Topic Editors

Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada
Prof. Dr. Linhai Jing
Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China

Information Fusion for Vegetation Characterization

Abstract submission deadline
closed (31 March 2024)
Manuscript submission deadline
30 June 2024
Viewed by
2559

Topic Information

Dear Colleagues,

Geospatial technologies are developing more rapidly than ever before, causing Earth observation (EO) data to be widely available in unprecedented volume and detail. Extracting the useful information buried in all these data is a challenge that calls for innovative solutions. In parallel with this increase in data, there is now an increased demand for the accurate determination of a growing number of attributes of vegetation canopies using remote sensing for inventory and sustainable management; analysis of wild-life habitats and biodiversity; renewable energy production; precision agriculture and forestry; carbon stock estimation, etc. The accurate characterization of vegetation canopies, such as the determination of cover types and species and the retrieval of their biophysical parameters, remains challenging using traditional data and stand-alone approaches. Significant advancements in machine learning, deep learning, and artificial intelligence have wide implications for this field. Data/information fusion has lately been attracting much interest in the remote sensing community to generate maximal values from the available data sets. This topic collection, thus, aims to disseminate innovative research in vegetation characterization using multi-source remotely sensed data and accumulated domain knowledge. The papers may be related to, but not limited to, the following topics:

  • Quantification and representation of uncertainty associated with information fusion.
  • Integration of physical principles and domain knowledge to data-driven machine learning and deep learning methods.
  • Development of novel information fusion algorithms for applications, such as: a. segmentation and classification of vegetation types; b. retrieval of physical/biophysical/biochemical parameters of vegetation canopies; c. change detection.
  • Development of frameworks, methods, and tools for large-scale operational implementation in vegetation characterization using multi-source data.

Prof. Dr. Baoxin Hu
Prof. Dr. Linhai Jing
Topic Editors

Keywords

  • information fusion
  • multi-source
  • vegetation characterization
  • deep learning
  • machine learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Forests
forests
2.9 4.5 2010 16.9 Days CHF 2600 Submit
Geomatics
geomatics
- - 2021 18.6 Days CHF 1000 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600 Submit

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Published Papers (2 papers)

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31 pages, 35626 KiB  
Article
Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network
by Maoyang Bai, Peihao Peng, Shiqi Zhang, Xueman Wang, Xiao Wang, Juan Wang and Petri Pellikka
Forests 2023, 14(9), 1823; https://doi.org/10.3390/f14091823 - 06 Sep 2023
Viewed by 922
Abstract
Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated [...] Read more.
Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field. Full article
(This article belongs to the Topic Information Fusion for Vegetation Characterization)
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23 pages, 10932 KiB  
Article
Fusion Approaches to Individual Tree Species Classification Using Multisource Remote Sensing Data
by Qian Li, Baoxin Hu, Jiali Shang and Hui Li
Forests 2023, 14(7), 1392; https://doi.org/10.3390/f14071392 - 07 Jul 2023
Cited by 3 | Viewed by 907
Abstract
With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined [...] Read more.
With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined their positive or negative contributions to tree species classification. This study aimed to investigate fusion approaches at the feature and decision levels deployed with support vector machine and random forest algorithms to classify five dominant tree species: Norway maple, honey locust, Austrian pine, white spruce, and blue spruce in individual crowns. Spectral, textural, and structural features derived from multispectral imagery (MSI), a very high-resolution panchromatic image (PAN), and LiDAR data were systematically exploited to assess their contributions to accurate classifications. Among the various classification schemes that were explored, both feature- and decision-level fusion approaches demonstrated significant improvements in tree species classification compared with the utilization of MSI (0.7), PAN (0.74), or LiDAR (0.8) in isolation. Notably, the decision-level fusion approach achieved the highest overall accuracies (0.86 for SVM and 0.84 for RF) and kappa coefficients (0.82 for SVM and 0.79 for RF). The misclassification analysis of fusion approaches highlighted the potential and flexibility of decision-level fusion in tree species classification. Full article
(This article belongs to the Topic Information Fusion for Vegetation Characterization)
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