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Special Issue "Advances in LiDAR Remote Sensing for Forestry and Ecology"

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 April 2022) | Viewed by 20723

Special Issue Editors

Dr. Jason Drake
E-Mail Website
Guest Editor
USDA Forest Service and Florida A&M University, USA
Interests: LiDAR remote sensing; ecological modeling; geospatial analysis; UAV applications
Dr. Paul Medley
E-Mail Website
Guest Editor
USDA Forest Service and Florida A&M University, USA
Interests: geospatial analysis and tool development; UAV applications; wildlife and fisheries

Special Issue Information

Dear Colleagues,

LiDAR technology is evolving very rapidly with applications in fields such as survey/engineering, natural resource assessment, manufacturing/design, autonomous vehicle navigation and even consumer electronics such as the latest iPad Pro which uses LiDAR for 3D scanning and augmented reality applications. In forestry and ecology LiDAR has proven to be an invaluable tool for assessment and monitoring of forest structure and condition. Advancements such as the miniaturization of LiDAR sensors so that they can be flown on UAVs and the new Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station open up many exciting opportunities at scales ranging from 10s of hectares to the globe.    

We invite authors to submit research papers on new and innovative applications using LiDAR remote sensing in forestry, ecology, and related fields for this special issue. This special issue is open to all types of LiDAR including terrestrial, UAV, aerial, and spaceborne sensors. Research involving the synergy of LiDAR with other sensors is encouraged, as well as innovative methods for processing and extracting useful information from LiDAR point clouds (e.g., machine learning techniques). A synthesis or review paper may also be considered which provides a thorough overview of the current methods, applications, and related best practices (e.g., recommendations for cost effective collection and processing of LiDAR data for forestry and ecological applications). We look forward to your submissions to this special issue.

Dr. Jason Drake
Dr. Paul Medley
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 2500 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

  • LiDAR
  • UAV LiDAR
  • Airborne LiDAR
  • Terrestrial LiDAR
  • Forestry
  • Ecology
  • Forest management

Published Papers (16 papers)

