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Remote Sens., Volume 13, Issue 18 (September-2 2021) – 243 articles

Cover Story (view full-size image): Remote sensing products are important for estimating landscape scale ecosystem services, such as carbon sequestration. Such efforts often use time series data of plant greenness, which can provide a proxy of plant biomass, coverage, and photosynthetic activity. However, this is difficult within tidal wetlands due to frequent flooding that leads to attenuations of the vegetation signal. This uncertainty contributes to gaps in upscaling and budgeting of these important “blue carbon” ecosystems and projecting their future. This study developed and investigated a strategy to model annual trajectories of a greenness indicator in a tidally affected estuarine setting by combining per-pixel historical multi-year phenological variability with relevant landscape properties, and current-year climatic characteristics from open access data. View this paper.
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Article
Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion
Remote Sens. 2021, 13(18), 3791; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183791 - 21 Sep 2021
Viewed by 456
Abstract
The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received [...] Read more.
The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework. Full article
(This article belongs to the Special Issue Radar Signal Processing and System Design for Urban Health)
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Article
Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East
Remote Sens. 2021, 13(18), 3790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183790 - 21 Sep 2021
Viewed by 503
Abstract
Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly [...] Read more.
Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination. Full article
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Article
Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach
Remote Sens. 2021, 13(18), 3789; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183789 - 21 Sep 2021
Viewed by 585
Abstract
This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach [...] Read more.
This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt. Full article
(This article belongs to the Special Issue Remote Sensing in Irrigated Crop Water Stress Assessment)
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Article
A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes
Remote Sens. 2021, 13(18), 3788; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183788 - 21 Sep 2021
Viewed by 651
Abstract
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from [...] Read more.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Article
Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019
Remote Sens. 2021, 13(18), 3787; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183787 - 21 Sep 2021
Viewed by 505
Abstract
Alpine glaciers are strongly suffering the consequences of the temperature rising and monitoring them over long periods is of particular interest for climate change tracking. A wide range of techniques can be successfully applied to survey and monitor glaciers with different spatial and [...] Read more.
Alpine glaciers are strongly suffering the consequences of the temperature rising and monitoring them over long periods is of particular interest for climate change tracking. A wide range of techniques can be successfully applied to survey and monitor glaciers with different spatial and temporal resolutions. However, going back in time to retrace the evolution of a glacier is still a challenging task. Historical aerial images, e.g., those acquired for regional cartographic purposes, are extremely valuable resources for studying the evolution and movement of a glacier in the past. This work analyzed the evolution of the Belvedere Glacier by means of structure from motion techniques applied to digitalized historical aerial images combined with more recent digital surveys, either from aerial platforms or UAVs. This allowed the monitoring of an Alpine glacier with high resolution and geometrical accuracy over a long span of time, covering the period 1977–2019. In this context, digital surface models of the area at different epochs were computed and jointly analyzed, retrieving the morphological dynamics of the Belvedere Glacier. The integration of datasets dating back to earlier times with those referring to surveys carried out with more modern technologies exploits at its full potential the information that at first glance could be thought obsolete, proving how historical photogrammetric datasets are a remarkable heritage for glaciological studies. Full article
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Article
Influence of Scale Effect of Canopy Projection on Understory Microclimate in Three Subtropical Urban Broad-Leaved Forests
Remote Sens. 2021, 13(18), 3786; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183786 - 21 Sep 2021
Viewed by 383
Abstract
The canopy is the direct receiver and receptor of external environmental variations, and affects the microclimate and energy exchange between the understory and external environment. After autumn leaf fall, the canopy structure of different forests shows remarkable variation, causes changes in the microclimate [...] Read more.
