Remote Sensing

31 pages, 4849 KiB

Open AccessArticle

The Estimation of the Growing Stock Volume of Forests with Synthetic Aperture Radar-Based Models: A Comparison of Model-Fitting Methods

by Maurizio Santoro, Oliver Cartus, Oleg Antropov and Jukka Miettinen

Remote Sens. 2024, 16(21), 4079; https://doi.org/10.3390/rs16214079 - 31 Oct 2024

Abstract

The satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such [...] Read more.

The satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. Full article

(This article belongs to the Special Issue SAR for Forest Mapping III)

14 pages, 1958 KiB

Open AccessTechnical Note

Mare Volcanism in Apollo Basin Evaluating the Mare Basalt Genesis Models on the Moon

by Xiaohui Fu, Chengxiang Yin, Jin Li, Jiang Zhang, Siyue Chi, Jian Chen and Bo Li

Remote Sens. 2024, 16(21), 4078; https://doi.org/10.3390/rs16214078 - 31 Oct 2024

Abstract

The Apollo basin is a well-preserved double-ringed impact basin located on the northeastern edge of the South Pole–Aitken (SPA) basin. The Apollo basin has been flooded and filled with large volumes of mare lavas, indicating an active volcanism history. Based on orbital data, [...] Read more.

The Apollo basin is a well-preserved double-ringed impact basin located on the northeastern edge of the South Pole–Aitken (SPA) basin. The Apollo basin has been flooded and filled with large volumes of mare lavas, indicating an active volcanism history. Based on orbital data, we reveal that the Apollo basin exhibits an overall asymmetric configuration in the distribution of mare basalts as well as its topography, chemical compositions, and crustal thickness. The Apollo basin is an excellent example for assessing the influences of the above factors on mare basalts petrogenesis and evaluating mare basalt genesis models. It was found that the generation of mare basalt magmas and their emplacement in the Apollo basin seems to be strongly related to local thin crust (<30 km), but the formation of basaltic magmas should be independent of the decompression melting because the mare units (3.34–1.79 Ga) are much younger than the pre-Nectarian Apollo basin. The mare basalts filled in the Apollo basin exhibits a large variation of TiO₂ abundances, indicating the heterogeneity of mantle sources, which is possible due to the lunar mantle overturn after the LMO solidification or the impact-induced mantle convection and migration. However, the prolonged mare volcanic history of the Apollo basin is not well explained, especially considering the low Th abundance (<2 ppm) of this region. In addition, the central mare erupted earlier than other mare units within the Apollo basin, which seems to contradict the predictions of the postbasin loading-induced stresses model. Laboratory investigations of the Chang’E-6 mare basalt samples could possibly answer the above questions and provide new insight into the mare volcanic history of the lunar farside and the connections between mare volcanism and impact basin formation/evolution. Full article

(This article belongs to the Special Issue Advances in Exploring the Moon, Mars, and Asteroids Based on In-Situ and Remote Sensing Measurements)

28 pages, 32933 KiB

Open AccessArticle

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

by Kyaw Soe Win and Jun Sasaki

Remote Sens. 2024, 16(21), 4077; https://doi.org/10.3390/rs16214077 - 31 Oct 2024

Abstract

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for [...] Read more.

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs. Full article

(This article belongs to the Special Issue Remote Sensing in Mangroves III)

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18 pages, 5420 KiB

Open AccessArticle

Artificial Intelligence-Based Precipitation Estimation Method Using Fengyun-4B Satellite Data

by Nianqing Liu, Jianying Jiang, Dongyan Mao, Meng Fang, Yun Li, Bowei Han and Suling Ren

Remote Sens. 2024, 16(21), 4076; https://doi.org/10.3390/rs16214076 - 31 Oct 2024

Abstract

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation [...] Read more.

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation model was constructed using the light gradient boosting machine (LGBM) algorithm. A comparative analysis of FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 revealed the following: (1) The results demonstrate that FY-4B_AI is more accurate than GPM/IMERG-L. Six of the eight evaluation indices exhibit superior performance for FY-4B_AI in comparison to GPM/IMERG-L. These indices include the mean absolute error (MAE), root mean square error (RMSE), relative error (RE), correlation coefficient (CC), probability of detection (POD), and critical success index (CSI). As for the MAE, the results are 1.67 (FY-4B_AI) and 1.92 (GPM/IMERG-L), respectively. The RMSEs are 3.68 and 4.07, respectively. The REs are 17.72% and 26.28%, respectively. The CCs are 0.44 and 0.36, respectively. The PODs are 61.84% and 47.31%, respectively. The CSIs are 0.30 and 0.27, respectively. However, with regard to the mean errors (MEs) and false alarm rates (FARs), FY-4B_AI (−0.88 and 62.85%, respectively) displays a slight degree of inferiority in comparison to GPM/IMERG-L (−0.80 and 62.21%, respectively). (2) An evaluation of two strong weather events to represent the spatial distribution of precipitation in different climatic zones revealed that both FY-4B_AI and GPM/IMERG-L are equally capable of accurately representing these phenomena, irrespective of whether the region in question is humid, as is the case in the southeast, or dry, as is the case in the northwest. Full article

