Search Results (969)

Post-fire soil and vegetation changes can intensify erosion and sediment yield by altering the factors controlling the runoff–infiltration balance. Erosion barriers (EBs) are widely used in hydrological and forest restoration to mitigate erosion, reduce sediment transport, and promote vegetation recovery. However, precise spatial assessments of their effectiveness remain scarce, requiring validation through operational methodologies. This study evaluates the impact of EB on post-fire vegetation recovery at two temporal and spatial scales: (1) Remotely Piloted Aircraft System (RPAS) imagery, acquired at high spatial resolution but limited to a single acquisition date coinciding with the field flight. These data were captured using a MicaSense RedEdge-MX multispectral camera and an RGB optical sensor (SODA), from which NDVI and vegetation height were derived through aerial photogrammetry and digital surface models (DSMs). (2) Sentinel-2 satellite imagery, offering coarser spatial resolution but enabling multi-temporal analysis, through NDVI time series spanning four consecutive years. The study was conducted in the area of the Luna Fire (northern Spain), which burned in July 2015. A paired sampling design compared upstream and downstream areas of burned wood stacks and control sites using NDVI values and vegetation height. Results showed slightly higher NDVI values (0.45) upstream of the EB (p < 0.05), while vegetation height was, on average, ~8 cm lower than in control sites (p > 0.05). Sentinel-2 analysis revealed significant differences in NDVI distributions between treatments (p < 0.05), although mean values were similar (~0.32), both showing positive trends over four years. This study offers indirect insight into the functioning and effectiveness of EB in post-fire recovery. The findings highlight the need for continued monitoring of treated areas to better understand environmental responses over time and to inform more effective land management strategies. Full article

Accurate semantic segmentation of high-resolution remote sensing imagery is challenged by substantial intra-class variability, inter-class similarity, and the limitations of single-modality data. This paper proposes MMFNet, a novel multimodal fusion network that leverages the Mamba architecture to efficiently capture long-range dependencies for semantic segmentation tasks. MMFNet adopts a dual-encoder design, combining ResNet-18 for local detail extraction and VMamba for global contextual modelling, striking a balance between segmentation accuracy and computational efficiency. A Multimodal Feature Fusion Block (MFFB) is introduced to effectively integrate complementary information from optical imagery and digital surface models (DSMs), thereby enhancing multimodal feature interaction and improving segmentation accuracy. Furthermore, a frequency-aware upsampling module (FreqFusion) is incorporated in the decoder to enhance boundary delineation and recover fine spatial details. Extensive experiments on the ISPRS Vaihingen and Potsdam benchmarks demonstrate that MMFNet achieves mean IoU scores of 83.50% and 86.06%, outperforming eight state-of-the-art methods while maintaining relatively low computational complexity. These results highlight MMFNet’s potential for efficient and accurate multimodal semantic segmentation in remote sensing applications. Full article

Gully erosion, driven by the interplay of natural processes and human activities, results in severe soil degradation and landscape alteration, yet approaches for accurately quantifying erosion triggered by extreme precipitation using multi-source high-resolution remote sensing remain limited. This study first extracted digital surface models (DSM) for the years 2014 and 2024 using Ziyuan-3 and GaoFen-7 satellite stereo imagery, respectively. Subsequently, the DSM was calibrated using high-resolution unmanned aerial vehicle photogrammetry data to enhance elevation accuracy. Based on the corrected DSMs, gully erosion depths from 2014 to 2024 were quantified. Erosion patches were identified through a deep learning framework applied to GaoFen-1 and GaoFen-2 imagery. The analysis further explored the influences of natural processes and anthropogenic activities on elevation changes within the gully erosion watershed. Topographic monitoring in the Sandu River watershed revealed a net elevation loss of 2.6 m over 2014–2024, with erosion depths up to 8 m in some sub-watersheds. Elevation changes are primarily driven by extreme precipitation-induced erosion alongside human activities, resulting in substantial spatial variability in surface lowering across the watershed. This approach provides a refined assessment of the spatial and temporal evolution of gully erosion, offering valuable insights for soil conservation and sustainable land management strategies in the Loess Plateau region. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify and characterize these water reservoirs, which played a crucial role in ancient Maya water management and continued to be vital for contemporary wildlife. By comparing different visualization techniques and imagery sources, the study demonstrates that while lidar data provides superior topographic detail, satellite imagery—particularly with nominal 3 m, or finer, spatial resolution with a near-infrared band—offers valuable complementary data including present-day hydrological and vegetative characteristics. In this study, 350 aguadas were identified in the broader region. The shapes, canopy cover, and topographic positions of these aguadas were documented, and the anthropogenic origin of most features was emphasized. The paper’s conclusion states that combining various remote sensing datasets enhances the identification and understanding of aguadas, providing insights into ancient Mayan adaptive strategies and contributing to ongoing archaeological and ecological research. Full article

