation missions are also equipped with laser retroreflector arrays, and produce a large number of SLR observations, which are only used for orbit validation. In this study, we focus on the contribution of multiple LEO satellites to ERP estimation. The SLR and Global Positioning System (GPS) observations of the current seven LEO satellites (Swarm-A/B/C, Gravity Recovery and Climate Experiment (GRACE)-C/D, and Sentinel-3A/B) are used. Several schemes are designed to investigate the impact of LEO orbit improvement, the ERP quality of the single-LEO solutions, and the contribution of multiple LEO combinations. We find that ERP estimation using an ambiguity-fixed orbit can attain a better result than that using ambiguity-float orbit. The introduction of an ambiguity-fixed orbit contributes to an accuracy improvement of 0.5%, 1.1% and 15% for X pole, Y pole and station coordinates, respectively. In the multiple LEO satellite solutions, the quality of ERP and station coordinates can be improved gradually with the increase in the involved LEO satellites. The accuracy of X pole, Y pole and length-of-day (LOD) is improved by 57.5%, 57.6% and 43.8%, respectively, when the LEO number increases from three to seven. Moreover, the combination of multiple LEO satellites is able to weaken the orbit-related signal existing in the single-LEO solution. We also investigate the combination of LEO satellites and LAGEOS satellites in the ERP estimation. Compared to the LAGEOS solution, the combination leads to an accuracy improvement of 0.6445 ms, 0.6288 ms and 0.0276 ms for X pole, Y pole and LOD, respectively. In addition, we explore the feasibility of a one-step method, in which ERP and the orbit parameters are jointly determined, based on SLR and GPS observations, and present a detailed comparison between the one-step solution and two-step solution. Full article

(This article belongs to the Special Issue High-Precision GNSS: Methods, Open Problems and Geoscience Applications—Part II)

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25 pages, 12636 KiB

Open AccessArticle

A Cross-Direction and Progressive Network for Pan-Sharpening

by Han Xu, Zhuliang Le, Jun Huang and Jiayi Ma

Remote Sens. 2021, 13(15), 3045; https://doi.org/10.3390/rs13153045 - 03 Aug 2021

Cited by 9 | Viewed by 1936

Abstract

In this paper, we propose a cross-direction and progressive network, termed CPNet, to solve the pan-sharpening problem. The full processing of information is the main characteristic of our model, which is reflected as follows: on the one hand, we process the source images [...] Read more.

In this paper, we propose a cross-direction and progressive network, termed CPNet, to solve the pan-sharpening problem. The full processing of information is the main characteristic of our model, which is reflected as follows: on the one hand, we process the source images in a cross-direction manner to obtain the source images of different scales as the input of the fusion modules at different stages, which maximizes the usage of multi-scale information in the source images; on the other hand, the progressive reconstruction loss is designed to boost the training of our network and avoid partial inactivation, while maintaining the consistency of the fused result with the ground truth. Since the extraction of the information from the source images and the reconstruction of the fused image is based on the entire image rather than a single type of information, there is little loss of partial spatial or spectral information due to insufficient information processing. Extensive experiments, including qualitative and quantitative comparisons demonstrate that our model can maintain more spatial and spectral information compared to the state-of-the-art pan-sharpening methods. Full article

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22 pages, 22593 KiB

Open AccessArticle

Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

by Mingjie Liao, Rui Zhang, Jichao Lv, Bin Yu, Jiatai Pang, Ran Li, Wei Xiang and Wei Tao

Remote Sens. 2021, 13(15), 3044; https://doi.org/10.3390/rs13153044 - 03 Aug 2021

Cited by 13 | Viewed by 2166

Abstract

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain [...] Read more.

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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24 pages, 804 KiB

Open AccessArticle

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

by Kang Li, Bo Jiu, Hongwei Liu and Wenqiang Pu

Remote Sens. 2021, 13(15), 3043; https://doi.org/10.3390/rs13153043 - 03 Aug 2021

Cited by 12 | Viewed by 2581

Abstract

To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in [...] Read more.

To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer. Full article

(This article belongs to the Special Issue Advances in Joint Radar-Communication Systems, Multi-Carrier Radars, Passive Radar Networks, and Waveform Design)

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17 pages, 7329 KiB

Open AccessArticle

Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

by Kateřina Gdulová, Jana Marešová, Vojtěch Barták, Marta Szostak, Jaroslav Červenka and Vítězslav Moudrý

Remote Sens. 2021, 13(15), 3042; https://doi.org/10.3390/rs13153042 - 03 Aug 2021

Cited by 8 | Viewed by 2133

Abstract

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a [...] Read more.

