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Remote Sens., Volume 15, Issue 1 (January-1 2023) – 288 articles

Cover Story (view full-size image): This paper presents a fast and accurate approach for the determination of snow properties from the measurements of the Ocean and Land Colour Instrument (OLCI) onboard Copernicus Sentinel-3 satellites (see also http://snow.geus.dk/, 22.12.2022). The algorithm targets the retrieval of snow optical properties, such as snow spectral and broadband albedo, and snow microstructure (snow specific surface area and effective ice grain size). Additionally, we propose a technique with which to retrieve the concentration of light-absorbing snow impurities. The retrievals are validated using ground measurements performed in Antarctica, Greenland, and the French Alps. Spaceborne observations make it possible to monitor Antarctica and Greenland ice sheets on a scale not accessible to ground-based networks. View this paper
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23 pages, 8239 KiB  
Article
A Parallel Principal Skewness Analysis and Its Application in Radar Target Detection
by Dahu Wang, Chang Liu and Chao Wang
Remote Sens. 2023, 15(1), 288; https://doi.org/10.3390/rs15010288 - 3 Jan 2023
Viewed by 2426
Abstract
Radar is often affected by various clutter backgrounds in complex environments, so clutter suppression has important practical significance for radar target detection. The clutter suppression process conforms to the blind source separation (BSS) model. The principal skewness analysis (PSA) algorithm is a BSS [...] Read more.
Radar is often affected by various clutter backgrounds in complex environments, so clutter suppression has important practical significance for radar target detection. The clutter suppression process conforms to the blind source separation (BSS) model. The principal skewness analysis (PSA) algorithm is a BSS algorithm with third-order statistics as the objective function, and its running speed is faster than the conventional BSS algorithm. Still, the PSA algorithm has the problem of error accumulation. This paper improves the PSA algorithm and proposes a parallel PSA (PPSA) algorithm. PPSA can estimate the directions corresponding to each independent component simultaneously and avoid the problem of error accumulation. PPSA uses parallel instead of serial computing, significantly improving the running speed. In this paper, the PPSA algorithm is applied to radar target detection. The simulation data and real data experiments verify the effectiveness and superiority of the PPSA algorithm in suppressing clutter. Full article
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22 pages, 2851 KiB  
Article
Ongoing Development of the Bass Strait GNSS/INS Buoy System for Altimetry Validation in Preparation for SWOT
by Boye Zhou, Christopher Watson, Benoit Legresy, Matt A. King and Jack Beardsley
Remote Sens. 2023, 15(1), 287; https://doi.org/10.3390/rs15010287 - 3 Jan 2023
Cited by 1 | Viewed by 2096
Abstract
GNSS equipped buoys remain an important tool in altimetry validation. Progressive advances in altimetry missions require associated development in such validation tools. In this paper, we enhanced an existing buoy approach and gained further understanding of the buoy dynamics based on in situ [...] Read more.
GNSS equipped buoys remain an important tool in altimetry validation. Progressive advances in altimetry missions require associated development in such validation tools. In this paper, we enhanced an existing buoy approach and gained further understanding of the buoy dynamics based on in situ observations. First, we implemented the capability to separate the ambiguity fixing strategy for different constellations in the processing software TRACK. A comparison between GPS and GNSS solutions suggested up to 3 cm reduction in the root mean square of the buoy minus co-located mooring SSH residuals over the selected sidereal periods. Then, comparison between double differencing and precise point positioning solutions suggested a possible common mode error external to GNSS processing. To assess buoy performance in different ocean conditions and sea states, GNSS and INS observations were used during periods where external forcings (waves, current and wind) were not interacting substantially. For the deployments investigated, no significant relationship was found, noting the maximum significant wave height and current velocity was ~2.3 m and ~0.3 m/s, respectively. In the lead up to the validation required for the SWOT mission, these results place important bounds on the performance of the buoy design under real operating conditions. Full article
(This article belongs to the Special Issue Advances in Satellite Altimetry)
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5 pages, 199 KiB  
Editorial
Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions
by Yaoming Ma, Lei Zhong, Li Jia and Massimo Menenti
Remote Sens. 2023, 15(1), 286; https://doi.org/10.3390/rs15010286 - 3 Jan 2023
Viewed by 1910
Abstract
The global climate has undergone unequivocal warming [...] Full article
14 pages, 4351 KiB  
Technical Note
High-Resolution Observation of Ionospheric E-Layer Irregularities Using Multi-Frequency Range Imaging Technology
by Bo Chen, Yi Liu, Jian Feng, Yuqiang Zhang, Yufeng Zhou, Chen Zhou and Zhengyu Zhao
Remote Sens. 2023, 15(1), 285; https://doi.org/10.3390/rs15010285 - 3 Jan 2023
Viewed by 1879
Abstract
E-region field-aligned irregularities (FAIs) are a hot topic in space research, since electromagnetic signal propagation through ionospheric irregularities can undergo sporadic enhancements and fading known as ionospheric scintillation, which could severely affect communication, navigation, and radar systems. However, the range resolution of very-high-frequency [...] Read more.
