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LiDAR Remote Sensing for Forest Mapping
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LiDAR Remote Sensing for Forest Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4325

Special Issue Editors

Dr. Jie Shao

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China
Interests: LiDAR remote sensing; forest inventory; point cloud processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yiming Chen

E-Mail Website
Guest Editor
Chinese Academy of Surveying & Mapping, Beijing 100036, China
Interests: forest inventory; tree species classification; LiDAR; UAV; point cloud processing
Dr. Lei Luo

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR remote sensing; understory exploration; forest ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Markus Hollaus

E-Mail Website
Guest Editor
Department of Geodesy and Geoinformation, University of Technology, 1040 Vienna, Austria
Interests: lidar; forest; biomass; vegetation; change detection; environmental studies; forest inventories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an active remote sensing technology, light detection and ranging (LiDAR) has unparalleled advantages in acquiring forest spatial structure information, offering opportunities for enhanced forest monitoring. Mapping has always been critical for LiDAR-based forest research and application. For example, mapping forest scenes can provide a data foundation for forest measurements and understory exploration (such as topographic surveys and archaeology); mapping forest structural traits and composition can provide valuable support for forest inventory, forest biomass assessment, forest carbon storage estimation, fire management, and wildlife habitat conservation. Data acquisition and processing are prerequisites for LiDAR-based forest mapping. Recently, developments in LiDAR sensors, platforms (such as terrestrial, handheld, backpack, aerial, drone, and satellite), and data processing techniques, have further promoted the application of LiDAR remote sensing in forests. In this context, exploring efficient methods to use LiDAR remote sensing technology in high-quality forest mapping has become an important research topic in relevant fields.

This Special Issue aims at contributions that focus on LiDAR remote sensing for forest mapping. We are particularly interested in original papers that have addressed innovative techniques for acquiring, handling, and analyzing forest data of multi-platform LiDAR, challenges in forest mapping based on LiDAR remote sensing, and developed new applications for LiDAR-based forest mapping.

  • Development and integration of novel LiDAR systems for forest mapping.
  • Acquiring LiDAR data of forests from different platforms.
  • Registration of multisource LiDAR point clouds for forest mapping.
  • Mapping individual trees and tree species with LiDAR data.
  • Tree stem extraction and wood-leaf separation from LiDAR point clouds.
  • Mapping forest structure (such as diameter of breast height, tree height, canopy cover, and leaf area index) with multi-platform LiDAR systems.
  • Mapping biomass and carbon storage of forests with LiDAR data.
  • Application of LiDAR-based forest mapping, such as topographic surveys and understory archaeology.
  • Fusing LiDAR with other remote sensing data (such as hyperspectral information) for forest mapping and application.

Dr. Jie Shao
Dr. Yiming Chen
Dr. Lei Luo
Dr. Markus Hollaus
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
  • multiplatform LiDAR
  • data acquisition
  • data fusion
  • point cloud processing
  • tree species classification
  • forest structure
  • forest inventory
  • understory exploration

Published Papers (3 papers)

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26 pages, 8059 KiB  
Article
Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method
by Mengting Sang, Hai Xiao, Zhili Jin, Junchen He, Nan Wang and Wei Wang
Remote Sens. 2023, 15(23), 5436; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15235436 - 21 Nov 2023
Viewed by 804
Abstract
Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. [...] Read more.
Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)
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17 pages, 4711 KiB  
Article
Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation
by Xiaoxiao Zhu, Sheng Nie, Yamin Zhu, Yiming Chen, Bo Yang and Wang Li
Remote Sens. 2023, 15(20), 4969; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15204969 - 15 Oct 2023
Cited by 1 | Viewed by 1802
Abstract
Two space-borne light detection and ranging (LiDAR) missions, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), have demonstrated high capabilities in extracting terrain and canopy heights in forest environments. However, there have been limited studies evaluating their performance [...] Read more.
Two space-borne light detection and ranging (LiDAR) missions, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), have demonstrated high capabilities in extracting terrain and canopy heights in forest environments. However, there have been limited studies evaluating their performance for terrain and canopy height retrievals in short-stature vegetation. This study utilizes airborne LiDAR data to validate and compare the accuracies of terrain and canopy height retrievals for short-stature vegetation using the latest versions of ICESat-2 (Version 5) and GEDI (Version 2). Furthermore, this study also analyzes the influence of various factors, such as vegetation type, terrain slope, canopy height, and canopy cover, on terrain and canopy height retrievals. The results indicate that ICESat-2 (bias = −0.05 m, RMSE = 0.67 m) outperforms GEDI (bias = 0.39 m, RMSE = 1.40 m) in terrain height extraction, with similar results observed for canopy height retrievals from both missions. Additionally, the findings reveal significant differences in terrain and canopy height retrieval accuracies between ICESat-2 and GEDI data under different data acquisition scenarios. Error analysis results demonstrate that terrain slope plays a pivotal role in influencing the accuracy of terrain height extraction for both missions, particularly for GEDI data, where the terrain height accuracy decreases significantly with increasing terrain slope. However, canopy height has the most substantial impact on the estimation accuracies of GEDI and ICESat-2 canopy heights. Overall, these findings confirm the strong potential of ICESat-2 data for terrain and canopy height retrievals in short-stature vegetation areas, and also provide valuable insights for future applications of space-borne LiDAR data in short-stature vegetation-dominated ecosystems. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)
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17 pages, 1913 KiB  
Technical Note
Identifying Old-Growth Forests in Complex Landscapes: A New LiDAR-Based Estimation Framework and Conservation Implications
by Raphaël Trouvé, Ruizhu Jiang, Patrick J. Baker, Sabine Kasel and Craig R. Nitschke
Remote Sens. 2024, 16(1), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010147 - 29 Dec 2023
Viewed by 1088
Abstract
Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation [...] Read more.
Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation algorithm for broadleaved forests, Gaussian mixture modelling, and a rule-based classification key to map the extent and location of old-growth forests across a topographically and ecologically complex landscape of 337,548 ha in southeastern Australia. We found that variation in old growth extent was largely driven by the old growth definition, which is a human construct, rather than by uncertainty in the technical aspect of the work. Current regulations define a stand as old growth if it was recruited prior to 1900 (i.e., >120 years old) and is undisturbed (i.e., <10% regrowth canopy cover and no visible disturbance traces). Only 2.7% (95% confidence intervals ranging from 1.4 to 4.9%) of the forests in the study landscape met these criteria. However, this definition is overly restrictive as it leaves many multi-aged stands with ecologically mature elements (e.g., one or more legacy trees amid regrowth) unprotected. Removing the regrowth filter, an indicator of past disturbances, increased the proportion of old-growth forests from 2.7% to 15% of the landscape. Our analyses also revealed that 60% of giant trees (>250 cm in diameter at breast height) were located within 50 m of cool temperate rainforests and cool temperate mixed forests (i.e., streamlines). We discuss the implication of our findings for the conservation and management of high-conservation-value forests in the region. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)
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