Remote Sensing

19 pages, 7591 KiB

Open AccessArticle

GNSS Localization in Constraint Environment by Image Fusing Techniques

by Ciprian David, Corina Nafornita, Vasile Gui, Andrei Campeanu, Guillaume Carrie and Michel Monnerat

Remote Sens. 2021, 13(10), 2021; https://doi.org/10.3390/rs13102021 - 20 May 2021

Cited by 1 | Viewed by 2689

Abstract

Satellite localization often suffers in terms of accuracy due to various reasons. One possible source of errors is represented by the lack of means to eliminate Non-Line-of-Sight satellite-related data. We propose here a method for fusing existing data with new information, extracted by [...] Read more.

Satellite localization often suffers in terms of accuracy due to various reasons. One possible source of errors is represented by the lack of means to eliminate Non-Line-of-Sight satellite-related data. We propose here a method for fusing existing data with new information, extracted by using roof-mounted cameras and adequate image processing algorithms. The roof-mounted camera is used to robustly segment the sky regions. The localization approach can benefit from this new information as it offers a way of excluding the Non-Line-of-Sight satellites. The output of the camera module is a probability map. One can easily decide which satellites should not be used for localization, by manipulating this probability map. Our approach is validated by extensive tests, which demonstrate the improvement of the localization itself (Horizontal Positioning Error reduction) and a moderate degradation of Horizontal Protection Level due to the Dilution of Precision phenomenon, which appears as a consequence of the reduction of the satellites’ number used for localization. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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16 pages, 5890 KiB

Open AccessArticle

Reverse-Time Migration Imaging of Ground-Penetrating Radar in NDT of Reinforced Concrete Structures

by Ruiqing Shen, Yonghui Zhao, Shufan Hu, Bo Li and Wenda Bi

Remote Sens. 2021, 13(10), 2020; https://doi.org/10.3390/rs13102020 - 20 May 2021

Cited by 14 | Viewed by 3728

Abstract

The evaluation and inspection of steel bars in reinforced concrete structures are critical for prolonging the service life of buildings. In this regard, ground-penetrating radar (GPR) has been a crucial alternative due to its non-invasiveness and convenience. This paper reports the experimental activities [...] Read more.

The evaluation and inspection of steel bars in reinforced concrete structures are critical for prolonging the service life of buildings. In this regard, ground-penetrating radar (GPR) has been a crucial alternative due to its non-invasiveness and convenience. This paper reports the experimental activities on a test-site area inside a camp in Shanghai, China. To assess the concrete structures of the building, GPR was employed for the detection and localization of rebars in columns, beams, and floors. From the GPR B-scan profiles acquired using a high-frequency antenna, the exact quantity of reinforcements was identified according to the hyperbola responses. Considering the difficulty of inferring the exact position and the scale of the rebars, we applied reverse time migration (RTM) to collapse the hyperbolic response and retrieve the target in a migrated image. To verify the effectiveness of the RTM algorithm, we carried out an experiment on a concrete model with three reinforced bars. We also utilized the RTM algorithm to process the B-scan profiles collected in a column that was later excavated. The imaging results validated the capacity of RTM in localizing and shaping rebars. Then, we employed the RTM algorithm for the GPR B-scan data collected from the other column. Based on the imaging profile, the quantity and positions of the rebars were correctly determined. Moreover, the thickness of the protective layer was evaluated according to the migrated result. These results demonstrate that GPR combined with RTM could provide useful foundation data for structural evaluation. Full article

(This article belongs to the Special Issue Data Fusion, Integration and Advances of Non-destructive Testing Methods in Engineering and Geosciences)

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21 pages, 10432 KiB

Open AccessArticle

New Constraints on Slip Behavior of the Jianshui Strike-Slip Fault from Faulted Stream Channel Risers and Airborne Lidar Data, SE Tibetan Plateau, China

by Peng Guo, Zhujun Han, Shaopeng Dong, Fan Gao and Jiani Li

Remote Sens. 2021, 13(10), 2019; https://doi.org/10.3390/rs13102019 - 20 May 2021

Cited by 3 | Viewed by 3009

Abstract

The temporal slip behavior of a fault from displaced landforms when there are no chronological data remains poorly understood. The southern segment of the Xiaojiang fault zone (XJFZ) plays an important role in accommodating the lateral extrusion of the SE Tibetan plateau. However, [...] Read more.

