Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery DOI Open Access

Álvaro Agustín Chávez-Durán,

Mariano Garcı́a, Miguel Olvera‐Vargas

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(2), P. 225 - 225

Published: Jan. 24, 2024

Canopy fuels determine the characteristics of entire complex forest due to their constant changes triggered by environment; therefore, development appropriate strategies for fire management and risk reduction requires an accurate description canopy fuels. This paper presents a method mapping spatial distribution fuel loads (CFLs) in alignment with natural variability three-dimensional distribution. The approach leverages object-based machine learning framework UAV multispectral data photogrammetric point clouds. proposed was developed mixed protected area “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from clouds, along spectral information, were used Random Forest model accurately estimate CFLs, yielding R2 = 0.75, RMSE 1.78 Mg, average Biasrel 18.62%. volume most significant explanatory variable, achieving mean decrease impurity values greater than 80%, while combination texture vegetation indices presented importance close 20%. Our modelling enables estimation accounting ecological context that governs dynamics variability. high precision achieved, at relatively low cost, encourages updating maps enable researchers managers streamline decision making on management.

Language: Английский

A critical review on multi-sensor and multi-platform remote sensing data fusion approaches: current status and prospects DOI Creative Commons
Farhad Samadzadegan, Ahmad Toosi, Farzaneh Dadrass Javan

et al.

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 76

Published: Dec. 11, 2024

Numerous remote sensing (RS) systems currently collect data about Earth and its environments. However, each system provides limited in terms of spatial resolution, spectral information, other parameters. Given technological constraints, combining from diverse sources can effectively enhance RS solutions through enrichment. Many studies have investigated the fusion acquired different sensors platforms. This paper a comprehensive review research on multi-platform -sensor fusion, encompassing visible-light images, multi/hyper-spectral RADAR LiDAR point clouds, thermal spectrometry samples, geophysical data. An analysis over 950 papers revealed that feature-level multi-sensor was most commonly employed technique, surpassing pixel- decision-level approaches. Moreover, satellite more prevalent than manned unmanned aerial vehicles. The integration initially gained traction applications such as precision agriculture before expanding to land use cover mapping. addresses previously overlooked issues presents framework facilitate seamless Guidelines for this include ensuring same acquisition time, co-registration, true orthorectification, consistent resolution or information content, radiometric consistency, wavelength band coverage.

Language: Английский

Citations

8
Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI DOI Creative Commons

Damini Raniga,

A. Narmilan, Juan Sandino

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1063 - 1063

Published: Feb. 6, 2024

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change ozone depletion, requires robust non-invasive methods monitor its health condition. Despite increasing use unmanned aerial vehicles (UAVs) acquire high-resolution data for vegetation analysis Antarctic regions through artificial intelligence (AI) techniques, multispectral imagery deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores potential a field notably limited implementations these datasets; (2) introduces an innovative workflow that compares performance between supervised machine (ML) classifiers: Extreme Gradient Boosting (XGBoost) U-Net. The proposed validated by detecting mapping lichen using collected highly biodiverse Specially Protected Area (ASPA) 135, situated near Casey Station, January February 2023. implemented ML models were trained against five classes: Healthy Moss, Stressed Moribund Lichen, Non-vegetated. In development U-Net model, applied: Method which utilised original labelled those used XGBoost; incorporated XGBoost predictions additional input version Results indicate demonstrated performance, exceeding 85% key metrics precision, recall, F1-score. suggested enhanced accuracy classification outputs U-Net, 2 substantial increase recall F1-score compared 1, notable improvements precision Moss (Method 2: 94% vs. 1: 74%) 86% 69%). These findings contribute advancing monitoring techniques delicate ecosystems, showcasing UAVs, imagery, remote sensing applications.

Language: Английский

Citations

7
Developing a new method using thermal drones for population surveys of the world's rarest great ape species, Pongo tapanuliensis DOI Creative Commons
Dede Aulia Rahman,

Haryanto R. Putro,

Tubagus Ahmad Mufawwaz

et al.

Global Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown, P. e03463 - e03463

Published: Jan. 1, 2025

Language: Английский

Citations

1
Compression Approaches for LiDAR Point Clouds and Beyond: A Survey DOI Open Access
Miaohui Wang, Runnan Huang, Wuyuan Xie

et al.

