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Remote Sens., Volume 14, Issue 3 (February-1 2022) – 375 articles

Cover Story (view full-size image): The degradation of forest roads in Canada was documented by identifying relevant spatiotemporal variables with (1) predictive models of gravel forest road degradation, and (2) using topography, roughness, and vegetation indices obtained from Airborne Laser Scanning and Sentinel-2 optical data to spatialise it. The field approach (n = 207) showed that after five years without maintenance, the rate of degradation on a road, regardless of its width, increased exponentially, exacerbated by a high slope gradient and loss of road surface. The remote sensing approach performed gives us valuable tools to document the state of gravel forest road degradation, providing us a piece of critical information for maintaining and sustaining access to Canada’s boreal forest. View this paper
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Article
A Voxel-Based Individual Tree Stem Detection Method Using Airborne LiDAR in Mature Northeastern U.S. Forests
Remote Sens. 2022, 14(3), 806; https://doi.org/10.3390/rs14030806 - 08 Feb 2022
Viewed by 798
Abstract
This paper describes a new method for detecting individual tree stems that was designed to perform well in the challenging hardwood-dominated, mixed-species forests common to the northeastern U.S., where canopy height-based methods have proven unreliable. Most prior research in individual tree detection has [...] Read more.
This paper describes a new method for detecting individual tree stems that was designed to perform well in the challenging hardwood-dominated, mixed-species forests common to the northeastern U.S., where canopy height-based methods have proven unreliable. Most prior research in individual tree detection has been performed in homogenous coniferous or conifer-dominated forests with limited hardwood presence. The study area in central Pennsylvania, United States, includes 17+ tree species and contains over 90% hardwoods. Existing methods have shown reduced performance as the proportion of hardwood species increases, due in large part to the crown-focused approaches they have employed. Top-down approaches are not reliable in deciduous stands due to the inherent complexity of the canopy and tree crowns in such stands. This complexity makes it difficult to segment trees and accurately predict tree stem locations based on detected crown segments. The proposed voxel column-based approach has advantages over both traditional canopy height model-based methods and computationally demanding point-based solutions. The method was tested on 1125 reference trees, ≥10 cm diameter at breast height (DBH), and it detected 68% of all reference trees and 87% of medium and large (sawtimber-sized) trees ≥28 cm DBH. Significantly, the commission rate (false predictions) was negligible as most raw false positives were confirmed in follow-up field visits to be either small trees below the threshold for recording or trees that were otherwise missed during the initial ground survey. Minimizing false positives was a priority in tuning the method. Follow-up in-situ evaluation of individual omission and commission instances was facilitated by the high spatial accuracy of predicted tree locations generated by the method. The mean and maximum predicted-to-reference tree distances were 0.59 m and 2.99 m, respectively, with over 80% of matches within <1 m. A new tree-matching method utilizing linear integer programming is presented that enables rigorous, repeatable matching of predicted and reference trees and performance evaluation. Results indicate this new tree detection method has potential to be operationalized for both traditional forest management activities and in providing the more frequent and scalable inventories required by a growing forest carbon offsets industry. Full article
(This article belongs to the Section Forest Remote Sensing)
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Article
Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables
Remote Sens. 2022, 14(3), 805; https://doi.org/10.3390/rs14030805 - 08 Feb 2022
Cited by 2 | Viewed by 891
Abstract
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes [...] Read more.
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries. Full article
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Technical Note
Local Freeman Decomposition for Robust Imaging and Identification of Subsurface Anomalies Using Misaligned Full-Polarimetric GPR Data
Remote Sens. 2022, 14(3), 804; https://doi.org/10.3390/rs14030804 - 08 Feb 2022
Cited by 1 | Viewed by 458
Abstract
A full-polarimetric ground penetrating radar (FP-GPR) uses an antenna array to detect subsurface anomalies. Compared to the traditional GPR, FP-GPR can obtain more abundant information about the subsurface. However, in field FP-GPR measurements, the arrival time of the received electromagnetic (EM) waves from [...] Read more.
