Remote Sensing

25 pages, 11278 KiB

Open AccessArticle

Analysis of Droughts and Floods Evolution and Teleconnection Factors in the Yangtze River Basin Based on GRACE/GFO

by Ruqing Ren, Tatsuya Nemoto, Venkatesh Raghavan, Xianfeng Song and Zheng Duan

Remote Sens. 2025, 17(14), 2344; https://doi.org/10.3390/rs17142344 - 8 Jul 2025

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is [...] Read more.

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)

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18 pages, 3618 KiB

Open AccessArticle

Quality Assessment of Dual-Polarization C-Band SAR Data Influenced by Precipitation Based on Normalized Polarimetric Radar Vegetation Index

by Jisung Geba Chang, Simon Kraatz, Yisok Oh, Feng Gao and Martha Anderson

Remote Sens. 2025, 17(14), 2343; https://doi.org/10.3390/rs17142343 - 8 Jul 2025

Abstract

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar [...] Read more.

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

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

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19 pages, 3553 KiB

Open AccessArticle

Research on the Autonomous Orbit Determination of Beidou-3 Assisted by Satellite Laser Ranging Technology

by Wei Xiao, Zhengcheng Wu, Zongnan Li, Lei Fan, Shiwei Guo and Yilun Chen

Remote Sens. 2025, 17(14), 2342; https://doi.org/10.3390/rs17142342 - 8 Jul 2025

Abstract

The Beidou Global System (BDS-3) innovatively achieves autonomous navigation using inter-satellite links (ISL) across the entire constellation, but it still faces challenges such as the limitations of the prior constraint orbital accuracy and the overall constellation rotation. The gradual availability of satellite laser [...] Read more.

The Beidou Global System (BDS-3) innovatively achieves autonomous navigation using inter-satellite links (ISL) across the entire constellation, but it still faces challenges such as the limitations of the prior constraint orbital accuracy and the overall constellation rotation. The gradual availability of satellite laser ranging (SLR) data, with advantages of high precision and no ambiguous parameters, can provide new ideas for solving the current problem. This work firstly deduces the mathematical model for orbit determination by combining inter-satellite links and the introduced satellite laser ranging observations, then designs orbit determination experiments with different prior orbit constraints and different observation data, and finally evaluates the impacts of the prior orbits and the introduction of SLR observations from two dimensions: orbit accuracy and constellation rotation. The experimental results using one month of measured data show the following: (1) There is good consistency among different days, and the accuracy of the prior orbits affects the performance of the orbit determination and the consistency. Compared with broadcast ephemerides, using precise ephemerides as prior constraints significantly improves the consistency, and the orbit accuracy can be increased by about 75%. (2) The type of observation data affects the performance of the orbit determination. Introducing SLR observations can improve the orbit accuracy by approximately 13% to 26%. (3) Regardless of whether broadcast ephemerides or precise ephemerides are used as prior constraints, the constellation translation and rotation still exist after introducing SLR observations. Among the translation parameters, TX is the largest, followed by TY, and TZ is the smallest; all three rotation parameters (RX, RY, and RZ) show relatively large values, which may be related to the limited number of available satellite laser ranging stations during this period. (4) After considering the constellation translation and rotation, the orbit accuracy under different prior constraints remains at the same level. The statistical root mean square error (RMSE) indicates that the orbit accuracy of inclined geosynchronous orbit (IGSO) satellites in three directions is better than 20 cm, while the accuracy of medium earth orbit (MEO) satellites in along-track, cross-track, and radial directions is better than 10 cm, 8 cm, and 5 cm, respectively. Full article

(This article belongs to the Special Issue Advances in Multi-Frequency GNSS High-Precision Positioning and Navigation Technology)

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20 pages, 6538 KiB

Open AccessArticle

A Deep Reinforcement Learning Method with a Low Intercept Probability in a Netted Synthetic Aperture Radar

by Longhao Xie, Ziyang Cheng, Ming Li and Huiyong Li

Remote Sens. 2025, 17(14), 2341; https://doi.org/10.3390/rs17142341 - 8 Jul 2025

Abstract

A deep reinforcement learning (DRL)-based power allocation method is proposed to achieve a low probability of intercept (LPI) in a netted synthetic aperture radar (SAR). To provide a physically meaningful and intuitive assessment of a netted radar for LPI performance, a netted circular [...] Read more.

