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Forests
Special Issues
Forest Parameter Detection and Modeling Using Remote Sensing Data
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Forest Parameter Detection and Modeling Using Remote Sensing Data

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (28 October 2024) | Viewed by 1701

Special Issue Editors

Prof. Dr. Marcos Benedito Schimalski

E-Mail Website
Guest Editor
Department of Forest Engineering, Santa Catarina State University (UDESC), Lages 88520-000, SC, Brazil
Interests: forest management; orbital imagery; Lidar; mapping; digital photogrammetry; image analysis and processing; GIS; machine learning
Dr. Vasco M. Mantas

E-Mail Website
Guest Editor
Department of Earth Sciences, Center for Earth and Space Research (CITEUC), University of Coimbra, 3040-004 Coimbra, Portugal
Interests: remote sensing; forest ecology; synthetic aperture; Radar; forest management; radar; earth observation; environment science
Dr. Camile Sothe

E-Mail Website
Guest Editor
School of Earth, Environment & Society, McMaster University, Hamilton, ON L8S 4L8, Canada
Interests: classification; machine learning; forests; deep learning; Lidar; remote sensing; geoscience; data fusion; feature selection; multispectral image analysis; soil carbon content

Special Issue Information

Dear Colleagues,

Measuring forest parameters is crucial for forest inventorying, management, and conservation and remote sensing data derived from both active and passive sensors provide valuable information to estimate these parameters. With the rapid development of artificial intelligence and technologies such as machine learning, research into forest resource inventorying as applied to forest management has gained significant importance.

The aim of this Special Issue is to present the latest developments and applications of deep learning techniques for extracting and modeling forest parameters from remote sensing data. Topics include feature engineering, data augmentation, network architecture adaptation, model interpretation, and uncertainty quantification.

This Special Issue will feature original research articles demonstrating the benefits and challenges of deep learning methods in solving forest parameter measurement problems. Applications include deep learning for tree detection and diameter estimation, forest inventorying and planning, and structural and forest health estimation.

We welcome contributions introducing novel and innovative deep learning approaches to forest parameter detection and tree modeling and reviewing the current state and future prospects of deep learning for forestry. Papers addressing the practical issues and limitations of deep learning for forestry applications are also highly encouraged.

Prof. Dr. Marcos Benedito Schimalski
Dr. Vasco M. Mantas
Dr. Camile Sothe
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • forest parameters
  • remote sensing
  • UAV
  • digital photogrammetry

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Published Papers (2 papers)

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21 pages, 36623 KiB  
Article
Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation
by Chige Li, Hengkai Li, Kunming Liu, Xiuli Wang and Xiaoyong Fan
Forests 2024, 15(11), 1885; https://doi.org/10.3390/f15111885 - 26 Oct 2024
Viewed by 519
Abstract
Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including [...] Read more.
Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth. Full article
(This article belongs to the Special Issue Forest Parameter Detection and Modeling Using Remote Sensing Data)
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22 pages, 7672 KiB  
Article
ALS-Based, Automated, Single-Tree 3D Reconstruction and Parameter Extraction Modeling
by Hong Wang, Dan Li, Jiaqi Duan and Peng Sun
Forests 2024, 15(10), 1776; https://doi.org/10.3390/f15101776 - 9 Oct 2024
Viewed by 843
Abstract
The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling [...] Read more.
The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R2s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R2s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3. Full article
(This article belongs to the Special Issue Forest Parameter Detection and Modeling Using Remote Sensing Data)
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