Remote Sensing

20 pages, 4160 KiB

Open AccessArticle

Enhancing Algal Bloom Level Monitoring with CYGNSS and Sentinel-3 Data

by Yan Jia, Zhiyu Xiao, Liwen Yang, Quan Liu, Shuanggen Jin, Yan Lv and Qingyun Yan

Remote Sens. 2024, 16(20), 3915; https://doi.org/10.3390/rs16203915 - 21 Oct 2024

Viewed by 884

Algal blooms, resulting from the overgrowth of algal plankton in water bodies, pose significant environmental problems and necessitate effective remote sensing methods for monitoring. In recent years, Global Navigation Satellite System–Reflectometry (GNSS-R) has rapidly advanced and made notable contributions to many surface observation [...] Read more.

Algal blooms, resulting from the overgrowth of algal plankton in water bodies, pose significant environmental problems and necessitate effective remote sensing methods for monitoring. In recent years, Global Navigation Satellite System–Reflectometry (GNSS-R) has rapidly advanced and made notable contributions to many surface observation fields, providing new means for identifying algal blooms. Additionally, meteorological parameters such as temperature and wind speed, key factors in the occurrence of algal blooms, can aid in their identification. This paper utilized Cyclone GNSS (CYGNSS) data, Sentinel-3 OLCI data, and ECMWF Re-Analysis-5 meteorological data to retrieve Chlorophyll-a values. Machine learning algorithms were then employed to classify algal blooms for early warning based on Chlorophyll-a concentration. Experiments and validations were conducted from May 2023 to September 2023 in the Hongze Lake region of China. The results indicate that classification and early warning of algal blooms based on CYGNSS data produced reliable results. The ability of CYGNSS data to accurately reflect the severity of algal blooms opens new avenues for environmental monitoring and management. Full article

(This article belongs to the Special Issue Latest Advances and Application in the GNSS-R Field)

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24 pages, 5589 KiB

Open AccessArticle

Ozone Detector Based on Ultraviolet Observations on the Martian Surface

by Daniel Viúdez-Moreiras, Alfonso Saiz-Lopez, Michael D. Smith, Víctor Apestigue, Ignacio Arruego, Elisa García, Juan J. Jiménez, José A. Rodriguez-Manfredi, Daniel Toledo, Mike Wolff and María-Paz Zorzano

Remote Sens. 2024, 16(20), 3914; https://doi.org/10.3390/rs16203914 - 21 Oct 2024

Viewed by 629

Abstract

Ozone plays a key role in both atmospheric chemistry and UV absorption in planetary atmospheres. On Mars, upper-tropospheric ozone has been widely characterized by space-based instruments. However, surface ozone remains poorly characterized, hindered by the limited sensitivity of orbiters to the lowest scale [...] Read more.

Ozone plays a key role in both atmospheric chemistry and UV absorption in planetary atmospheres. On Mars, upper-tropospheric ozone has been widely characterized by space-based instruments. However, surface ozone remains poorly characterized, hindered by the limited sensitivity of orbiters to the lowest scale height of the atmosphere and challenges in delivering payloads to the surface of Mars, which have prevented, to date, the measurement of ozone from the surface of Mars. Systematic measurements from the Martian surface could advance our knowledge of the atmospheric chemistry and habitability potential of this planet. NASA’s Mars 2020 mission includes the first ozone detector deployed on the Martian surface, which is based on discrete photometric observations in the ultraviolet band, a simple technology that could obtain the first insights into total ozone abundance in preparation for more sophisticated measurement techniques. This paper describes the Mars 2020 ozone detector and its retrieval algorithm, including its performance under different sources of uncertainty and the potential application of the retrieval algorithm on other missions, such as NASA’s Mars Science Laboratory. Pre-landing simulations using the UVISMART radiative transfer model suggest that the retrieval is robust and that it can deal with common issues affecting surface operations in Martian missions, although the expected low ozone abundance and instrument uncertainties could challenge its characterization in tropical latitudes of the planet. Other space missions will potentially include sensors of similar technology. Full article

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15 pages, 2289 KiB

Open AccessTechnical Note

Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

by Damianos F. Mantsis, Anastasia Moumtzidou, Ioannis Lioumbas, Ilias Gialampoukidis, Aikaterini Christodoulou, Alexandros Mentes, Stefanos Vrochidis and Ioannis Kompatsiaris

Remote Sens. 2024, 16(20), 3913; https://doi.org/10.3390/rs16203913 - 21 Oct 2024

Viewed by 571

Abstract

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) [...] Read more.

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques. Full article

(This article belongs to the Section Ocean Remote Sensing)