The temporal slip behavior of a fault from displaced landforms when there are no chronological data remains poorly understood. The southern segment of the Xiaojiang fault zone (XJFZ) plays an important role in accommodating the lateral extrusion of the SE Tibetan plateau. However, there are few reports on the evolution of the offset landforms and slip behavior of the fault due to the dense vegetation in the region. Here, offset landforms along the Jianshui fault (JSF) in the southern segment of the XJFZ are systematically interpreted and measured using high-resolution satellite imagery, field investigations, and airborne lidar. The risers on the right banks of three stream channels feature similar left-lateral offset characteristics near the town of Dongshanzhai. The left-lateral offsets consist of multiple inflections produced by seismic events, and the offset of each event is similar. These inflections are distributed downstream in a stair-stepped manner. The newly formed inflections are located close to the fault, and the earlier formed ones are eroded by flowing water and migrate downstream. The difference between the amount of downstream erosion of two adjacent inflections varies. Assuming the stream’s long-term erosion rate remains steady, the estimated time intervals between seismic events are different. Combined with the cumulative offset probability density calculation for 92 offsets, the JSF is considered to show a nonperiodic characteristic earthquake recurrence pattern. We also propose a multistage offset evolution model of the stream channel riser. This provides a new way to analyze the seismic recurrence pattern of the fault over a relative time scale. Full article

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14 pages, 5241 KiB

Open AccessArticle

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

by Zofia Jabs-Sobocińska, Andrzej N. Affek, Ireneusz Ewiak and Mihai Daniel Nita

Remote Sens. 2021, 13(10), 2018; https://doi.org/10.3390/rs13102018 - 20 May 2021

Cited by 8 | Viewed by 4899

Abstract

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine [...] Read more.

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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18 pages, 5826 KiB

Open AccessArticle

Spherically Optimized RANSAC Aided by an IMU for Fisheye Image Matching

by Anbang Liang, Qingquan Li, Zhipeng Chen, Dejin Zhang, Jiasong Zhu, Jianwei Yu and Xu Fang

Remote Sens. 2021, 13(10), 2017; https://doi.org/10.3390/rs13102017 - 20 May 2021

Cited by 10 | Viewed by 3217

Abstract

Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random [...] Read more.

Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios. Full article

(This article belongs to the Special Issue GPS/INS and Mapping Techniques for Environmental and Infrastructure Monitoring)

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25 pages, 7006 KiB

Open AccessArticle

Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data

by Xiufang Zhu, Rui Guo, Tingting Liu and Kun Xu

Remote Sens. 2021, 13(10), 2016; https://doi.org/10.3390/rs13102016 - 20 May 2021

Cited by 17 | Viewed by 4900

Abstract

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and [...] Read more.

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R² values of the eight yield estimation models established in the two provinces were all above 0.7, Lin’s concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 °C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)

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21 pages, 70540 KiB

Open AccessArticle

A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars

by Yusheng Wang, Yidong Lou, Yi Zhang, Weiwei Song, Fei Huang and Zhiyong Tu

Remote Sens. 2021, 13(10), 2015; https://doi.org/10.3390/rs13102015 - 20 May 2021

Cited by 13 | Viewed by 5169

Abstract

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics [...] Read more.

