Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 290 - 290

Published: Jan. 11, 2024

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery ground-based observations coastal marine environments with high spatial resolution. The dynamic nature of water surfaces poses challenge for photogrammetric techniques due to absence fixed reference points. Addressing these issues, this study introduces an innovative, efficient, accurate workflow georeferencing mosaicking that overcomes previous limitations. Using open-source Python libraries, employs direct produce georeferenced orthomosaic integrates multiple UAV captures, has been tested locations worldwide optical RGB, thermal, multispectral imagery. best case achieved Root Mean Square Error 4.52 m standard deviation 2.51 accuracy, thus UAV’s centimeter-scale This represents significant advancement processes, resolving major limitation facing technology remote observation local-scale phenomena over surfaces.

Language: Английский

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 446 - 446

Published: Jan. 23, 2024

Since 1971, remote sensing techniques have been used to map and monitor phenomena parameters of the coastal zone. However, updated reviews only considered one phenomenon, parameter, data source, platform, or geographic region. No review has offered an overview that can be accurately mapped monitored with data. This systematic was performed achieve this purpose. A total 15,141 papers published from January 2021 June 2023 were identified. The 1475 most cited screened, 502 eligible included. Web Science Scopus databases searched using all possible combinations between two groups keywords: geographical names in areas platforms. demonstrated that, date, many (103) (39) (e.g., coastline land use cover changes, climate change, urban sprawl). Moreover, authors validated 91% retrieved parameters, 39 1158 times (88% combined together other parameters), 75% over time, 69% several compared results each available products. They obtained 48% different methods, their 17% GIS model techniques. In conclusion, addressed requirements needed more effectively analyze employing integrated approaches: they data, merged

Language: Английский

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Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario

Remote Sensing, Journal Year: 2023, Volume and Issue: 16(1), P. 68 - 68

Published: Dec. 23, 2023

Water scarcity and quality deterioration, driven by rapid population growth, urbanization, intensive industrial agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model comprehensively monitor various parameters (WQP) evaluate feasibility of implementing this in real-world scenarios, addressing limitations conventional in-situ sampling. Thus, comprehensive monitoring WQP was developed using 38-year dataset Landsat imagery data from Information System Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Nitrogen (TN), Phosphorus (TP). Optimal band combinations each parameter were identified, enhancing accuracy estimation. The ANN-based exhibited very high accuracy, particularly chl-a TSS (R2 > 0.90, NRMSE < 0.79%), surpassing previous studies. independent validation showcased accurate classification TN, while DO estimation faced challenges during variation periods, highlighting complexity dynamics. usability successfully tested real-case scenario, proving be an operational tool Future research avenues include exploring additional sources improved potentially predictions expanding model’s utility diverse environmental contexts.

Language: Английский

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Water Sustainability Enhancement with UAV and AIoT: An Integrated Technology for Water Quality and Flood Hazard Monitoring using the Internet of Drones

Journal of Geosciences and Environmental Studies., Journal Year: 2025, Volume and Issue: 2(1), P. 13 - 13

Published: Jan. 26, 2025

Globally, there are challenges in minimizing the effects of water pollution and global warming everywhere. We want to apply a sensor network connected an Esp32 Tensorflow lite integrated system map flood conditions for drone-based surface waste collection. Finally, GSM sim 800L Module is incorporated notify user about monitored conditions, such as trash level other data. An ultrasonic utilized detect level. The outcome shows high chance tracking levels monitoring floods. This innovative technology allows users receive warnings be warned remotely. Inception-v3 model on clean unclean images obtained 97% accuracy testing USING Inception-v3, using proposed circuit diagram, prototype developed possible deployment resource region operation application presented paper.