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition. Full article

(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)

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18 pages, 1814 KiB

Open AccessArticle

Interferometric Phase Error Analysis and Compensation in GNSS-InSAR: A Case Study of Structural Monitoring

by Zhanze Wang, Feifeng Liu, Tao Zeng and Chenghao Wang

Remote Sens. 2021, 13(15), 3041; https://doi.org/10.3390/rs13153041 - 03 Aug 2021

Cited by 10 | Viewed by 2183

Abstract

Global navigation satellite system (GNSS)-based synthetic aperture radar interferometry (InSAR) employs GNSS satellites as transmitters of opportunity and a fixed receiver with two channels, i.e., direct wave and echo, on the ground. The repeat-pass concept is adopted in GNSS-based InSAR to retrieve the [...] Read more.

Global navigation satellite system (GNSS)-based synthetic aperture radar interferometry (InSAR) employs GNSS satellites as transmitters of opportunity and a fixed receiver with two channels, i.e., direct wave and echo, on the ground. The repeat-pass concept is adopted in GNSS-based InSAR to retrieve the deformation of the target area, and it has inherited advantages from the GNSS system, such as a short repeat-pass period and multi-angle retrieval. However, several interferometric phase errors, such as inter-channel and atmospheric errors, are introduced into GNSS-based InSAR, which seriously decreases the accuracy of the retrieved deformation. In this paper, a deformation retrieval algorithm is presented to assess the compensation of the interferometric phase errors in GNSS-based InSAR. Firstly, the topological phase error was eliminated based on accurate digital elevation model (DEM) information from a light detection and ranging (lidar) system. Secondly, the inter-channel phase error was compensated, using direct wave in the echo channel, i.e., a back lobe signal. Finally, by modeling the atmospheric phase, the residual atmospheric phase error was compensated for. This is the first realization of the deformation detection of urban scenes using a GNSS-based system, and the results suggest the effectiveness of the phase error compensation algorithm. Full article

(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)

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23 pages, 11267 KiB

Open AccessEditor’s ChoiceArticle

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

by Binita Shrestha, Haroon Stephen and Sajjad Ahmad

Remote Sens. 2021, 13(15), 3040; https://doi.org/10.3390/rs13153040 - 03 Aug 2021

Cited by 24 | Viewed by 3337

Abstract

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have [...] Read more.

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities. Full article

(This article belongs to the Special Issue Data Fusion for Urban Applications)

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25 pages, 9736 KiB

Open AccessArticle

On the Structure and Kinematics of an Algerian Eddy in the Southwestern Mediterranean Sea

by Pierre-Marie Poulain, Luca Centurioni, Tamay Özgökmen, Daniel Tarry, Ananda Pascual, Simon Ruiz, Elena Mauri, Milena Menna and Giulio Notarstefano

Remote Sens. 2021, 13(15), 3039; https://doi.org/10.3390/rs13153039 - 03 Aug 2021

Cited by 6 | Viewed by 2075

Abstract

An Algerian Eddy, anticyclonic vortex generated by the instability of the Algerian Current in the southwestern Mediterranean Sea, is studied using data provided by drifters (surface currents), Argo floats (temperature and salinity profiles), environmental satellites (absolute dynamic topography maps and ocean color images) [...] Read more.

An Algerian Eddy, anticyclonic vortex generated by the instability of the Algerian Current in the southwestern Mediterranean Sea, is studied using data provided by drifters (surface currents), Argo floats (temperature and salinity profiles), environmental satellites (absolute dynamic topography maps and ocean color images) and operational oceanography products. The eddy was generated in May 2018 and lasted as an isolated vortex until November 2018. Its morphology and kinematics are described in June–July 2018 when drifters were trapped in its core. During that period, the eddy was slowly moving to the NE (~2 km/day), with an overall diameter of about 200 km (slowly growing with time) and maximal surface swirl velocity of ~50 cm/s at a radius of ~50 km. Geostrophic currents derived from satellite altimetry data compare well with low-pass filtered drifter velocities, with only a slight overestimation, which is expected as its maximum vorticity corresponds to a small Rossby number of ~0.6. Satellite ocean color images and some drifters show that the eddy has an elliptical spiral structure. The looping tracks of the drifters trapped in the eddy were analyzed using two statistical methods: least-squares ellipse fitting and wavelet ridge analysis, revealing a typical eccentricity of about 0.5, a wide range of inclination and a rotation period between 3 and 10 days. Clusters of drifters on the northeastern limb of the eddy were also considered to estimate divergence and vorticity. The results indicate convergence (divergence) and downwelling (upwelling) at scales of 20–50 km near the northeastern (northwestern) edge of the eddy, in agreement with the quasi-geostrophic theory. Vertically, the eddy extends mostly down to 250 m depth, with a warm, low-salinity and low-density signature and with geostrophic currents near 50 cm/s in the top layer (down to ~80 m) reducing to less than 10 cm/s near 250 m. Near the surface, colder water is advected into it. Full article