The canopy is the direct receiver and receptor of external environmental variations, and affects the microclimate and energy exchange between the understory and external environment. After autumn leaf fall, the canopy structure of different forests shows remarkable variation, causes changes in the microclimate and is essential for understory vegetation growth. Moreover, the microclimate is influenced by the scale effect of the canopy. However, the difference in influence between different forests remains unclear on a small scale. In this study, we aimed to analyze the influence of the scale effect of canopy projection on understory microclimate in three subtropical broad-leaved forests. Three urban forests: evergreen broad-leaved forest (EBF), deciduous broad-leaved forest (DBF), and mixed evergreen and deciduous broad-leaved forest (MBF) were selected for this study. Sensors for environmental monitoring were used to capture the microclimate data (temperature (T), relative humidity (RH), and light intensity (LI)) for each forest. Terrestrial laser scanning was employed to obtain the canopy projection intensity (CPI) at each sensor location. The results indicate that the influence range of canopy projection on the microclimate was different from stand to stand (5.5, 5, and 3 m). Moreover, there was a strong negative correlation between T and RH, and the time for T and LI to reach a significant correlation in different urban forests was different, as well as the time for RH and LI during the day. Finally, the correlation between CPI and the microclimate showed that canopy projection had the greatest effect on T and RH in MBF, followed by DBF and EBF. In conclusion, our findings confirm that canopy projection can significantly affect understory microclimate. This study provides a reference for the conservation of environmentally sensitive organisms for urban forest management. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forest Structure)
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Article
Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite
Remote Sens. 2021, 13(18), 3785; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183785 - 21 Sep 2021
Viewed by 469
Abstract
The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading [...] Read more.
The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE. Full article
(This article belongs to the Special Issue Estimating Inland Water Quality from Remote Sensing Data)
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Article
Reduction of Air Pollution in Poland in Spring 2020 during the Lockdown Caused by the COVID-19 Pandemic
Remote Sens. 2021, 13(18), 3784; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183784 - 21 Sep 2021
Viewed by 546
Abstract
The COVID-19 pandemic has affected many aspects of human well-being including air quality. The present study aims at quantifying this effect by means of ground-level concentrations of NO2, PM2.5, as well as aerosol optical depth (AOD) measurements and tropospheric [...] Read more.
The COVID-19 pandemic has affected many aspects of human well-being including air quality. The present study aims at quantifying this effect by means of ground-level concentrations of NO2, PM2.5, as well as aerosol optical depth (AOD) measurements and tropospheric NO2 column number density (NO2 TVCD), during the imposed governmental restrictions in spring 2020. The analyses were performed for both urban and non-built-up areas across the whole of Poland accompanied by Warsaw (urban site) and Strzyzow (a background site). The results revealed that mean PM2.5 concentrations in spring 2020 for urban and non-built-up areas across Poland and for Warsaw were 20%, 23%, 15% lower than the 10-year average, respectively. Analogous mean NO2 concentrations were lower by 20%, 18%, 30% and NO2 TVCD revealed 9%, 4%, 9% reductions in 2020 as compared to 2019. Regarding mean AOD, retrieved from MERRA-2 reanalysis, it was found that for the whole of Poland during spring 2020 the reduction in AOD as compared to the 10-year average was 15%. The contribution of the lockdown within total air pollution reduction is not easily assessable due to anomalous weather conditions in 2020 which resulted in advection of clean air masses identified from MERRA-2 reanalysis and Strzyzow observatory. Full article
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Communication
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic
Remote Sens. 2021, 13(18), 3783; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183783 - 21 Sep 2021
Viewed by 408
Abstract
For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. [...] Read more.
For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of <15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping. Full article
(This article belongs to the Section Remote Sensing Letter)
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Article
Improving the Robustness of the MTI-Estimated Mining-Induced 3D Time-Series Displacements with a Logistic Model
Remote Sens. 2021, 13(18), 3782; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183782 - 21 Sep 2021
Viewed by 382
Abstract
The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous [...] Read more.