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Figure 1
Schematic showing the work flow of our methodology. Full article ">Figure 2
North–south cross-section of the 18 January 2021 formation, showing the remote sensing reflectance in all available bands after atmospheric corrections have been applied. For ACOLITE, all corrected bands are displayed, and for iCOR, only the 10 m bands are displayed. Full article ">Figure 3
Visualization of the 18 January 2021 formation with B5 (560 nm) after C2RCC, iCOR, Polymer, and ACOLITE corrections have been implemented. Note that the color bar is not the same for all images to improve the direct comparison of formation characteristics after different atmospheric corrections have been applied. Full article ">Figure 4
Visualization of the 18 January 2021 formation with NIR (833 nm) after C2RCC, iCOR, Polymer, and ACOLITE corrections have been implemented. Note that for C2RCC, we plot the 865 nm band, given that the NIR band is not available. Lack of data in the polymer illustration indicates that the polymer algorithm overestimates the correction resulting in a negative <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, and therefore is masked out. Full article ">Figure 5
Visualization of the 30 December 2017 formation with B4 (665 nm) after C2RCC, iCOR, Polymer, and ACOLITE corrections have been implemented. Note that the color bar scale is lower for C2RCC. The feature in the upper right corner represents land. Full article ">Figure 6
(a) False color image where three patches of different sizes (30 December 2017) are “puzzled together” to form one of the patches that is chosen for annotation, (b) formation annotation, (c) DNN prediction after ACOLITE is applied, and (d) DNN prediction without ACOLITE. For (b–d) oil spill pixels appear with yellow. Full article ">Figure 7
(a) False color image of one of the formations (18 January 2021) chosen for annotation, (b) formation annotation, (c) DNN prediction after ACOLITE is applied, and (d) DNN prediction without ACOLITE. For (b–d) oil spill pixels appear with yellow. Full article ">Figure 8
Red–NIR <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math> scatterplot for the formation and clear water pixels that are annotated and used for the DNN training. Full article ">Figure 9
Schematic showing the DNN in three different configurations corresponding to the three types of experiments: (a) Red-NIR, (b) Green-NIR, and (c) 4-Bands. Full article ">Figure 10
(a) Sentinel-2 false color image (green–red–NIR) showing the oil spill case for the 27 February 2017, (b) zoomed area in yellow box, (c) DNN prediction. Full article ">

22 pages, 7929 KiB

Open AccessArticle

Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder–Decoder Network

by Fang Wang, Xingqian Du, Weiguang Zhang, Liang Nie, Hu Wang, Shun Zhou and Jun Ma

Remote Sens. 2024, 16(20), 3912; https://doi.org/10.3390/rs16203912 - 21 Oct 2024

Viewed by 914

Abstract

The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized [...] Read more.

The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods. Full article

(This article belongs to the Special Issue 3D Scene Reconstruction, Modeling and Analysis Using Remote Sensing)

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Overall framework of the proposed MGEN for multi-modal data classification. Full article ">Figure 2
Structure of the graph encoder. Full article ">Figure 3
Visualized classification results of proposed MGEN and compared methods on Trento dataset. Full article ">Figure 4
Visualized classification results of proposed MGEN and compared methods on MUUFL dataset. Full article ">Figure 5
Visualized classification results of proposed MGEN and compared methods on Houston dataset. Full article ">Figure 6
Experimental results of using a single scale with different values of <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. Full article ">Figure 7
Classification accuracy results of the proposed MGEN with different scale parameters on MUUFL dataset. OA, AA, and Kappa are displayed by colors of blue, orange, and green, respectively, with the intensity of the colors indicating the magnitude of the values. Full article ">

22 pages, 6149 KiB

Open AccessArticle

ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data

by Mingyue Lu, Jingke Zhang, Manzhu Yu, Hui Liu, Caifen He, Tongtong Dong and Yongwei Mao

Remote Sens. 2024, 16(20), 3911; https://doi.org/10.3390/rs16203911 - 21 Oct 2024

Viewed by 548

Abstract

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for [...] Read more.

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels. Full article

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21 pages, 14622 KiB

Open AccessArticle

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

by Massimiliano Bussolino, Gaia Letizia Civardi, Matteo Quirino, Michele Bechini and Michèle Lavagna

Remote Sens. 2024, 16(20), 3910; https://doi.org/10.3390/rs16203910 - 21 Oct 2024

Viewed by 580

Abstract

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and [...] Read more.

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains. Full article

(This article belongs to the Special Issue Advances in Detection-Oriented Multi-sensor Fusion beyond the Visible Spectrum)

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16 pages, 12826 KiB

Open AccessArticle

Seasonal and Interannual Variations in Sea Ice Thickness in the Weddell Sea, Antarctica (2019–2022) Using ICESat-2

by Mansi Joshi, Alberto M. Mestas-Nuñez, Stephen F. Ackley, Stefanie Arndt, Grant J. Macdonald and Christian Haas

Remote Sens. 2024, 16(20), 3909; https://doi.org/10.3390/rs16203909 - 21 Oct 2024

Viewed by 806

Abstract

The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 [...] Read more.

The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness. Full article

(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)

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21 pages, 13880 KiB

Open AccessArticle

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

by Zhida Guo, Lei Cheng, Liwei Chang, Shiqiong Li and Yuzhu Li

Remote Sens. 2024, 16(20), 3908; https://doi.org/10.3390/rs16203908 - 21 Oct 2024

Viewed by 697

Abstract

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have [...] Read more.

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km² to 22,500 km². Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km²) with an average

R^{2}

improvement of 33.75%. The average

M A E

is 1.63 °C, and the average

R M S E

is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an

M A E

of 2.03 °C and an

R M S E

of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes. Full article

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17 pages, 9573 KiB

Open AccessArticle

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

by Chengjin Zhan, Shuning Zhang, Chenyu Sun and Si Chen

Remote Sens. 2024, 16(20), 3907; https://doi.org/10.3390/rs16203907 - 21 Oct 2024

Viewed by 585

Abstract

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly [...] Read more.

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions. Full article

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21 pages, 10239 KiB

Open AccessArticle

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

by Xiaolong Hu, Liangsheng Shi, Lin Lin, Shenji Li, Xianzhi Deng, Jinmin Li, Jiang Bian, Chenye Su, Shuai Du, Tinghan Wang, Yujie Wang and Zhitao Zhang

Remote Sens. 2024, 16(20), 3906; https://doi.org/10.3390/rs16203906 - 21 Oct 2024

Viewed by 787

Abstract

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for [...] Read more.