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy. Full article

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23 pages, 4168 KiB

Open AccessArticle

Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

by Celina Aznarez, Patricia Jimeno-Sáez, Adrián López-Ballesteros, Juan Pablo Pacheco and Javier Senent-Aparicio

Remote Sens. 2021, 13(10), 2014; https://doi.org/10.3390/rs13102014 - 20 May 2021

Cited by 49 | Viewed by 6899

Abstract

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water [...] Read more.

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment. Full article

(This article belongs to the Special Issue South American Hydrology and Remote Sensing (South America Water from Space))

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31 pages, 7098 KiB

Open AccessArticle

Feature-Aided RTK/LiDAR/INS Integrated Positioning System with Parallel Filters in the Ambiguity-Position-Joint Domain for Urban Environments

by Wenyi Li, Gang Liu, Xiaowei Cui and Mingquan Lu

Remote Sens. 2021, 13(10), 2013; https://doi.org/10.3390/rs13102013 - 20 May 2021

Cited by 13 | Viewed by 3058

Abstract

As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible [...] Read more.

As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible solution to the issue. For prior-map-free situations, as the only sensor with a global frame, RTK determines and maintains the global positioning precision of the integrated system. However, RTK performance degrades greatly in GNSS-challenged environments, and most of the existing integrated systems adopt loose coupling mode, which does nothing to improve RTK and, thus, prevents integrated systems from further improvement. Aiming at improving RTK performance in the RTK/LiDAR/INS integrated system, we proposed an innovative integrated algorithm that utilizes RTK to register LiDAR features while integrating the pre-registered LiDAR features to RTK and adopts parallel filters in the ambiguity-position-joint domain to weaken the effects of low satellite availability, cycle slips, and multipath. By doing so, we can improve the RTK fix rate and stability in GNSS-challenged environments. The results of the theoretical analyses, simulation experiments, and a road test proved that the proposed method improved RTK performance in GNSS-challenged environments and, thus, guaranteed the global positioning precision of the whole system. Full article

(This article belongs to the Special Issue GNSS for Urban Transport Applications)

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24 pages, 11227 KiB

Open AccessArticle

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

by Yue Yu, Jinmei Pan and Jiancheng Shi

Remote Sens. 2021, 13(10), 2012; https://doi.org/10.3390/rs13102012 - 20 May 2021

Cited by 1 | Viewed by 2276

Abstract

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is [...] Read more.

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (T_B) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow T_B from the snow’s surface was compared with the bulk T_B of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk T_B error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk T_B of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm. Full article

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12 pages, 5181 KiB

Open AccessTechnical Note

Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3

by Sehwan Park, Jinpyung Kim, Kyoyoung Jeon, Junkyeong Kim and Seunghee Park

Remote Sens. 2021, 13(10), 2011; https://doi.org/10.3390/rs13102011 - 20 May 2021

Cited by 20 | Viewed by 3544

Abstract

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because [...] Read more.

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified. Full article

(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications)

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18 pages, 14748 KiB

Open AccessArticle

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

by Johanna Roiha, Einari Heinaro and Markus Holopainen

Remote Sens. 2021, 13(10), 2010; https://doi.org/10.3390/rs13102010 - 20 May 2021

Cited by 9 | Viewed by 3253

Abstract

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause [...] Read more.

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed. Full article

(This article belongs to the Special Issue Protection and Conservation of Cultural Heritage: A Role for Remote Sensing)

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19 pages, 3498 KiB

Open AccessArticle

Modeling Gully Erosion Susceptibility to Evaluate Human Impact on a Local Landscape System in Tigray, Ethiopia

by Robert Busch, Jacob Hardt, Nadav Nir and Brigitta Schütt

Remote Sens. 2021, 13(10), 2009; https://doi.org/10.3390/rs13102009 - 20 May 2021

Cited by 17 | Viewed by 3360

Abstract

In recent years, modeling gully erosion susceptibility has become an increasingly popular approach for assessing the impact of different land degradation factors. However, different forms of human influence have so far not been identified in order to form an independent model. We investigate [...] Read more.