The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous MTI method, no redundant observations are provided to estimate 3D displacements, causing poor robustness and further degrading the accuracy of the 3D displacement estimation. This study presents an improved MTI method to significantly improve the robustness of the 3D mining displacements derived by the previous MTI method. In this new method, a fused-track strategy, instead of the previous track-by-track one, is proposed to process the multi-track InSAR measurements by introducing a logistic model. In doing so, redundant observations are generated and further incorporated into the prior model to solve 3D displacements. The improved MTI method was tested on the Datong coal mining area, China, with Sentinel-1 InSAR datasets from three tracks. The results show that the 3D mining displacements estimated by the improved MTI method had the same spatial–temporal resolution as those estimated by the previous MTI method and about 33.5% better accuracy. The more accurate 3D displacements retrieved from the improved MTI method can offer better data for scientifically understanding the mechanism of mining deformation and assessing mining-related geohazards. Full article
(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)
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Article
Recovery of Tropical Cyclone Induced SST Cooling Observed by Satellite in the Northwestern Pacific Ocean
Remote Sens. 2021, 13(18), 3781; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183781 - 21 Sep 2021
Viewed by 560
Abstract
Based on the satellite observed sea surface temperature (SST), the recovery of SST cooling induced by the tropical cyclones (TCs) over the northwestern Pacific Ocean is investigated. The results show that the passage of a TC induces a mean maximum cooling in the [...] Read more.
Based on the satellite observed sea surface temperature (SST), the recovery of SST cooling induced by the tropical cyclones (TCs) over the northwestern Pacific Ocean is investigated. The results show that the passage of a TC induces a mean maximum cooling in the SST of roughly −1.25 °C. It was also found that most of this cooling (~87%) is typically erased within 30 days of TC passage. This recovery time depends upon the degree of cooling, with stronger (weaker) SST cooling corresponding to longer (shorter) recovery time. Further analyses show that the mixed layer depth (MLD) and the upper layer thermocline temperature gradient (UTTG) also play an important role in the SST response to TCs. The maximum cooling increases ~0.1 °C for every 7 m decrease in the MLD or every 0.04 °C/m increase in the UTTG. The combined effects of MLD and TC intensity and translation speed on the SST response are also discussed. Full article
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Article
A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction
Remote Sens. 2021, 13(18), 3780; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183780 - 21 Sep 2021
Viewed by 406
Abstract
One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to [...] Read more.
One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC. Full article
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Article
Satellite-Derived Barrier Response and Recovery Following Natural and Anthropogenic Perturbations, Northern Chandeleur Islands, Louisiana
Remote Sens. 2021, 13(18), 3779; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183779 - 21 Sep 2021
Viewed by 551
Abstract
The magnitude and frequency of storm events, relative sea-level rise (RSLR), sediment supply, and anthropogenic alterations drive the morphologic evolution of barrier island systems, although the relative importance of any one driver will vary with the spatial and temporal scales considered. To explore [...] Read more.
The magnitude and frequency of storm events, relative sea-level rise (RSLR), sediment supply, and anthropogenic alterations drive the morphologic evolution of barrier island systems, although the relative importance of any one driver will vary with the spatial and temporal scales considered. To explore the relative contributions of storms and human alterations to sediment supply on decadal changes in barrier landscapes, we applied Otsu’s thresholding method to multiple satellite-derived spectral indices for coastal land-cover classification and analyzed Landsat satellite imagery to quantify changes to the northern Chandeleur Islands barrier system since 1984. This high temporal-resolution dataset shows decadal-scale land-cover oscillations related to storm–recovery cycles, suggesting that shorter and (or) less resolved time series are biased toward storm impacts and may significantly overpredict land-loss rates and the timing of barrier morphologic state changes. We demonstrate that, historically, vegetation extent and persistence were the dominant controls on alongshore-variable landscape response and recovery following storms, and are even more important than human-mediated sediment input. As a result of extensive vegetation losses over the past few decades, however, the northern Chandeleur Islands are transitioning to a new morphologic state in which the landscape is dominated by intertidal environments, indicating reduced resilience to future storms and possibly rapid transitions in morphologic state with increasing rates of RSLR. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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Review
Progress and Trends in the Application of Google Earth and Google Earth Engine
Remote Sens. 2021, 13(18), 3778; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183778 - 21 Sep 2021
Viewed by 729
Abstract
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 [...] Read more.
