Measurement and Modeling of Forest Vegetation Structures with Remote Sensing Technology

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: 30 October 2025 | Viewed by 662

Special Issue Editors


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Guest Editor
Institute of Botany, Jiangsu Province and Chinese Academy of Sciences (Nanjing Botanical Garden Mem. Sun Yat-Sen), Nanjing 210014, China
Interests: forest vegetation structure; remote sensing; LiDAR; forest management; forest ecology; tree size distribution; biodiversity

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Guest Editor
Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina, SK S4S 0A2, Canada
Interests: tree demography; stand dynamics; forest simulation modelling; global change; forest distributions; carbon sequestration; LiDAR
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Special Issue Information

Dear Colleagues,

Forest vegetation structures are one of the key drivers that sustain ecosystem functions and biodiversity. The use of remote sensing technology with multi-sensors (e.g., LiDAR, radar, multispectral, and hyperspectral imagery) on multiple platforms (e.g., UAV-borne, airborne, and spaceborne) to evaluate forest vegetation structures and monitor their dynamics has become a trending research topic. Compared to traditional field forest inventories, remote sensing has an unsurpassed capability to observe forest resources in fine/large scales across space and time. Accurate, timely, and effective measurement and modeling of forest vegetation structures through multi-source remote sensing data benefits ecologists, forest science experts, and foresters by providing a comprehensive understanding of forest ecosystem services and assisting in efficient forest management decisions. It also helps address forest and biodiversity loss in the context of climate change and human activities. 

With this Special Issue, we promote knowledge and strategies for measuring and modeling forest vegetation structures using remote sensing technology. Potential topics may include, but are not limited to, forest structure, vegetation functional diversity, forest dynamics, forest growth and productivity, forest management and decision, and forest disturbance.

We look forward to receiving your contributions.

Dr. Zhengnan Zhang
Dr. Mark Vanderwel
Guest Editors

Manuscript Submission Information

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Keywords

  • forest vegetation structure
  • vegetation functional diversity
  • remote sensing
  • forest management and decision
  • forest structure dynamic monitoring
  • forest disturbance
  • climate change and human activities

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Published Papers (1 paper)

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Research

26 pages, 5576 KiB  
Article
Comparison Between Traditional Forest Inventory and Remote Sensing with Random Forest for Estimating the Periodic Annual Increment in a Dry Tropical Forest
by Anelisa Pedroso Finger, Rinaldo Luiz Caraciolo Ferreira, Mayara Dalla Lana, José Antônio Aleixo da Silva, Emanuel Araújo Silva, Fábio Marcelo Breunig, Polyanna da Conceição Bispo, Veraldo Liesenberg and Sara Sebastiana Nogueira
Forests 2025, 16(6), 998; https://doi.org/10.3390/f16060998 - 13 Jun 2025
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Abstract
This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The analysis integrates forest inventory data collected from [...] Read more.
This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The analysis integrates forest inventory data collected from permanent plots monitored between 2011 and 2019 with Landsat satellite imagery processed through the Google Earth Engine platform. By incorporating surface reflectance and vegetation indices, the approach significantly improved the accuracy of productivity estimates while reducing the costs and efforts associated with traditional field-based methods. The Random Forest model achieved a strong performance (R2 = 0.8867; RMSE = 0.87), and its predictions were further refined using post-processing correction factors. These results demonstrate the potential of data-driven modeling to support forest monitoring and sustainable management practices, especially in ecosystems vulnerable to the impacts of climate change. Full article
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