A full-polarimetric ground penetrating radar (FP-GPR) uses an antenna array to detect subsurface anomalies. Compared to the traditional GPR, FP-GPR can obtain more abundant information about the subsurface. However, in field FP-GPR measurements, the arrival time of the received electromagnetic (EM) waves from different channels cannot be strictly aligned due to the limitations of human operation errors and the craftsmanship of the equipment. Small misalignments between the radargrams acquired from different channels of an FP-GPR can lead to erroneous identification results of the classic Freeman decomposition (FD) method. Here, we propose a local Freeman decomposition (LFD) method to enhance the robustness of the classic FD method when managing with misaligned FP-GPR data. The tests on three typical targets demonstrate that misalignments will severely interfere with the imaging and the identification results of the classic FD method for the plane and dihedral scatterers. In contrast, the proposed LFD method can produce smooth images and accurate identification results. Besides, the identification of the volume scatterer is not affected by misalignments. A test of ice-fracture detection further verifies the capability of the LFD method in field measurements. Due to the different relative magnitudes of the permittivity of the media on two sides of the interfaces, the ice surface and ice fracture show the features of surface-like and double-bounce scattering, respectively. However, the definition of double-bounce scattering is different from the definition in polarimetric synthetic aperture radar (SAR). Finally, a quantitative analysis shows that the sensitivities of the FD and LFD methods to misalignments are related to both the type of target and the polarized mode of the misaligned data. The tolerable range of the LFD method for misalignments is approximately ±0.2 times the wavelength of the EM wave, which is much wider than that of the FD method. In most cases, the LFD method can guarantee an accurate result of identification. Full article
(This article belongs to the Special Issue Latest Results on GPR Algorithms, Applications and Systems)
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Article
Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms
Remote Sens. 2022, 14(3), 803; https://doi.org/10.3390/rs14030803 - 08 Feb 2022
Cited by 2 | Viewed by 616
Abstract
Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of [...] Read more.
Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of forest disturbances. Time series analyses, such as Breaks for Additive Season and Trend (BFAST), have been frequently used to map tropical forest disturbances with promising results. Previous studies suggest that in addition to magnitude of change, disturbance accuracy could be enhanced by using other components of BFAST that describe additional aspects of the model, such as its goodness-of-fit, NDVI seasonal variation, temporal trend, historical length of observations and data quality, as well as by using separate thresholds for distinct forest types. The objective of this study is to determine if the BFAST algorithm can benefit from using these model components in a supervised scheme to improve the accuracy to detect forest disturbance. A random forests and support vector machines algorithms were trained and verified using 238 points in three different datasets: all-forest, tropical dry forest, and temperate forest. The results show that the highest accuracy was achieved by the support vector machines algorithm using the all-forest dataset. Although the increase in accuracy of the latter model vs. a magnitude threshold model is small, i.e., 0.14% for sample-based accuracy and 0.71% for area-weighted accuracy, the standard error of the estimated total disturbed forest area was 4352.59 ha smaller, while the annual disturbance rate was also smaller by 1262.2 ha year−1. The implemented approach can be useful to obtain more precise estimates in forest disturbance, as well as its associated carbon emissions. Full article
(This article belongs to the Special Issue Advances in the Remote Sensing of Forest Cover Change)
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Article
Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Québec, Canada)
Remote Sens. 2022, 14(3), 802; https://doi.org/10.3390/rs14030802 - 08 Feb 2022
Viewed by 420
Abstract
In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Active and passive microwave remote sensing has shown a principal capacity to provide effective monitoring of landscape FT dynamics. [...] Read more.
In the terrestrial cryosphere, freeze/thaw (FT) state transitions play an important and measurable role in climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes. Active and passive microwave remote sensing has shown a principal capacity to provide effective monitoring of landscape FT dynamics. The study presents a seasonal threshold approach, which examines the timeseries progression of remote sensing measurements relative to signatures acquired during seasonal frozen and thawed reference states. This is used to estimate the FT state from the Sentinel-1 database and applied and evaluated for the region of Eastern Nunavik (Québec, Canada). An optimization process of the threshold is included. In situ measurements from the meteorological station network were used for the validation process. Overall, acceptable estimation accuracy (>70%) was achieved in most tests; on the best-performing sites, an accuracy higher than 90% was reached. The performance of the seasonal threshold approach over the study region was further discussed with consideration of land cover, spatial heterogeneity, and soil depth. This work is dedicated to providing more accurate data to capture the spatiotemporal heterogeneity of freeze/thaw transitions and to improving our understanding of related processes in permafrost landscapes. Full article
(This article belongs to the Section Earth Observation Data)
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Article
Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City
Remote Sens. 2022, 14(3), 801; https://doi.org/10.3390/rs14030801 - 08 Feb 2022
Viewed by 680
Abstract
The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of [...] Read more.