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective. Full article
(This article belongs to the Special Issue Feature Papers for Remote Sensing Image Processing Section)
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Article
Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches
Remote Sens. 2021, 13(18), 3777; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183777 - 20 Sep 2021
Viewed by 456
Abstract
Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the [...] Read more.
Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests. Full article
(This article belongs to the Special Issue UAV Applications for Forest Management: Wood Volume, Biomass, Mapping)
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Article
Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images
Remote Sens. 2021, 13(18), 3776; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183776 - 20 Sep 2021
Viewed by 501
Abstract
It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art [...] Read more.
It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented. Full article
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Article
Quantitative Assessment of Riverbed Planform Adjustments, Channelization, and Associated Land Use/Land Cover Changes: The Ingauna Alluvial-Coastal Plain Case (Liguria, Italy)
Remote Sens. 2021, 13(18), 3775; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183775 - 20 Sep 2021
Viewed by 494
Abstract
The active-channel planform adjustments that have occurred along the Centa, lower Arroscia and lower Neva rivers since 1930, along with the riverbed channelization processes and the land-use and land-cover changes in disconnected riverine areas, were investigated through a multitemporal analysis based on remote [...] Read more.
The active-channel planform adjustments that have occurred along the Centa, lower Arroscia and lower Neva rivers since 1930, along with the riverbed channelization processes and the land-use and land-cover changes in disconnected riverine areas, were investigated through a multitemporal analysis based on remote sensing and geographical information systems (GIS). These watercourses flow through the largest Ligurian alluvial-coastal plain in a completely anthropogenic landscape. This research is based on the integrated use of consolidated and innovative metrics for riverbed planform analysis. Specific indices were introduced to assess active-channel lateral migration in relation to the active-channel area abandonment and formation processes. The Arroscia and Neva riverbeds experienced narrowing, progressive stabilization, and braiding phenomena disappearance from 1930 to the early 1970s, and then slight narrowing up to the late 1980s. Subsequently, generalized stability was observed. Conversely, the Centa was not affected by relevant planform changes. Recently, all rivers underwent a slight to very slight width increase triggered by the November 2016 high-magnitude flood. The active-channel adjustments outlined in this paper reflect the relevant role in conditioning the river morphology and dynamics played by channelization works built from the 1920s to the early 1970s. They (i) narrowed, straightened, and stabilized the riverbed and (ii) reduced the floodable surface over the valley-floor. Thus, large disconnected riverine areas were occupied by human activities and infrastructures, resulting in a progressive increase in vulnerable elements exposed to hydrogeomorphic hazards. The outlined morphological dynamics (i) display significant differences in terms of chronology, type, and magnitude of active-channel planform adjustments with respect to the medium- and short-term morphological evolution of most Italian rivers and (ii) reflect the widespread urbanization of Ligurian major valley floors that occurred over the 20th century. The outcomes from this study represent an essential knowledge base from a river management perspective; the novel metrics enlarge the spectrum of available GIS tools for active-channel planform analysis. Full article
(This article belongs to the Special Issue Geomorphological Mapping and Process Monitoring Using Remote Sensing)
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Article
IFRAD: A Fast Feature Descriptor for Remote Sensing Images
Remote Sens. 2021, 13(18), 3774; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183774 - 20 Sep 2021
Viewed by 373
Abstract
Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art [...] Read more.
Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Article
Combined GRACE and MT-InSAR to Assess the Relationship between Groundwater Storage Change and Land Subsidence in the Beijing-Tianjin-Hebei Region
Remote Sens. 2021, 13(18), 3773; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183773 - 20 Sep 2021
Viewed by 485
Abstract
Beijing-Tianjin-Hebei (BTH) has been suffering from severe groundwater storage (GWS) consumption and land subsidence (LS) for a long period. The overexploitation of groundwater brings about severe land subsidence, which affects the safety and development of BTH. In this paper, we utilized multi-frame synthetic [...] Read more.