E-region field-aligned irregularities (FAIs) are a hot topic in space research, since electromagnetic signal propagation through ionospheric irregularities can undergo sporadic enhancements and fading known as ionospheric scintillation, which could severely affect communication, navigation, and radar systems. However, the range resolution of very-high-frequency (VHF) radars, which is widely used to observe E-region FAIs, is limited due to its bandwidth. As a technology that is widely used in atmosphere radars to improve the range resolution of pulsed radars by transmitting multiple frequencies, this paper employed the multifrequency radar imaging (RIM) technique in a Wuhan VHF radar. The results showed that the range resolution of E-region FAIs greatly improved when compared with the results in traditional single-frequency mode, and that finer structures of E-region FAIs can be obtained. Specifically, the imaging results in multifrequency mode show that E-region FAIs demonstrate an overall descending trend at night, and it could be related to the tides or gravity waves due to their downward phase velocities or even driven by downwind shear. In addition, typical quasi-periodic (QP) echoes with a time period of around 10 min could be clearly seen using the RIM technique, and the features of the echoes suggest that they could be modulated by gravity waves. Furthermore, the RIM technique can be used to obtain the fine structure of irregularities within a short time period, and the hierarchical structure of E-region FAIs can be easily found. Therefore, the multifrequency imaging RIM technique is suitable for observing E-region FAIs and their evolution, as well as for identifying the different layers of E-region FAIs. Combined with the RIM technique, a VHF radar provides an effective and promising way to observe the structure of E-region FAIs in more detail to study the physical mechanism behind the formation and evolution of ionospheric E-region irregularities. Full article
(This article belongs to the Special Issue Ionosphere Monitoring with Remote Sensing II)
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19 pages, 5134 KiB  
Article
Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China
by Huimian Li, Guilian Zhang, Qicheng Zhong, Luqi Xing and Huaqiang Du
Remote Sens. 2023, 15(1), 284; https://doi.org/10.3390/rs15010284 - 3 Jan 2023
Cited by 11 | Viewed by 2900
Abstract
The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be [...] Read more.
The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be leveraged to accurately monitor forest AGC, whereas machine learning is an important algorithm for the accurate prediction of AGC. Therefore, in this study, single Landsat 8 (L) remote sensing data, single Sentinel-2 (S) remote sensing data, and combined Landsat 8 and Sentinel-2 (L + S) data are used as data sources. Four machine learning methods, support vector regression (SVR), random forest (RF), XGBoost (extreme gradient boosting), and CatBoost (categorical boosting), are used to predict forest AGC based on two phases of forest sample plots in Shanghai. We chose the optimal model to predict the AGC and simulate the spatiotemporal distribution. The study shows that both machine learning models based on separate Landsat 8 OLI and Sentinel-2 satellite remote sensing data can accurately predict the AGC and spatiotemporal distribution of the Shanghai urban forest. Nevertheless, the accuracy of the combined data (L + S) and CatBoost-integrated AGC models is higher than the others, with fitting and validation accuracy R2 values of 0.99 and 0.70, respectively. The RMSE was also smaller at 0.67 and 6.29 Mg/ha, respectively. The uncertainty of the AGC spatial distribution in the Shanghai urban forest derived from the CatBoost model prediction from the 2016–2019 data was small and consistent with the actual situation. Furthermore, the statistics showed that the AGC of the Shanghai forest increased from 24.90 Mg/ha in 2016 to 25.61 Mg/ha in 2019. Full article
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7 pages, 191 KiB  
Editorial
Geodetic Monitoring for Land Deformation
by Alex Hay-Man Ng, Linlin Ge, Hsing-Chung Chang and Zheyuan Du
Remote Sens. 2023, 15(1), 283; https://doi.org/10.3390/rs15010283 - 3 Jan 2023
Cited by 1 | Viewed by 2708
Abstract
Land deformation is a pervasive hazard that could lead to serious problems, for example, increasing risk of flooding in coastal areas, damaging buildings and infrastructures, destructing groundwater systems, generating tension cracks on land, and reactivating faults, to name only a few [...] Full article
(This article belongs to the Special Issue Geodetic Monitoring for Land Deformation)
18 pages, 6856 KiB  
Article
Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia
by Julian Bolaños, Juan Carlos Corrales and Liseth Viviana Campo
Remote Sens. 2023, 15(1), 282; https://doi.org/10.3390/rs15010282 - 3 Jan 2023
Cited by 1 | Viewed by 2512
Abstract
Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating [...] Read more.
Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in Precision Agriculture)
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10 pages, 7924 KiB  
Communication
A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia
by Valery Bondur, Tumen Chimitdorzhiev and Aleksey Dmitriev
Remote Sens. 2023, 15(1), 281; https://doi.org/10.3390/rs15010281 - 3 Jan 2023
Viewed by 1589
Abstract
Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term [...] Read more.
Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term heaving mounds that turn into craters as a result are relevant. A workflow for detecting and assessing anomalous dynamics of heaving mounds in the Arctic regions is proposed. Areas with anomalous increase of ALOS-2 PALSAR-2 synthetic aperture radar (SAR) backscattering intensity are detected in the first stage. These increases take place due to sudden changes in local terrain slopes when the scattering surface (mound slope) turns toward the radar. Radar backscattering intensity also rises due to depolarization at newly formed frost cracks. Validation of the detected anomaly is carried out at the second stage through a comparison of multi-temporal digital elevation models obtained from bistatic radar interferometry TerraSAR-X/TanDEM-X data. At the final stage, the deformations are assessed within the detected areas using differential SAR interferometry (DInSAR) technique by ALOS-2 PALSAR-2 data. The magnitude of the heaving along the line of sight (LOS) was 22–24 cm in the period from January 2019 to January 2020. In general, effectiveness for detecting the perennial heaving mounds and the rate assessment of their increase were demonstrated in the suggested workflow. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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19 pages, 12021 KiB  
Article
Mid-Wave Infrared Snapshot Compressive Spectral Imager with Deep Infrared Denoising Prior
by Shuowen Yang, Hanlin Qin, Xiang Yan, Shuai Yuan and Qingjie Zeng
Remote Sens. 2023, 15(1), 280; https://doi.org/10.3390/rs15010280 - 3 Jan 2023
Cited by 2 | Viewed by 2221
Abstract
Although various infrared imaging spectrometers have been studied, most of them are developed under the Nyquist sampling theorem, which severely burdens 3D data acquisition, storage, transmission, and processing, in terms of both hardware and software. Recently, computational imaging, which avoids direct imaging, has [...] Read more.
Although various infrared imaging spectrometers have been studied, most of them are developed under the Nyquist sampling theorem, which severely burdens 3D data acquisition, storage, transmission, and processing, in terms of both hardware and software. Recently, computational imaging, which avoids direct imaging, has been investigated for its potential in the visible field. However, it has been rarely studied in the infrared domain, as it suffers from inconsistency in spectral response and reconstruction. To address this, we propose a novel mid-wave infrared snapshot compressive spectral imager (MWIR-SCSI). This design scheme provides a high degree of randomness in the measurement projection, which is more conducive to the reconstruction of image information and makes spectral correction implementable. Furthermore, leveraging the explainability of model-based algorithms and the high efficiency of deep learning algorithms, we designed a deep infrared denoising prior plug-in for the optimization algorithm to perform in terms of both imaging quality and reconstruction speed. The system calibration obtains 111 real coded masks, filling the gap between theory and practice. Experimental results on simulation datasets and real infrared scenarios prove the efficacy of the designed deep infrared denoising prior plug-in and the proposed acquisition architecture that acquires mid-infrared spectral images of 640 pixels × 512 pixels × 111 spectral channels at an acquisition frame rate of 50 fps. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing Imaging and Processing)
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19 pages, 12057 KiB  
Article
Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years
by Yang Yi, Mingchang Shi, Jie Wu, Na Yang, Chen Zhang and Xiaoding Yi
Remote Sens. 2023, 15(1), 279; https://doi.org/10.3390/rs15010279 - 3 Jan 2023
Cited by 7 | Viewed by 2496
Abstract
Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag [...] Read more.
Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. Full article
(This article belongs to the Special Issue Remote Sensing with Landscape Ecology and Landscape Sustainability)
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25 pages, 6659 KiB  
Article
Towards Unified Online-Coupled Aerosol Parameterization for the Brazilian Global Atmospheric Model (BAM): Aerosol–Cloud Microphysical–Radiation Interactions
by Jayant Pendharkar, Silvio Nilo Figueroa, Angel Vara-Vela, R. Phani Murali Krishna, Daniel Schuch, Paulo Yoshio Kubota, Débora Souza Alvim, Eder Paulo Vendrasco, Helber Barros Gomes, Paulo Nobre and Dirceu Luís Herdies
Remote Sens. 2023, 15(1), 278; https://doi.org/10.3390/rs15010278 - 3 Jan 2023
Cited by 1 | Viewed by 2071
Abstract
In this work, we report the ongoing implementation of online-coupled aerosol–cloud microphysical–radiation interactions in the Brazilian global atmospheric model (BAM) and evaluate the initial results, using remote-sensing data for JFM 2014 and JAS 2019. Rather than developing a new aerosol model, which incurs [...] Read more.