The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of adequate phytosanitary control has negative social, economic, and environmental impacts. However, pest management is a challenging task due to the difficulty of early detection for proper control of mistletoe infestations. Automating the detection of this pest is important due to its rapid spread and the high costs of field identification tasks. This paper presents a Genetic Programming (GP) approach for the automatic design of an algorithm to detect mistletoe using multispectral aerial images. Our study area is located in a conservation area of Mexico City, in the San Bartolo Ameyalco community. Images of 148 hectares were acquired by means of an Unmanned Aerial Vehicle (UAV) carrying a sensor sensitive to the R, G, B, red edge, and near-infrared bands, and with an average spatial resolution of less than 10 cm per pixel. As a result, it was possible to obtain an algorithm capable of classifying mistletoe P. velutinum at its flowering stage for the specific case of the study area in conservation area with an Overall Accuracy (OA) of 96% and a value of fitness function based on weighted Cohen’s Kappa (kw) equal to 0.45 in the test data set. Additionally, our method’s performance was compared with two traditional image classification methods; in the first, a classical spectral index, named Intensive Pigment Index of Structure 2 (SIPI2), was considered for the detection of P. velutinum. The second method considers the well-known Support Vector Machine classification algorithm (SVM). We also compare the accuracy of the best GP individual with two additional indices obtained during the solution analysis. According to our experimental results, our GP-based algorithm outperforms the results obtained by the aforementioned methods for the identification of P. velutinum. Full article
(This article belongs to the Special Issue Detecting Anomalies and Tracking Biodiversity for Forest Monitoring)
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Article
Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images
Remote Sens. 2022, 14(3), 800; https://doi.org/10.3390/rs14030800 - 08 Feb 2022
Viewed by 414
Abstract
Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) [...] Read more.
Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale residual network is established for the spatial deep feature branch. In addition, an enhanced residual network is established for the spectral deep feature branch This operation is adopted to enhance the spectral and spatial deep features. Finally, this method establishes the spectral–spatial deep feature simultaneity to circumvent the independence of spectral and spatial deep features. The proposed model was evaluated on three groups of real-world hyperspectral and panchromatic image datasets which are collected with a ZY-1E sensor and are located at Baiyangdian, Chaohu and Dianchi, respectively. The experimental results and quality evaluation values, including RMSE, SAM, SCC, spectral curve comparison, PSNR, SSIM ERGAS and Q metric, confirm the superior performance of the proposed model compared with the state-of-the-art methods, including AWLP, CNMF, GIHS, MTF_GLP, HPF and SFIM methods. Full article
(This article belongs to the Section AI Remote Sensing)
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Article
Early Detection of Basal Stem Rot Disease in Oil Palm Tree Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging
Remote Sens. 2022, 14(3), 799; https://doi.org/10.3390/rs14030799 - 08 Feb 2022
Viewed by 600
Abstract
Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not [...] Read more.
Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Article
Spatio-Temporal Quality Indicators for Differential Interferometric Synthetic Aperture Radar Data
Remote Sens. 2022, 14(3), 798; https://doi.org/10.3390/rs14030798 - 08 Feb 2022
Cited by 1 | Viewed by 613
Abstract
Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR [...] Read more.
Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DInSAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs. Full article
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Article
Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection
Remote Sens. 2022, 14(3), 797; https://doi.org/10.3390/rs14030797 - 08 Feb 2022
Cited by 1 | Viewed by 389
Abstract
Anomaly targets in a hyperspectral image (HSI) are often multi-pixel, rather than single-pixel, objects. Therefore, algorithms using a test point vector may ignore the spatial characteristics of the test point. In addition, hyperspectral anomaly detection (AD) algorithms usually use original spectral signatures. In [...] Read more.