Beijing-Tianjin-Hebei (BTH) has been suffering from severe groundwater storage (GWS) consumption and land subsidence (LS) for a long period. The overexploitation of groundwater brings about severe land subsidence, which affects the safety and development of BTH. In this paper, we utilized multi-frame synthetic aperture radar datasets obtained by the Rardarsat-2 satellite to monitor land subsidence’s temporal and spatial distribution in the BTH from 2012 to 2016 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). In addition, we also employed the Gravity Recovery and Climate Experiment (GRACE) mascon datasets acquired by the Center for Space Research (CSR) and Jet Propulsion Laboratory (JPL) to obtain the GWS anomalies (GWSA) of BTH from 2003 to 2016. Then we evaluate the accuracy of the results obtained. Furthermore, we explored the relationship between the regional GWSA and the average cumulative subsidence in the BTH. The total volume change of subsidence is 59.46% of the total volume change of groundwater storage. Moreover, the long-term decreasing trend of the GWSA (14.221 mm/year) and average cumulative subsidence (17.382 mm/year) show a relatively high consistency. Finally, we analyze the heterogeneity of GWS change (GWSC) and LS change (LSC) in the four typical areas by the Lorenz curve model. The implementation of the South-to-North Water Diversion Project (MSWDP) affects the heterogeneity of GWSC and LSC. It can be seen that the largest heterogeneity of LSC lags behind the GWSC in the Tianjin-Langfang-Hengshui-Baoding area. The largest uneven subsidence in Beijing and Tianjin occurred in 2015, and the largest uneven subsidence in Hengshui-Baoding occurred in 2014. After that, the heterogeneity of subsidence gradually tends to stable. Full article
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Article
Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar
Remote Sens. 2021, 13(18), 3772; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183772 - 20 Sep 2021
Viewed by 385
Abstract
Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can [...] Read more.
Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method. Full article
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Article
Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images
Remote Sens. 2021, 13(18), 3771; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183771 - 20 Sep 2021
Viewed by 320
Abstract
Land cover classification from very high-resolution (VHR) remote sensing images is a challenging task due to the complexity of geography scenes and the varying shape and size of ground targets. It is difficult to utilize the spectral data directly, or to use traditional [...] Read more.
Land cover classification from very high-resolution (VHR) remote sensing images is a challenging task due to the complexity of geography scenes and the varying shape and size of ground targets. It is difficult to utilize the spectral data directly, or to use traditional multi-scale feature extraction methods, to improve VHR remote sensing image classification results. To address the problem, we proposed a multi-modality and multi-scale attention fusion network for land cover classification from VHR remote sensing images. First, based on the encoding-decoding network, we designed a multi-modality fusion module that can simultaneously fuse more useful features and avoid redundant features. This addresses the problem of low classification accuracy for some objects caused by the weak ability of feature representation from single modality data. Second, a novel multi-scale spatial context enhancement module was introduced to improve feature fusion, which solves the problem of a large-scale variation of objects in remote sensing images, and captures long-range spatial relationships between objects. The proposed network and comparative networks were evaluated on two public datasets—the Vaihingen and the Potsdam datasets. It was observed that the proposed network achieves better classification results, with a mean F1-score of 88.6% for the Vaihingen dataset and 92.3% for the Potsdam dataset. Experimental results show that our model is superior to the state-of-the-art network models. Full article
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Article
Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models
Remote Sens. 2021, 13(18), 3770; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183770 - 20 Sep 2021
Viewed by 538
Abstract
Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few [...] Read more.
Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets. Full article
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Article
A High Latitude Model for the E Layer Dominated Ionosphere
Remote Sens. 2021, 13(18), 3769; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183769 - 20 Sep 2021
Viewed by 461
Abstract
Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of [...] Read more.
Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of the ELDI for the Northern and Southern Hemispheres. Our proposed Neustrelitz ELDI Event Model (NEEM) is an empirical, climatological model that describes ELDI characteristics by means of four submodels for selected model observables, considering the dependencies on appropriate model drivers. The observables include the occurrence probability of ELDI events and typical E layer parameters that are important to describe the propagation medium for High Frequency (HF) radio waves. The model drivers are the geomagnetic latitude, local time, day of year, solar activity and the convection electric field. During our investigation, we found clear trends for the model observables depending on the drivers, which can be well represented by parametric functions. In this regard, the submodel NEEM-N characterizes the peak electron density NmE of the E layer, while the submodels NEEM-H and NEEM-W describe the corresponding peak height hmE and the vertical width wvE of the E layer electron density profile, respectively. Furthermore, the submodel NEEM-P specifies the ELDI occurrence probability %ELDI. The dataset underlying our studies contains more than two million vertical electron density profiles covering a period of almost 13 years. These profiles were derived from ionospheric GPS radio occultation observations on board the six COSMIC/FORMOSAT-3 satellites (Constellation Observing System for Meteorology, Ionosphere and Climate/Formosa Satellite Mission 3). We divided the dataset into a modeling dataset for determining the model coefficients and a test dataset for subsequent model validation. The normalized root mean square deviation (NRMS) between the original and the predicted model observables yields similar values across both datasets and both hemispheres. For NEEM-N, we obtain an NRMS varying between 36.1% and 47.1% and for NEEM-H, between 6.1% and 6.3%. In the case of NEEM-W, the NRMS varies between 38.5% and 41.1%, while it varies between 56.5% and 60.3% for NEEM-P. In summary, the proposed NEEM utilizes primary relationships with geophysical and solar wind observables, which are useful for describing ELDI occurrences and the associated changes of the E layer properties. In this manner, the NEEM paves the way for future prediction of the ELDI and of its characteristics in technical applications, especially from the fields of telecommunications and navigation. Full article
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Article
Approaching Global Instantaneous Precise Positioning with the Dual- and Triple-Frequency Multi-GNSS Decoupled Clock Model
Remote Sens. 2021, 13(18), 3768; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183768 - 20 Sep 2021
Viewed by 443
Abstract
Precise Point Positioning (PPP), as a global precise positioning technique, suffers from relatively long convergence times, hindering its ability to be the default precise positioning technique. Reducing the PPP convergence time is a must to reach global precise positions, and doing so in [...] Read more.
Precise Point Positioning (PPP), as a global precise positioning technique, suffers from relatively long convergence times, hindering its ability to be the default precise positioning technique. Reducing the PPP convergence time is a must to reach global precise positions, and doing so in a few minutes to seconds can be achieved thanks to the additional frequencies that are being broadcast by the modernized GNSS constellations. Due to discrepancies in the number of signals broadcast by each satellite/constellation, it is necessary to have a model that can process a mix of signals, depending on availability, and perform ambiguity resolution (AR), a technique that proved necessary for rapid convergence. This manuscript does so by expanding the uncombined Decoupled Clock Model to process and fix ambiguities on up to three frequencies depending on availability for GPS, Galileo, and BeiDou. GLONASS is included as well, without carrier-phase ambiguity fixing. Results show the possibility of consistent quasi-instantaneous global precise positioning through an assessment of the algorithm on a network of global stations, as the 67th percentile solution converges below 10 cm horizontal error within 2 min, compared to 8 min with a triple-frequency solution, showing the importance of having a flexible PPP-AR model frequency-wise. In terms of individual datasets, 14% of datasets converge instantaneously when mixing dual- and triple-frequency measurements, compared to just 0.1% in that of dual-frequency mode without ambiguity resolution. Two kinematic car datasets were also processed, and it was shown that instantaneous centimetre-level positioning with a moving receiver is possible. These results are promising as they only rely on ultra-rapid global satellite products, allowing for instantaneous real-time precise positioning without the need for any local infrastructure or prior knowledge of the receiver’s environment. Full article
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Article
Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel
Remote Sens. 2021, 13(18), 3767; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183767 - 20 Sep 2021
Viewed by 291
Abstract
In this work we demonstrate that restoration interventions in arid to semi-arid landscapes can be independently assessed by remote sensing methods throughout all phases. For early verification, we use Sentinel-1 radar imagery that is sensitive to changes in soil roughness and thus able [...] Read more.