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26 pages, 284813 KiB

Open AccessArticle

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

by Bo Deng, Qiang Xu, Xiujun Dong, Weile Li, Mingtang Wu, Yuanzhen Ju and Qiulin He

Remote Sens. 2024, 16(21), 4075; https://doi.org/10.3390/rs16214075 - 31 Oct 2024

Abstract

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently [...] Read more.

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. Full article

(This article belongs to the Topic Landslides and Natural Resources)

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22 pages, 1538 KiB

Open AccessArticle

A Novel Beam-Domain Direction-of-Arrival Tracking Algorithm for an Underwater Target

by Xianghao Hou, Weisi Hua, Yuxuan Chen and Yixin Yang

Remote Sens. 2024, 16(21), 4074; https://doi.org/10.3390/rs16214074 - 31 Oct 2024

Abstract

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested [...] Read more.

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach. Full article

(This article belongs to the Section Ocean Remote Sensing)

33 pages, 11487 KiB

Open AccessArticle

Cross Attention-Based Multi-Scale Convolutional Fusion Network for Hyperspectral and LiDAR Joint Classification

by Haimiao Ge, Liguo Wang, Haizhu Pan, Yanzhong Liu, Cheng Li, Dan Lv and Huiyu Ma

Remote Sens. 2024, 16(21), 4073; https://doi.org/10.3390/rs16214073 - 31 Oct 2024

Abstract

In recent years, deep learning-based multi-source data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, has gained significant attention in the field of remote sensing. However, the traditional convolutional neural network fusion techniques always provide poor extraction of [...] Read more.

In recent years, deep learning-based multi-source data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, has gained significant attention in the field of remote sensing. However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. Furthermore, the mere act of stacking multi-source feature embeddings fails to represent the deep semantic relationships among them. In this paper, we propose a cross attention-based multi-scale convolutional fusion network for HSI-LiDAR joint classification. It contains three major modules: spatial–elevation–spectral convolutional feature extraction module (SESM), cross attention fusion module (CAFM), and classification module. In the SESM, improved multi-scale convolutional blocks are utilized to extract features from HSI and LiDAR to ensure discriminability and comprehensiveness in diversified land cover conditions. Spatial and spectral pseudo-3D convolutions, pointwise convolutions, residual aggregation, one-shot aggregation, and parameter-sharing techniques are implemented in the module. In the CAFM, a self-designed local-global cross attention block is utilized to collect and integrate relationships of the feature embeddings and generate joint semantic representations. In the classification module, average polling, dropout, and linear layers are used to map the fused semantic representations to the final classification results. The experimental evaluations on three public HSI-LiDAR datasets demonstrate the competitiveness of the proposed network in comparison with state-of-the-art methods. Full article

(This article belongs to the Special Issue Image Processing and Analysis: Trends in Registration, Data Fusion, 3D Reconstruction, and Change Detection (Third Edition))

22 pages, 9742 KiB

Open AccessArticle

Three-Dimensional Thermohaline Reconstruction Driven by Satellite Sea Surface Data Based on Sea Ice Seasonal Variation in the Arctic Ocean

by Xiangyu Wu, Jinlong Li, Xidong Wang, Zikang He, Zhiqiang Chen, Shihe Ren and Xi Liang

Remote Sens. 2024, 16(21), 4072; https://doi.org/10.3390/rs16214072 (registering DOI) - 31 Oct 2024

Abstract

This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to [...] Read more.