In this work, we report the ongoing implementation of online-coupled aerosol–cloud microphysical–radiation interactions in the Brazilian global atmospheric model (BAM) and evaluate the initial results, using remote-sensing data for JFM 2014 and JAS 2019. Rather than developing a new aerosol model, which incurs significant overheads in terms of fundamental research and workforce, a simplified aerosol module from a preexisting global aerosol–chemistry–climate model is adopted. The aerosol module is based on a modal representation and comprises a suite of aerosol microphysical processes. Mass and number mixing ratios, along with dry and wet radii, are predicted for black carbon, particulate organic matter, secondary organic aerosols, sulfate, dust, and sea salt aerosols. The module is extended further to include physically based parameterization for aerosol activation, vertical mixing, ice nucleation, and radiative optical properties computations. The simulated spatial patterns of surface mass and number concentrations are similar to those of other studies. The global means of simulated shortwave and longwave cloud radiative forcing are comparable with observations with normalized mean biases ≤11% and ≤30%, respectively. Large positive bias in BAM control simulation is enhanced with the inclusion of aerosols, resulting in strong overprediction of cloud optical properties. Simulated aerosol optical depths over biomass burning regions are moderately comparable. A case study simulating an intense biomass burning episode in the Amazon is able to reproduce the transport of smoke plumes towards the southeast, thus showing a potential for improved forecasts subject to using near-real-time remote-sensing fire products and a fire emission model. Here, we rely completely on remote-sensing data for the present evaluation and restrain from comparing our results with previous results until a complete representation of the aerosol lifecycle is implemented. A further step is to incorporate dry deposition, in-cloud and below-cloud scavenging, sedimentation, the sulfur cycle, and the treatment of fires. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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18 pages, 4703 KiB  
Technical Note
False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel
by Yang Li, Yonghui Wei, Yanping Wang, Yun Lin, Wenjie Shen and Wen Jiang
Remote Sens. 2023, 15(1), 277; https://doi.org/10.3390/rs15010277 - 3 Jan 2023
Cited by 4 | Viewed by 2067
Abstract
Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM [...] Read more.
Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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24 pages, 4926 KiB  
Article
Environmental Performance of Regional Protected Area Network: Typological Diversity and Fragmentation of Forests
by Tatiana Chernenkova, Ivan Kotlov, Nadezhda Belyaeva, Elena Suslova and Natalia Lebedeva
Remote Sens. 2023, 15(1), 276; https://doi.org/10.3390/rs15010276 - 3 Jan 2023
Cited by 1 | Viewed by 1884
Abstract
Protected areas (PAs) are among the main tools for preserving biodiversity and creating an environment for the natural course of ecological processes. The identification of forest biodiversity is especially important for large metropolitan areas. An obvious problem in assessing the efficiency of the [...] Read more.
Protected areas (PAs) are among the main tools for preserving biodiversity and creating an environment for the natural course of ecological processes. The identification of forest biodiversity is especially important for large metropolitan areas. An obvious problem in assessing the efficiency of the PAs network is the lack of up-to-date cartographic materials representing the typological diversity of vegetation. The aim of the paper is to identify forest biodiversity and fragmentation in the example of the Moscow region (MR)—the largest metropolis in Eastern Europe. The typological classification was carried out at a detailed hierarchical level—33 association groups (ass. gr.) considering the diversity of the land cover. A random forest algorithm was used for cartographic mapping (overall accuracy 0.59). Remote sensing (RS) data included Sentinel-2A, DEM SRTM, and PALSAR radar images. Six fragmentation metrics were calculated based on the raster map of forest typological diversity. A significant correlation between the forest diversity and PAs forest patch fragmentation metrics was noted. It has been established that the PAs proportion of the territory accounts for almost 20% only within the northernmost district and noticeably decreases to the south to 1–2%. At the same time, fragmentation noticeably increases from Northeast to Southwest. The category of PAs does not affect the state of the forest cover. Additionally, there was no direct influence of the anthropogenic factor from both local sources and a large regional source, i.e., the city of Moscow. It is shown that the average area of PAs, supporting 75% of the typological diversity of regional communities, was about 1000 ha. The results of the study suggest that there is a general lack of environmental protection measures in the region. It is recommended to increase the area of PAs, primarily for less fragmented forest patches, including indigenous forest-steppe and forest types of communities. Full article
(This article belongs to the Special Issue Application of Earth Observation for Monitoring Biodiversity)
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18 pages, 5702 KiB  
Article
Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China
by Jie Jiang, Jiaxin Liu, Donglai Jiao, Yong Zha and Shusheng Cao
Remote Sens. 2023, 15(1), 275; https://doi.org/10.3390/rs15010275 - 3 Jan 2023
Cited by 3 | Viewed by 2125
Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of [...] Read more.
The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of MODIS C6.1 Dark Target (DT), Deep Blue (DB), and C6.0 Multi-angle Implementation of Atmospheric Correction (MAIAC) products under different land cover types, aerosol types, and observation geometries were analyzed. About 65.64% of MAIAC AOD is within the expected error (Within EE), which is significantly higher than 41.43% for DT and 56.98% for DB. The DT product accuracy varies most obviously with the seasons, and the Within EE in winter is more than three times that in spring. The DB and MAIAC products have low accuracy in summer but high in other seasons. The accuracy of the DT product gradually decreases with the increase in urban and water land-cover proportion. After being corrected by bias and mean relative error, the DT accuracy is significantly improved, and the Within EE increases by 24.12% and 32.33%, respectively. The observation geometries and aerosol types were also examined to investigate their effects on AOD retrieval. Full article
(This article belongs to the Special Issue Aerosol and Atmospheric Correction)
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15 pages, 2774 KiB  
Article
Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients
by Achala Shakya, Mantosh Biswas and Mahesh Pal
Remote Sens. 2023, 15(1), 274; https://doi.org/10.3390/rs15010274 - 3 Jan 2023
Cited by 5 | Viewed by 2767
Abstract
This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset. For the fusion of SAR and [...] Read more.