Anomaly targets in a hyperspectral image (HSI) are often multi-pixel, rather than single-pixel, objects. Therefore, algorithms using a test point vector may ignore the spatial characteristics of the test point. In addition, hyperspectral anomaly detection (AD) algorithms usually use original spectral signatures. In a fractional Fourier transform (FrFT), the signals in the fractional Fourier domain (FrFD) possess complementary characteristics of both the original reflectance spectrum and its Fourier transform. In this paper, a tensor RX (TRX) algorithm based on FrFT (FrFT-TRX) is proposed for hyperspectral AD. First, the fractional order of FrFT is selected by fractional Fourier entropy (FrFE) maximization. Then, the HSI is transformed into the FrFD by FrFT. Next, TRX is employed in the FrFD. Finally, according to the optimal spatial dimensions of the target and background tensors, the optimal AD result is achieved by adjusting the fractional order. TRX employs a test point tensor, making better use of the spatial characteristics of the test point. TRX in the FrFD exploits the complementary advantages of the intermediate domain to increase discrimination between the target and background. Six existing algorithms are used for comparison in order to verify the AD performance of the proposed FrFT-TRX over five real HSIs. The experimental results demonstrate the superiority of the proposed algorithm. Full article
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Article
A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model
Remote Sens. 2022, 14(3), 796; https://doi.org/10.3390/rs14030796 - 08 Feb 2022
Viewed by 395
Abstract
Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising [...] Read more.
Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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Article
DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information
Remote Sens. 2022, 14(3), 795; https://doi.org/10.3390/rs14030795 - 08 Feb 2022
Viewed by 716
Abstract
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application [...] Read more.
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes. Full article
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Article
Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China
Remote Sens. 2022, 14(3), 794; https://doi.org/10.3390/rs14030794 - 08 Feb 2022
Cited by 1 | Viewed by 433
Abstract
Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in [...] Read more.
Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in structuring a species richness pattern is generally downplayed, especially in temperate regions. Here we developed a suite of winter habitat indices at 500 m spatial resolution by fusing MODIS snow products and NASA MEaSUREs daily freeze/thaw records from passive microwave sensors and tested how these indices could improve the explanation of species richness patterns across China. We found that the winter habitat indices provided unique and mutually complementary environmental information compared to the commonly used Dynamic Habitat Indices (DHIs). Winter habitat indices significantly increased the explanatory power for species richness of all mammal and bird groups. Particularly, winter habitat indices contributed more to the explanation of bird species than mammals. Regarding the independent contribution, winter season length made the largest contributions to the explained variance of winter birds (30%), resident birds (27%), and mammals (18%), while the frequency of snow-free frozen ground contributed the most to the explanation of species richness for summer birds (23%). Our research provides new insights into the interpretation of broad-scale species diversity, which has great implications for biodiversity assessment and conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems in Cold Regions)
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Article
A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea
Remote Sens. 2022, 14(3), 793; https://doi.org/10.3390/rs14030793 - 08 Feb 2022
Viewed by 406
Abstract
Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method [...] Read more.
Accurate cloud-masking procedures to distinguish cloud-free pixels from cloudy pixels are essential for optical satellite remote sensing. Many studies on satellite-based cloud-detection have been performed using the spectral characteristics of clouds in terms of reflectance and temperature. This study proposes a cloud-detection method using reflectance in four bands: 0.56 μm, 0.86 μm, 1.38 μm, and 1.61 μm. Methodologically, we present a conversion relationship between the normalized difference water index (NDWI) and the green band in the visible spectrum for thick cloud detection using moderate-resolution imaging spectroradiometer (MODIS) observations. NDWI consists of reflectance at the 0.56 and 0.86 μm bands. For thin cloud detection, the 1.38 and 1.61 μm bands were applied with empirically determined threshold values. Case study analyses for the four seasons from 2000 to 2019 were performed for the sea surface area of the Yellow Sea and Bohai Sea. In the case studies, the comparison of the proposed cloud-detection method with the MODIS cloud mask (CM) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data indicated a probability of detection of 0.933, a false-alarm ratio of 0.086, and a Heidke Skill Score of 0.753. Our method demonstrated an additional important benefit in distinguishing clouds from sea ice or yellow dust, compared to the MODIS CM products, which usually misidentify the latter as clouds. Consequently, our cloud-detection method could be applied to a variety of low-orbit and geostationary satellites with 0.56, 0.86, 1.38, and 1.61 μm bands. Full article
(This article belongs to the Special Issue Advances in Ocean Remote Sensing through Data and Algorithm Fusion)
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Article
Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model
Remote Sens. 2022, 14(3), 792; https://doi.org/10.3390/rs14030792 - 08 Feb 2022
Viewed by 444
Abstract
Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected [...] Read more.
Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural Hydrology and Water Resources Modeling)
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Article
Development of an App and Teaching Concept for Implementation of Hyperspectral Remote Sensing Data into School Lessons Using Augmented Reality
Remote Sens. 2022, 14(3), 791; https://doi.org/10.3390/rs14030791 - 08 Feb 2022
Viewed by 624
Abstract
For the purpose of expanding STEM (science, technology, engineering, mathematics) education with remote sensing (RS) data and methods, an augmented reality (AR) app was developed in combination with a worksheet and lesson plan. Data from the Hyperspectral Imager for the Coastal Ocean (HICO) [...] Read more.
For the purpose of expanding STEM (science, technology, engineering, mathematics) education with remote sensing (RS) data and methods, an augmented reality (AR) app was developed in combination with a worksheet and lesson plan. Data from the Hyperspectral Imager for the Coastal Ocean (HICO) was searched for topics applicable to STEM curricula, which was found in the example of a harmful algal bloom in Lake Erie, USA, in 2011. Spectral shape algorithms were applied to differentiate between less harmful green and more harmful blue algae in the lake. The data was pre-processed to reduce its size significantly without losing too much information and then integrated into an app that was developed in Unity with the Vuforia extension. It was designed to let students browse and understand the raw data in RGB and a tangible hyperspectral cube, as well as to analyze algae maps derived from it. The app runs on Android smartphones with minimized data usage to make it less dependent on school funding and the socioeconomic background of students. Using educational concepts, such as active and collaborative learning, moderate constructivism, and scientific inquiry, the data was integrated into a lesson about environmental problems that was enhanced by the AR app. The app and worksheet were evaluated in two advanced geography courses (n = 36) and found to be complex, but doable and understandable, for the target group of German high school students in their final two school years. Thus, hyperspectral data can be used for STEM lessons using AR technology on students’ smartphones with several limitations both in the technology used and gainable knowledge. Full article
(This article belongs to the Collection Teaching and Learning in Remote Sensing)
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Article
Wildfire Dynamics along a North-Central Siberian Latitudinal Transect Assessed Using Landsat Imagery
Remote Sens. 2022, 14(3), 790; https://doi.org/10.3390/rs14030790 - 08 Feb 2022
Viewed by 569
Abstract
The history of wildfires along a latitudinal transect from forest–tundra to middle taiga in North-Central Siberia was reconstructed for the period from 1985 to 2020 using Landsat imagery. The transect passed through four key regions (75 × 75 km2) with different [...] Read more.
The history of wildfires along a latitudinal transect from forest–tundra to middle taiga in North-Central Siberia was reconstructed for the period from 1985 to 2020 using Landsat imagery. The transect passed through four key regions (75 × 75 km2) with different climate and landscape conditions that allowed us to evaluate regional wildfire dynamics as well as estimate differences in post-fire forest recovery. The Level-2A Landsat data (TM, ETM+, and OLI) were used to derive: (i) burned area (BA) locations, (ii) timing of wildfire occurrence (date, month, or season), (iii) fire severity, and (iv) trends in post-fire vegetation recovery. We used pre-selected and pre-processed scenes suitable for BA mapping taken within four consecutive time intervals covering the entire period of data analysis (1985–2020). Pre- and post-fire dynamics of forest vegetation were described using spectral indices, i.e., NBR and NDVI. We found that during the last three decades, the maximum BA occurred in the southernmost Vanavara region where ≈58% of the area burned. Total BA gradually decreased to the northwest with a minimum in the Igarka region (≈1%). Nearly half of these BAs appeared between summer 2013 and autumn 2020 due to higher frequency of hot and dry weather. The most severe wildfires were detected in the most northeastern Tura region. Analysis of NDVI and NBR dynamics showed that the mean period of post-fire vegetation recovery ranged between 20 and 25 years. The time of vegetation recovery at BAs with repeat wildfires and high severity was significantly longer. Full article
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Article
Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet++ and Two-Step Post-Processing
Remote Sens. 2022, 14(3), 789; https://doi.org/10.3390/rs14030789 - 08 Feb 2022
Viewed by 475
Abstract
In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning [...] Read more.