Language: Английский

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Location optimization of unmanned aerial vehicle (UAV) drone port for coastal zone management: The case of Guangdong coastal zone in China

Ocean & Coastal Management, Journal Year: 2025, Volume and Issue: 262, P. 107576 - 107576

Published: Feb. 12, 2025

Language: Английский

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Addressing complex challenges in water quality management: emerging technologies and sustainable strategies

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 251 - 276

Published: Jan. 1, 2025

Language: Английский

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A Novel Method for Eliminating Glint in Water-Leaving Radiance from UAV Multispectral Imagery

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 996 - 996

Published: March 12, 2025

Unmanned Aerial Vehicle (UAV) high-resolution remote sensing imagery has been used for unprecedented coastal environment monitoring with ground sampling distance and time intervals of a few centimeters seconds, respectively. However, high spatial-time resolutions UAV data consist unexpected signals from water surface level changes induced by wind-driven currents waves. This leads to non-linear non-stationary forms sun sky glints in the sea image. Consequently, these interfere detection body reflections objects, reducing accuracy usability measurements. study employed Fast Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) separate spatial periodicity time-continuous multispectral images original retain non-oscillatory called residual images. The effectively represented spatial-temporal radiance flow variations correcting regions glint. presents three key findings: First, homogeneous glint removed raw image sequence was acquired using FA-MEMD. Second, continuous removal effect is validated through water-leaving (Lw-SBA) measurements obtained via Skylight-Blocked Approach (SBA) method. Comparisons showed that R2 values clear before after were 0.02 0.56 RMSE 8.37 × 10−5 5.51 W·m−2·sr−1, respectively, indicating an improvement rate 34.19%. Third, comparative analysis previous methods demonstrated our approach yielded spatially temporally uniform less variability than traditional methods. synchronized Lw-SBA similarity, confirming FA-MEMD technique wave-induced roughness, enhancing reliability color observations.

Language: Английский

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Water quality parameters inversion based on multispectral remote sensing

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 73, P. 107707 - 107707

Published: April 15, 2025

Language: Английский

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Detection of Small Water Bodies for Vector Control Using Deep Learning on Unmanned Aerial Vehicle Multispectral Imagery

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 30, 2025

Vector-borne diseases pose a persistent public health challenge in tropical regions such as Vietnam, where traditional ground-based surveillance methods struggle with scale and accuracy. This study presents framework that integrates Unmanned Aerial Vehicle (UAV) multispectral imagery deep learning techniques to detect small-to-medium-sized water bodies, important habitats for arbovirus vectors. High-resolution images were captured the DJI Phantom 4 (P4M) Multispectral UAV rural peri-urban areas of Binh Duong province Vietnam. A curated dataset 982 annotated was created, comprising RGB, near-infrared (NIR), normalized difference index (NDWI) bands. Six state-of-the-art object detection segmentation models evaluated, including YOLOv7, YOLOv7x, DocF, U-Net, MSNet, RTFNet. Among them, (U-Net MSNet) using RGB + Green NIR NDWI achieved best performance dice scores above 0.92. The results show combination significantly improves accuracy bodies complex conditions. approach provides scalable, cost-effective solution mapping small contributes targeted vector control disease prevention measures arbovirus-prone regions.

Language: Английский

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Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming

Drones, Journal Year: 2023, Volume and Issue: 7(7), P. 410 - 410

Published: June 21, 2023

This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms estimate chlorophyll-a (Chl-a) cyanobacteria in experimental fishponds Brazil. In addition spectral resolutions, tested differ price, payload, imaging system, processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard DJI Matrice 600 UAV. Multispectral global shutter-frame 4-band Parrot Sequoia Phantom 4 Water quality field measurements acquired portable fluorometer laboratory analysis. The concentration ranged from 14.3 290.7 µg/L 0 112.5 for Chl-a cyanobacteria, respectively. Forty-one bio-optical retrieval models tested. UAV hyperspectral image achieved robust assessments, with RMSE values 32.8 12.1 µg/L, images 47.6 35.1 respectively, efficiently mapping broad classes. are ideal monitoring CyanoHABs; however, integrated platform has high cost. More accessible multispectral may represent trade-off between efficiency deployment costs, provided that cameras offer narrow bands 660–690 nm 700–730 ranges 600–625 cyanobacteria.

Language: Английский

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