Advances in Sensor Data Fusion and AI for Environmental Monitoring

Abstract submission deadline

30 July 2026

Manuscript submission deadline

30 September 2026

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2094

Topic Information

Dear Colleagues,

Environmental monitoring increasingly demands high-resolution, real-time, and reliable information to guide sustainability, disaster response, and ecosystem management. Advances in sensor technologies—including satellites, airborne platforms, in situ stations, and Internet of Things (IoT) devices—have enabled the collection of vast amounts of heterogeneous data (e.g., spectral, structural, chemical, and meteorological observations). However, transforming these multimodal streams into actionable insights remains challenging, necessitating effective data fusion strategies and robust artificial intelligence frameworks. This Topic emphasizes research at the intersection of sensor data fusion and AI-driven analytics, aiming to highlight innovations that integrate multi-source data for accurate environmental assessment and predictive modeling.

Contributions are encouraged in areas such as:

(1) Novel fusion algorithms for heterogeneous sensor integration, especially those improving spatial and temporal resolution;

(2) Deep learning models tailored to fused data for applications such as land-cover change, air and water quality, forest health, and disaster prediction;

(3) Scalable and efficient architectures—e.g., edge-to-cloud systems or federated learning—for near‑real‑time monitoring;

(4) Case studies demonstrating improved decision-making in forestry, agriculture, urban planning, or conservation. Ultimately, this Topic seeks to showcase multidisciplinary approaches that leverage sensor fusion and AI to advance environmental science and inform sustainable resource management.

Dr. Zhenyu Yu
Prof. Dr. Mohd Yamani Idna Idris
Prof. Dr. Yu Li
Dr. Aleksandar Dj Valjarević
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geosciences geosciences	2.1	5.1	2011	23.4 Days	CHF 1800	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	34.2 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Data data	2.0	5.0	2016	25.2 Days	CHF 1600	Submit

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Published Papers (2 papers)

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All Geosciences IJGI Remote Sensing Sensors Data

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24 pages, 10966 KB  

Open AccessArticle

UAV-Based Wellsite Reclamation Monitoring Using Transformer-Based Deep Learning on Multi-Seasonal LiDAR and Multispectral Data

by Dmytro Movchan, Zhouxin Xi, Angeline Van Dongen, Charumitha Selvaraj and Dani Degenhardt

Remote Sens. 2025, 17(20), 3440; https://doi.org/10.3390/rs17203440 - 15 Oct 2025

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Abstract

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and [...] Read more.

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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27 pages, 16753 KB  

Open AccessArticle

A 1°-Resolution Global Ionospheric TEC Modeling Method Based on a Dual-Branch Input Convolutional Neural Network

by Nian Liu, Yibin Yao and Liang Zhang

Remote Sens. 2025, 17(17), 3095; https://doi.org/10.3390/rs17173095 - 5 Sep 2025

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Abstract

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. [...] Read more.

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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