A deep reinforcement learning (DRL)-based power allocation method is proposed to achieve a low probability of intercept (LPI) in a netted synthetic aperture radar (SAR). To provide a physically meaningful and intuitive assessment of a netted radar for LPI performance, a netted circular equivalent vulnerable radius (NCEVR) is proposed and adopted. For SAR detection performance, the resolution, signal-to-noise ratio in a single pulse, and signal-to-noise ratio in SAR imaging are integrated at the task level. The LPI performance is achieved by minimizing NCEVR within the constraints of SAR detection performance. The powers in multiple moments are optimized using the DRL proximal policy optimization algorithm with the designed reward and observation. A DRL-based solver is provided for LPI radar, which handles problems that are difficult to optimize using traditional methods. The effectiveness is verified by simulations. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar (Second Edition))

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31 pages, 6764 KiB

Open AccessArticle

Upscaling Frameworks Drive Prediction Accuracy and Uncertainty When Mapping Aboveground Biomass Density from the Synergism of Spaceborne LiDAR, SAR, and Passive Optical Data

by Inacio T. Bueno, Carlos A. Silva, Monique B. Schlickmann, Victoria M. Donovan, Jeff W. Atkins, Kody M. Brock, Jinyi Xia, Denis R. Valle, Jiangxiao Qiu, Jason Vogel, Andres Susaeta, Ajay Sharma, Carine Klauberg, Midhun Mohan and Ana Paula Dalla Corte

Remote Sens. 2025, 17(14), 2340; https://doi.org/10.3390/rs17142340 - 8 Jul 2025

Abstract

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for integrating GEDI LiDAR, [...] Read more.

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for integrating GEDI LiDAR, SAR, and optical satellite data to create wall-to-wall AGBD maps. The frameworks tested in this paper were: (1) a single-step approach using optical imagery, (2) a two-stage approach with GEDI-derived variables, and (3) a three-stage approach combining imagery and in situ-derived allometries. Internal validation showed that framework 1 achieved the lowest root mean square difference (%RMSD) of 53.3% and highest coefficient of determination (R²) of 0.53. An independent external validation of the AGBD map was performed using in situ observations, also revealing that framework 1 was the most accurate (%RMSD = 39.3% and R² = 0.93), while frameworks 2 and 3 were less accurate (%RMSD = 54.7, 44.7 and R² = 0.95, 0.90, respectively). Herein, we show that upscaling frameworks significantly impacted AGBD map uncertainty and the magnitude of estimate differences. Our findings suggest that upscaling framework 1 based on a single step approach was the most effective for capturing detailed AGBD variations, while careful consideration of model sensitivity and map uncertainties is essential for reliable AGBD estimation. This study provides valuable insights for advancing forest AGBD monitoring and highlights the potential for further enhancements in remote sensing methodologies. Full article

(This article belongs to the Section Forest Remote Sensing)

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22 pages, 6546 KiB

Open AccessArticle

Remote Sensing-Based Assessment of Evapotranspiration Patterns in a UNESCO World Heritage Site Under Increasing Water Competition

by Maria C. Moyano, Monica Garcia, Luis Juana, Laura Recuero, Lucia Tornos, Joshua B. Fisher, Néstor Fernández and Alicia Palacios-Orueta

Remote Sens. 2025, 17(14), 2339; https://doi.org/10.3390/rs17142339 - 8 Jul 2025

Abstract

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity ( [...] Read more.

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural areas and rice fields surrounding the National Park (R = 0.3), contrasted by a strong negative ET trend in wetlands (R < −0.5). These opposing patterns suggest a growing diversion of water toward irrigation at the expense of natural ecosystems. The impact was especially marked during droughts, such as the 2011–2016 period, when precipitation declined by 16%. In wetlands, ET was significantly correlated with precipitation (R > 0.4), highlighting their vulnerability to reduced water inputs. These findings offer crucial insights to support sustainable water management strategies that balance agricultural productivity with the preservation of ecologically valuable systems under mounting climatic and anthropogenic pressures typical of semi-arid Mediterranean environments. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Structure and Function Dynamics Due to Climate Change and Human Activities)

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46 pages, 5911 KiB

Open AccessArticle

Leveraging Prior Knowledge in Semi-Supervised Learning for Precise Target Recognition

by Guohao Xie, Zhe Chen, Yaan Li, Mingsong Chen, Feng Chen, Yuxin Zhang, Hongyan Jiang and Hongbing Qiu

Remote Sens. 2025, 17(14), 2338; https://doi.org/10.3390/rs17142338 - 8 Jul 2025

Abstract

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, [...] Read more.

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

(This article belongs to the Special Issue Remote Sensing Target Recognition and Detection: Theory and Applications (Second Edition))

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25 pages, 8372 KiB

Open AccessArticle

CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers

by Ahcene Zetout and Mohand Saïd Allili

Remote Sens. 2025, 17(14), 2337; https://doi.org/10.3390/rs17142337 - 8 Jul 2025

Abstract

Accurate multi-class semantic segmentation of disaster-affected areas is essential for rapid response and effective recovery planning. We present CSDNet, a context-aware segmentation model tailored to disaster scene scenarios, designed to improve segmentation of both large-scale disaster zones and small, underrepresented classes. The architecture [...] Read more.

