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Forests
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Applications of Optical and Active Remote Sensing in Forestry
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Applications of Optical and Active Remote Sensing in Forestry

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 June 2025 | Viewed by 1686

Special Issue Editors

Dr. Dandan Xu

E-Mail Website
Guest Editor
College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
Interests: UAV; multispectral remote sensing; LiDAR; Geographic Information Systems (GIS); satellite imagery; forest ecosystems
Prof. Dr. Xulin Guo

E-Mail Website
Guest Editor
Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada
Interests: shrubland; grassland; biogeography; remote sensing

Special Issue Information

Dear Colleagues,

Forests are critical ecosystems for human life and carbon sinks. Accurate estimations of forest biophysical parameters (biomass, timber volume, crown parameters, etc.) are the foundation of the timber industry and forest management. Remote sensing data, with variable spatial and temporal scales and the advantage of non-destructive monitoring, are widely applied to forest monitoring. However, the variety of vegetation dynamics and complex forest structures brings challenges to forest monitoring in different scales via remote sensing.

This Special Issue invites contributions that report new research and findings about the monitoring of forest biophysical parameters using remote sensing approaches, either through optical satellite images, satellite LiDAR point clouds, UAV images (RGB, multispectral or hyperspectral data), or UAV LiDAR point clouds. Original research and reviews in all types of forest ecosystems will be welcome, including, but not limited to, rain forests, mangrove forests, subtropical evergreen forests, boreal forests, forest–grassland transition ecotones, and various forest plantations.

The outcomes of this Special Issue will be very helpful in establishing future guidelines for forest monitoring using remote sensing in different temporal and spatial scales, which is fundamental for forest management when facing anthropogenic pressures and climate change.

Dr. Dandan Xu
Prof. Dr. Xulin Guo
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

  • forests
  • plantations
  • satellite images
  • UAV images
  • UAV-LiDAR
  • biophysical parameters

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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25 pages, 17010 KiB  
Article
Estimation of Tree Species Diversity in Warm Temperate Forests via GEDI and GF-1 Imagery
by Lei Zhang, Liu Yang, Jinhua Sun, Qimeng Zhu, Ting Wang and Hui Zhao
Forests 2025, 16(4), 570; https://doi.org/10.3390/f16040570 - 25 Mar 2025
Viewed by 419
Abstract
Estimates of tree species diversity via traditional optical remote sensing are based only on the spectral variation hypothesis (SVH); however, this approach does not account for the vertical structure of a forest. The relative height (RH) indices derived from GEDI spaceborne LiDAR provide [...] Read more.
Estimates of tree species diversity via traditional optical remote sensing are based only on the spectral variation hypothesis (SVH); however, this approach does not account for the vertical structure of a forest. The relative height (RH) indices derived from GEDI spaceborne LiDAR provide vertical vegetation structure information through waveform decomposition. Although RH indices have been widely studied, the optimal RH index for tree species diversity estimation remains unclear. This study integrated GF-1 optical imagery and GEDI LiDAR data to estimate tree species diversity in a warm temperate forest. First, random forest plus residual kriging (RFRK) was employed to achieve wall-to-wall mapping of the GEDI-derived indices. Second, recursive feature elimination (RFE) was applied to select relevant spectral and LiDAR features. The random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) methods were subsequently applied to estimate tree species diversity through remote sensing data. The results indicated that multisource data achieved greater accuracy in tree species diversity estimation (average R2 = 0.675, average RMSE = 0.750) than single-source data (average R2 = 0.636, average RMSE = 0.754). Among the three machine learning methods, the RF model (R2 = 0.760, RMSE = 2.090, MAE = 1.624) was significantly more accurate than the SVM (R2 = 0.571, RMSE = 2.556, MAE = 1.995) and kNN (R2 = 0.715, RMSE = 2.084, MAE = 1.555) models. Moreover, mean_mNDVI, mean_RDVI, and mean_Blue were identified as the most important spectral features, whereas RH30 and RH98 were crucial features derived from LiDAR for establishing models of tree species diversity. Spatially, tree species diversity was high in the west and low in the east in the study area. This study highlights the potential of integrating optical imagery and spaceborne LiDAR for tree species diversity modeling and emphasizes that low RH indices are most indicative of middle- to lower-canopy tree species diversity. Full article
(This article belongs to the Special Issue Applications of Optical and Active Remote Sensing in Forestry)
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9 pages, 2413 KiB  
Technical Note
TR-SNP v1.0: A Desktop Tool to Link Tree Ring Width with Annual Aboveground Biomass Increment
by Yizhao Chen, Zhongyi Lin, Zhixin Shi and Yang Li
Forests 2024, 15(12), 2148; https://doi.org/10.3390/f15122148 - 5 Dec 2024
Viewed by 885
Abstract
The past couple of decades have witnessed an increasing application of tree ring observations to assess forest carbon (C) balance and its historical dynamics. To address the growing need for understanding long-term forest C sequestration dynamics through tree rings, we developed a new [...] Read more.
The past couple of decades have witnessed an increasing application of tree ring observations to assess forest carbon (C) balance and its historical dynamics. To address the growing need for understanding long-term forest C sequestration dynamics through tree rings, we developed a new desktop tool (TR-SNP v1.0) that estimates the annual aboveground biomass increment (AABI) of trees from tree ring width (TRW). Users can easily process and convert TRW into AABI using either the built-in dataset or by uploading local TRW data. TR-SNP offers methods for correcting potential bias from unmeasured initial core width, converting TRW to diameter at breast height (DBH), and estimating AABI using species-specific allometric relationships. We provide examples from specific sites to demonstrate how TR-SNP functions and its potential for identifying bias sources of AABI estimation. We anticipate that TR-SNP will streamline the analysis of tree ring data and advance our understanding of forest biomass increment dynamics. Full article
(This article belongs to the Special Issue Applications of Optical and Active Remote Sensing in Forestry)
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