In this work we demonstrate that restoration interventions in arid to semi-arid landscapes can be independently assessed by remote sensing methods throughout all phases. For early verification, we use Sentinel-1 radar imagery that is sensitive to changes in soil roughness and thus able to rapidly detect disturbances due to mechanised ploughing, including identification of the time of occurrence and the surface area prepared for planting. Subsequently, time series of the normalized difference vegetation index (NDVI) derived from high-resolution imagery enabled tracking and verifying of the increase in biomass and the long-term impact of restoration interventions. We assessed 111 plots within the Great Green Wall area in Burkina Faso, Niger, Nigeria and Senegal. For 58 plots, the interventions were successfully verified, corresponding to an area of more than 7000 ha of degraded land. Comparatively, these computerised data were matched with field data and high-resolution imagery, for which the NDVI was used as an indicator of subsequent biomass growth in the plots. The trends were polynomial and presented clear vegetation gains for the monthly aggregates over the last 2 years (2018–2020). The qualitative data on planted species also showed an increase in biodiversity as direct sown seeds of a minimum of 10 native Sahel species (six woody mixed with four fodder herbaceous species) were planted per hectare. This innovative and standardised monitoring method provides an objective and timely assessment of restoration interventions and will likely appeal more actors to confidently invest in restoration as a part of zero-net climate mitigation. Full article
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Article
Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation
Remote Sens. 2021, 13(18), 3766; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183766 - 20 Sep 2021
Viewed by 460
Abstract
Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes [...] Read more.
Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods. Full article
(This article belongs to the Special Issue Remote Sensing Based Building Extraction II)
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Article
Potential Impacts of Assimilating Every-10-Minute Himawari-8 Satellite Radiance with the POD-4DEnVar Method
Remote Sens. 2021, 13(18), 3765; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183765 - 20 Sep 2021
Viewed by 483
Abstract
The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove [...] Read more.
The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove cloudy and precipitation-affected observations. Historical samples and physical ensembles are combined to construct four-dimensional ensembles according to the observed frequency of the Himawari-8 satellite. The purpose of this study was to test the potential impacts of assimilating high temporal resolution observations with POD-4DEnVar in a numerical weather prediction (NWP) system. Two parallel experiments were performed with and without Himawari-8 radiance assimilation during the entire month of July 2020. The results of the experiment with radiance assimilation show that it improves the analysis and forecast accuracy of geopotential, horizontal wind field and relative humidity compared to the experiment without radiance assimilation. Moreover, the equitable threat score (ETS) of 24-h accumulated precipitation shows that assimilating Himawari-8 radiance improves the rainfall forecast accuracy. Improvements were found in the structure, amplitude and location of the precipitation. In addition, the ETS of hourly accumulated precipitation indicates that assimilating high temporal resolution Himawari-8 radiance can improve the prediction of rapidly developed rainfall. Overall, assimilating every-10-min AHI radiance from Himawari-8 with POD-4DEnVar has positive impacts on NWP. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Article
Modeling the Near-Surface Energies and Water Vapor Fluxes Behavior in Response to Summer Canopy Density across Yanqi Endorheic Basin, Northwestern China
Remote Sens. 2021, 13(18), 3764; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183764 - 20 Sep 2021
Viewed by 558
Abstract
The Yanqi basin is the main irrigated and active agroecosystem in semi-arid Xinjiang, northwestern China, which further seeks responses to the profound local water-related drawbacks in relation to the unceasing landscape desiccation and scant precipitation. Yet, it comes as an astonishment that a [...] Read more.