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (

V_{c m a x, 0}^{25}

), was quantified using various spatial information representation methods, including mean (μ_ref) and standard deviation (σ_ref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of

V_{c m a x, 0}^{25}

and GPP estimation. CNN methods achieved the best

V_{c m a x, 0}^{25}

estimation, with an R of 0.94, an RMSE of 19.44 μmol m⁻² s⁻¹, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m⁻² s⁻¹, and an MdAPE of 23%. The μ_ref-GLCM texture features and μ_ref-LBPH joint-driven models also gave promising results. However, σ_ref contributed less to

V_{c m a x, 0}^{25}

estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μ_ref-LBPH joint-driven model mainly prioritized reflectance information. The μ_ref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. Full article

(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)

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Figure 1
Flowchart of the study (μref: the mean value of UAV reflectance; σref: the standard deviation value of UAV reflectance; GLCM: gray-level co-occurrence matrix-based texture features; LBPH: local binary pattern histogram texture feature; GPP: gross primary production; SWIN: incoming shortwave radiation; Ca: ambient CO2 concentration; Ta: air temperature; RH: relative humidity; u: wind speed). Full article ">Figure 2
The location of the experiment site. The red five-pointed star represents the EC system. The contour lines of the footprint during the growing period in (3) are shown in steps of 10% from 10 to 90%. The background maps in (a,b) are RGB images from Google Earth. The background map in (c) is the RGB image from the UAV platform. Full article ">Figure 3
Seasonal variation in daily carbon fluxes, environmental elements and biophysical indicators. (a) Carbon fluxes, including net ecosystem exchange, gross primary production, and ecosystem respiration; (b) incoming shortwave radiation; (c) air temperature; (d) humidity; (e) wind speed; (f) ambient CO2 concentration; (g) precipitation; (h) soil moisture; (i) plant area index; (j) leaf area index; (k) clumping index; (l) canopy height. The term ‘Doy’ means day of the year. Full article ">Figure 4
Seasonal variation in field-scale <math display="inline"><semantics> <msubsup> <mi>V</mi> <mrow> <mi>c</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mn>0</mn> </mrow> <mn>25</mn> </msubsup> </semantics></math>. Full article ">Figure 5
The accuracy of 8 field-scale <math display="inline"><semantics> <msubsup> <mi>V</mi> <mrow> <mi>c</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mn>0</mn> </mrow> <mn>25</mn> </msubsup> </semantics></math> models. The blue points represent the training samples, and the orange points represent the validation samples. The sample numbers of the training and validation dataset are 224 and 57, respectively. Full article ">Figure 6
The accuracy of GPP estimation based on the 8 field-scale <math display="inline"><semantics> <msubsup> <mi>V</mi> <mrow> <mi>c</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mn>0</mn> </mrow> <mn>25</mn> </msubsup> </semantics></math> models. The blue bar represents the accuracy metrics on the training dataset, and the orange bar represents the accuracy metrics on the testing dataset. Full article ">Figure 7
SHAP-based feature importance analysis for the DNN <math display="inline"><semantics> <msubsup> <mi>V</mi> <mrow> <mi>c</mi> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>,</mo> <mn>0</mn> </mrow> <mn>25</mn> </msubsup> </semantics></math> model. The feature importance is defined as the mean of the absolute value of the SHAP for each feature (mean(|SHAP|)). Only the top 5 most important features are displayed. Full article ">Figure 8
The spatial distribution of the AlexNet-model-based SHAP. Full article ">

27 pages, 31281 KiB

Open AccessArticle

Tracking Moisture Dynamics in a Karst Rock Formation Combining Multi-Frequency 3D GPR Data: A Strategy for Protecting the Polychrome Hall Paintings in Altamira Cave

by Vicente Bayarri, Alfredo Prada, Francisco García, Carmen De Las Heras and Pilar Fatás

Remote Sens. 2024, 16(20), 3905; https://doi.org/10.3390/rs16203905 - 21 Oct 2024

Viewed by 921

Abstract

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and [...] Read more.

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m² area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution. Full article

(This article belongs to the Special Issue Multi-data Applied to Near-Surface Geophysics (Second Edition))

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15 pages, 2422 KiB

Open AccessArticle

Multi-Modal Object Detection Method Based on Dual-Branch Asymmetric Attention Backbone and Feature Fusion Pyramid Network

by Jinpeng Wang, Nan Su, Chunhui Zhao, Yiming Yan and Shou Feng

Remote Sens. 2024, 16(20), 3904; https://doi.org/10.3390/rs16203904 - 21 Oct 2024

Viewed by 660

Abstract

With the simultaneous acquisition of the infrared and optical remote sensing images of the same target becoming increasingly easy, using multi-modal data for high-performance object detection has become a research focus. In remote sensing multi-modal data, infrared images lack color information, it is [...] Read more.