This study has compiled the first impact-crater dataset for Mercury with diameters greater than 400 m by a multimodal deep-learning pipeline. We present an enhanced deep learning framework for large-scale planetary crater detection, extending the YOLOLens architecture through the integration of multimodal inputs: optical imagery, digital terrain models (DTMs), and hillshade derivatives. By incorporating morphometric data, the model achieves robust detection of impact craters that are often imperceptible in optical imagery alone, especially in regions affected by low contrast, degraded rims, or shadow-dominated illumination. The resulting catalogs LU6M371TGT for the Moon and ME6M300TGT for Mercury constitute the most comprehensive automated crater inventories to date, demonstrating the effectiveness of multimodal learning and cross-planet transfer. This work highlights the critical role of terrain information in planetary object detection and establishes a scalable, high-throughput pipeline for planetary surface analysis using modern deep learning tools. To validate the pipeline, we compare its predictions against the manually annotated catalogs for the Moon, Mercury, and several regional inventories, observing close agreement across the full diameter spectrum, revealing a high level of confidence in our approach. This work presents a spatial density analysis, comparing the spatial density maps of small and large craters highlighting the uneven distribution of crater sizes across Mercury. We explore the prevalence of kilometer-scale (1–5 km range) impact craters, demonstrating that these dominate the crater population in certain regions of Mercury’s surface. Full article

This article explores how Toru Takemitsu transforms literary and natural imagery into sacred soundscapes in his Rain Tree Sketches, drawing on Ōe Kenzaburō’s short story “The Clever Rain Tree” as a starting point for musical meditation on nature and spirituality. This research employs three different approaches to study the transformation process. First, it traces the transformation of Ōe’s literary symbols into Takemitsu’s musical vocabulary while explaining how Zen aesthetics and Japanese shizen (nature) concepts unite text and sound domains. Second, it undertakes a systematic study of musical parameters in the composition to show how motivic development, textural transformation, and temporal organization express water imagery and embody the Zen principle of ma (emptiness). Third, it critically examines modern multimedia visualizations of Rain Tree Sketches to explore both the potential and the limitations of digital technology in mediating the composition’s spiritual dimensions. The analysis demonstrates how Takemitsu created a modernist sacred space through musical techniques that enable listeners to experience transcendence via the deliberate orchestration of sound, silence, and suspended time. More broadly, it shows how modern composers can transform literary spiritual content into abstract musical compositions while preserving their meditative character. This article significantly expands upon preliminary ideas presented at KAMC 2024 conference, 2024, incorporating new theoretical frameworks, extensive analysis of spiritual dimensions, and critical examination of digital mediation not present in the original conference presentation. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article

Rural road networks are vital for rural development, yet narrow alleys and occluded segments remain underrepresented in digital maps due to irregular morphology, spectral ambiguity, and limited model generalization. Traditional segmentation models struggle to balance local detail preservation and long-range dependency modeling, prioritizing either local features or global context alone. Hypothesizing that integrating hierarchical local features and global context will mitigate these limitations, this study aims to accurately segment such rural roads by proposing R-SWTNet, a context-aware U-Net-based framework, and constructing the SQVillages dataset. R-SWTNet integrates ResNet34 for hierarchical feature extraction, Swin Transformer for long-range dependency modeling, ASPP for multi-scale context fusion, and CAM-Residual blocks for channel-wise attention. The SQVillages dataset, built from multi-source remote sensing imagery, includes 18 diverse villages with adaptive augmentation to mitigate class imbalance. Experimental results show R-SWTNet achieves a validation IoU of 54.88% and F1-score of 70.87%, outperforming U-Net and Swin-UNet, and with less overfitting than R-Net and D-LinkNet. Its lightweight variant supports edge deployment, enabling on-site road management. This work provides a data-driven tool for infrastructure planning under China’s Rural Revitalization Strategy, with potential scalability to global unstructured rural road scenes. Full article