This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset. For the fusion of SAR and optical images, Sentinel 1A and Sentinel 2B data covering Central State Farm in Hissar (India) was used. The major agricultural crops grown in this area include paddy, maize, cotton, and pulses during kharif (summer) and wheat, sugarcane, mustard, gram, and peas during rabi (winter) seasons. The gradient method using a Sobel operator and color components for three directions (i.e., x, y, and z) are used for image fusion. To judge the quality of fused image, several fusion metrics are calculated. After obtaining the resultant fused image, gradient based classification methods, including Stochastic Gradient Descent Classifier, Stochastic Gradient Boosting Classifier, and Extreme Gradient Boosting Classifier, are used for the final classification. The classification accuracy is represented using overall classification accuracy and kappa value. A comparison of classification results indicates a better performance by the Extreme Gradient Boosting Classifier. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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28 pages, 13830 KiB  
Article
Evaluation of InSAR Tropospheric Correction by Using Efficient WRF Simulation with ERA5 for Initialization
by Qinghua Liu, Qiming Zeng and Zhiliang Zhang
Remote Sens. 2023, 15(1), 273; https://doi.org/10.3390/rs15010273 - 2 Jan 2023
Cited by 5 | Viewed by 2395
Abstract
The delay caused by the troposphere is one of the major sources of errors limiting the accuracy of InSAR measurements. The tropospheric correction of InSAR measurements is important. The Weather Research and Forecasting (WRF) Model is a state-of-the-art mesoscale numerical weather prediction system [...] Read more.
The delay caused by the troposphere is one of the major sources of errors limiting the accuracy of InSAR measurements. The tropospheric correction of InSAR measurements is important. The Weather Research and Forecasting (WRF) Model is a state-of-the-art mesoscale numerical weather prediction system designed for atmospheric research applications. It can be applied to InSAR tropospheric correction. Its parameters can be altered according to the requirements of the given application. WRF is usually initialized based on 3 h- or 6 h temporal resolution data in InSAR tropospheric correction studies, a lower temporal resolution compared to ERA5 data. A lower time resolution means a longer integration time for WRF to simulate from the initial time to the target time. Initialization with a higher resolution can shorten the integration time of the simulation theoretically and improve its accuracy. However, an evaluation of the effectiveness of ERA5_WRF for InSAR tropospheric correction is lacking. To evaluate the efficiency of WRF tropospheric correction, we used Reanalysis v5 (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF) for initialization to drive the WRF (ERA5_WRF) for efficient applications in InSAR. Three methods based on global atmospheric models—FNL_WRF (tropospheric correction method based on WRF driven by NCEP FNL), Generic Atmospheric Correction Online Service for InSAR (GACOS), and ERA5—were used to evaluate the corrective effects of ERA5_WRF. The reliability of ERA5_WRF in different scenarios with large tropospheric delay was evaluated from the spatial and temporal perspectives by considering seasonal, topographic, and climatic factors. Its applications in the local space showed that ERA5_WRF could adequately correct tropospheric delay. Benefits include its high-quality data sources and the simulation of WRF, and its application in different seasons had proven superior to other methods in terms of the corrective effects of elevation-related and spatially related delays in summer. By analyzing the data sources and downscaling methods of correction methods and weather conditions of cases, ERA5_WRF had superior performance under the condition of large content and hourly variation of tropospheric delay. Furthermore, WRF showed the potential for tropospheric correction when other higher-quality data appear in the future. Full article
(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)
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17 pages, 12419 KiB  
Article
FlexibleNet: A New Lightweight Convolutional Neural Network Model for Estimating Carbon Sequestration Qualitatively Using Remote Sensing
by Mohamad M. Awad
Remote Sens. 2023, 15(1), 272; https://doi.org/10.3390/rs15010272 - 2 Jan 2023
Cited by 6 | Viewed by 2575
Abstract
Many heavy and lightweight convolutional neural networks (CNNs) require large datasets and parameter tuning. Moreover, they consume time and computer resources. A new lightweight model called FlexibleNet was created to overcome these obstacles. The new lightweight model is a CNN scaling-based model (width, [...] Read more.