In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning model PointNet++, where the features of the input data include not only those selected from raw LiDAR points, such as 3D coordinate values, intensity, etc., but also the digital number (DN) of co-registered images and generated geometric features to describe a strip-like road. Then, the road points from PointNet++ were post-processed based on graph-cut and constrained triangulation irregular networks, where both the commission and omission errors were greatly reduced. Finally, collinearity and width similarity were proposed to estimate the connection probability of road segments, thereby improving the connectivity and completeness of the road network represented by centerlines. Experiments conducted on the Vaihingen data show that the proposed framework outperformed others in terms of completeness and correctness; in addition, some narrower residential streets with 2 m width, which have normally been neglected by previous studies, were extracted. The completeness and the correctness of the extracted road points were 84.7% and 79.7%, respectively, while the completeness and the correctness of the extracted centerlines were 97.0% and 86.3%, respectively. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas II)
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Article
Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm
Remote Sens. 2022, 14(3), 788; https://doi.org/10.3390/rs14030788 - 08 Feb 2022
Viewed by 636
Abstract
After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, [...] Read more.
After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m2, respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation. Full article
(This article belongs to the Special Issue SAR in Big Data Era II)
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Article
Radiometric Assessment of ICESat-2 over Vegetated Surfaces
Remote Sens. 2022, 14(3), 787; https://doi.org/10.3390/rs14030787 - 08 Feb 2022
Cited by 1 | Viewed by 668
Abstract
The ice, cloud, and land elevation satellite-2 (ICESat-2) is providing global elevation measurements to the science community. ICESat-2 measures the height of the Earth’s surface using a photon counting laser altimeter, ATLAS (advanced topographic laser altimetry system). As a photon counting system, the [...] Read more.
The ice, cloud, and land elevation satellite-2 (ICESat-2) is providing global elevation measurements to the science community. ICESat-2 measures the height of the Earth’s surface using a photon counting laser altimeter, ATLAS (advanced topographic laser altimetry system). As a photon counting system, the number of reflected photons per shot, or radiometry, is a function primarily of the transmitted laser energy, solar elevation, surface reflectance, and atmospheric scattering and attenuation. In this paper, we explore the relationship between detected scattering and attenuation in the atmosphere against the observed radiometry for three general forest types, as well as the radiometry as a function of day versus night. Through this analysis, we found that ATLAS strong beam radiometry exceeds the pre-launch design cases for boreal and tropical forests but underestimates the predicted radiometry over temperate forests by approximately half a photon. The weak beams, in contrast, exceed all pre-launch conditions by a factor of two to six over all forest types. We also observe that the signal radiometry from day acquisitions is lower than night acquisitions by 10% and 40% for the strong and weak beams, respectively. This research also found that the detection ratio between each beam-pair was lower than the predicted 4:1 values. This research also presents the concept of ICESat-2 radiometric profiles; these profiles provide a path for calculating vegetation structure. The results from this study are intended to be informative and perhaps serve as a benchmark for filtering or analysis of the ATL08 data products over vegetated surfaces. Full article
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Article
Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro
Remote Sens. 2022, 14(3), 786; https://doi.org/10.3390/rs14030786 - 08 Feb 2022
Viewed by 653
Abstract
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates [...] Read more.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results. Full article
(This article belongs to the Special Issue Machine Learning Methods for Environmental Monitoring)
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Article
An Investigation of a Multidimensional CNN Combined with an Attention Mechanism Model to Resolve Small-Sample Problems in Hyperspectral Image Classification
Remote Sens. 2022, 14(3), 785; https://doi.org/10.3390/rs14030785 - 08 Feb 2022
Cited by 1 | Viewed by 813
Abstract
The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, [...] Read more.