With the simultaneous acquisition of the infrared and optical remote sensing images of the same target becoming increasingly easy, using multi-modal data for high-performance object detection has become a research focus. In remote sensing multi-modal data, infrared images lack color information, it is hard to detect difficult targets with low contrast, and optical images are easily affected by illuminance. One of the most effective ways to solve this problem is to integrate multi-modal images for high-performance object detection. The challenge of fusion object detection lies in how to fully integrate multi-modal image features with significant modal differences and avoid introducing interference information while taking advantage of complementary advantages. To solve these problems, a new multi-modal fusion object detection method is proposed. In this paper, the method is improved in terms of two aspects: firstly, a new dual-branch asymmetric attention backbone network (DAAB) is designed, which uses a semantic information supplement module (SISM) and a detail information supplement module (DISM) to supplement and enhance infrared and RGB image information, respectively. Secondly, we propose a feature fusion pyramid network (FFPN), which uses a Transformer-like strategy to carry out multi-modal feature fusion and suppress features that are not conducive to fusion during the fusion process. This method is a state-of-the-art process for both FLIR-aligned and DroneVehicle datasets. Experiments show that this method has strong competitiveness and generalization performance. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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The overall scheme of the proposed method. The input arrows for each cube are the variables involved in the calculation not the fixed calculation method. The green, orange, and yellow in the figure mark the calculation process for optical, infrared, and fusion features, respectively, as in the following text. Full article ">Figure 2
The main structure diagram of DAAN. The area highlighted by the red line can be seen as a loop structure. The part of the figure highlighted by the red line is a loop structure. Full article ">Figure 3
(a) DISM structure diagram; (b) SISM structure diagram. In the figure, C refers to the concatenation operation, the plus sign refers to the addition operation of the elements, and 1× 1 (3 × 3) refers to the convolution operation with a kernel size of 1 (3). Full article ">Figure 4
The main structure diagram of FFPN. The symbols in the figure are the same as described above. Full article ">Figure 5
Our method detects results on some typical images from the FLIR dataset. The prediction box is marked as orange-red. Full article ">Figure 6
Our method detects results on some typical images in the DroneVehicle dataset. The prediction box for the baseline method is marked in green, and the prediction box for the present method is marked in red. Full article ">

27 pages, 3720 KiB

Open AccessArticle

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques

by Yannick Useni Sikuzani, Médard Mpanda Mukenza, John Kikuni Tchowa, Delphin Kabamb Kanyimb, François Malaisse and Jan Bogaert

Remote Sens. 2024, 16(20), 3903; https://doi.org/10.3390/rs16203903 - 21 Oct 2024

Viewed by 1043

Abstract

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas [...] Read more.

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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Geographical map of the Lualaba Province in the DRC, detailing its five territories, including key agricultural zones (IV) Sandoa, (V) Kapanga, and (III) Dilolo and major mining areas (I) Lubudi and (II) Mutshatsha. The map also identifies seven protected areas within Lualaba Province: (a) Basse Kando Hunting Domain (BKHD), (b) Lac Tshangalele Hunting Domain (LTHD), (c) Mulumbu Hunting Domain (MHD), (f) Alunda and Tutshokwe Hunting Reserve (ATHR), (d) Mwene Kay Hunting Domain (MKHD), (e) Mwene Musoma Hunting Domain (MMHD), and (g) Tshikamba Hunting Reserve (THR). Full article ">Figure 2
Spatial mapping of land cover dynamics in the Lualaba province landscape from 1990 to 2024, utilizing supervised classification of Landsat images with the Random Forest classifier. The land cover classes are denoted as follows: MW (Miombo woodland), SV (savanna), AG (agriculture), BBS (built-up and bare soil), and other land cover. Intermediate dates not displayed on this map did not exhibit significant perceptible changes in the landscape. Full article ">Figure 3
Landscape composition evolution in Lualaba province (A) (DRC) and their territories, (B) Lubudi, (C) Mutshatsha, (D) Dilolo, (E) Sandoa, and (F) Kapanga, from 1990 to 2024. MW (Miombo woodland), SV (savanna), AG (agriculture), and BBS (built-up and bare soil). The total landscape proportion in Lualaba province and for each territory does not sum to 100% as other land cover classes were excluded from the analyses due to their relatively stable nature. Full article ">Figure 4
Dynamics of landscape diversity in Lualaba province (DRC) and its territories (Lubudi, Mutshatsha, Dilolo, Kapanga, and Sandoa). The overall landscape of Lualaba province has remained relatively stable between 1990 and 2024, though with notable variations during this period. Full article ">Figure 5
Landscape composition evolution in protected areas in Lualaba province from 1990 to 2024: The Basse Kando Hunting Domain (A); the Lac Tshangalele Hunting Domain (B); the Mulumbu Hunting Domain (C); the Alunda and Tutshokwe Hunting Reserve (D); The Mwene Kay Hunting Domain (E); the Mwene Musoma Hunting Domain (F); the Tshikamba Hunting Reserve (G). MW (Miombo woodland), SV (savanna), AG (agriculture) and BBS (built-up and bare soil). The total landscape proportion for each protected area does not sum to 100% as other land cover classes were excluded from the analyses due to their relatively stable nature. Full article ">Figure 6
Dynamics of landscape diversity in the protected areas of Lualaba Province between 1990 and 2024. The protected areas have experienced variations in landscape homogeneity and heterogeneity over this period. Basse Kando Hunting Domain (BKHD), Lac Tshangalele Hunting Domain (LTHD), Mulumbu Hunting Domain (MHD), Alunda and Tutshokwe Hunting Reserve (ATHR), Mwene Kay Hunting Domain (MKHD), Mwene Musoma Hunting Domain (MMHD), and Tshikamba Hunting Reserve (THR). Full article ">Figure 7
Dynamics of Miombo woodland spatial patterns (1990–2024). (A) displays the class area (CA, in km2) of Miombo woodlands, with absolute values calculated by dividing the total Miombo woodland area for each year by the sum of Miombo woodland areas across all studied years. (B) illustrates the patch number (PN, also in absolute values) of Miombo woodland patches across the landscapes of Lualaba Province and its territories from 1990 to 2024. The variations in CA and PN during this period enabled the identification of spatial transformation processes, which were analyzed using the decision tree algorithm developed by Bogaert et al. [<a href="#B62-remotesensing-16-03903" class="html-bibr">62</a>]. (C) shows the evolution of the largest patch index (LPI, in %), which indicates the proportion of the landscape occupied by the largest Miombo woodland patch. (D) depicts the edge density (ED, in m/ha), reflecting the amount of edge habitat in relation to the Miombo woodland area. Finally, (E) presents the Mean Euclidean Nearest-Neighbor Distance (ENN, in meters), which measures the average distance between the nearest neighboring patches, providing insights into Miombo woodland connectivity. Full article ">Figure 8
Dynamics of Miombo woodland spatial patterns in protected areas of Lualaba Province (1990–2024). (A) displays the class area (CA, in km2) of Miombo woodlands, with absolute values calculated by dividing the total Miombo woodland area for each year by the sum of Miombo woodland areas across all studied years. (B) illustrates the patch number (PN, also in absolute values) of Miombo woodland patches across the protected areas in Lualaba Province from 1990 to 2024. The variations in CA and PN during this period enabled the identification of spatial transformation processes, which were analyzed using the decision tree algorithm developed by Bogaert et al. [<a href="#B62-remotesensing-16-03903" class="html-bibr">62</a>]. (C) shows the evolution of the largest patch index (LPI, in %), which indicates the proportion of the landscape occupied by the largest Miombo woodland patch. (D) depicts the edge density (ED, in m/ha), reflecting the amount of edge habitat in relation to Miombo woodland area. Finally, (E) presents the Mean Euclidean Nearest-Neighbor Distance (ENN, in meters), which measures the average distance between the nearest neighboring patches, providing insights into Miombo woodland connectivity. Basse Kando Hunting Domain (BKHD), Lac Tshangalele Hunting Domain (LTHD), Mulumbu Hunting Domain (MHD), Alunda and Tutshokwe Hunting Reserve (ATHR), Mwene Kay Hunting Domain (MKHD), Mwene Musoma Hunting Domain (MMHD), and Tshikamba Hunting Reserve (THR). Full article ">