Accurate multi-class semantic segmentation of disaster-affected areas is essential for rapid response and effective recovery planning. We present CSDNet, a context-aware segmentation model tailored to disaster scene scenarios, designed to improve segmentation of both large-scale disaster zones and small, underrepresented classes. The architecture combines a lightweight transformer module for global context modeling with depthwise separable convolutions (DWSCs) to enhance efficiency without compromising representational capacity. Additionally, we introduce a detection-guided feature fusion mechanism that integrates outputs from auxiliary detection tasks to mitigate class imbalance and improve discrimination of visually similar categories. Extensive experiments on several public datasets demonstrate that our model significantly improves segmentation of both man-made infrastructure and natural damage-related features, offering a robust and efficient solution for post-disaster analysis. Full article

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21 pages, 3305 KiB

Open AccessArticle

Unlocking Potato Phenology: Harnessing Sentinel-1 and Sentinel-2 Synergy for Precise Crop Stage Detection

by Diego Gomez, Pablo Salvador, Jorge Gil and Juan Fernando Rodrigo

Remote Sens. 2025, 17(14), 2336; https://doi.org/10.3390/rs17142336 - 8 Jul 2025

Abstract

Global challenges such as climate change and population growth require improvements in crop monitoring models. To address these issues, this study advances the identification of potato crop phenological stages using satellite remote sensing, a field where cereals have been the primary focus. We [...] Read more.

Global challenges such as climate change and population growth require improvements in crop monitoring models. To address these issues, this study advances the identification of potato crop phenological stages using satellite remote sensing, a field where cereals have been the primary focus. We introduce a methodology using Sentinel-1 (S1) and Sentinel-2 (S2) time series data to pinpoint critical phenological stages—emergence, canopy closure, flowering, senescence onset, and harvest timing—at the field scale. Our approach utilizes analysis of NDVI, fAPAR, and IRECI2 from S2, alongside VH and VV polarizations from S1, informed by domain knowledge of the spectral and morphological responses of potato crops. We propose the integration of NDVI and VH indices, NDVI_VH, to improve stage detection accuracy. Comparative analysis with ground-observed stages validated the method’s effectiveness, with NDVI proving to be one of the most informative indices, achieving RMSEs of 12 and 14 days for emergence and closure, and 17 days for the onset of senescence. The integrated NDVI_VH approach complemented NDVI, particularly in harvest and flowering stages, where VH enhanced accuracy, achieving an overall R² value of 0.80. The study demonstrates the potential of combining SAR and optical data for post-season crop phenology analysis, providing insights that can inform the development of new methods and strategies to enhance on-season crop monitoring and yield forecasting. Full article

(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)

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18 pages, 4682 KiB

Open AccessArticle

UAS Remote Sensing for Coastal Wetland Vegetation Biomass Estimation: A Destructive vs. Non-Destructive Sampling Experiment

by Grayson R. Morgan, Lane Stevenson, Cuizhen Wang and Ram Avtar

Remote Sens. 2025, 17(14), 2335; https://doi.org/10.3390/rs17142335 - 8 Jul 2025

Abstract

Coastal wetlands are critical ecosystems that require effective monitoring to support conservation and restoration efforts. This study evaluates the use of small unmanned aerial systems (sUAS) and multispectral imagery to estimate aboveground biomass (AGB) in tidal marshes, comparing models calibrated with destructive versus [...] Read more.

Coastal wetlands are critical ecosystems that require effective monitoring to support conservation and restoration efforts. This study evaluates the use of small unmanned aerial systems (sUAS) and multispectral imagery to estimate aboveground biomass (AGB) in tidal marshes, comparing models calibrated with destructive versus non-destructive in situ sampling methods. Imagery was collected over South Carolina’s North Inlet-Winyah Bay National Estuarine Research Reserve, and vegetation indices (VIs) were derived from sUAS imagery to model biomass. Stepwise linear regression was used to develop and validate models based on both sampling approaches. Destructive sampling models, particularly those using the Normalized Difference Vegetation Index (NDVI) and Difference Vegetation Index (DVI), achieved the lowest root mean square error (RMSE) values (as low as 70.91 g/m²), indicating higher predictive accuracy. Non-destructive models, while less accurate (minimum RMSE of 214.86 g/m²), demonstrated higher R² values (0.44 and 0.61), suggesting the potential for broader application with further refinement. These findings highlight the trade-offs between ecological impact and model performance, and support the viability of non-destructive methods for biomass estimation in sensitive wetland environments. Future work should explore machine learning approaches and improved temporal alignment of data collection to enhance model robustness. Full article