The Yanqi basin is the main irrigated and active agroecosystem in semi-arid Xinjiang, northwestern China, which further seeks responses to the profound local water-related drawbacks in relation to the unceasing landscape desiccation and scant precipitation. Yet, it comes as an astonishment that a few reported near-surface items and water vapor fluxes as so far required for water resources decision support, particularly in a scarce observation data region. As a contributive effort, here we adjusted the sensible heat flux (H) calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to high-resolution satellite dataset coupled with in-situ observation, through a wise guided “anchor” pixel assortment from surface reflectance-α, Leaf area index-LAI, vegetation index-NDVI, and surface temperature (Pcold, Phot) to model the robustness of energy fluxes and Evapotranspiration-ETa over the basin. Results reasonably reflected ETa which returned low RMSE (0.6 mm d1), MAE (0.48 mm d1) compared to in-situ recordings, indicating the competence of SEBAL to predict vapor fluxes in this region. The adjustment unveiled the estimates of the land-use contribution to evapotranspiration with an average ranging from 3 to 4.69 mm d1, reaching a maximum of 5.5 mm d1. Furthermore, findings showed a high striking energy dissipation (LE/Rn) across grasslands and wetlands. The vegetated surfaces with a great evaporative fraction were associated with the highest LE/Rn (70–90%), and water bodies varying between 20% and 60%, while the desert ecosystem dissipated the least energy with a low evaporative fraction. Still, besides high portrayed evaporation in water, grasslands and wetlands varied interchangeably in accounting for the highest ETa followed by cropland. Finally, a substantial nexus between available energy (Rn-G) and ETa informed the available energy, influenced by NDVI to be the primary driver of these oases’ transpiration. This study provides essentials of near-surface energy fluxes and the likelihood of ETa with considerable baseline inferences for Yanqi that may be beneficial for long-term investigations that will attend in agrometeorological services and sustainable management of water resources in semi-arid regions. Full article
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Technical Note
Development and Validation of Machine-Learning Clear-Sky Detection Method Using 1-Min Irradiance Data and Sky Imagers at a Polluted Suburban Site, Xianghe
Remote Sens. 2021, 13(18), 3763; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183763 - 20 Sep 2021
Viewed by 487
Abstract
Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution [...] Read more.
Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m−2). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Remote Sensing of Atmospheric Environment)
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Article
Landscape Structure and Seasonality: Effects on Wildlife Species Richness and Occupancy in a Fragmented Dry Forest in Coastal Ecuador
Remote Sens. 2021, 13(18), 3762; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183762 - 19 Sep 2021
Viewed by 607
Abstract
Despite high fragmentation and deforestation, little is known about wildlife species richness and occurrence probabilities in tropical dry forest (TDF) landscapes. To fill this gap in knowledge, we used a Sentinel-2-derived land-cover map, Normalized Difference Vegetation Index (NDVI) data and a multi-species occupancy [...] Read more.
Despite high fragmentation and deforestation, little is known about wildlife species richness and occurrence probabilities in tropical dry forest (TDF) landscapes. To fill this gap in knowledge, we used a Sentinel-2-derived land-cover map, Normalized Difference Vegetation Index (NDVI) data and a multi-species occupancy model to correct for detectability to assess the effect of landscape characteristics on medium and large mammal occurrence and richness in three TDF areas that differ in disturbance and seasonality in Ecuador. We recorded 15 species of medium and large mammals, distributed in 12 families; 1 species is critically Endangered, and 2 are Near-Threatened. The results indicate that species occupancy is related to low forest cover and high vegetation seasonality (i.e., high difference in NDVI between the wet and dry seasons). We believe that the apparent negative effect of forest cover is an indicator of species tolerance for disturbance. The three sampling areas varied from 98% to 40% forest cover, yet species richness and occupancy were not significantly different among them. Vegetation seasonality indicates that more seasonal forests (i.e., those where most tree species lose their leaves during the dry season) tend to have higher mammal species occupancy compared to less seasonal, semi-deciduous forests. Overall, occupancy did not vary between the dry and wet seasons, but species-specific data indicate that some species exhibit higher occupancy during the wet season. This research offers a good understanding of mammal species’ responses to habitat disturbance and fragmentation in TDFs and provides insights to promote their conservation. Full article
(This article belongs to the Special Issue Wildlife Ecology for a Dynamic Future)
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