In recent years, modeling gully erosion susceptibility has become an increasingly popular approach for assessing the impact of different land degradation factors. However, different forms of human influence have so far not been identified in order to form an independent model. We investigate the spatial relation between gully erosion and distance to settlements and footpaths, as typical areas of human interaction, with the natural environment in rural African areas. Gullies are common features in the Ethiopian Highlands, where they often hinder agricultural productivity. Within a catchment in the north Ethiopian Highlands, 16 environmental and human-related variables are mapped and categorized. The resulting susceptibility to gully erosion is predicted by applying the Random Forest (RF) machine learning algorithm. Human-related and environmental factors are used to generate independent susceptibility models and form an additional inclusive model. The resulting models are compared and evaluated by applying a change detection technique. All models predict the locations of most gullies, while 28% of gully locations are exclusively predicted using human-related factors. Full article

(This article belongs to the Special Issue Quantifying Landscape Evolution and Erosion by Remote Sensing)

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24 pages, 9277 KiB

Open AccessEditor’s ChoiceArticle

Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas

by Juan López-Vinielles, José A. Fernández-Merodo, Pablo Ezquerro, Juan C. García-Davalillo, Roberto Sarro, Cristina Reyes-Carmona, Anna Barra, José A. Navarro, Vrinda Krishnakumar, Massimiliano Alvioli and Gerardo Herrera

Remote Sens. 2021, 13(10), 2008; https://doi.org/10.3390/rs13102008 - 20 May 2021

Cited by 23 | Viewed by 4371

Abstract

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The [...] Read more.

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas. Full article

(This article belongs to the Special Issue Remote Sensing-Based Monitoring and Modeling of Ground Movements)

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21 pages, 16325 KiB

Open AccessArticle

Performance Improvement of Spaceborne Carbon Dioxide Detection IPDA LIDAR Using Linearty Optimized Amplifier of Photo-Detector

by Yadan Zhu, Juxin Yang, Xiaoxi Zhang, Jiqiao Liu, Xiaopeng Zhu, Huaguo Zang, Tengteng Xia, Chuncan Fan, Xiao Chen, Yanguang Sun, Xia Hou and Weibiao Chen

Remote Sens. 2021, 13(10), 2007; https://doi.org/10.3390/rs13102007 - 20 May 2021

Cited by 9 | Viewed by 3099

Abstract

The spaceborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was found to be helpful in observing atmospheric CO₂ and understanding the carbon cycle. The airborne experiments of a scale prototype of China’s planned spaceborne IPDA LIDAR was implemented [...] Read more.

The spaceborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was found to be helpful in observing atmospheric CO₂ and understanding the carbon cycle. The airborne experiments of a scale prototype of China’s planned spaceborne IPDA LIDAR was implemented in 2019. A problem with data inversion caused by the detector module nonlinearity was found. Through many experiments, the amplifier circuit board (ACB) of the detector module was proved to be the main factor causing the nonlinearity. Through amplifier circuit optimization, the original bandwidth of the ACB was changed to 1 MHz by using a fifth-order active filter. Compared with the original version, the linearity of optimized ACB is improved from 42.6% to 0.0747%. The optimized ACB was produced and its linearity was verified by experiments. In addition, the output waveform of the optimized ACB changes significantly, which will affect the random error (RE) of the optimized IPDA LIDAR system. Through the performance simulation, the RE of more than 90% of the global area is less than 0.728 ppm. Finally, the transfer model of the detector module was given, which will be helpful for the further optimization of the CO₂ column-averaged dry-air mixing ratio (XCO₂) inversion algorithm. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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19 pages, 32130 KiB

Open AccessArticle

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

by Jun Hu, Qiaoqiao Ge, Jihong Liu, Wenyan Yang, Zhigui Du and Lehe He

Remote Sens. 2021, 13(10), 2006; https://doi.org/10.3390/rs13102006 - 20 May 2021

Cited by 8 | Viewed by 3352

Abstract

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated [...] Read more.