The convolutional neural network (CNN) method has been widely used in the classification of hyperspectral images (HSIs). However, the efficiency and accuracy of the HSI classification are inevitably degraded when small samples are available. This study proposes a multidimensional CNN model named MDAN, which is constructed with an attention mechanism, to achieve an ideal classification performance of CNN within the framework of few-shot learning. In this model, a three-dimensional (3D) convolutional layer is carried out for obtaining spatial–spectral features from the 3D volumetric data of HSI. Subsequently, the two-dimensional (2D) and one-dimensional (1D) convolutional layers further learn spatial and spectral features efficiently at an abstract level. Based on the most widely used convolutional block attention module (CBAM), this study investigates a convolutional block self-attention module (CBSM) to improve accuracy by changing the connection ways of attention blocks. The CBSM model is used with the 2D convolutional layer for better performance of HSI classification purposes. The MDAN model is applied for classification applications using HSI, and its performance is evaluated by comparing the results with the support vector machine (SVM), 2D CNN, 3D CNN, 3D–2D–1D CNN, and CBAM. The findings of this study indicate that classification results from the MADN model show overall classification accuracies of 97.34%, 96.43%, and 92.23% for Salinas, WHU-Hi-HanChuan, and Pavia University datasets, respectively, when only 1% HSI data were used for training. The training and testing times of the MDAN model are close to those of the 3D–2D–1D CNN, which has the highest efficiency among all comparative CNN models. The attention model CBSM is introduced into MDAN, which achieves an overall accuracy of about 1% higher than that of the CBAM model. The performance of the two proposed methods is superior to the other models in terms of both efficiency and accuracy. The results show that the combination of multidimensional CNNs and attention mechanisms has the best ability for small-sample problems in HSI classification. Full article
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Article
Integration of DInSAR Time Series and GNSS Data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications
Remote Sens. 2022, 14(3), 784; https://doi.org/10.3390/rs14030784 - 08 Feb 2022
Viewed by 538
Abstract
With approximately 800 million people globally living within 100 km of a volcano, it is essential that we build a reliable observation system capable of delivering early warnings to potentially impacted nearby populations. Global Navigation Satellite System (GNSS) and satellite Synthetic Aperture Radar [...] Read more.
With approximately 800 million people globally living within 100 km of a volcano, it is essential that we build a reliable observation system capable of delivering early warnings to potentially impacted nearby populations. Global Navigation Satellite System (GNSS) and satellite Synthetic Aperture Radar (SAR) document comprehensive ground motions or ruptures near, and at, the Earth’s surface and may be used to detect and analyze natural hazard phenomena. These datasets may also be combined to improve the accuracy of deformation results. Here, we prepare a differential interferometric SAR (DInSAR) time series and integrate it with GNSS data to create a fused dataset with enhanced accuracy of 3D ground motions over Hawaii island from November 2015 to April 2021. We present a comparison of the raw datasets against the fused time series and give a detailed account of observed ground deformation leading to the May 2018 and December 2020 volcanic eruptions. Our results provide important new estimates of the spatial and temporal dynamics of the 2018 Kilauea volcanic eruption. The methodology presented here can be easily repeated over any region of interest where an SAR scene overlaps with GNSS data. The results will contribute to diverse geophysical studies, including but not limited to the classification of precursory movements leading to major eruptions and the advancement of early warning systems. Full article
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Communication
Investigation of Turbulent Tidal Flow in a Coral Reef Channel Using Multi-Look WorldView-2 Satellite Imagery
Remote Sens. 2022, 14(3), 783; https://doi.org/10.3390/rs14030783 - 08 Feb 2022
Viewed by 441
Abstract
The general topic here is the application of high-resolution satellite imagery to the study of ocean phenomena having horizontal length scales of several meters to a few kilometers. The present study investigates whether multiple images acquired quite closely in time can be used [...] Read more.
The general topic here is the application of high-resolution satellite imagery to the study of ocean phenomena having horizontal length scales of several meters to a few kilometers. The present study investigates whether multiple images acquired quite closely in time can be used to derive a spatial map of the surface current in situations where the near-surface hydrodynamics are dominated by bed-generated turbulence and associated wave–current interaction. The approach is illustrated using imagery of turbulent tidal flow in a channel through the outer part of the Great Barrier Reef. The main result is that currents derived from the imagery are found to reach speeds of nearly 4 m/s during a flooding tide—three times larger than published values for other parts of the Reef. These new findings may have some impact on our understanding of the transport of tracers and particles over the shelf. Full article
(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)
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Article
Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms
Remote Sens. 2022, 14(3), 782; https://doi.org/10.3390/rs14030782 - 08 Feb 2022
Viewed by 798
Abstract
Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this [...] Read more.
Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping. Full article
(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)
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Article
Finding Mesolithic Sites: A Multichannel Ground-Penetrating Radar (GPR) Investigation at the Ancient Lake Duvensee
Remote Sens. 2022, 14(3), 781; https://doi.org/10.3390/rs14030781 - 08 Feb 2022
Viewed by 698
Abstract
The shift to the early Holocene in northern Europe is strongly associated with major environmental and climatic changes that influenced hunter-gatherers’ activities and occupation during the Mesolithic period. The ancient lake Duvensee (10,000–6500 cal. BCE) has been studied for almost a century, providing [...] Read more.