This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to validate the model output. The study indicates that the multivariate statistical regression model effectively captures the characteristics of the three-dimensional thermohaline structure of the Arctic Ocean. Areas with large reconstruction errors are primarily observed in the salinity values of ice-free regions and the temperature values of ice-covered regions. The statistical regression experiments reveal that salinity errors in ice-free regions are caused by inaccuracies in the satellite salinity data, while temperature errors in ice-covered areas mainly result from the inadequate representation of the under-ice temperature structure of the reanalysis data. The continuous and stable thermohaline data produced using near real-time satellite data as input provide an important foundation for studying Arctic marine environmental characteristics and assessing climate change. Full article

(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)

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23 pages, 11676 KiB

Open AccessArticle

A Multi-Scale Feature Fusion Deep Learning Network for the Extraction of Cropland Based on Landsat Data

by Huiling Chen, Guojin He, Xueli Peng, Guizhou Wang and Ranyu Yin

Remote Sens. 2024, 16(21), 4071; https://doi.org/10.3390/rs16214071 - 31 Oct 2024

Abstract

In the face of global population growth and climate change, the protection and rational utilization of cropland are crucial for food security and ecological balance. However, the complex topography and unique ecological environment of the Qinghai-Tibet Plateau results in a lack of high-precision [...] Read more.

In the face of global population growth and climate change, the protection and rational utilization of cropland are crucial for food security and ecological balance. However, the complex topography and unique ecological environment of the Qinghai-Tibet Plateau results in a lack of high-precision cropland monitoring data. Therefore, this paper constructs a high-quality cropland dataset for the YarlungZangbo-Lhasa-Nyangqv River region of the Qinghai-Tibet Plateau and proposes an MSC-ResUNet model for cropland extraction based on Landsat data. The dataset is annotated at the pixel level, comprising 61 Landsat 8 images in 2023. The MSC-ResUNet model innovatively combines multiscale features through residual connections and multiscale skip connections, effectively capturing features ranging from low-level spatial details to high-level semantic information and further enhances performance by incorporating depthwise separable convolutions as part of the feature fusion process. Experimental results indicate that MSC-ResUNet achieves superior accuracy compared to other models, with F1 scores of 0.826 and 0.856, and MCC values of 0.816 and 0.847, in regional robustness and temporal transferability tests, respectively. Performance analysis across different months and band combinations demonstrates that the model maintains high recognition accuracy during both growing and non-growing seasons, despite the study area’s complex landforms and diverse crops. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

24 pages, 7524 KiB

Open AccessArticle

A Study on Typhoon Center Localization Based on an Improved Spatio-Temporally Consistent Scale-Invariant Feature Transform and Brightness Temperature Perturbations

by Chaoyu Yan, Jie Guang, Zhengqiang Li, Gerrit de Leeuw and Zhenting Chen

Remote Sens. 2024, 16(21), 4070; https://doi.org/10.3390/rs16214070 (registering DOI) - 31 Oct 2024

Abstract

Extreme weather events like typhoons have become more frequent due to global climate change. Current typhoon monitoring methods include manual monitoring, mathematical morphological methods, and artificial intelligence. Manual monitoring is accurate but labor-intensive, while AI offers convenience but requires accuracy improvements. Mathematical morphology [...] Read more.

Extreme weather events like typhoons have become more frequent due to global climate change. Current typhoon monitoring methods include manual monitoring, mathematical morphological methods, and artificial intelligence. Manual monitoring is accurate but labor-intensive, while AI offers convenience but requires accuracy improvements. Mathematical morphology methods, such as brightness temperature perturbation (BTP) and a spatio-temporally consistent (STC) Scale-Invariant Feature Transform (SIFT), remain mainstream for typhoon positioning. This paper enhances BTP and STC SIFT methods for application to Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) L1 data, incorporating parallax correction for more accurate surface longitude and latitude positioning. The applicability of these methods for different typhoon intensities and monitoring time resolutions is analyzed. Automated monitoring with one-hour observation intervals in the northwest Pacific region demonstrates high positioning accuracy, reaching 25 km or better when compared to best path data from the China Meteorological Administration (CMA). For 1 h remote sensing observations, BTP is more accurate for typhoons at or above typhoon intensity, while STC SIFT is more accurate for weaker typhoons. In the current era of a high temporal resolution of typhoon monitoring using geostationary satellites, the method presented in this paper can serve the national meteorological industry for typhoon monitoring, which is beneficial to national pre-disaster prevention work as well as global meteorological research. Full article

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23 pages, 30736 KiB

Open AccessArticle

Ku-Band SAR-Drone System and Methodology for Repeat-Pass Interferometry

by Gerard Ruiz-Carregal, Marc Lort Cuenca, Luis Yam, Gerard Masalias, Eduard Makhoul, Rubén Iglesias, Antonio Heredia, Álex González, Giuseppe Centolanza, Albert Gili-Zaragoza, Azadeh Faridi, Dani Monells and Javier Duro

Remote Sens. 2024, 16(21), 4069; https://doi.org/10.3390/rs16214069 - 31 Oct 2024

Abstract

In recent years, drone-based Synthetic Aperture Radar (SAR) systems have emerged as flexible and cost-efficient solutions for detecting changes in the Earth’s surface, retrieving topographic data, or detecting ground displacement processes in localized areas, among other applications. These systems offer a unique combination [...] Read more.

