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LiDAR Remote Sensing for Forest Mapping

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

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 11876

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Special Issue Editors

Dr. Jie Shao

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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
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Dr. Yiming Chen

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Guest Editor

Chinese Academy of Surveying & Mapping, Beijing 100036, China
Interests: forest inventory; tree species classification; LiDAR; UAV; point cloud processing

Dr. Lei Luo

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR remote sensing; understory exploration; forest ecology
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Dr. Markus Hollaus

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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

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Keywords

forests
multiplatform LiDAR
data acquisition
data fusion
point cloud processing
tree species classification
forest structure
forest inventory
understory exploration

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Published Papers (6 papers)

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16 pages, 1905 KiB

Open AccessArticle

Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe

by Devara P. Adiningrat, Andrew Skidmore, Michael Schlund, Tiejun Wang, Haidi Abdullah and Marco Heurich

Remote Sens. 2025, 17(2), 251; https://doi.org/10.3390/rs17020251 - 12 Jan 2025

Viewed by 725

Abstract

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)

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16 pages, 1563 KiB

Open AccessArticle

Tree Species Classification from UAV Canopy Images with Deep Learning Models

by Yunmei Huang, Botong Ou, Kexin Meng, Baijian Yang, Joshua Carpenter, Jinha Jung and Songlin Fei

Remote Sens. 2024, 16(20), 3836; https://doi.org/10.3390/rs16203836 - 15 Oct 2024

Cited by 1 | Viewed by 1609

Abstract

Forests play a critical role in the provision of ecosystem services, and understanding their compositions, especially tree species, is essential for effective ecosystem management and conservation. However, identifying tree species is challenging and time-consuming. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have emerged as a promising technology for species identification due to their relatively low cost and high spatial and temporal resolutions. Moreover, the advancement of various deep learning models makes remote sensing based species identification more a reality. However, three questions remain to be answered: first, which of the state-of-the-art models performs best for this task; second, which is the optimal season for tree species classification in a temperate forest; and third, whether a model trained in one season can be effectively transferred to another season. To address these questions, we focus on tree species classification by using five state-of-the-art deep learning models on UAV-based RGB images, and we explored the model transferability between seasons. Utilizing UAV images taken in the summer and fall, we captured 8799 crown images of eight species. We trained five models using summer and fall images and compared their performance on the same dataset. All models achieved high performances in species classification, with the best performance on summer images, with an average F1-score was 0.96. For the fall images, Vision Transformer (ViT), EfficientNetB0, and YOLOv5 achieved F1-scores greater than 0.9, outperforming both ResNet18 and DenseNet. On average, across the two seasons, ViT achieved the best accuracy. This study demonstrates the capability of deep learning models in forest inventory, particularly for tree species classification. While the choice of certain models may not significantly affect performance when using summer images, the advanced models prove to be a better choice for fall images. Given the limited transferability from one season to another, further research is required to overcome the challenge associated with transferability across seasons. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)

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20 pages, 12334 KiB

Open AccessArticle

Derivation and Evaluation of LAI from the ICESat-2 Data over the NEON Sites: The Impact of Segment Size and Beam Type

by Yao Wang and Hongliang Fang

Remote Sens. 2024, 16(16), 3078; https://doi.org/10.3390/rs16163078 - 21 Aug 2024

Cited by 1 | Viewed by 1049

Abstract

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing for Forest Mapping)

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26 pages, 8059 KiB

Open AccessArticle

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://doi.org/10.3390/rs15235436 - 21 Nov 2023

Cited by 4 | Viewed by 1469

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. 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

Open AccessEditor’s ChoiceArticle

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://doi.org/10.3390/rs15204969 - 15 Oct 2023

Cited by 11 | Viewed by 3335

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 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

Open AccessTechnical 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://doi.org/10.3390/rs16010147 - 29 Dec 2023

Cited by 1 | Viewed by 2250

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 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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