(This article belongs to the Special Issue Observing the Flow of Ocean Currents and Circulation Using Remote Sensing)

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18 pages, 11123 KiB

Open AccessArticle

Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks

by Huy Hoa Huynh, Jaehung Yu, Lei Wang, Nam Hoon Kim, Bum Han Lee, Sang-Mo Koh, Sehyun Cho and Trung Hieu Pham

Remote Sens. 2021, 13(15), 3037; https://doi.org/10.3390/rs13153037 - 03 Aug 2021

Cited by 7 | Viewed by 3628

Abstract

This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed [...] Read more.

This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed the limestone well coincided with the reference spectra, while the dolostone did not show clear absorption features compared to the reference spectra, indicating a mixture of clay minerals. The spectral indices based on SWIR hyperspectral images were derived for limestone and dolostone using aluminum hydroxide (AlOH), hydroxide (OH), iron hydroxide (FeOH), magnesium hydroxide (MgOH) and carbonate ion (CO₃²⁻) absorption features based on random forest and logistic regression models with an accuracy over 87%. Given that the indices were derived from field data with consideration of commonly occurring geological units, the indices have better applicability for real world cases. The integrative 3D geological model developed by co-registration between hyperspectral map and UAV-based DEM using best matching SIFT descriptor pairs showed the 3D rock formations between limestone and dolostone. Moreover, additional geological information of the outcrop was extracted including thickness, slope, rock classification, strike, and dip. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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14 pages, 7065 KiB

Open AccessTechnical Note

A Waveform-Encoded SAR Implementation Using a Limited Number of Cyclically Shifted Chirps

by Se-Yeon Jeon, Fabian Glatz and Michelangelo Villano

Remote Sens. 2021, 13(15), 3038; https://doi.org/10.3390/rs13153038 - 02 Aug 2021

Viewed by 1955

Abstract

Synthetic aperture radar (SAR) provides high-resolution images of the Earth’s surface irrespective of sunlight and weather conditions. In conventional spaceborne SAR, nadir echoes caused by the pulsed operation of SAR may significantly affect the SAR image quality. Therefore, the pulse repetition frequency (PRF) [...] Read more.

Synthetic aperture radar (SAR) provides high-resolution images of the Earth’s surface irrespective of sunlight and weather conditions. In conventional spaceborne SAR, nadir echoes caused by the pulsed operation of SAR may significantly affect the SAR image quality. Therefore, the pulse repetition frequency (PRF) is constrained within the SAR system design to avoid the appearance of nadir echoes in the SAR image. As an alternative, the waveform-encoded SAR concept using a pulse-to-pulse variation of the transmitted waveform and dual-focus postprocessing can be exploited for nadir echo removal and to alleviate the PRF constraints. In particular, cyclically shifted chirps have been proposed as a possible waveform variation scheme. However, a large number of distinct waveforms is required to enable the simple implementation of the concept. This work proposes a technique based on the Eulerian circuit for generating a waveform sequence starting from a reduced number of distinct cyclically shifted chirps that can be effectively exploited for waveform-encoded SAR. The nadir echo suppression performance of the proposed scheme is analyzed through simulations using real TerraSAR-X data and a realistic nadir echo model that shows how the number of distinct waveforms and therefore the system complexity can be reduced without significant performance loss. These developments reduce the calibration burden and make the concept viable for implementation in future SAR systems. Full article

(This article belongs to the Special Issue Earth Environment Monitoring with Advanced Spaceborne Synthetic Aperture Radar: New Architectures, Operational Modes, and Processing Techniques)

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17 pages, 2573 KiB

Open AccessArticle

A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

by Jinling Song, Xiao Zhu, Jianbo Qi, Yong Pang, Lei Yang and Lihong Yu

Remote Sens. 2021, 13(15), 3036; https://doi.org/10.3390/rs13153036 - 02 Aug 2021

Cited by 9 | Viewed by 4905

Abstract

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the [...] Read more.

