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Forests
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Methodology and Theory of Forest Parameters Estimation Using Multi-Source Remote...
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Methodology and Theory of Forest Parameters Estimation Using Multi-Source Remote Sensing

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 (30 October 2024) | Viewed by 4167

Special Issue Editors

Dr. Shiming Li

E-Mail Website
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest inventory; LiDAR remote sensing; point cloud processing; forest disturbance
Prof. Dr. Steven L. Petersen

E-Mail Website
Guest Editor
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA
Interests: forest landscape ecology and GIS; remote sensing (satellite, very high resolution); disturbance and succession ecology; forest and rangeland dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Cangjiao Wang

E-Mail
Guest Editor
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest structure and functioning; biomass estimation; LiDAR; land use and land cover change

Special Issue Information

Dear Colleagues,

Forest structure and functioning parameters can be directly extracted at the plot level by destructive and/or non-destructive manual measurements, which are widely recognized as expensive and labor-intensive. The advancement of multi-source remote sensing, e.g., airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), high spatial resolution (HSR)/super high spatial resolution (VHSR) optical imagery, and near-surface remote sensing, has the potential to revolutionize the way forest parameters are estimated.

This Special Issue is open to contributions dealing with many aspects of new insights, current challenges, recent advances, operational use, and future perspectives in the field of forest parameters derived from remote sensing technologies. Contributions on the use of multi- and hyperspectral remote sensing, terrestrial, airborne, and spaceborne laser scanning, and near-surface remote sensing (drones, wireless sensor networks) are welcome. Reviews are also welcomed. Topics may include, but are not limited to:

  • Forest parameters estimation and characterization;
  • Forest structure and functioning;
  • Multi- and hyperspectral remote sensing;
  • Digital aerial photogrammetry;
  • High spatial resolution (HSR)/very high spatial resolution (VHSR) optical satellite imagery;
  • Near-surface remote sensing;
  • Terrestrial laser scanning/ground-based LiDAR;
  • Mobile laser scanning;
  • Airborne laser scanning;
  • Spaceborne LiDAR;
  • Tree height;
  • Tree density;
  • Above-ground biomass;
  • Leaf area index;
  • Canopy structure.

Submitted manuscripts must be original contributions, not ones previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered, provided that they are considerably extended and improved. Papers must follow the instructions for authors at https://www.mdpi.com/journal/forests/instructions.

Dr. Shiming Li
Prof. Dr. Steven L. Petersen
Dr. Cangjiao Wang
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

  • forest inventory
  • tree height
  • tree density
  • DBH, above-ground biomass
  • canopy structure
  • LiDAR
  • photogrammetry
  • forest ecosystem
  • remote sensing

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Published Papers (3 papers)