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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15 pages, 6160 KiB

Open AccessArticle

GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain

by Rui Jorge Oliveira, Bento Caldeira, Teresa Teixidó and José Fernando Borges

Remote Sens. 2021, 13(10), 2005; https://doi.org/10.3390/rs13102005 - 20 May 2021

Cited by 11 | Viewed by 3081

Abstract

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be [...] Read more.

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation. Full article

(This article belongs to the Special Issue Advanced Techniques for Ground Penetrating Radar Imaging)

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18 pages, 4181 KiB

Open AccessArticle

Geophysical Signal Detection in the Earth’s Oblateness Variation and Its Climate-Driven Source Analysis

by Hongjuan Yu, Qiujie Chen, Yu Sun and Krzysztof Sosnica

Remote Sens. 2021, 13(10), 2004; https://doi.org/10.3390/rs13102004 - 20 May 2021

Cited by 4 | Viewed by 2978

Abstract

This study analyzes the geophysical signals in J₂ time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a [...] Read more.

This study analyzes the geophysical signals in J₂ time series from 1976 to 2020 by using singular spectrum analysis (SSA) and the Lomb-Scargle (L-S) periodogram for the first time. The results of SSA indicate that the secular trend is characterized by a superposition of the secular linear decrease with a rate of approximately (−5.80 ± 0.08) × 10⁻¹¹/yr and an obvious quadratic rate of (2.38 ± 0.02) × 10⁻¹³/yr². Besides, the annual, semi-annual, and 10.6-year signals with determining for the first time its amplitude of 5.01 × 10⁻¹¹, are also detected by SSA, where their stochastic behavior can be maintained to the greatest extent. The 18.6-year signal cannot be detected by SSA even when the window size of 18.6 years was selected, while L-S periodogram can detect the signal of 18.6 years after removing the 18.6-year tidal theoretical value and the linear trend, proving the existence of the tidal variations of 18.6 years in the residual time series. Nevertheless, the 10.6-year signal can be found only after removing the secular trend. This fact suggests that the advantages of different methods used will lead to different sensitivity to the particular signals hard to be detected. Finally, the reconstructed ΔJ₂ time series through the sum of the climate-driven contributions from glacial isostatic adjustment (GIA), Antarctic ice sheets (ANT), atmosphere (ATM), continental glaciers (GLA), Greenland ice sheets (GRE), ocean bottom pressure (OBP), and terrestrial water storage (TWS) by using GRACE gravity field solution and geophysical models agrees very well with that of the observed ΔJ₂ from SLR in terms of the amplitude and phase. About 81.5% of observed ΔJ₂ can be explained by the reconstructed value. ATM, TWS, and OBP are the most significant contributing sources for seasonal signals in ΔJ₂ time series, explaining up to 40.1%, 31.9%, and 26.3% of the variances of observed ΔJ₂. These three components contribute to the annual and semi-annual variations of the observed ΔJ₂ up to 30.1% and 1.6%, 30.8% and 1.0%, as well as 25.4% and 0.7%, respectively. GRE, ANT, and GLA have ~3 to ~7-year periodic fluctuations and a positive linear trend, excluding GIA. Full article

(This article belongs to the Special Issue GRACE Satellite Gravimetry for Geosciences)

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19 pages, 8297 KiB

Open AccessArticle

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

by Daeyong Jin, Eojin Lee, Kyonghwan Kwon and Taeyun Kim

Remote Sens. 2021, 13(10), 2003; https://doi.org/10.3390/rs13102003 - 20 May 2021

Cited by 25 | Viewed by 4185

Abstract

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired [...] Read more.