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R² of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data. Full article

(This article belongs to the Special Issue Leaf and Canopy Biochemical and Biophysical Variables Retrieval)

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16 pages, 20205 KiB

Open AccessArticle

Impact of GPS/BDS Satellite Attitude Quaternions on Precise Point Positioning with Ambiguity Resolution

by Songfeng Yang, Qiyuan Zhang, Xi Zhang and Donglie Liu

Remote Sens. 2021, 13(15), 3035; https://doi.org/10.3390/rs13153035 - 02 Aug 2021

Cited by 1 | Viewed by 2268

Abstract

Precise point positioning with ambiguity resolution (PPP-AR) based on multiple global navigation satellite system (multi-GNSS) constellations is an important high-precision positioning tool. However, some unmodeled satellite and receiver biases (such as errors in satellite attitude) make it difficult to fix carrier-phase ambiguities. In [...] Read more.

Precise point positioning with ambiguity resolution (PPP-AR) based on multiple global navigation satellite system (multi-GNSS) constellations is an important high-precision positioning tool. However, some unmodeled satellite and receiver biases (such as errors in satellite attitude) make it difficult to fix carrier-phase ambiguities. In order to fix ambiguities of eclipsing satellites, accurate integer clock and satellite attitude products (i.e., attitude quaternion) have been provided by the International GNSS Service (IGS). Nevertheless, the quality of these products and their positioning performance in multi-GNSS PPP-AR have not been investigated yet. Using the PRIDE PPP-AR II software associated with the corresponding rapid satellite orbit, integer clock and attitude quaternion products of Wuhan University (WUM), we carried out GPS/BDS PPP-AR using 30 days of data in an eclipsing season of 2020. We found that about 75% of GPS, 60% of BDS-2 and 57% of BDS-3 narrow-lane ambiguity residuals after integer clock corrections fall within ±0.1 cycles in the case of using nominal attitudes. However, when using attitude quaternions, these percentages will rise to 80% for GPS, 70% for BDS-2 and 60% for BDS-3. GPS/BDS daily kinematic PPP-AR after integer clock and nominal attitude corrections can usually achieve a positioning precision of about 10, 10 and 30 mm for the east, north and up components, respectively. In contrast, the counterparts are 8, 8 and 20 mm when using attitude quaternions. Compared with the case of using attitude quaternions only at the network end for the integer clock estimation, using attitude quaternions only at the user end shows a pronounced improvement of 15% in the east component and less than 10% in the north and up components. Therefore, we suggest PPP users apply integer clock and satellite attitude quaternion products to realize more efficient ambiguity fixing, especially in satellite eclipsing seasons. Full article

(This article belongs to the Special Issue GNSS Precise Point Positioning: Towards Global Instantaneous cm-Level Accuracy)

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18 pages, 5827 KiB

Open AccessArticle

The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

by Yujin Zhao, Yihan Sun, Wenhe Chen, Yanping Zhao, Xiaoliang Liu and Yongfei Bai

Remote Sens. 2021, 13(15), 3034; https://doi.org/10.3390/rs13153034 - 02 Aug 2021

Cited by 14 | Viewed by 4353

Abstract

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent [...] Read more.

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R² = 0.73, p < 0.001) and remotely sensed functional traits (R² = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystems)

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18 pages, 5211 KiB

Open AccessArticle

A Second-Order Time-Difference Position Constrained Reduced-Dynamic Technique for the Precise Orbit Determination of LEOs Using GPS

by Hui Wei, Jiancheng Li, Xinyu Xu, Shoujian Zhang and Kaifa Kuang

Remote Sens. 2021, 13(15), 3033; https://doi.org/10.3390/rs13153033 - 02 Aug 2021

Cited by 1 | Viewed by 1785

Abstract

In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show [...] Read more.