Digital platforms have become primary channels for cultural heritage transmission, yet how individuals visually represent their personal heritage online remains unexplored. This study investigates the visual patterns in personal heritage representation across digital platforms, examining whether platform affordances or demographics influence these patterns. Through the LAVIS multimodal AI system, we analyzed 588 heritage images from Instagram and “Personas y Patrimonios”, combining automated content, composition, color, and saturation analyses with human validation. Our findings revealed that intimate, portable objects—particularly jewelry (22.79%)—dominate personal heritage representations, with no content differences between platforms or genders. Small but statistically significant platform differences emerged in color patterns (Cohen’s d = −0.215) and compositional attention (Cohen’s d = 0.147), while gender showed no significant differences in any visual dimension. These findings may indicate that personal heritage representation follows universal visual patterns, emphasizing personal bonds that transcend both platform affordances and demographic differences. These results advance understanding of personal digital heritage communication by identifying the universal patterns in its visualization. Beyond establishing a methodological framework for AI-assisted heritage image analysis, this research provides practical insights for heritage educators and digital platform designers while illuminating how biographical objects function in digital environments, ultimately underscoring the pivotal role of imagery in contemporary cultural transmission. Full article

Historic buildings and urban streetscapes face increasing threats from climate change, development, and aging infrastructure, creating a pressing need for accurate and scalable documentation methods. This review assesses the combined use of photogrammetry and unmanned aerial vehicle (UAV) technologies in preserving built cultural heritage. We systematically analyze the end-to-end workflow, from the sophisticated processing of imagery into highly detailed and accurate 3D models in photogrammetry software via data acquisition using diverse UAV platforms and sensor payloads. Through case studies, including the mapping of ancient Maya sites in the Yucatán Peninsula and the conservation of the Notre Dame Cathedral, the review highlights the accuracy, efficiency, and accessibility offered by this technological synergy, underscoring its significance for heritage conservation, research, and the development of digital twins. Furthermore, it explores how these advancements foster public engagement and virtual accessibility, enabling immersive experiences and enriched educational opportunities. The paper also critically assesses the inherent technical, ethical, and legal challenges associated with this methodology, offering a balanced perspective on its application. By synthesizing the current knowledge, this review proposes future research trajectories and advocates for best practices, aiming to guide heritage professionals in leveraging photogrammetry and UAVs for the effective documentation and safeguarding of global cultural heritage. Full article

Assessing the impact of land use and land cover (LULC) on water quality (WQ) is central to land-based environmental research. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study analyses recent trends using LULC maps to assess stream WQ within buffers, focusing on papers published between 2020 and 2024. It identifies relevant remote sensing practices for LULC mapping, landscape metrics, WQ physicochemical parameters, statistical techniques for correlating LULC and WQ, and conventions for configuring buffers. Materials include Scopus, Web of Science, and Atlas.ti, which support both qualitative data analysis and Conversational Artificial Intelligence (CAI) tasks via its integration with OpenAI’s large language models. The methodology highlights creating a bibliographic database, coding, CAI, and validating prompts. Official maps and visual or digital interpretations of optical imagery provided inputs for LULC. Classifiers from earlier generations have shaped LULC cartography. The most employed WQ parameters were phosphorus, total nitrogen, and pH. The three most referenced landscape metrics were the Largest Patch Index, Patch Density, and Landscape Shape Index. The literature mainly relied on Redundancy Analysis, Principal Component Analysis, and alternative correlation approaches. Buffer configurations varied in size. CAI facilitated an agile systematic review; however, it encountered challenges related to a phenomenon known as hallucination, which hampers its optimal performance. Full article