(This article belongs to the Special Issue Coastal and Marine Monitoring and Restoration Mapping Using UAS and Remote Sensing Systems)

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20 pages, 3609 KiB

Open AccessArticle

Beyond the Grid: GLRT-Based TomoSAR Fast Detection for Retrieving Height and Thermal Dilation

by Nabil Haddad, Karima Hadj-Rabah, Alessandra Budillon and Gilda Schirinzi

Remote Sens. 2025, 17(14), 2334; https://doi.org/10.3390/rs17142334 - 8 Jul 2025

Abstract

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building [...] Read more.

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

(This article belongs to the Section Urban Remote Sensing)

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24 pages, 5310 KiB

Open AccessArticle

Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection

by Joe Soundararajan, Andrew Kalukin, Jordan Malof and Dong Xu

Remote Sens. 2025, 17(14), 2333; https://doi.org/10.3390/rs17142333 - 8 Jul 2025

Abstract

Deforestation and forest fires are escalating global threats that require timely, scalable, and cost-effective monitoring systems. While UAV and ground-based solutions offer fine-grained data, they are often constrained by limited spatial coverage, high operational costs, and logistical challenges. In contrast, satellite imagery provides [...] Read more.

Deforestation and forest fires are escalating global threats that require timely, scalable, and cost-effective monitoring systems. While UAV and ground-based solutions offer fine-grained data, they are often constrained by limited spatial coverage, high operational costs, and logistical challenges. In contrast, satellite imagery provides broad, repeatable, and economically feasible coverage. This study presents a deep learning framework that combines the DeepLabV3+ architecture with an EfficientNet-B08 backbone to address both deforestation and wildfire detection using satellite imagery. The system utilizes advanced multi-scale feature extraction and Group Normalization to enable robust semantic segmentation under challenging atmospheric conditions and complex forest structures. It is evaluated on two benchmark datasets. In the Amazon forest segmentation dataset, the model achieves a validation Intersection over Union (IoU) of 0.9100 and a pixel accuracy of 0.9605, demonstrating strong performance in delineating forest boundaries. In FireDataset_20m, which presents a severe class imbalance between fire and non-fire pixels, the framework achieves 99.95% accuracy, 93.16% precision, and 91.47% recall. A qualitative analysis confirms the model’s ability to accurately localize fire hotspots and deforested areas. These results highlight the model’s dual-purpose utility for high-resolution, multi-temporal environmental monitoring. Its balanced performance across metrics and adaptability to complex terrain conditions make it a promising tool for supporting forest conservation, early fire detection, and evidence-based policy interventions. Full article

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24 pages, 2201 KiB

Open AccessArticle

TIMA-Net: A Lightweight Remote Sensing Image Change Detection Network Based on Temporal Interaction Enhancement and Multi-Scale Aggregation

by Zhijun Zhou, Xuejie Zhang, Xiaoliang Luo, Lvchun Wang, Wei Yu, Shufang Xu and Longbao Wang

Remote Sens. 2025, 17(14), 2332; https://doi.org/10.3390/rs17142332 - 8 Jul 2025

Abstract

Remote sensing image change detection (RSCD) holds significant application value in fields such as environmental monitoring, post-disaster assessment, and urban planning. However, existing deep learning methods often face challenges of high computational complexity and insufficient detail capture, particularly demonstrating limited performance in detecting [...] Read more.

Remote sensing image change detection (RSCD) holds significant application value in fields such as environmental monitoring, post-disaster assessment, and urban planning. However, existing deep learning methods often face challenges of high computational complexity and insufficient detail capture, particularly demonstrating limited performance in detecting high-resolution images and complex change regions. To address these issues, this paper proposes a novel network architecture, TIMA-Net, which is designed for efficient remote sensing image change detection. By introducing the timing interaction enhancement module and the progressive decoder based on multi-scale fusion, TIMA-Net improves the accuracy of change detection while ensuring efficient computing performance. Specifically, TIMA-Net designs a temporal interaction enhancement module based on dual-branch and coordinate attention and combines channel segmentation and multi-scale features to enhance the representation ability of the changed region. The comparative experimental results show that TIMA-Net outperforms several state-of-the-art methods on multiple remote sensing datasets, especially in terms of accuracy and computational efficiency. The ablation experiment results show that each module contributes to the final performance. In summary, TIMA-Net not only provides an efficient and accurate remote sensing image change detection solution but also shows its strong potential and broad application prospects in practical applications. Full article

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Remote Sens., Volume 17, Issue 14 (July-2 2025) – 13 articles

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