Many heavy and lightweight convolutional neural networks (CNNs) require large datasets and parameter tuning. Moreover, they consume time and computer resources. A new lightweight model called FlexibleNet was created to overcome these obstacles. The new lightweight model is a CNN scaling-based model (width, depth, and resolution). Unlike the conventional practice, which arbitrarily scales these factors, FlexibleNet uniformly scales the network width, depth, and resolution with a set of fixed scaling coefficients. The new model was tested by qualitatively estimating sequestered carbon in the aboveground forest biomass from Sentinel-2 images. We also created three different sizes of training datasets. The new training datasets consisted of six qualitative categories (no carbon, very low, low, medium, high, and very high). The results showed that FlexibleNet was better or comparable to the other lightweight or heavy CNN models concerning the number of parameters and time requirements. Moreover, FlexibleNet had the highest accuracy compared to these CNN models. Finally, the FlexibleNet model showed robustness and low parameter tuning requirements when a small dataset was provided for training compared to other models. Full article
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20 pages, 6526 KiB  
Article
Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System
by Miguel Lourenço, Diogo Estima, Henrique Oliveira, Luís Oliveira and André Mora
Remote Sens. 2023, 15(1), 271; https://doi.org/10.3390/rs15010271 - 2 Jan 2023
Cited by 3 | Viewed by 2539
Abstract
To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by [...] Read more.
To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications. Full article
(This article belongs to the Special Issue Information Retrieval from Remote Sensing Images)
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20 pages, 3029 KiB  
Article
UAV Propeller Rotational Speed Measurement through FMCW Radars
by Gianluca Ciattaglia, Grazia Iadarola, Linda Senigagliesi, Susanna Spinsante and Ennio Gambi
Remote Sens. 2023, 15(1), 270; https://doi.org/10.3390/rs15010270 - 2 Jan 2023
Cited by 6 | Viewed by 3308
Abstract
The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous [...] Read more.
The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach. Full article
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22 pages, 28258 KiB  
Article
Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR
by Shuming Si, Han Hu, Yulin Ding, Xuekun Yuan, Ying Jiang, Yigao Jin, Xuming Ge, Yeting Zhang, Jie Chen and Xiaocui Guo
Remote Sens. 2023, 15(1), 269; https://doi.org/10.3390/rs15010269 - 2 Jan 2023
Cited by 2 | Viewed by 2524
Abstract
Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on [...] Read more.
Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas. Full article
(This article belongs to the Special Issue Machine Learning for LiDAR Point Cloud Analysis)
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26 pages, 9169 KiB  
Article
Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020
by Jing Zhang, Jiaqiang Du, Shifeng Fang, Zhilu Sheng, Yangchengsi Zhang, Bingqing Sun, Jialin Mao and Lijuan Li
Remote Sens. 2023, 15(1), 268; https://doi.org/10.3390/rs15010268 - 2 Jan 2023
Cited by 3 | Viewed by 2025
Abstract
Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In [...] Read more.
Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries. Full article
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19 pages, 3572 KiB  
Article
Comparison of Lake Extraction and Classification Methods for the Tibetan Plateau Based on Topographic-Spectral Information
by Xiaoliang Wang, Guangsheng Zhou, Xiaomin Lv, Li Zhou, Mingcheng Hu, Xiaohui He and Zhihui Tian
Remote Sens. 2023, 15(1), 267; https://doi.org/10.3390/rs15010267 - 2 Jan 2023
Viewed by 2177
Abstract
Accurate identification and extraction of lake boundaries are the basis of the accurate assessment of lake changes and their responses to climate change. To reduce the effects of lake ice and snow cover, mountain shadows, cloud and fog shielding, alluvial and proluvial deposits, [...] Read more.
Accurate identification and extraction of lake boundaries are the basis of the accurate assessment of lake changes and their responses to climate change. To reduce the effects of lake ice and snow cover, mountain shadows, cloud and fog shielding, alluvial and proluvial deposits, and shoals on the extraction of lake boundaries on the Tibetan Plateau, this study developed an RNSS water index to increase the contrast between the lake and similar surface objects of the spectral curve, and constructed a new method flow for lake extraction on the Tibetan Plateau based on image synthesis, topographic-spectral feature indexes, and machine learning algorithms. The lake extraction effects of three common machine learning classification algorithms were compared: the Cart decision tree, random forest (RF), and gradient boosting decision tree (GBDT). The results show that the new lake extraction method based on topographic-spectral characteristics and the GBDT classification method had the highest extraction accuracy for Tibetan Plateau lakes in 2016 and 2021. Its overall accuracy, Kappa coefficient, user’s accuracy, and producer’s accuracy for 2016 and 2021 were 99.81%, 0.887, 83.55%, 94.67% and 99.88%, 0.933, 89.18%, 98.24%, respectively, and the total area of lake extraction was the most consistent with the validation datasets. The three classification methods can effectively extract lakes covered by ice and snow, and the extraction effect was ranked as GBDT > RF > Cart. The lake extraction effect under mountain shadow was ranked as Cart > GBDT > RF, and the lake extraction effect under alluvial deposits and shoals was ranked as GBDT > RF > Cart. The results may provide technical support for extracting lakes from long time series and reveal the impact of climate change on Tibetan Plateau lakes. Full article
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26 pages, 31710 KiB  
Article
MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network
by Dong Chen, Fan Hu, P. Takis Mathiopoulos, Zhenxin Zhang and Jiju Peethambaran
Remote Sens. 2023, 15(1), 266; https://doi.org/10.3390/rs15010266 - 2 Jan 2023
Cited by 8 | Viewed by 2237
Abstract
Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from [...] Read more.
Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2∼32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F1-score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net. Full article
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
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12 pages, 12117 KiB  
Communication
Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks
by Zheng Zhang, Zhe Yan, Jiankun Jing, Hanming Gu and Haiying Li
Remote Sens. 2023, 15(1), 265; https://doi.org/10.3390/rs15010265 - 2 Jan 2023
Cited by 2 | Viewed by 1939
Abstract
Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic [...] Read more.
Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic data and manually labeling the real data. However, synthetic data have certain feature differences from real data, and the manual labeling of data is time-consuming and subjective. These factors limit the application of deep learning algorithms in seismic data interpretation. To obtain realistic seismic training data, we propose label-to-data networks based on cycle-consistent adversarial networks in this work. These networks take random labels and unlabeled real seismic data as input and generate synthetic seismic data that match the random labels and have similar features to the real seismic data. Quantitative analysis of the generated data demonstrate the effectiveness of the proposed methods. Meanwhile, test results on different data indicate that the generated data are reliable and can be applied for seismic fault detection. Full article
(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)
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22 pages, 18793 KiB  
Article
Generation of Multiple Frames for High Resolution Video SAR Based on Time Frequency Sub-Aperture Technique
by Congrui Yang, Zhen Chen, Yunkai Deng, Wei Wang, Pei Wang and Fengjun Zhao
Remote Sens. 2023, 15(1), 264; https://doi.org/10.3390/rs15010264 - 2 Jan 2023
Cited by 4 | Viewed by 2168
Abstract
Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, [...] Read more.
Video Synthetic Aperture Radar (ViSAR) operating in spotlight mode has received widespread attention in recent years because of its ability to form a sequence of SAR images for a region of interest (ROI). However, due to the heavy computational burden of data processing, the application of ViSAR is limited in practice. Although back projection (BP) can avoid unnecessary repetitive processing of overlapping parts between consecutive video frames, it is still time-consuming for high-resolution video-SAR data processing. In this article, in order to achieve the same or a similar effect to BP and reduce the computational burden as much as possible, a novel time-frequency sub-aperture technology (TFST) is proposed. Firstly, based on azimuth resampling and full aperture azimuth scaling, a time domain sub-aperture (TDS) processing algorithm is proposed to process ViSAR data with large coherent integration angles to ensure the continuity of ViSAR monitoring. Furthermore, through frequency domain sub-aperture (FDS) processing, multiple high-resolution video frames can be generated efficiently without sub-aperture reconstruction. In addition, TFST is based on the range migration algorithm (RMA), which can take into account the accuracy while ensuring efficiency. The results of simulation and X-band airborne SAR experimental data verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition)
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21 pages, 5991 KiB  
Article
Quantitative Evaluation of Bathymetric LiDAR Sensors and Acquisition Approaches in Lærdal River in Norway
by Mahmoud Omer Mahmoud Awadallah, Christian Malmquist, Morten Stickler and Knut Alfredsen
Remote Sens. 2023, 15(1), 263; https://doi.org/10.3390/rs15010263 - 2 Jan 2023
Cited by 5 | Viewed by 3197
Abstract
The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This [...] Read more.
The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This study evaluates the performance of three bathymetric LiDAR sensors, CZMIL Supernova, Riegl VQ880-G, and Riegl VQ840-G, used with different acquisition approaches, in mapping Lærdal River bathymetry in Norway. The performance was evaluated based on comparing the sensors against a multibeam echosounder (MBES), a terrestrial laser scanner (TLS), and by an intercomparison between the individual sensors. The comparison was completed by comparing point clouds from the instruments and through the comparison of DEMs created from the point clouds. For the comparison against the MBES, the results show that the median residuals range between 3 to 13 cm, while against the TLS the median residuals range between 0 to 5 cm. The comparison of the CZMIL sensor against the two Riegl sensors shows median residuals of around 12 cm where the CZMIL map is shallower against the VQ880-G and deeper against the VQ840-G sensor. For the two Riegl sensors, the results show a median difference of 2 cm with the VQ880-G map deeper. We do observe that areas with high residuals are linked to river features such as large substrate variability, steep banks, and whitewater/turbulent flow. The study shows that all the LiDAR instruments provide high-quality representations of the river geometry and create a solid foundation for planning, modelling, or other work in rivers where detailed bathymetry is needed. Full article
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22 pages, 5998 KiB  
Article
On the Choice of the Most Suitable Period to Map Hill Lakes via Spectral Separability and Object-Based Image Analyses
by Antonino Maltese
Remote Sens. 2023, 15(1), 262; https://doi.org/10.3390/rs15010262 - 2 Jan 2023
Cited by 2 | Viewed by 1677
Abstract
Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their [...] Read more.
