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3D Point Clouds in Forest Remote Sensing II
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3D Point Clouds in Forest Remote Sensing II

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 42232

Special Issue Editors

Prof. Dr. Sandra Buján

E-Mail Website
Guest Editor
Escuela Superior y Técnica de Ingenieros de Minas, University of León, 24401 Ponferrada, Spain
Interests: remote sensing data processing; spatial analysis; development of data processing algorithms; free software; land cover/use classification
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Hevia

E-Mail Website
Guest Editor
Departamento de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21819 Huelva, Spain
Interests: forest management and silviculture; forest inventory and monitoring; forest structure; biomass and carbon; wood properties; forest modelling; forest risks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

3D point clouds have become a well stablished data source for characterizing and monitoring forest structure. Particularly, the use of such data from active sensors, like airborne LiDAR, has confirmed its interest in forest studies from its early development in the 1970’s and 1980’s, to the establishment of robust and cost-efficient systems from the 1990’s onwards, due to the improvement of global positioning and inertial units (GNSS/IMU). Even though airborne LiDAR has been the prevalent technology in forest 3D point cloud acquisition, other alternative or complementary technologies have also been presented in forest studies at different scales in the last decades, namely airborne/shuttle/satellite radar, terrain laser scanning or photogrammetry from either photogrammetric or consumer grade cameras. Regarding the latter, the fast evolution of the Remotely Piloted Aircraft Systems (RPAS), along with the streamlining of consumer grade cameras data processing by computer vision software, have popularized the use of ultra-high resolution 3D point clouds at an unprecedent cost-efficiency and spatial-temporal flexibility for local scale studies.

This Special Issue aims at studies covering different uses of 3D point clouds acquired by different sensors and platforms in forest sciences. Topics may cover anything from the classical estimation of forest variables at a tree or stand level, to more comprehensive aims and scales. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches or studies focused on forest ecosystem services monitoring, among other issues, are welcome. Articles may address, but are not limited to the following topics:

  • Tree and stand variables inventory
  • Forest land cover mapping and pattern analysis
  • Forest planning and management
  • Forest ecology
  • Forest change
  • Biodiversity and wildlife
  • Forest fuel and fire studies
  • Biotic and abiotic forest damage
  • Biomass
  • Forest plants functional traits
  • Carbon cycle/sequestration
  • Terrain analysis
  • Forest ecosystem services
  • Forests and climate change

Dr. Sandra Buján
Dr. Andrea Hevia
Guest Editors

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Keywords

  • Forest inventory
  • Forest structure and function
  • Forest dynamics
  • Structure from motion
  • Airborne laser scanning
  • Terrain laser scanning
  • 3D point cloud analysis
  • Spectral and structural data fusion

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Related Special Issue

Published Papers (15 papers)