In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show that the accuracies of integrating the STPs of LEOs at 30 s intervals are better than 0.01 m when the forces (<10⁻⁵ ms⁻²) acting on the LEOs are ignored. Therefore, only using the Earth’s gravity model is good enough for the proposed RD_STP method. All unmodeled dynamic models (e.g., luni-solar gravitation, tide forces) are treated as the error sources of the STP pseudo-observation. In addition, there are no pseudo-stochastic orbit parameters to be estimated in the RD_STP method. Finally, we use the RD_STP method to process 15 days of GPS data from the GOCE mission. The results show that the accuracy of the RD_STP solution is more accurate and smoother than the kinematic solution in nearly polar and equatorial regions, and consistent with the RD solution. The 3D RMS of the differences between the RD_STP and RD solutions is 1.93 cm for 1 s sampling. This indicates that the proposed method has a performance comparable to the RD method, and could be an alternative for the POD of LEOs. Full article

(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)

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26 pages, 6118 KiB

Open AccessEditor’s ChoiceArticle

Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia

by Debbie A. Chamberlain, Stuart R. Phinn and Hugh P. Possingham

Remote Sens. 2021, 13(15), 3032; https://doi.org/10.3390/rs13153032 - 02 Aug 2021

Cited by 20 | Viewed by 5395

Abstract

Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity [...] Read more.

Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity in estuarine and mangrove ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing in Mangroves II)

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14 pages, 11142 KiB

Open AccessArticle

Time-Series of Cloud-Free Sentinel-2 NDVI Data Used in Mapping the Onset of Growth of Central Spitsbergen, Svalbard

by Stein Rune Karlsen, Laura Stendardi, Hans Tømmervik, Lennart Nilsen, Ingar Arntzen and Elisabeth J. Cooper

Remote Sens. 2021, 13(15), 3031; https://doi.org/10.3390/rs13153031 - 02 Aug 2021

Cited by 20 | Viewed by 3542

Abstract

The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, [...] Read more.

The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, the study area was the central part of Spitsbergen, Svalbard, located between 77.28°N and 78.44°N. The goals of this study were: (1) to prepare, analyze and present a cloud-free time-series of daily Sentinel-2 NDVI datasets for the 2016 to 2019 seasons, and (2) to demonstrate the use of the dataset in mapping the onset of growth. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud-free Sentinel-2 data were used. The onset of growth was then mapped by a NDVI threshold method, which showed significant correlation (r² = 0.47, n = 38, p < 0.0001) with ground-based phenocam observation of the onset of growth in seven vegetation types. However, large bias was found between the Sentinel-2 NDVI-based mapped onset of growth and the phenocam-based onset of growth in a moss tundra, which indicates that the data in these vegetation types must be interpreted with care. In 2018, the onset of growth was about 10 days earlier compared to 2017. Full article

(This article belongs to the Special Issue Earth Observation (EO), Remote Sensing (RS), and Geoinformation (GI) Applications in Svalbard)

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19 pages, 2364 KiB

Open AccessArticle

Coastal Wetland Shoreline Change Monitoring: A Comparison of Shorelines from High-Resolution WorldView Satellite Imagery, Aerial Imagery, and Field Surveys

by Kathryn E. L. Smith, Joseph F. Terrano, Jonathan L. Pitchford and Michael J. Archer

Remote Sens. 2021, 13(15), 3030; https://doi.org/10.3390/rs13153030 - 02 Aug 2021

Cited by 17 | Viewed by 3972

Abstract

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques [...] Read more.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr⁻¹). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices. Full article

(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)

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20 pages, 893 KiB

Open AccessArticle

A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

by Jimin Yu, Guangyu Zhou, Shangbo Zhou and Jiajun Yin

Remote Sens. 2021, 13(15), 3029; https://doi.org/10.3390/rs13153029 - 02 Aug 2021

Cited by 16 | Viewed by 2179

Abstract

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and [...] Read more.

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and noise corruption. To improve the recognition performance, methods based on convolutional neural networks (CNN) have been introduced to solve such problems and have shown outstanding performance. However, most of these methods rely on continuously increasing the width and depth of networks. This adds a large number of parameters and computational overhead, which is not conducive to deployment on edge devices. To solve these problems, a novel lightweight fully convolutional neural network based on Channel-Attention mechanism, Channel-Shuffle mechanism, and Inverted-Residual block, namely the ASIR-Net, is proposed in this paper. Specifically, we deploy Inverted-Residual blocks to extract features in high-dimensional space with fewer parameters and design a Channel-Attention mechanism to distribute different weights to different channels. Then, in order to increase the exchange of information between channels, we introduce the Channel-Shuffle mechanism into the Inverted-Residual block. Finally, to alleviate the matter of the scarcity of SAR images and strengthen the generalization performance of the network, four approaches of data augmentation are proposed. The effect and generalization performance of the proposed ASIR-Net have been proved by a lot of experiments under both SOC and EOCs on the MSTAR dataset. The experimental results indicate that ASIR-Net achieves higher recognition accuracy rates under both SOC and EOCs, which is better than the existing excellent ATR methods. Full article

