Search Results (18,111)

In recent years, unmanned aerial vehicles (UAVs) and Big Data analytics have both emerged as increasingly important approaches in advanced traffic monitoring. UAVs provide high-resolution spatial data and operational flexibility, supporting automated vehicle detection and the construction of origin–destination (O/D) matrices through video processing. Conversely, Big Data offers a passive and non-invasive approach based on heterogeneous sources such as mobile devices, satellite navigation systems, and digital applications, ensuring continuous temporal coverage for mobility pattern analysis. This study evaluates the combined use of UAVs and Big Data for traffic flow monitoring as an alternative to traditional manual methods. Focusing on two case studies in Trapani (Italy), the research assesses the advantages and limitations of each technology and their complementary use. Results show that Big Data effectively captures large-scale temporal dynamics but lacks accuracy for detailed O/D estimation, while UAVs provide precise spatial and behavioural information despite operational constraints. A key objective of this study is to investigate the potential complementarity between UAV observations and Big Data traffic monitoring technologies, highlighting the main strengths and limitations of each method under complex study sites and challenging operational conditions for traffic data acquisition using UAVs. Full article

This study quantifies coastal mangrove carbon stocks and their interannual variability along the Pakistan coastline by developing a multi-sensor fusion framework integrated with a process-based light use efficiency (LUE) modeling approach. To ensure high-cadence monitoring and overcome persistent cloud cover over the Indus Delta, data from multiple satellite sensors including Landsat 8/9 and Sentinel-2 within Google Earth Engine were utilized. Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) data composited for the January–March period was processed to estimate vegetation productivity. Field-based validation of biomass estimates was conducted using 57 georeferenced sampling points, cross-compared with Sentinel-2 data. Mangrove extent was delineated through land use and land cover (LULC) classification into water bodies, mangroves, mudflats, land parcels, and sand surfaces. The LUE model incorporated environmental stress scalars, including temperature, vapor pressure deficit (VPD), salinity, and photosynthetically active radiation (PAR) to estimate gross primary productivity and derive total biomass, which was subsequently converted into carbon stocks. Results indicate a mean carbon stock of 31.95 Mg C ha⁻¹ (equivalent to 117.3 Mg CO₂ ha⁻¹), with significant interannual variation (coefficient of variation = 19.8%). A significant decline in carbon stocks was observed in 2021 (−11.11%; 3.56 Mg C ha⁻¹), corresponding to a reduction in NDVI value (0.55 compared to 0.58 in other years). Spatial analysis revealed substantial heterogeneity in carbon distribution (20.51 to 55.93 Mg C ha⁻¹), primarily influenced by localized salinity gradients and water stress conditions. This study mapped mangrove extent, quantified environmental stress, and estimated carbon stocks across Pakistan’s coast from 2020 to 2024, delivering a spatially resolved, multi-year baseline for coastal carbon assessment and ecosystem monitoring in arid tidal environments. Full article

This study examines the seasonal and diurnal variations in ultrafine particles (PM_0.1) and their carbonaceous components (OC and EC), collected at the Institute of Technology of Cambodia in Phnom Penh. Sampling was conducted over 14 consecutive days in September 2024 (during the wet season) and February 2025 (during the dry season). The average mass concentration of PM_0.1 in February (8.5 μg/m³; range: 3.9–11.3 μg/m³) was approximately three times greater than that in September, driven by a corresponding increase in OC concentration. Conversely, average EC concentrations remained almost stable across both seasons, indicating consistent local emission sources. Total carbonaceous compounds (OC + EC) constitute approximately 50% of the PM_0.1 mass in both seasons. Primary organic carbon (POC) concentration increases almost four times in February compared to September. Secondary organic carbon (SOC) concentrations were significantly elevated during February daytime (1.4 ± 1.0 μg/m³), indicating active photochemical formation. Backward trajectory analysis and satellite hotspot data revealed that September air masses originated from maritime sources without significant local burning influences, while February pollution events were likely influenced by short-range transboundary transport from biomass-burning areas across the Cambodia–Vietnam border. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Open-access satellite data from major Earth observation (EO) missions, including Landsat, Sentinel, and MODIS, have transformed environmental monitoring globally, yet in most least developed countries (LDCs) this data abundance has not translated into operational decisions or policy impact. This review argues that the dominant narrative in the remote sensing community, that open data leads to democratized impact, is fundamentally incomplete. Using Nepal as an illustrative case study, we demonstrate that legal openness alone is insufficient without parallel advances in technical usability and institutional accessibility, the two layers of EO accessibility that the community has largely overlooked. Through a cross-sectoral synthesis spanning forests, agriculture, disaster management, and land cover monitoring, we identify a persistent “last-mile problem”: the systematic gap between data availability and operational governance integration. Systemic barriers including limited internet infrastructure, skills gaps compounded by brain drain, fragmented institutional mandates, and the absence of a national EO coordination mechanism collectively prevent technically sound EO outputs from informing routine planning and policy decisions. Nepal’s small geographic extent, growing digital literacy, and ongoing governance reforms create strategic opportunities for transition, but realizing these requires a functioning geospatial ecosystem integrating data systems, technical infrastructure, human capital, and institutional frameworks. We propose the “Pixels to Policy” framework to operationalize this ecosystem and identify three priority research directions for the global remote sensing community: lightweight data formats for low-bandwidth settings, capacity-aware tool design, and implementation science for EO uptake. These directions reframe the community’s responsibility from delivering open data to ensuring it can be used. Full article