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29 pages, 5634 KiB  
Article
Mapping of the Successional Stage of a Secondary Forest Using Point Clouds Derived from UAV Photogrammetry
by Ricardo Pinheiro Cabral, Gilson Fernandes da Silva, André Quintão de Almeida, Santiago Bonilla-Bedoya, Henrique Machado Dias, Adriano Ribeiro De Mendonça, Nívea Maria Mafra Rodrigues, Carem Cristina Araujo Valente, Klisman Oliveira, Fábio Guimarães Gonçalves and Tathiane Santi Sarcinelli
Remote Sens. 2023, 15(2), 509; https://doi.org/10.3390/rs15020509 - 14 Jan 2023
Cited by 1 | Viewed by 3378
Abstract
The definition of strategies for forest restoration projects depends on information of the successional stage of the area to be restored. Usually, classification of the successional stage is carried out in the field using forest inventory campaigns. However, these campaigns are costly, time-consuming, [...] Read more.
The definition of strategies for forest restoration projects depends on information of the successional stage of the area to be restored. Usually, classification of the successional stage is carried out in the field using forest inventory campaigns. However, these campaigns are costly, time-consuming, and limited in terms of spatial coverage. Currently, forest inventories are being improved using 3D data obtained from remote sensing. The objective of this work was to estimate several parameters of interest for the classification of the successional stages of secondary vegetation areas using 3D digital aerial photogrammetry (DAP) data obtained from unmanned aerial vehicles (UAVs). A cost analysis was also carried out considering the costs of equipment and data collection, processing, and analysis. The study was carried out in southeastern Brazil in areas covered by secondary Atlantic Forest. Regression models were fit to estimate total height (h), diameter at breast height (dbh), and basal area (ba) of trees in 40 field inventory plots (0.09 ha each). The models were fit using traditional metrics based on heights derived from DAP and a portable laser scanner (PLS). The prediction models based on DAP data yielded a performance similar to models fit with LiDAR, with values of R² ranging from 88.3% to 94.0% and RMSE between 11.1% and 28.5%. Successional stage maps produced by DAP were compatible with the successional classes estimated in the 40 field plots. The results show that UAV photogrammetry metrics can be used to estimate h, dbh, and ba of secondary vegetation with an accuracy similar to that obtained from LiDAR. In addition to presenting the lowest cost, the estimates derived from DAP allowed for the classification of successional stages in the analyzed secondary forest areas. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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26 pages, 10813 KiB  
Article
Tree Species Classification Using Airborne LiDAR Data Based on Individual Tree Segmentation and Shape Fitting
by Chen Qian, Chunjing Yao, Hongchao Ma, Junhao Xu and Jie Wang
Remote Sens. 2023, 15(2), 406; https://doi.org/10.3390/rs15020406 - 9 Jan 2023
Cited by 7 | Viewed by 6185
Abstract
Individual tree species classification is of strategic importance for forest monitoring, analysis, and management, which are critical for sustainable forestry development. In this regard, the paper proposes a method based on the profile of segmented individual tree laser scanning points to identify tree [...] Read more.
Individual tree species classification is of strategic importance for forest monitoring, analysis, and management, which are critical for sustainable forestry development. In this regard, the paper proposes a method based on the profile of segmented individual tree laser scanning points to identify tree species. The proposed methodology mainly takes advantage of three-dimensional geometric features of a tree crown captured by a laser point cloud to identify tree species. Firstly, the Digital Terrain Model (DTM) and Digital Surface Model (DSM) are used for Crown Height Model (CHM) generation. Then, local maximum algorithms and improved rotating profile-based delineations are used to segment individual trees from the profile CHM point data. In the next step, parallel-line shape fitting is used to fit the tree crown shape. In particular, three basic geometric shapes, namely, triangle, rectangle, and arc are used to fit the tree crown shapes of different tree species. If the crown belongs to the same crown shape or shape combination, parameter classification is used, such as the ratio of crown width and crown height or the apex angle range of the triangles. The proposed method was tested by two real datasets which were acquired from two different sites located at Tiger and Leopard National Park in Northeast China. The experimental results indicate that the average tree classification accuracy is 90.9% and the optimal classification accuracy reached 95.9%, which meets the accuracy requirements for rapid forestry surveying. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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24 pages, 30475 KiB  
Article
Tree Reconstruction Using Topology Optimisation
by Thomas Lowe and Joshua Pinskier
Remote Sens. 2023, 15(1), 172; https://doi.org/10.3390/rs15010172 - 28 Dec 2022
Cited by 7 | Viewed by 3169 | Correction
Abstract
Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying fall hazards, estimating trees’ biomass and calculating traversability. Existing methods for tree reconstruction rely on sparse feature identification to segment a [...] Read more.
Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying fall hazards, estimating trees’ biomass and calculating traversability. Existing methods for tree reconstruction rely on sparse feature identification to segment a forest into individual trees and generate a branch structure graph, limiting their application to easily separable trees and uniform forests. However, the natural world is a messy place in which trees present with significant heterogeneity and are frequently encroached upon by the surrounding environment. We present a general method for extracting the branch structure of trees from point cloud data, which estimates the structure of trees by adapting the methods of structural topology optimisation to find the optimal material distribution to interpolate the input data. We present the results of this optimisation over a wide variety of scans, and discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Our method generates detailed and accurate tree structures, with a mean Surface Error (SE) of 15 cm over 13 diverse tree datasets. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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24 pages, 15994 KiB  