The shift to the early Holocene in northern Europe is strongly associated with major environmental and climatic changes that influenced hunter-gatherers’ activities and occupation during the Mesolithic period. The ancient lake Duvensee (10,000–6500 cal. BCE) has been studied for almost a century, providing archaeological sites consisting of bark mats and hazelnut-roasting hearths situated on small sand banks deposited by the glacier. No method is yet available to locate these features before excavation. Therefore, a key method for understanding the living conditions of hunter-gatherer groups is to reconstruct the paleoenvironment with a focus on the identification of areas that could possibly host Mesolithic camps and well-preserved archaeological artefacts. We performed a 16-channel MALÅ Imaging Radar Array (MIRA) system survey aimed at understanding the landscape surrounding the find spot Duvensee WP10, located in a hitherto uninvestigated part of the bog. Using an integrated approach of high-resolution ground radar mapping and targeted excavations enabled us to derive a 3D spatio-temporal landscape reconstruction of the investigated sector, including paleo-bathymetry, stratigraphy, and shorelines around the Mesolithic camps. Additionally, we detected previously unknown islands as potential areas for yet unknown dwelling sites. We found that the growth rates of the islands were in the order of approximately 0.3 m2/yr to 0.7 m2/yr between the late Preboreal and the Subboreal stages. The ground-penetrating radar surveying performed excellently in all aspects of near-surface landscape reconstruction as well as in identifying potential dwellings; however, the direct identification of small-scale artefacts, such as fireplaces, was not successful because of their similarity to natural structures. Full article
(This article belongs to the Special Issue Advanced Ground Penetrating Radar Theory and Applications II)
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Article
Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China
Remote Sens. 2022, 14(3), 780; https://doi.org/10.3390/rs14030780 - 08 Feb 2022
Viewed by 497
Abstract
To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to [...] Read more.
To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone. Full article
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Article
Strain-Rates from GPS Measurements in the Ordos Block, China: Implications for Geodynamics and Seismic Hazards
Remote Sens. 2022, 14(3), 779; https://doi.org/10.3390/rs14030779 - 07 Feb 2022
Viewed by 504
Abstract
A number of devastating earthquakes have occurred around the Ordos Block in recent history. For the purpose of studying where the next major event will occur surrounding the Ordos Block, much work has been done, particularly in the investigation of the Earth’s surface [...] Read more.
A number of devastating earthquakes have occurred around the Ordos Block in recent history. For the purpose of studying where the next major event will occur surrounding the Ordos Block, much work has been done, particularly in the investigation of the Earth’s surface strain rates based on GPS measurements. However, there exist striking differences between the results from different authors although they used almost the same GPS data. Therefore, we validated the method for the calculation of GPS strain rates developed by Zhu et al. (2005, 2006) and found that the method is feasible and has high precision. With this approach and the updated GPS data, we calculated the strain rates in the region around the Ordos Block. The computed results show that the total strain rates in the interior of the Block are very small, and the high values are mainly concentrated on the peripheral zones of the Ordos Block and along the large-scale active faults, such as the Haiyuan fault, which are closely aligned to the results by geological and geophysical observations. Additionally, the strain rate results demonstrated that all rifted grabens on the margin of the Ordos Block exhibit extensional deformation. Finally, based on the strain rate, seismicity, and tectonic structures, we present some areas of high earthquake risk surrounding the Ordos Block in the future, which are located on the westernmost of the Weihe Graben, both the east and westernmost of the Hetao Graben, and in the middle of the Shanxi Graben. Hence, this work is significant in contributing to a better understanding of the geodynamics and seismic hazard assessment. Full article
(This article belongs to the Special Issue Geodetic Observations for Earth System)
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Review
The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture
Remote Sens. 2022, 14(3), 778; https://doi.org/10.3390/rs14030778 - 07 Feb 2022
Viewed by 753
Abstract
The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in [...] Read more.
The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future. Full article
(This article belongs to the Special Issue Remote Sensing for Water Resources Assessment in Agriculture)
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Article
Object Tracking in Satellite Videos Based on Correlation Filter with Multi-Feature Fusion and Motion Trajectory Compensation
Remote Sens. 2022, 14(3), 777; https://doi.org/10.3390/rs14030777 - 07 Feb 2022
Viewed by 552
Abstract
As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's [...] Read more.
As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object’s position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object’s bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics. Full article
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