In recent years, drone-based Synthetic Aperture Radar (SAR) systems have emerged as flexible and cost-efficient solutions for detecting changes in the Earth’s surface, retrieving topographic data, or detecting ground displacement processes in localized areas, among other applications. These systems offer a unique combination of short and versatile revisit times and flexible acquisition geometries that are not achievable with space-borne, airborne, or ground-based SAR sensors. However, due to platform limitations and flight stability issues, they also present significant challenges regarding instrument design and data processing, particularly when generating interferometric repeat-pass datasets. This paper demonstrates the feasibility of repeat-pass interferometry using a Ku-band drone-based SAR system. The system integrates a dual-channel Ku-band Frequency Modulated Continuous Wave (FMCW) radar with cross-track single-pass interferometric capabilities, mounted on a drone platform. The proposed repeat-pass interferometric processing chain leverages an accurate Digital Elevation Model (DEM), generated from the single-pass interferograms, to precisely coregister the entire stack of acquisitions, thereby producing repeat-pass interferograms free from residual motion errors. The results underscore the potential of this system and the processing chain proposed for generating multi-temporal repeat-pass stacks suitable for repeat-pass applications. Full article

20 pages, 1335 KiB

Open AccessArticle

TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection

by Hongzhu Wang, Zhaoyi Ye, Chuan Xu, Liye Mei, Cheng Lei and Du Wang

Remote Sens. 2024, 16(21), 4068; https://doi.org/10.3390/rs16214068 - 31 Oct 2024

Abstract

Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise [...] Read more.

Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions. Full article

(This article belongs to the Special Issue Application of Spatial Information Science and Cartography in the Big Remotely Sensed Data Era)

18 pages, 2705 KiB

Open AccessArticle

Climatic Influence on the Carotenoids Concentration in a Mediterranean Coastal Lagoon Through Remote Sensing

by Juan Víctor Molner, Rebeca Pérez-González, Xavier Sòria-Perpinyà and Juan Soria

Remote Sens. 2024, 16(21), 4067; https://doi.org/10.3390/rs16214067 - 31 Oct 2024

Abstract

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been [...] Read more.

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

28 pages, 6037 KiB

Open AccessArticle

Statistical and Independent Component Analysis of Sentinel-1 InSAR Time Series to Assess Land Subsidence Trends

by Celina Anael Farías, Michelle Lenardón Sánchez, Roberta Bonì and Francesca Cigna

Remote Sens. 2024, 16(21), 4066; https://doi.org/10.3390/rs16214066 - 31 Oct 2024

Abstract

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to [...] Read more.

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection. Full article

(This article belongs to the Special Issue Monitoring Geohazard from Synthetic Aperture Radar Interferometry)

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20 pages, 8552 KiB

Open AccessArticle

A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection

by Justin Butler and Henry Leung

Remote Sens. 2024, 16(21), 4065; https://doi.org/10.3390/rs16214065 - 31 Oct 2024

Abstract

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in [...] Read more.

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role. Full article

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17 pages, 16134 KiB

Open AccessArticle

Mapping Leaf Mass Per Area and Equivalent Water Thickness from PRISMA and EnMAP

by Xi Yang, Hanyu Shi and Zhiqiang Xiao

Remote Sens. 2024, 16(21), 4064; https://doi.org/10.3390/rs16214064 - 31 Oct 2024

Abstract

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can [...] Read more.

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm⁻² and 9.08 mg·cm⁻², respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSE_LMA = 5.78 mg·cm⁻², RMSE_EWT = 6.84 mg·cm⁻²). The results demonstrated the hybrid inversion model’s universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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30 pages, 5364 KiB

Open AccessArticle

Characterizing Chromophoric Dissolved Organic Matter Spatio-Temporal Variability in North Andean Patagonian Lakes Using Remote Sensing Information and Environmental Analysis

by Ayelén Sánchez Valdivia, Lucia G. De Stefano, Gisela Ferraro, Diamela Gianello, Anabella Ferral, Ana I. Dogliotti, Mariana Reissig, Marina Gerea, Claudia Queimaliños and Gonzalo L. Pérez

Remote Sens. 2024, 16(21), 4063; https://doi.org/10.3390/rs16214063 - 31 Oct 2024

Abstract

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated [...] Read more.