Radio frequency interference (RFI) segmentation in Ku-band satellite communications remains challenging because of weak, non-stationary interference characteristics and the scarcity of pixel-level annotated empirical data. To address this limitation, this study proposes a synthetic-to-real deep learning framework in which four parameterized RFI morphologies—narrowband, broadband, impulsive, and frequency-varying—are superimposed onto empirical Ku-band spectrogram backgrounds acquired from a 12-m ground-station platform. A conditional Generative Adversarial Network (cGAN) is then employed for domain adaptation to reduce the synthetic-to-real gap by harmonizing the hybrid spectrograms with empirical thermal noise characteristics. The refined spectrograms and their exact binary masks are subsequently used to train a U-Net model for pixel-level segmentation. Quantitative evaluation on held-out empirical-background hybrid test data shows that the proposed framework achieves an Intersection over Union (IoU) of 0.849 and an F1-score of 0.918, outperforming traditional threshold-based methods and unrefined learning baselines. Additional qualitative validation on naturally observed empirical Ku-band RFI recordings further supports the practical applicability of the proposed framework beyond controlled hybrid test data. These results indicate that generative domain adaptation provides a practical and scalable alternative to manual labeling for automated RFI monitoring in operational Ku-band environments. Full article

Atmospheric correction (AC) uncertainties critically constrain satellite chlorophyll-a (CHLA) retrieval in optically complex coastal waters. Existing AC algorithms perform divergently across water types, and no single algorithm is universally optimal. Although multi-source fusion has been widely explored, current studies predominantly integrate satellite sensors or inversion models while neglecting uncertainties inherent to the preprocessing AC step. In this study, we developed a pixel-wise AC optimization method using the QA score model to evaluate and select spectrally complementary outputs from multiple AC algorithms. Applied to GOCI data over the Shandong Peninsula, four algorithms (GDPS 1.3, GDPS 2.0, Seadas_Default, and Seadas_MUMM) were employed. For each pixel, the optimal remote sensing reflectance (R_rs) was selected based on QA scores, followed by CHLA retrieval via the YOC model. Validation against 96 in situ measurements demonstrated significantly improved accuracy (r = 0.868, RMSE = 0.582 μg/L, ε = 16.9%) compared with any single AC method. This study confirms that pixel-wise AC optimization and selection effectively suppress algorithm-specific uncertainties, providing a robust strategy for enhancing satellite-derived CHLA estimates in complex coastal waters. Full article

Fine particulate matter (PM_2.5) is studied for the first time at ground level in different sites of Greater Rosario (GR), an urban and industrial area of central-eastern Argentina. Twelve sites were selected according to land use, and 87 samples were analyzed during winter 2021 and summer 2022. The spatial and temporal distribution of PM_2.5 was examined, comparing results among sites and with global data. Ground-based data were complemented with satellite-derived Aerosol Optical Depth (AOD) and nitrogen dioxide vertical column density (NO₂ VCD). During winter, the highest PM_2.5 was obtained at an industrial site in northern GR, while in summer, maximum values were observed in the center of Rosario. Summer rain events could contribute to the wet deposition of suspended particles, resulting in lower PM_2.5 concentrations. Satellite data indicate higher average AOD in summer (attributable to forest fires in NE Argentina) and higher NO₂ VCD in winter, coinciding with burning events in the Paraná Delta islands and reflected in some PM_2.5 peaks. This analysis represents the first approach to assessing the air quality of Rosario and its surroundings, with on-site data collected in association with land use. Full article

In Space–Air–Ground Integrated Networks (SAGINs), the inherent high bit error rate (BER) and prolonged propagation latency of satellite links, compounded by the highly dynamic topologies and multi-hop nature of Unmanned Aerial Vehicle (UAV) networks, present severe bottlenecks to end-to-end transport performance. To mitigate performance degradation within these heterogeneously converged SAGIN-UAV architectures, this paper proposes a SAGIN-enabled Adaptive End-to-End Congestion Control scheme. By exploiting the distinct transmission characteristics of long-delay, high-BER satellite links alongside terrestrial mobile multi-hop UAV networks, the Proposed Scheme optimizes data injection during the slow-start phase and introduces a high-precision loss differentiation mechanism during the congestion avoidance phase. This framework accurately distinguishes non-congestive losses (e.g., channel errors or topology switching induced by UAV mobility) from genuine buffer overflows. The simulation results demonstrate that the proposed adaptive scheme significantly reduces queuing delays at UAV nodes, accelerates transmission efficiency across multi-hop terminals, and enhances data throughput in high-latency environments. Ultimately, this scheme offers a resilient solution for optimizing end-to-end transport control and maximizing the overall transmission capability of SAGIN-enabled UAV networks. Full article