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Article
Fine Resolution Imagery and LIDAR-Derived Canopy Heights Accurately Classify Land Cover with a Focus on Shrub/Sapling Cover in a Mountainous Landscape
Remote Sens. 2022, 14(6), 1364; https://doi.org/10.3390/rs14061364 - 11 Mar 2022
Viewed by 646
Abstract
Publicly available land cover maps do not accurately represent shrubs and saplings, an uncommon but ecologically relevant cover type represented by woody vegetation <4 m tall. This omission likely occurs because (1) the resolution is too coarse, (2) poor training data are available, [...] Read more.
Publicly available land cover maps do not accurately represent shrubs and saplings, an uncommon but ecologically relevant cover type represented by woody vegetation <4 m tall. This omission likely occurs because (1) the resolution is too coarse, (2) poor training data are available, and/or (3) shrub/saplings are difficult to discriminate from spectrally similar classes. We present a framework for classifying land cover, including shrub/saplings, by combining open-source fine-resolution (1 m) spectral and structural data across a large (>6000 km2) mountainous region. We hypothesized that the combination of spectral (imagery) and structural (LIDAR) data would allow for discrimination of shrub/sapling cover from other cover types. Specifically, we created training data using segmented four-band imagery from the National Agricultural Imagery Program (NAIP). In addition to spectral information from imagery, we used topographic information (elevation, slope, and aspect) and a LIDAR-derived canopy height model to classify land cover within a pixel-based random forests framework. To assess model accuracy, we used image interpretation and an independent sample of validation points. Due to the fine resolution of predictor rasters across such a large geographic region, we classified five subregions (counties) separately. We also compared the landscape metrics calculated for our custom classification at fine (1 m) and coarse resolution (resampled to 30 m) to metrics calculated with National Land Cover Data (NLCD). We achieved an overall accuracy of 89% and >80% accuracy for each land cover class. The LIDAR-derived canopy height model was consistently ranked as the most important predictor of vegetative land cover classes. Compared with our custom classification, NLCD underrepresented pasture/grassland by up to 10% and overrepresented forest up to 30%. There was no correlation between percent shrub/sapling cover in our custom classification and NLCD, suggesting that NLCD is not reliable for applications concerned with this ecologically relevant cover type. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks
Remote Sens. 2022, 14(6), 1362; https://doi.org/10.3390/rs14061362 - 11 Mar 2022
Viewed by 588
Abstract
Sustainable forest management requires accurate fine-scale description of wood resources. Stem size distribution (SSD) by species is used by foresters worldwide as a representative overview of forest structure and species composition suitable for informing management decisions at shorter and longer terms. In mixed [...] Read more.
Sustainable forest management requires accurate fine-scale description of wood resources. Stem size distribution (SSD) by species is used by foresters worldwide as a representative overview of forest structure and species composition suitable for informing management decisions at shorter and longer terms. In mixed uneven-aged deciduous forests, tree data required for SSD estimation are most often collected in the field through traditional forest management inventories (FMIs), but these are time-consuming and costly with respect to the sampled area. Combining FMIs with remote sensing methods such as airborne laser scanning (ALS), which has high potential for predicting forest structure and composition, and is becoming increasingly accessible and affordable, could provide cheaper and faster SSD data across large areas. In this study, we developed a method for estimating species-specific SSDs by combining FMIs and dual-wavelength ALS data using neural networks (NNs). The proposed method was tested and validated using 178 FMI plots within 22,000 ha of a mixed uneven-aged deciduous forest in Belgium. The forest canopy was segmented, and metrics were derived from the ALS point cloud. A NN with a custom architecture was set up to simultaneously predict the three components required to compute species-specific SSDs (species, circumference, and number of stems) at segment level. Species-specific SSDs were thereafter estimated at stand level by aggregating the estimates for the segments. A robustness test was set up using fully independent plots to thoroughly assess the method precision at stand-level on a larger area. The global Reynolds index for the species-specific SSDs was 21.2 for the training dataset and 54.0 for the independent dataset. The proposed method does not require allometric models, prior knowledge of the structure, or the predefinition of variables; it is versatile and thus potentially adaptable to other forest types having different structures and compositions. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow
Remote Sens. 2021, 13(23), 4763; https://doi.org/10.3390/rs13234763 - 24 Nov 2021
Viewed by 704
Abstract
Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used [...] Read more.
Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds
Remote Sens. 2021, 13(22), 4677; https://doi.org/10.3390/rs13224677 - 19 Nov 2021
Cited by 1 | Viewed by 1707
Abstract
Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed [...] Read more.
Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning
Remote Sens. 2021, 13(20), 4168; https://doi.org/10.3390/rs13204168 - 18 Oct 2021
Viewed by 798
Abstract
Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data [...] Read more.
Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information
Remote Sens. 2021, 13(20), 4050; https://doi.org/10.3390/rs13204050 - 11 Oct 2021
Viewed by 672
Abstract
Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we [...] Read more.
Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 s per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations
Remote Sens. 2021, 13(17), 3536; https://doi.org/10.3390/rs13173536 - 06 Sep 2021
Cited by 1 | Viewed by 1265
Abstract
Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of [...] Read more.
Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Woody Surface Area Measurements with Terrestrial Laser Scanning Relate to the Anatomical and Structural Complexity of Urban Trees
Remote Sens. 2021, 13(16), 3153; https://doi.org/10.3390/rs13163153 - 09 Aug 2021
Viewed by 935
Abstract
Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models [...] Read more.
Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models to provide comprehensive characterizations of the woody surface area allometry of urban trees and relate them to urban tree anatomy, physiology, and structural complexity. Fifty-six trees of three species (Gleditsia triacanthos L., Quercus macrocarpa Michx., Metasequoia glyptostroboides Hu & W.C. Cheng) were sampled on the Michigan State University campus. Variations in surface area allocation to non-photosynthesizing components (main stem, branches) are related to the fractal dimension of tree architecture, in terms of structural complexity (box-dimension metric) and the distribution of “path” lengths from the tree base to every branch tip. The total woody surface area increased with the box-dimension metric, but it was most strongly correlated with the 25th percentile of path lengths. These urban trees mainly allocated the woody surface area to branches, which changed with branch order, branch-base diameter, and branch-base height. The branch-to-stem area ratio differed among species and increased with the box-dimension metric. Finally, the woody surface area increased with the crown surface area of the study trees across all species combined and within each species. The results of this study provide novel data and new insights into the surface area properties of urban tree species and the links with structural complexity and constraints on tree morphology. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level
Remote Sens. 2021, 13(14), 2796; https://doi.org/10.3390/rs13142796 - 16 Jul 2021
Cited by 2 | Viewed by 2698
Abstract
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the [...] Read more.
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)
Remote Sens. 2021, 13(12), 2307; https://doi.org/10.3390/rs13122307 - 12 Jun 2021
Cited by 2 | Viewed by 754
Abstract
The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The [...] Read more.
The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data
Remote Sens. 2021, 13(5), 948; https://doi.org/10.3390/rs13050948 - 03 Mar 2021
Cited by 2 | Viewed by 867
Abstract
Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical [...] Read more.
Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Estimating Net Primary Productivity (NPP) and Debris-Fall in Forests Using Lidar Time Series
Remote Sens. 2021, 13(5), 891; https://doi.org/10.3390/rs13050891 - 27 Feb 2021
Cited by 1 | Viewed by 1137
Abstract
Temporal series of lidar, properly field-validated, can provide critical information allowing in-ferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine [...] Read more.
Temporal series of lidar, properly field-validated, can provide critical information allowing in-ferences about the dynamics of biomass and carbon in forest canopies. Forest canopies gain carbon through net primary production (NPP) and lose carbon through canopy component damage and death, such as fine and coarse woody debris and litterfall (collectively, debris-fall). We describe a statistical method to extract gamma distributions of NPP and debris-fall rates in forest canopies from lidar missions repeated through time and we show that the means of these distributions covary with ecologically meaningful variables: topography, canopy structure, and taxonomic composition. The method employed is the generalized method of moments that applies the R package gmm to uncover the distribution of latent variables. We present an example with eco-logical interpretations that support the method’s application to change in biomass estimated for a boreal forest in southcentral Alaska. The deconvolution of net change from remote sensing products as distributions of NPP and debris-fall rates can inform carbon cycling models of can-opy-level NPP and debris-fall rates. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters
Remote Sens. 2020, 12(23), 3948; https://doi.org/10.3390/rs12233948 - 02 Dec 2020
Cited by 9 | Viewed by 1873
Abstract
Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver [...] Read more.
Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry
Remote Sens. 2020, 12(21), 3649; https://doi.org/10.3390/rs12213649 - 06 Nov 2020
Cited by 6 | Viewed by 1451
Abstract
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple [...] Read more.
Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Article
Radiometric Calibration for Incidence Angle, Range and Sub-Footprint Effects on Hyperspectral LiDAR Backscatter Intensity
Remote Sens. 2020, 12(17), 2855; https://doi.org/10.3390/rs12172855 - 02 Sep 2020
Cited by 7 | Viewed by 1546
Abstract
Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle [...] Read more.
Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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Technical Note
UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy
Remote Sens. 2020, 12(22), 3829; https://doi.org/10.3390/rs12223829 - 21 Nov 2020
Cited by 2 | Viewed by 1335
Abstract
High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and [...] Read more.
High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels. Full article
(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)
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