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Research

20 pages, 16040 KiB  
Article
Unveiling Anomalies in Terrain Elevation Products from Spaceborne Full-Waveform LiDAR over Forested Areas
by Hailan Jiang, Yi Li, Guangjian Yan, Weihua Li, Linyuan Li, Feng Yang, Anxin Ding, Donghui Xie, Xihan Mu, Jing Li, Kaijian Xu, Ping Zhao, Jun Geng and Felix Morsdorf
Forests 2024, 15(10), 1821; https://doi.org/10.3390/f15101821 - 17 Oct 2024
Viewed by 706
Abstract
Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on “normal” data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and [...] Read more.
Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on “normal” data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community. Full article
(This article belongs to the Special Issue Methodology and Theory of Forest Parameters Estimation Using Multi-Source Remote Sensing)
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24 pages, 3816 KiB  
Article
Response of Individual-Tree Aboveground Biomass to Spatial Effects in Pinus kesiya var. langbianensis Forests by Stand Origin and Tree Size
by Chunxiao Liu, Yong Wu, Xiaoli Zhang, Hongbin Luo, Zhibo Yu, Zihao Liu, Wenfang Li, Qinling Fan and Guanglong Ou
Forests 2024, 15(2), 349; https://doi.org/10.3390/f15020349 - 10 Feb 2024
Viewed by 1128
Abstract
To enhance forest carbon sequestration capacity, it is important to optimize forest structure by revealing the spatial effects of the aboveground biomass of individual trees, with particular emphasis on stand origin and tree size. Here, 0.3 ha clear-cut plots of Pinus kesiya var. [...] Read more.
To enhance forest carbon sequestration capacity, it is important to optimize forest structure by revealing the spatial effects of the aboveground biomass of individual trees, with particular emphasis on stand origin and tree size. Here, 0.3 ha clear-cut plots of Pinus kesiya var. langbianensis forest were selected in a typical plantation and natural stand. Then, the ordinary least squares model and spatial regression models were used to analyze the different responses between spatial position and individual tree biomass based on the stand origin and diameter at breast height (DBH) of the tree. Our study shows the following: (1) The spatial effect produced a stronger response in the natural stand than in the plantation. The amount of change in the adjusted R-squared (ΔRadj2) of tree component totaled 0.34 and 0.57 for Pinus kesiya var. langbianensis and other trees in the natural stand, compared to only 0.2 and 0.42 in the plantation; (2) Spatial effects had a stronger impact on the accuracy of the fit for the crown (ΔRadj2 = 0.52) compared to the wood and bark (ΔRadj2 = 0.03) in the plantation, and there were no significant differences in the natural stand (ΔRadj2 = 0.42, ΔRadj2 = 0.43); (3) When DBH reached a certain size, the impact of spatial effect for the crown showed a significant change from positive to negative. The sizes of DBH were 19.5 cm, 14 cm and 34.6 cm, 19 cm for branches of Pinus kesiya var. langbianensis and other tree species in the plantation and natural stand, and were 20.3 cm and 31.4 cm for the foliage of Pinus kesiya var. langbianensis. Differences in stand structure led to varied responses in the biomass of tree components to spatial effects. Full article
(This article belongs to the Special Issue Methodology and Theory of Forest Parameters Estimation Using Multi-Source Remote Sensing)
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14 pages, 2902 KiB  
Article
Multifiltering Algorithm for Enhancing the Accuracy of Individual Tree Parameter Extraction at Eucalyptus Plantations Using LiDAR Data
by Jinjun Huang, Wen He and Yuefeng Yao
Forests 2024, 15(1), 81; https://doi.org/10.3390/f15010081 - 30 Dec 2023
Viewed by 1802
Abstract
Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) [...] Read more.
Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) alone. Moreover, there is still limited research on the effect of point cloud filtering algorithms on the extraction of individual tree parameters from multiplatform LiDAR data. Here, we employed a multifiltering algorithm to increase the accuracy of individual tree parameter (tree height and diameter at breast height (DBH)) extraction with the fusion of TLS and UAV-LiDAR (TLS-UAV-LiDAR) data. The results showed that compared to a single filtering algorithm (improved progressive triangulated irregular network densification, IPTD, or a cloth simulation filter, CSF), the multifiltering algorithm (IPTD + CSF) improves the accuracy of tree height extraction with TLS, UAV-LiDAR, and TLS-UAV-LiDAR data (with R2 improvements from 1% to 7%). IPTD + CSF also enhances the accuracy of DBH extraction with TLS and TLS-UAV-LiDAR. In comparison to single-platform LiDAR (TLS or UAV-LiDAR), TLS-UAV-LiDAR can compensate for the missing crown and stem information, enabling a more detailed depiction of the tree structure. The highest accuracy of individual tree parameter extraction was achieved using the multifiltering algorithm combined with TLS-UAV-LiDAR data. The multifiltering algorithm can facilitate the application of multiplatform LiDAR data and offers an accurate way to quantify individual tree parameters. Full article
(This article belongs to the Special Issue Methodology and Theory of Forest Parameters Estimation Using Multi-Source Remote Sensing)
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