25 pages, 27207 KiB

Open AccessArticle

From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland

by Khansa Gulshad, Andaleeb Yaseen and Michał Szydłowski

Remote Sens. 2024, 16(20), 3902; https://doi.org/10.3390/rs16203902 - 20 Oct 2024

Viewed by 1014

Abstract

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. [...] Read more.

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models’ performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability. Full article

(This article belongs to the Special Issue Advances in GIS and Remote Sensing Applications in Natural Hazards)

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18 pages, 4741 KiB

Open AccessArticle

Estimation of Glacier Outline and Volume Changes in the Vilcanota Range Snow-Capped Mountains, Peru, Using Temporal Series of Landsat and a Combination of Satellite Radar and Aerial LIDAR Images

by Nilton Montoya-Jara, Hildo Loayza, Raymundo Oscar Gutiérrez-Rosales, Marcelo Bueno and Roberto Quiroz

Remote Sens. 2024, 16(20), 3901; https://doi.org/10.3390/rs16203901 - 20 Oct 2024

Viewed by 700

Abstract

The Vilcanota is the second-largest snow-capped mountain range in Peru, featuring 380 individual glaciers, each with its own unique characteristics that must be studied independently. However, few studies have been conducted in the Vilcanota range to monitor and track the area and volume [...] Read more.

The Vilcanota is the second-largest snow-capped mountain range in Peru, featuring 380 individual glaciers, each with its own unique characteristics that must be studied independently. However, few studies have been conducted in the Vilcanota range to monitor and track the area and volume changes of the Suyuparina and Quisoquipina glaciers. Notably, there are only a few studies that have approached this issue using LIDAR technology. Our methodology is based on a combination of optical, radar and LIDAR data sources, which allowed for constructing coherent temporal series for the both the perimeter and volume changes of the Suyuparina and Quisoquipina glaciers while accounting for the uncertainty in the perimeter detection procedure. Our results indicated that, from 1990 to 2013, there was a reduction in snow cover of 12,694.35 m² per year for Quisoquipina and 16,599.2 m² per year for Suyuparina. This represents a loss of 12.18% for Quisoquipina and 22.45% for Suyuparina. From 2006 to 2013, the volume of the Quisoquipina glacier decreased from 11.73 km³ in 2006 to 11.04 km³ in 2010, while the Suyuparina glacier decreased from 6.26 km³ to 5.93 km³. Likewise, when analyzing the correlation between glacier area and precipitation, a moderate inverse correlation (R = −0.52, p < 0.05) was found for Quisoquipina. In contrast, the correlation for Suyuparina was low and nonsignificant, showing inconsistency in the effect of precipitation. Additionally, the correlation between the snow cover area and the annual mean air temperature (R = −0.34, p > 0.05) and annual minimum air temperature (R = −0.36, p > 0.05) was low, inverse, and not significant for Quisoquipina. Meanwhile, snow cover on Suyuparina had a low nonsignificant correlation (R = −0.31, p > 0.05) with the annual maximum air temperature, indicating a minimal influence of the measured climatic variables near this glacier on its retreat. In general, it was possible to establish a reduction in both the area and volume of the Suyuparina and Quisoquipina glaciers based on freely accessible remote sensing data. Full article

(This article belongs to the Section Remote Sensing and Geo-Spatial Science)