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24 pages, 6201 KiB

Open AccessArticle

Influence of Wind-Induced Effects on Laser Disdrometer Measurements: Analysis and Compensation Strategies

by Vincenzo Capozzi, Clizia Annella, Mario Montopoli, Elisa Adirosi, Giannetta Fusco and Giorgio Budillon

Remote Sens. 2021, 13(15), 3028; https://doi.org/10.3390/rs13153028 - 02 Aug 2021

Cited by 10 | Viewed by 2410

Abstract

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are [...] Read more.

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are affected by various sources of error being some of them related to environmental conditions. This work presents an assessment of Thies Clima laser disdrometer performance with a focus on the relationship between wind and the accuracy of the disdrometer output products. The 10-min average rainfall rate and total rainfall accumulation obtained by the disdrometer are systematically compared with the collocated measures of a standard tipping bucket rain gauge, the FAK010AA sensor, in terms of familiar statistical scores. A total of 42 rainy events, collected in a mountainous site of Southern Italy (Montevergine observatory), are used to support our analysis. The results show that the introduction of a new adaptive filtering in the disdrometric data processing can reduce the impact of sampling errors due to strong winds and heavy rain conditions. From a quantitative perspective, the novel filtering procedure improves by 8% the precipitation estimates with respect to the standard approach widely used in the literature. A deeper examination revealed that the signature of wind speed on raw velocity-diameter spectrographs gradually emerges with the rise of wind strength, thus causing a progressive increase of the wrongly allocated hydrometeors (which reaches 70% for wind speed greater than 8 m s⁻¹). With the aid of reference rain-gauge rainfall data, we designed a second simple methodology that makes use of a correction factor to mitigate the wind-induced bias in disdrometric rainfall estimates. The resulting correction factor could be applied as an alternative to the adaptive filtering suggested by this study and may be of practical use when dealing with disdrometric data processing. Full article

(This article belongs to the Special Issue Retrieval of Cloud and Precipitation by Ground-Based Radar and In Situ Observations: Application to Atmospheric and Volcanic Ash Clouds)

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13 pages, 2647 KiB

Open AccessArticle

Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe

by Saleem Ibrahim, Martin Landa, Ondřej Pešek, Karel Pavelka and Lena Halounova

Remote Sens. 2021, 13(15), 3027; https://doi.org/10.3390/rs13153027 - 02 Aug 2021

Cited by 11 | Viewed by 2725

Abstract

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose [...] Read more.

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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43 pages, 7536 KiB

Open AccessArticle

Nearshore Benthic Mapping in the Great Lakes: A Multi-Agency Data Integration Approach in Southwest Lake Michigan

by Molly K. Reif, Brandon S. Krumwiede, Steven E. Brown, Ethan J. Theuerkauf and Joseph H. Harwood

Remote Sens. 2021, 13(15), 3026; https://doi.org/10.3390/rs13153026 - 01 Aug 2021

Cited by 4 | Viewed by 2807

Abstract

The Laurentian Great Lakes comprise the largest assemblage of inland waterbodies in North America, with vast geographic, environmentally complex nearshore benthic substrate and associated habitat. The Great Lakes Water Quality Agreement, originally signed in 1972, aims to help restore and protect the basin, [...] Read more.