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R²) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Full article

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

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23 pages, 7410 KiB

Open AccessArticle

A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study

by Hannah Vickers, Eirik Malnes, Ward J. J. van Pelt, Veijo A. Pohjola, Mari Anne Killie, Tuomo Saloranta and Stein Rune Karlsen

Remote Sens. 2021, 13(10), 2002; https://doi.org/10.3390/rs13102002 - 20 May 2021

Cited by 4 | Viewed by 4050

Abstract

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the [...] Read more.

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Full article

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

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19 pages, 10270 KiB

Open AccessArticle

Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations

by Antonella Boselli, Alessia Sannino, Mariagrazia D’Emilio, Xuan Wang and Salvatore Amoruso

Remote Sens. 2021, 13(10), 2001; https://doi.org/10.3390/rs13102001 - 20 May 2021

Cited by 10 | Viewed by 2496

Abstract

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius [...] Read more.

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere. Full article

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18 pages, 10615 KiB

Open AccessArticle

Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments

by Zhiyong Tu, Yidong Lou, Wenfei Guo, Weiwei Song and Yusheng Wang

Remote Sens. 2021, 13(10), 2000; https://doi.org/10.3390/rs13102000 - 20 May 2021

Cited by 8 | Viewed by 2558

Abstract

This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these [...] Read more.

This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these vehicles contain fast changing doppler frequency shifts and the first and second derivatives of doppler frequency, which will directly cause a negative impact on the receiver’s stable tracking of the signals. In order to guarantee the dynamic performance and the tracking accuracy, this paper designs a vector carrier structure to estimate the doppler component of a signal. Firstly, after the coherence integral, the IQ values are reorganized into new observations. Secondly, the phase error and frequency of the carrier are estimated through the pre-filter. Then, the pseudorange and carrier frequency are used as the observations of the main filter to estimate the motion state of the aircraft. Finally, the current state is fed back to the carrier Numerical Controlled Oscillator (NCO) as a complete closed loop. In the whole structure, the cascading vector loop replaces the original carrier tracking loop, and the stable signal tracking of code loop is guaranteed by carrier assisted pseudo-code method. In this paper, with the high dynamic signals generated by the GNSS signal simulator, this designed algorithm is validated by a software receiver. The results show that this loop has a wider dynamic tracking range and lower tracking error than the second-order frequency locked loop assisted third-order phase locked loop in high dynamic circumstances. When the acceleration of carrier is 100 g, the convergence time of vector structure is about 100 ms, and the carrier phase error is lower than 0.6 mm. Full article

(This article belongs to the Topic GNSS Measurement Technique in Aerial Navigation)

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17 pages, 10853 KiB

Open AccessArticle

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

by Audrey Minghelli, Sayoob Vadakke-Chanat, Malik Chami, Mireille Guillaume, Emmanuelle Migne, Patrick Grillas and Olivier Boutron

Remote Sens. 2021, 13(10), 1999; https://doi.org/10.3390/rs13101999 - 20 May 2021

Cited by 11 | Viewed by 3716

Abstract

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry [...] Read more.

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68. Full article

(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)

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17 pages, 2235 KiB

Open AccessArticle

Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery

by Mallory Liebl Barnes, Landon Yoder and Mahsa Khodaee

Remote Sens. 2021, 13(10), 1998; https://doi.org/10.3390/rs13101998 - 20 May 2021

Cited by 16 | Viewed by 4852

Abstract

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation [...] Read more.

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change. Full article

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18 pages, 7376 KiB

Open AccessArticle

Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

by Joan Grau, Kang Liang, Jae Ogilvie, Paul Arp, Sheng Li, Bonnie Robertson and Fan-Rui Meng

Remote Sens. 2021, 13(10), 1997; https://doi.org/10.3390/rs13101997 - 20 May 2021

Cited by 8 | Viewed by 2987

Abstract

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we [...] Read more.