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An Airborne LIDAR point cloud of 3.2 m spatial resolution was acquired on the Suyuparina and Quisoquipina glaciers in the province of Canchis, Cusco. Full article ">Figure 2
Binarized NDSI images recovered from Landsat 5 images from May 1990 (A) and Landsat 7 from April 2013 (B) for the Suyuparina and Quisoquipina glaciers. In orange and red, the shapefiles of the Suyuparina and Quisoquipina glaciers are delimited by expert criteria. Full article ">Figure 3
Processing scheme. Blue represents inputs, green represents processing, yellow represents intermedium processing, and purple represents outputs. Full article ">Figure 4
(A) Glacierized area of Quisoquipina, and (B) Suyuparina glaciers. In gray is the uncertainty band of the estimated glacier area. Full article ">Figure 5
(Up) Glaciated area of Quisoquipina, and Suyuparina glaciers (Down) analyzed from 1990 to 1999 and from 2000 to 2013. Full article ">Figure 6
(A) Volume changes of the Quisoquipina and (B) Suyuparina glaciers. Confidence intervales to the linear fitted model, shown in gray. Full article ">Figure 7
(A) Glacierized outlines of Suyuparina and Quisoquipina glaciers. (B) Elevation change based on ALOS and LIDAR DEM analysis. Snow glaciological stakes installed between 2014 to 2016 are shown as reference. The background image corresponds to Google Earth 2019. Full article ">Figure 8
Scatterplots of climatic and glacier surface changes. MeanAPE: Mean annual potential evapotranspiration (mm/day), MeanAP: Annual mean precipitation (mm/day), MaxAAT: Max. annual air temperature (°C), MinAAT: Min. annual air temperature (°C), MeanAMAT: Mean annual mean air temperature (°C). Full article ">

18 pages, 9065 KiB

Open AccessArticle

Modeling of Solar Radiation Pressure for BDS-3 MEO Satellites with Inter-Satellite Link Measurements

by Yifei Lv, Zihao Liu, Rui Jiang and Xin Xie

Remote Sens. 2024, 16(20), 3900; https://doi.org/10.3390/rs16203900 - 20 Oct 2024

Viewed by 661

Abstract

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. [...] Read more.

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. Since the BDS-3’s inter-satellite link (ISL) can enhance the orbit estimation of BDS-3 satellites, the aim of this study is to establish an a priori SRP model for the satellite body using 281-day ISL observations to reduce the systematic errors in the final orbits. The adjustable box wind (ABW) model is employed to refine the optical parameters for the satellite buses. The self-shadow effect caused by the search and rescue (SAR) antenna is considered. Satellite laser ranging (SLR), day-boundary discontinuity (DBD), and overlapping Allan deviation (OADEV) are utilized as indicators to assess the performance of the a priori model. With the a priori model developed by both ISL and ground observation, the slopes of SLR residual for the China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM) satellites decrease from −0.097 cm/deg and 0.067 cm/deg to −0.004 cm/deg and −0.009 cm/deg, respectively. The standard deviation decreased by 21.8% and 26.6%, respectively. There are slight enhancements in the average values of DBD and OADEV, and a reduced β-dependent variation is observed in the OADEV of the corresponding clock offset. We also found that considering the SAR antenna only slightly improves the orbit accuracy. These results demonstrate that an a priori model established for the BDS-3 MEO satellite body can reduce the systematic errors in orbits, and the parameters estimated using both ISL and ground observation are superior to those estimated using only ground observation. Full article

(This article belongs to the Special Issue GNSS Positioning and Navigation in Remote Sensing Applications)

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16 pages, 5879 KiB

Open AccessArticle

The Characterization of Electric Field Pulses Observed in the Preliminary Breakdown Processes of Normal and Inverted Intracloud Flashes

by Dongdong Shi, Jinlai Zhang, Panliang Gao, Dong Zheng, Qi Qi, Jie Shao, Shiqi Kan, Daohong Wang and Ting Wu

Remote Sens. 2024, 16(20), 3899; https://doi.org/10.3390/rs16203899 - 20 Oct 2024

Viewed by 454

Abstract

We have studied the parameters of preliminary breakdown (PB) pulses in 395 normal and 319 inverted intracloud (IC) flashes observed in Gifu, Japan, and Ningxia, China, respectively, by using a low-frequency mapping system called fast antenna lightning mapping array (FALMA). These parameters are [...] Read more.

We have studied the parameters of preliminary breakdown (PB) pulses in 395 normal and 319 inverted intracloud (IC) flashes observed in Gifu, Japan, and Ningxia, China, respectively, by using a low-frequency mapping system called fast antenna lightning mapping array (FALMA). These parameters are extracted from the first half of the PB pulses. It is found that compared to normal IC flashes, inverted IC flashes exhibited PB pulses with slower rise times (6.8 vs. 3.1 μs), wider half-peak widths (3.8 vs. 2.5 μs), longer zero-crossing times (26.2 vs. 14 μs), and extended fall times (4 vs. 3.2 μs). We further demonstrated that such discrepancies between normal and inverted IC flashes should not be caused by subjective factors, like noise threshold setting, or objective factors, like signal propagation distance. Based on this analysis, finally, we inferred that the discrepancies should be a reflection of the PB channel properties of normal and inverted IC flashes. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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15 pages, 1077 KiB

Open AccessTechnical Note

Quantifying Annual Glacier Mass Change and Its Influence on the Runoff of the Tuotuo River

by Lin Liu, Xueyu Zhang and Zhimin Zhang

Remote Sens. 2024, 16(20), 3898; https://doi.org/10.3390/rs16203898 - 20 Oct 2024

Viewed by 550

Abstract

Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this [...] Read more.

Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this study, we employed an albedo-based model with a daily land surface albedo dataset to derive the annual glacier mass balance over the Tuotuo River Basin (TRB). During 2000–2022, an annual glacier mass balance range of −0.89 ± 0.08 to 0.11 ± 0.11 m w.e. was estimated. By comparing with river runoff records from the hydrometric station, the contribution of glacier mass change to river runoff was calculated to be 0.00–31.14% for the studied period, with a mean value of 9.97%. Moreover, we found that the mean contribution in drought years is 20.07%, which is approximately five times that in wet years (4.30%) and twice that in average years (9.49%). Therefore, our results verify that mountain glaciers act as a significant buffer against drought in the TRB, at least during the 2000–2022 period. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere)

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The geographic location of the Tuotuo River Basin (Olivine Yellow) and the three study sites (A, B, and C). Glacier boundaries were obtained from the second Chinese glacier inventory. The location of the Tuotuohe hydrometric station is indicated as a red five-pointed star. Full article ">Figure 2
The extracted annual minimum regional-average surface albedo time series for the three study sites in the TRB between 2000 and 2022. The red vertical dashed line indicates the year 2012. Full article ">Figure 3
The observed annual runoff of the Tuotuo River between 2000 and 2022. Full article ">Figure 4
Annual basin-wide average precipitation (a) and evaporation (b) for the Tuotuo River Basin between 2000 and 2022. Full article ">Figure 5
Comparison between annual glacier mass change and annual river runoff for the Tuotuo River Basin during the period of 2000–2022. Full article ">Figure 6
The amount of annual precipitation minus annual evaporation in 2000–2022. Full article ">Figure 7
Relationship between river runoff and the difference in precipitation and evaporation over the Tuotuo River Basin during the period of 2000–2022. Full article ">

26 pages, 28365 KiB

Open AccessArticle

Three-Dimensional Geometric-Physical Modeling of an Environment with an In-House-Developed Multi-Sensor Robotic System

by Su Zhang, Minglang Yu, Haoyu Chen, Minchao Zhang, Kai Tan, Xufeng Chen, Haipeng Wang and Feng Xu

Remote Sens. 2024, 16(20), 3897; https://doi.org/10.3390/rs16203897 - 20 Oct 2024

Viewed by 725

Abstract

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral [...] Read more.

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning. Full article

(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)

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20 pages, 6289 KiB

Open AccessArticle

A High-Resolution Remote Sensing Road Extraction Method Based on the Coupling of Global Spatial Features and Fourier Domain Features

by Hui Yang, Caili Zhou, Xiaoyu Xing, Yongchuang Wu and Yanlan Wu

Remote Sens. 2024, 16(20), 3896; https://doi.org/10.3390/rs16203896 - 20 Oct 2024

Viewed by 761

Abstract

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of [...] Read more.

Remote sensing road extraction based on deep learning is an important method for road extraction. However, in complex remote sensing images, different road information often exhibits varying frequency distributions and texture characteristics, and it is usually difficult to express the comprehensive characteristics of roads effectively from a single spatial domain perspective. To address the aforementioned issues, this article proposes a road extraction method that couples global spatial learning with Fourier frequency domain learning. This method first utilizes a transformer to capture global road features and then applies Fourier transform to separate and enhance high-frequency and low-frequency information. Finally, it integrates spatial and frequency domain features to express road characteristics comprehensively and overcome the effects of intra-class differences and occlusions. Experimental results on HF, MS, and DeepGlobe road datasets show that our method can more comprehensively express road features compared with other deep learning models (e.g., Unet, D-Linknet, DeepLab-v3, DCSwin, SGCN) and extract road boundaries more accurately and coherently. The IOU accuracy of the extracted results also achieved 72.54%, 55.35%, and 71.87%. Full article

(This article belongs to the Special Issue Road Extraction and Distress Assessment by Spaceborne, Airborne and Terrestrial Platforms (Second Edition))

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17 pages, 17273 KiB

Open AccessArticle

Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia

by Zeineb Kassouk, Emna Ayari, Benoit Deffontaines and Mohamed Ouaja

Remote Sens. 2024, 16(20), 3895; https://doi.org/10.3390/rs16203895 - 19 Oct 2024

Viewed by 1027

Abstract

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, [...] Read more.

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth^® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth^®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article

(This article belongs to the Special Issue Advances in Remote Sensing in Coastal Geomorphology (Third Edition))

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17 pages, 9005 KiB

Open AccessArticle

NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area

by Dimitri Charrière, Loïc Francon and Gregory Giuliani

Remote Sens. 2024, 16(20), 3894; https://doi.org/10.3390/rs16203894 - 19 Oct 2024

Viewed by 1152

Abstract

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and [...] Read more.

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments. Full article

(This article belongs to the Special Issue Advances in Satellite Image Analysis and Applications for Earth Observation)

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Figure 1
Localization of the canton of Valais (CH) and altitudinal range (projection: CH1903+/LV95). Full article ">Figure 2
General Workflow for the comparative assessment of PPI and NDVI for phenological metrics retrieval. The Vegetation Cover map is produced using Sentinel-2 images and “High Resolution Layers” from the Copernicus Land Monitoring Service (CLMS). This map is then used together with pre-processed PPI and NDVI rasters to retrieve PPI and NDVI values per vegetation classes. In a subsequent step, these values are used to process and retrieve time-series and seasonality parameters necessary for the analysis. Full article ">Figure 3
Vegetation distribution in Valais (projection: CH1903+/LV95). Full article ">Figure 4
NDVI time-series per vegetation classes (top) with raw data (grey) and the double logistic function derived (colored). The bottom chart depicts all double logistic functions. Full article ">Figure 5
PPI time-series per vegetation classes (top) with raw data (grey) and the double logistic function derived (colored). The bottom chart depicts all double logistic functions. Full article ">Figure 6
PPI and NDVI time-series for each vegetation class. Full article ">

32 pages, 100733 KiB

Open AccessArticle

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

by Hongyu Wu, Guanzhou Chen, Yang Bai, Ying Peng, Qianqian Ba, Shuai Huang, Xing Zhong, Haijiang Sun, Lei Zhang and Fuyu Feng

Remote Sens. 2024, 16(20), 3893; https://doi.org/10.3390/rs16203893 - 19 Oct 2024

Viewed by 918

Abstract

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km [...] Read more.