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated for the estimation of CDOM concentrations in surface lake water as the absorption coefficient at 440 nm (

a_{c d o m}^{440}

, m⁻¹). Of the five atmospheric corrections evaluated, the QUAC (Quick Atmospheric Correction) method demonstrated the highest accuracy for the remote estimation of CDOM. The application of separate models for deep and shallow lakes yielded superior results compared to a combined model, with R² values of 0.76 and 0.82 and mean absolute percentage errors (MAPEs) of 14% and 22% for deep and shallow lakes, respectively. The spatio-temporal variability of CDOM was characterized over a five-year period using satellite-derived

a_{c d o m}^{440}

values. CDOM concentrations varied widely, with very low values in deep lakes and moderate values in shallow lakes. Additionally, significant seasonal fluctuations were evident. Lower CDOM concentrations were observed during the summer to early autumn period, while higher concentrations were observed in the winter to spring period. A gradient boosting regression tree analysis revealed that inter-lake differences were primarily influenced by the lake perimeter to lake area ratio, mean lake depth, and watershed area to lake volume ratio. However, seasonal CDOM variation was largely influenced by Lake Nahuel Huapi water storage (a proxy for water level variability at a regional scale), followed by precipitation, air temperature, and wind. This research presents a robust method for estimating low to moderate CDOM concentrations, improving environmental monitoring of North Andean Patagonian Lake ecosystems. The results deepen the understanding of CDOM dynamics in low-impact lakes and its main environmental drivers, enhance the ability to estimate lacustrine carbon stocks on a regional scale, and help to predict the effects of climate change on this important variable. Full article

(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)

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19 pages, 4696 KiB

Open AccessArticle

Evaluating the Effects of Wetland Restoration on Ecosystem Services Using InVEST and Geostatistics: A Case Study of Dongting Lake in China

by Jiaying Zhou, Zijian Wang, Ying He, Peizhong Liu, Jie Xu, Cai Lu, Guangchun Lei and Li Wen

Remote Sens. 2024, 16(21), 4062; https://doi.org/10.3390/rs16214062 - 31 Oct 2024

Abstract

Ecological restoration projects aim to recover ecosystems that have been degraded, damaged, or destroyed. The effective assessment of these projects is crucial for ensuring that restoration goals are met and for guiding future efforts. The Poplar Ecological Restoration Project (PERP), focused on removing [...] Read more.

Ecological restoration projects aim to recover ecosystems that have been degraded, damaged, or destroyed. The effective assessment of these projects is crucial for ensuring that restoration goals are met and for guiding future efforts. The Poplar Ecological Restoration Project (PERP), focused on removing floodplain poplar plantations, is a key environmental initiative in China to preserve the ecological functions and services of the Dongting Lake freshwater wetland complex. Understanding the dynamic changes in ecosystem services and their trade-offs/synergies before and after the project is crucial for ensuring that ecosystem services are maintained and enhanced. This study selected three key ecosystem services, water conservation, carbon storage, and habitat quality, as indicators to assess the PERP. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and geostatistical methods, including the Spearman correlation analysis and spatial autocorrelation analysis, we explored the ecological outcomes of the project. The results indicated that since its implementation in 2014, the PERP has significantly altered the land use and land cover (LULC) in the lake. Most of the former plantations have been converted into reedbeds (17,404 ha), followed by sedgelands (3841 ha), mudflats (443 ha), shallow waters (382 ha), and deep waters (301 ha). During this period, water conservation and habitat quality initially decreased and then increased, while carbon storage continuously declined. Overall, all assessed services showed an increasing trend except for carbon storage, resulting in a slight decrease in the integrated ecosystem services. Moreover, there were strong bivariate associations among the three investigated ecosystem services, and the synergies were strengthened during the project. Importantly, these synergies became positive after the PERP, meaning that the improvement of one service enhanced others, potentially leading to a more sustainable and resilient ecosystem. Full article

(This article belongs to the Section Environmental Remote Sensing)

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24 pages, 12756 KiB

Open AccessArticle

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

by Vu Son Nguyen, Hubert Loisel, Vincent Vantrepotte, Xavier Mériaux and Dinh Lan Tran

Remote Sens. 2024, 16(21), 4061; https://doi.org/10.3390/rs16214061 - 31 Oct 2024

Abstract

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption [...] Read more.

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, a_cdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating a_cdom at the wavelength 412 nm (a_cdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, R_rs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite R_rs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate a_cdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m⁻¹ from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived a_cdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of a_cdom (412) is provided for a coastal transect in the south of Vietnam. Full article

(This article belongs to the Special Issue Remote Sensing Band Ratios for the Assessment of Water Quality)

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