Technological advances in Earth observation made images characterized by high spatial and temporal resolutions available, nevertheless bringing with them the radiometric heterogeneity of small geographical entities, often also changing in time. Among small geographical entities, hill lakes exhibit a widespread distribution, and their census is sometimes partial or shows unreliable data. High resolution and heterogeneity have boosted the development of geographic object-based image analysis algorithms. This research analyzes which is the most suitable period for acquiring satellite images to identify and delimitate hill lakes. This is achieved by analyzing the spectral separability of the surface reflectance of hill lakes from surrounding bare or vegetated soils and by implementing a semiautomatic procedure to enhance the segmentation phase of a GEOBIA algorithm. The proposed procedure was applied to high spatial resolution satellite images acquired in two different climate periods (arid and temperate), corresponding to dry and vegetative seasons. The segmentation parameters were tuned by minimizing an under- and oversegmentation metric on surfaces and perimeters of hill lakes selected as the reference. The separability of hill lakes from their surrounding was evaluated using Euclidean and divergence metrics both in the arid and temperate periods. The classification accuracy was evaluated by calculating the error matrix and normalized error matrix. Classes’ reflectances in the image acquired in the arid period show the highest average separability (3–4 higher than in the temperate one). The segmentation based on the reference areas performs more than that based on the reference perimeters (metric ≈ 20% lower). Both separability metrics and classification accuracies indicate that images acquired in the arid period are more suitable than temperate ones to map hill lakes. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
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21 pages, 18888 KiB  
Article
Shallow-to-Deep Spatial–Spectral Feature Enhancement for Hyperspectral Image Classification
by Lijian Zhou, Xiaoyu Ma, Xiliang Wang, Siyuan Hao, Yuanxin Ye and Kun Zhao
Remote Sens. 2023, 15(1), 261; https://doi.org/10.3390/rs15010261 - 1 Jan 2023
Cited by 14 | Viewed by 2531
Abstract
Since Hyperspectral Images (HSIs) contain plenty of ground object information, they are widely used in fine-grain classification of ground objects. However, some ground objects are similar and the number of spectral bands is far higher than the number of the ground object categories. [...] Read more.
Since Hyperspectral Images (HSIs) contain plenty of ground object information, they are widely used in fine-grain classification of ground objects. However, some ground objects are similar and the number of spectral bands is far higher than the number of the ground object categories. Therefore, it is hard to deeply explore the spatial–spectral joint features with greater discrimination. To mine the spatial–spectral features of HSIs, a Shallow-to-Deep Feature Enhancement (SDFE) model with three modules based on Convolutional Neural Networks (CNNs) and Vision-Transformer (ViT) is proposed. Firstly, the bands containing important spectral information are selected using Principal Component Analysis (PCA). Secondly, a two-layer 3D-CNN-based Shallow Spatial–Spectral Feature Extraction (SSSFE) module is constructed to preserve the spatial and spectral correlations across spaces and bands at the same time. Thirdly, to enhance the nonlinear representation ability of the network and avoid the loss of spectral information, a channel attention residual module based on 2D-CNN is designed to capture the deeper spatial–spectral complementary information. Finally, a ViT-based module is used to extract the joint spatial–spectral features (SSFs) with greater robustness. Experiments are carried out on Indian Pines (IP), Pavia University (PU) and Salinas (SA) datasets. The experimental results show that better classification results can be achieved by using the proposed feature enhancement method as compared to other methods. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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25 pages, 6324 KiB  
Article
Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation
by Gabriel B. Senay, Gabriel E. L. Parrish, Matthew Schauer, MacKenzie Friedrichs, Kul Khand, Olena Boiko, Stefanie Kagone, Ray Dittmeier, Saeed Arab and Lei Ji
Remote Sens. 2023, 15(1), 260; https://doi.org/10.3390/rs15010260 - 1 Jan 2023
Cited by 11 | Viewed by 5229
Abstract
Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of [...] Read more.
Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications. Full article
(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)
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18 pages, 7374 KiB  
Article
Pre-Seismic Anomaly Detection from Multichannel Infrared Images of FY-4A Satellite
by Yingbo Yue, Fuchun Chen and Guilin Chen
Remote Sens. 2023, 15(1), 259; https://doi.org/10.3390/rs15010259 - 1 Jan 2023
Cited by 2 | Viewed by 1886
Abstract
Research of seismic infrared remote sensing has been undertaken for several decades, but there is no stable and effective earthquake prediction method. A new algorithm combining the long short-term memory and the density-based spatial clustering of applications with noise models is proposed to [...] Read more.
Research of seismic infrared remote sensing has been undertaken for several decades, but there is no stable and effective earthquake prediction method. A new algorithm combining the long short-term memory and the density-based spatial clustering of applications with noise models is proposed to extract the anomalies from the multichannel infrared remote sensing images of the Fengyun-4 satellites. A statistical analysis is used to validate the correlation between the anomalies and earthquakes. The results show that the correlation rate is 64.29%, the hit rate is 68.75%, and the probability gain is about 1.91. In the Madoi and YangBi earthquake cases, the infrared anomaly detected in this paper is correlated with the TEC anomaly found in the previous research. This indicates that it is feasible to combine multi-source data to improve the accuracy of earthquake prediction in future studies. Full article
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