ACM Transactions on Multimedia Computing Communications and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

With the widespread use of LiDAR sensors in autonomous driving, point cloud compression (LPCC) plays an important role effectively managing storage, transmission, and perception growing volume data. Despite this need, there has been a noticeable absence comprehensive investigations specifically dedicated to LPCC methods. To address issue, paper presents systematic survey existing LPCCs, aiming summarize recent progress inspire future research field. We begin by providing general introduction fundamentals, covering latest (LPC) datasets, distinctive attributes, evaluation metrics, data formats. then conduct careful review comparison examining image-based, octree-based, deep-learned, other approaches, offering valuable insights into strengths weaknesses cutting-edge models. Finally, we propose directions based on limitations LPCCs. believe that findings presented will contribute deeper understanding LPCCs promote further development sensor-based systems.

Language: Английский

Citations

1
Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning DOI

Xiaokai Chen,

Fenling Li, Qingrui Chang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110072 - 110072

Published: Feb. 6, 2025

Language: Английский

Citations

1
Mapping Harmful Algae Blooms: The Potential of Hyperspectral Imaging Technologies DOI Creative Commons
Fernando Arias,

Mayteé Zambrano,

Edson Galagarza

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 608 - 608

Published: Feb. 11, 2025

Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, biodiversity impacts. These blooms, increasingly exacerbated climate change, compromise water quality in both marine freshwater ecosystems, significantly affecting life coastal economies based on fishing tourism while also posing serious risks inland bodies. This article examines the role of hyperspectral imaging (HSI) monitoring HABs. HSI, with its superior spectral resolution, enables precise classification mapping diverse species, emerging as a pivotal tool environmental surveillance. An array HSI techniques, algorithms, deployment platforms are evaluated, analyzing efficacy across varied geographical contexts. Notably, sensor-based studies achieved up 90% accuracy, regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients determination (R2) above 0.80. quantitative findings underscore potential for robust HAB diagnostics early warning systems. Furthermore, we explore current limitations future management, highlighting strategic importance addressing growing economic challenges posed paper seeks provide comprehensive insight into HSI’s capabilities, fostering integration global strategies against proliferation.

Language: Английский

Citations

1
Cross-industry Review of Autonomous Alignment Technologies: Adaptation Potential for Modular Construction DOI Creative Commons
Sulemana Fatoama Abdulai, Tarek Zayed, Ibrahim Yahaya Wuni

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: 495, P. 145101 - 145101

Published: Feb. 21, 2025

Language: Английский

Citations

1
Spatial predictions of soil moisture across a longitudinal gradient in semiarid ecosystems using UAV and RGB sensors DOI Creative Commons
Alexander A. Hernandez, Efraín Duarte,

Peter Porter

et al.

Geocarto International, Journal Year: 2025, Volume and Issue: 40(1)

Published: March 2, 2025

Language: Английский

Citations

1
What Is the Predictive Capacity of Sesamum indicum L. Bioparameters Using Machine Learning with Red–Green–Blue (RGB) Images? DOI Creative Commons
Edimir Xavier Leal Ferraz, Alan Cézar Bezerra, Raquele Mendes de Lira

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 64 - 64

Published: March 3, 2025

The application of machine learning techniques to determine bioparameters, such as the leaf area index (LAI) and chlorophyll content, has shown significant potential, particularly with use unmanned aerial vehicles (UAVs). This study evaluated RGB images obtained from UAVs estimate bioparameters in sesame crops, utilizing data selection methods. experiment was conducted at Federal Rural University Pernambuco involved using a portable AccuPAR ceptometer measure LAI spectrophotometry photosynthetic pigments. Field were captured DJI Mavic 2 Enterprise Dual remotely piloted aircraft equipped thermal cameras. To manage high dimensionality data, CRITIC Pearson correlation methods applied select most relevant indices for XGBoost model. divided into training, testing, validation sets ensure model generalization, performance assessed R2, MAE, RMSE metrics. effectively estimated LAI, a, total chlorophyll, carotenoids (R2 > 0.7) but had limited b. found be effective method algorithm.

Language: Английский

Citations

1
Knowledge graph exploitation to enhance the usability of risk assessment in construction safety planning DOI Creative Commons
Karsten Winther Johansen, Carl Schultz, Jochen Teizer

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103305 - 103305

Published: April 23, 2025

Language: Английский

Citations

1