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites. Full article

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16 pages, 2967 KiB

Open AccessTechnical Note

Field Programmable Gate Array (FPGA) Implementation of Parallel Jacobi for Eigen-Decomposition in Direction of Arrival (DOA) Estimation Algorithm

by Shuang Zhou and Li Zhou

Remote Sens. 2024, 16(20), 3892; https://doi.org/10.3390/rs16203892 - 19 Oct 2024

Viewed by 702

Abstract

The eigen-decomposition of a covariance matrix is a key step in the Direction of Arrival (DOA) estimation algorithms such as subspace classes. Eigen-decomposition using the parallel Jacobi algorithm implemented on FPGA offers excellent parallelism and real-time performance. Addressing the high complexity and resource [...] Read more.

The eigen-decomposition of a covariance matrix is a key step in the Direction of Arrival (DOA) estimation algorithms such as subspace classes. Eigen-decomposition using the parallel Jacobi algorithm implemented on FPGA offers excellent parallelism and real-time performance. Addressing the high complexity and resource consumption of the traditional parallel Jacobi algorithm implemented on FPGA, this study proposes an improved FPGA-based parallel Jacobi algorithm for eigen-decomposition. By analyzing the relationship between angle calculation and rotation during the Jacobi algorithm decomposition process, leveraging parallelism in the data processing, and based on the concepts of time-division multiplexing and parallel partition processing, this approach effectively reduces FPGA resource consumption. The improved parallel Jacobi algorithm is then applied to the classic DOA estimation algorithm, the MUSIC algorithm, and implemented on Xilinx’s Zynq FPGA. Experimental results demonstrate that this parallel approach can reduce resource consumption by approximately 75% compared to the traditional method but introduces little additional time consumption. The proposed method in this paper will solve the problem of great hardware consumption of eigen-decomposition based on FPGA in DOA applications. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing)

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22 pages, 11121 KiB

Open AccessArticle

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

by Longshuai Wang, Liwen Ma, Tao Wu, Jiaji Wu and Xiang Luo

Remote Sens. 2024, 16(20), 3891; https://doi.org/10.3390/rs16203891 - 19 Oct 2024

Viewed by 848

Abstract

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type [...] Read more.

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 10462 KiB

Open AccessArticle

Multi-Year Hurricane Impacts Across an Urban-to-Industrial Forest Use Gradient

by Carlos Topete-Pozas, Steven P. Norman and William M. Christie

Remote Sens. 2024, 16(20), 3890; https://doi.org/10.3390/rs16203890 - 19 Oct 2024

Viewed by 665

Abstract

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 [...] Read more.

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling)

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19 pages, 3739 KiB

Open AccessArticle

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

by Bahareh Ghanati and Joan Serra-Sagristà

Remote Sens. 2024, 16(20), 3889; https://doi.org/10.3390/rs16203889 - 19 Oct 2024

Viewed by 498

Abstract

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies [...] Read more.

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions. Full article

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14 pages, 313 KiB

Open AccessReview

Progress in Remote Sensing of Heavy Metals in Water

by Xiaoling Xu, Jiayi Pan, Hua Zhang and Hui Lin

Remote Sens. 2024, 16(20), 3888; https://doi.org/10.3390/rs16203888 - 19 Oct 2024

Viewed by 808

Abstract

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are [...] Read more.

This review article details the advancements in detecting heavy metals in aquatic environments using remote sensing methodologies. Heavy metals are significant pollutants in aquatic environment, and their detection and monitoring are crucial for predicting water quality. Traditional in situ water sampling methods are time-consuming and costly, highlighting the advantages of remote sensing techniques. Analysis of the reflectance and absorption characteristics of heavy metals has identified the red and near-infrared bands as the sensitive wavelengths for heavy metal detection in aquatic environments. Several studies have demonstrated a correlation between total suspended matter and heavy metals, which forms the basis for retrieving heavy metal content from TSM data. Recent developments in hyperspectral remote sensing and machine (deep) learning technologies may pave the way for developing more effective heavy metal detection algorithms. Full article

(This article belongs to the Section Environmental Remote Sensing)

21 pages, 19359 KiB

Open AccessArticle

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

by Guangming Li, Yu Zhang, Yuhua Zhang, Zizheng Guo, Yuanbo Liu, Xinyong Zhou, Zhanxu Guo, Wei Guo, Lihang Wan, Liang Duan, Hao Luo and Jun He

Remote Sens. 2024, 16(20), 3887; https://doi.org/10.3390/rs16203887 - 19 Oct 2024

Viewed by 691

Abstract

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides [...] Read more.

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article

(This article belongs to the Special Issue Aerial and Drone LiDAR Data for Geomorphological Mapping, Landform Extraction and Landscape Evolution)

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22 pages, 12339 KiB

Open AccessArticle

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

by Oliver Gutiérrez-Hernández and Luis V. García

Remote Sens. 2024, 16(20), 3886; https://doi.org/10.3390/rs16203886 - 19 Oct 2024

Cited by 1 | Viewed by 695

Abstract

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false [...] Read more.

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

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Remote Sens., Volume 16, Issue 20 (October-2 2024) – 163 articles

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