The Laurentian Great Lakes comprise the largest assemblage of inland waterbodies in North America, with vast geographic, environmentally complex nearshore benthic substrate and associated habitat. The Great Lakes Water Quality Agreement, originally signed in 1972, aims to help restore and protect the basin, and ecosystem monitoring is a primary objective to support adaptive management, environmental policy, and decision making. Yet, monitoring ecosystem trends remains challenging, potentially hindering progress in lake management and restoration. Consistent, high-resolution maps of nearshore substrate and associated habitat are fundamental to support management needs, and the nexus of high-quality remotely sensed data with improvements to analytical methods are increasing opportunities for large-scale nearshore benthic mapping at project-relevant spatial resolutions. This study attempts to advance the integration of high-fidelity data (airborne imagery and lidar, satellite imagery, in situ observations, etc.) and machine learning to identify and classify nearshore benthic substrate and associated habitat using a case study in southwest Lake Michigan along Illinois Beach State Park, Illinois, USA. Data inputs and analytical methods were evaluated to better understand their implications with respect to the Coastal and Marine Ecological Classification Standard (CMECS) classification hierarchy, resulting in an approach that could be easily applied to other shallow coastal environments. Classification of substrate and biotic components were iteratively classified in two Tiers in which classes with increasing specificity were identified using different combinations of airborne and satellite data inputs. Classification accuracy assessments revealed that for the Tier 1 substrate component (3 classes), average overall accuracy was 90.10 ± 0.60% for 24 airborne data combinations and 89.77 ± 1.02% for 12 satellite data combinations, whereas the Tier 1 biotic component (2 classes) average overall accuracy was 93.58 ± 0.91% for 24 airborne data combinations and 92.67 ± 0.71% for 11 satellite data combinations. The Tier 2 result for the substrate component (2 classes) was 93.28% for 2 airborne data combinations and 95.25% for the biotic component (2 classes). The study builds on foundational efforts to move towards a more integrated data approach, whereby data strengths and limitations for mapping nearshore benthic substrate and associated habitat, expressed through classification accuracy, were evaluated within the context of the CMECS classification hierarchy, and has direct applicability to critical monitoring needs in the Great Lakes. Full article

(This article belongs to the Section Environmental Remote Sensing)

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19 pages, 11384 KiB

Open AccessArticle

Characteristic Analysis of Sea Surface Currents around Taiwan Island from CODAR Observations

by Yu-Hao Tseng, Ching-Yuan Lu, Quanan Zheng and Chung-Ru Ho

Remote Sens. 2021, 13(15), 3025; https://doi.org/10.3390/rs13153025 - 01 Aug 2021

Cited by 10 | Viewed by 3502

Abstract

Sea surface currents observed by high-frequency (HF) radars have been widely used in ocean circulation research. In this study, hourly sea surface currents observed by the Taiwan Coastal Ocean Dynamics Applications Radar (CODAR) system from 2015 to 2019 were analyzed by the empirical [...] Read more.

Sea surface currents observed by high-frequency (HF) radars have been widely used in ocean circulation research. In this study, hourly sea surface currents observed by the Taiwan Coastal Ocean Dynamics Applications Radar (CODAR) system from 2015 to 2019 were analyzed by the empirical orthogonal function (EOF) analysis to reveal the characteristics of the sea surface currents around Taiwan Island. The study area is divided into two regions, the Kuroshio region east of Taiwan Island and the Taiwan Strait west of Taiwan Island. In the Kuroshio region, the first EOF mode shows that the Kuroshio is characterized by higher current speeds with greater variability in summer. The second and third EOF modes present a dipole eddy pair and single eddy impingement on the Kuroshio during different periods. The seasonal variation of the dipole eddy pair indicates that the cyclonic/anticyclonic eddy on the north/south side appears more frequently in summer. Single eddy impingement occurs at multiple periods, including daily, intraseasonal, interseasonal, and annual periods. For the Taiwan Strait, the first EOF mode displays the tide signals. The tides enter the Taiwan Strait from the north and south, forming strong sea surface currents around the northern tip of Taiwan Island and the Penghu Archipelago. The second EOF mode exhibits the seasonal changes of the sea surface currents driven by the monsoon winds. The sea surface currents in the northern Taiwan Strait are relatively strong, possibly due to the narrow and shallow terrain there. The high spatiotemporal resolution of sea surface currents derived from CODAR observations provide more detailed characteristics of sea surface circulation around Taiwan Island. Full article

(This article belongs to the Special Issue Selected Papers from the “International Symposium on Remote Sensing 2021”)

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16 pages, 26173 KiB

Open AccessArticle

Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

by Huiqin Ma, Wenjiang Huang, Yingying Dong, Linyi Liu and Anting Guo

Remote Sens. 2021, 13(15), 3024; https://doi.org/10.3390/rs13153024 - 01 Aug 2021

Cited by 23 | Viewed by 3572

Abstract

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features [...] Read more.

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R² of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively. Full article

(This article belongs to the Special Issue The Emerging Trends and Applications of Big Data and Machine Learning/Artificial Intelligence (AI) in Remote Sensing)

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Remote Sens., Volume 13, Issue 15 (August-1 2021) – 205 articles

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