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Human-Natural Systems Management)

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21 pages, 4033 KiB

Open AccessArticle

Detection of Root-Knot Nematode Meloidogyne luci Infestation of Potato Tubers Using Hyperspectral Remote Sensing and Real-Time PCR Molecular Methods

by Uroš Žibrat, Barbara Gerič Stare, Matej Knapič, Nik Susič, Janez Lapajne and Saša Širca

Remote Sens. 2021, 13(10), 1996; https://doi.org/10.3390/rs13101996 - 20 May 2021

Cited by 18 | Viewed by 4673

Abstract

Root-knot nematodes (Meloidogyne spp.) are considered the most aggressive, damaging, and economically important group of plant-parasitic nematodes and represent a significant limiting factor for potato (Solanum tuberosum) production and tuber quality. Meloidogyne luci has previously been shown to be a [...] Read more.

Root-knot nematodes (Meloidogyne spp.) are considered the most aggressive, damaging, and economically important group of plant-parasitic nematodes and represent a significant limiting factor for potato (Solanum tuberosum) production and tuber quality. Meloidogyne luci has previously been shown to be a potato pest having significant reproductive potential on the potato. In this study we showed that M. luci may develop a latent infestation without visible symptoms on the tubers. This latent infestation may pose a high risk for uncontrolled spread of the pest, especially via seed potato. We developed efficient detection methods to prevent uncontrolled spread of M. luci via infested potato tubers. Using hyperspectral imaging and a molecular approach to detection of nematode DNA with real-time PCR, it was possible to detect M. luci in both heavily infested potato tubers and tubers without visible symptoms. Detection of infested tubers with hyperspectral imaging achieved a 100% success rate, regardless of tuber preparation. The real-time PCR approach detected M. luci with high sensitivity. Full article

(This article belongs to the Special Issue Plant Phenotyping for Disease Detection)

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18 pages, 6420 KiB

Open AccessArticle

On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning

by Pan Xu, Qingyang Li, Bo Zhang, Fan Wu, Ke Zhao, Xin Du, Cankun Yang and Ruofei Zhong

Remote Sens. 2021, 13(10), 1995; https://doi.org/10.3390/rs13101995 - 19 May 2021

Cited by 67 | Viewed by 6201

Abstract

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis [...] Read more.

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Imaging of the Sea Surface: Simulation, Modelling, and Processing)

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22 pages, 10975 KiB

Open AccessArticle

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

by Kai-Yun Li, Niall G. Burnside, Raul Sampaio de Lima, Miguel Villoslada Peciña, Karli Sepp, Ming-Der Yang, Janar Raet, Ants Vain, Are Selge and Kalev Sepp

Remote Sens. 2021, 13(10), 1994; https://doi.org/10.3390/rs13101994 - 19 May 2021

Cited by 11 | Viewed by 4165

Abstract

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool [...] Read more.

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R² = 0.90, NRMSE = 0.12), followed by RFR (R² = 0.90 NRMSE = 0.15), and SVR (R² = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage. Full article

(This article belongs to the Special Issue New Insights into the Use of Small-Unmanned Aircraft Systems for Environmental Assessment and Monitoring)

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12 pages, 2150 KiB

Open AccessCommunication

GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season

by Xingcai Liu, Qiuhong Tang, Seyed-Mohammad Hosseini-Moghari, Xiaogang Shi, Min-Hui Lo and Bridget Scanlon

Remote Sens. 2021, 13(10), 1993; https://doi.org/10.3390/rs13101993 - 19 May 2021

Cited by 2 | Viewed by 2718

Abstract

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow [...] Read more.

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas. Full article

(This article belongs to the Special Issue Terrestrial Hydrology Using GRACE and GRACE-FO)

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16 pages, 4545 KiB

Open AccessTechnical Note

Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

by Alessio Lattanzio, Michael Grant, Marie Doutriaux-Boucher, Rob Roebeling and Jörg Schulz

Remote Sens. 2021, 13(10), 1992; https://doi.org/10.3390/rs13101992 - 19 May 2021

Viewed by 2670

Abstract

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations [...] Read more.

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface and Earth System Modelling)

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