Scene matching-based localization systems (SMLSs) offer an effective solution to the failure of Global Navigation Satellite System (GNSS) positioning in complex environments. This paper designs and implements a vision-based autonomous localization system for unmanned aerial vehicles (UAVs), compatible with both optical and infrared sensors, delivering high frame rates and high-precision positioning performance. First, to address the issue of uneven texture distribution in natural terrain features, an adaptive expansion sliding window model is constructed to accurately extract texture-rich regions, which effectively improves matching precision. Second, considering the differences in edge characteristics between optical and infrared images, the Sobel operator and Scharr operator are introduced, respectively, to construct gradient features, achieving high-precision, high-frame-rate heterogeneous image matching. Furthermore, to significantly improve the system frame rate, this paper designs an embedded parallel acceleration strategy based on a multi-core CPU architecture. The strategy achieves task-level concurrency between the front-end stages (pre-correction and feature refinement) and matching, and implements parallel optimization for feature construction and correlation computation within the matching module. On the algorithmic level, the correlation computation is further accelerated by replacing spatial-domain convolution with frequency-domain multiplication. Finally, the algorithm is deployed on an RK3588 embedded platform. The effectiveness of the proposed system is validated using offline flight data from a medium-altitude fixed-wing UAV and real-time flight experiments with a low-altitude rotary-wing UAV. In the medium-altitude UAV flight data validation, optical visual localization achieves an average position error of 20.94 m with a processing time of 0.123 s/frame, while infrared visual localization yields a position error of 11.77 m at 0.128 s/frame. In the low-altitude UAV flight experiment, optical visual localization achieves an average position error of 9.68 m at 0.15 s/frame, and infrared visual localization achieves an average position error of 11.22 m at 0.15 s/frame. Full article

Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial resolution, viewing geometry, and computational constraints present challenges for developing unified detection models. This study presents a comparative benchmarking analysis of the lightweight YOLOv26-nano model for forest fire detection using the FASDD dataset, comprising satellite, UAV, and ground-based imagery. A unified experimental protocol with five-fold cross-validation is adopted to ensure robustness and cross-platform generalization. Performance is enhanced through data augmentation, contrast-limited adaptive histogram equalization, and stochastic gradient descent optimization. Experimental results demonstrate that YOLOv26-nano achieves reliable detection accuracy and demonstrates promising computational characteristics under simulated resource-constrained edge-computing conditions. The proposed benchmarking framework provides a standardized reference for multi-platform fire detection and highlights the suitability of nano-scale object detection models for scalable wildfire monitoring and early-warning systems. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R² = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes. Full article

Maritime piracy remains a significant threat to the safety of commercial shipping and the stability of global supply chains. Contemporary maritime surveillance systems increasingly employ multisensor data fusion to build a coherent operational situation picture. This paper presents a comparative review of commercially available maritime surveillance systems integrating data from radar, AIS, and visual and satellite sensors. Systems were selected based on their representativeness across different application domains (coastal surveillance, VTS, MDA) and the availability of sufficient technical documentation. The review of these solutions enables an assessment of the current level of technological maturity and the identification of limitations of existing architectures in the context of dynamic piracy-related threats. On this basis, the need for developing autonomous early-warning systems utilizing data fusion and artificial intelligence algorithms operating at the ship’s operational level has been identified. Full article

Multisource remote sensing and Earth observation (EO) products provide scalable covariates for regional mineral prospectivity mapping, but their integration with incomplete and preferentially sampled occurrence records can produce biased prediction maps. We present a bias-aware machine learning workflow for porphyry copper prospectivity mapping that integrates satellite-derived alteration proxies, topographic variables, regional geology, structural context, and accessibility-related EO layers on a harmonized 1 km grid. The workflow separates remote sensing/geological predictors from survey-effort proxies and combines this decomposition with positive-unlabeled learning, stacked ensembling, rank-optimized blending, fold-wise calibration, and spatial block cross-validation. The case study covers the eastern Central Asian Orogenic Belt (CAOB) and uses porphyry Cu occurrences together with covariates derived from ASTER short-wave infrared information, Landsat 8 reflectance, SRTM topography, VIIRS night-time lights, GHSL population data, geological units, and active fault information. Across held-out spatial folds, the final RO-BAB ensemble provides a modest but exploration-relevant improvement in ranking relative to the all-covariate XGBoost baseline, increasing PR-AUC from 0.0297 to 0.0364 and recovering 26.75% of known deposits within the top 5% of ranked cells. The resulting maps delineate coherent remote sensing-supported prospective corridors while exposing regions where predictions may be influenced by historical accessibility and recording effort. The study demonstrates how machine learning that accounts for sampling bias can improve the reliability and interpretability of remote sensing mineral prospectivity products in the presence of only reference data. Full article