Article
Optimization Method of Airborne LiDAR Individual Tree Segmentation Based on Gaussian Mixture Model
by Zhenyu Zhang, Jian Wang, Zhiyuan Li, Youlong Zhao, Ruisheng Wang and Ayman Habib
Remote Sens. 2022, 14(23), 6167; https://doi.org/10.3390/rs14236167 - 5 Dec 2022
Cited by 4 | Viewed by 2200
Abstract
Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon [...] Read more.
Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typical of a high-density multistoried mixed forest area. Therefore, this paper proposes an ITS optimization method based on Gaussian mixture model for airborne LiDAR data. First, the mean shift (MS) algorithm is used for the initial ITS of the pre-processed airborne LiDAR data. Next, under-segmented samples are extracted by integrated learning, normally segmented samples are classified by morphological approximation, and the approximate distribution uncertainty of the normal samples is described with a covariance matrix. Finally, the class composition among the under-segmented samples is determined, and the under-segmented samples are re-segmented using Gaussian mixture model (GMM) clustering, in light of the optimal covariance matrix of the corresponding categories. Experiments with two datasets, Trento and Qingdao, resulted in ITS recall of 94% and 96%, accuracy of 82% and 91%, and F-scores of 0.87 and 0.93. Compared with the MS algorithm, our method is more accurate and less likely to under-segment individual trees in many cases. It can provide data support for the management and conservation of high-density multistoried mixed forest areas. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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21 pages, 2058 KiB  
Article
UAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardens
by Erica Lombardi, Francisco Rodríguez-Puerta, Filippo Santini, Maria Regina Chambel, José Climent, Víctor Resco de Dios and Jordi Voltas
Remote Sens. 2022, 14(22), 5904; https://doi.org/10.3390/rs14225904 - 21 Nov 2022
Cited by 5 | Viewed by 3246
Abstract
Remote sensing is increasingly used in forest inventories. However, its application to assess genetic variation in forest trees is still rare, particularly in conifers. Here we evaluate the potential of LiDAR and RGB imagery obtained through unmanned aerial vehicles (UAVs) as high-throughput phenotyping [...] Read more.
Remote sensing is increasingly used in forest inventories. However, its application to assess genetic variation in forest trees is still rare, particularly in conifers. Here we evaluate the potential of LiDAR and RGB imagery obtained through unmanned aerial vehicles (UAVs) as high-throughput phenotyping tools for the characterization of tree growth and crown structure in two representative Mediterranean pine species. To this end, we investigated the suitability of these tools to evaluate intraspecific differentiation in a wide array of morphometric traits for Pinus nigra (European black pine) and Pinus halepensis (Aleppo pine). Morphometric traits related to crown architecture and volume, primary growth, and biomass were retrieved at the tree level in two genetic trials located in Central Spain and compared with ground-truth data. Both UAV-based methods were then tested for their accuracy to detect genotypic differentiation among black pine and Aleppo pine populations and their subspecies (black pine) or ecotypes (Aleppo pine). The possible relation between intraspecific variation of morphometric traits and life-history strategies of populations was also tested by correlating traits to climate factors at origin of populations. Finally, we investigated which traits distinguished better among black pine subspecies or Aleppo pine ecotypes. Overall, the results demonstrate the usefulness of UAV-based LiDAR and RGB records to disclose tree architectural intraspecific differences in pine species potentially related to adaptive divergence among populations. In particular, three LiDAR-derived traits related to crown volume, crown architecture, and main trunk—or, alternatively, the latter (RGB-derived) two traits—discriminated the most among black pine subspecies. In turn, Aleppo pine ecotypes were partly distinguishable by using two LiDAR-derived traits related to crown architecture and crown volume, or three RGB-derived traits related to tree biomass and main trunk. Remote-sensing-derived-traits related to main trunk, tree biomass, crown architecture, and crown volume were associated with environmental characteristics at the origin of populations of black pine and Aleppo pine, thus hinting at divergent environmental stress-induced local adaptation to drought, wildfire, and snowfall in both species. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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17 pages, 6845 KiB  
Article
Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests
by Maryam Poorazimy, Ghasem Ronoud, Xiaowei Yu, Ville Luoma, Juha Hyyppä, Ninni Saarinen, Ville Kankare and Mikko Vastaranta
Remote Sens. 2022, 14(19), 4845; https://doi.org/10.3390/rs14194845 - 28 Sep 2022
Cited by 4 | Viewed by 2629
Abstract
The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring [...] Read more.
The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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17 pages, 3970 KiB  
Article
An Automatic Individual Tree 3D Change Detection Method for Allometric Parameters Estimation in Mixed Uneven-Aged Forest Stands from ALS Data
by Claudio Spadavecchia, Elena Belcore, Marco Piras and Milan Kobal
Remote Sens. 2022, 14(18), 4666; https://doi.org/10.3390/rs14184666 - 19 Sep 2022
Cited by 2 | Viewed by 2402
Abstract
Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been [...] Read more.
Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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16 pages, 4568 KiB  
Article
Promising Uses of the iPad Pro Point Clouds: The Case of the Trunk Flare Diameter Estimation in the Urban Forest
by Rogério Bobrowski, Monika Winczek, Lucas Polo Silva, Tarik Cuchi, Marta Szostak and Piotr Wężyk
Remote Sens. 2022, 14(18), 4661; https://doi.org/10.3390/rs14184661 - 19 Sep 2022
Cited by 4 | Viewed by 2597
Abstract
The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could [...] Read more.
The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could help the planning process to give tree roots more space to grow over the years. As such, we compared the applicability of point clouds based on iPad Pro 2020 image processing and a precise terrestrial laser scanner (TLS FARO) for the modeling of the TFD using different modeling procedures. For both scanning methods, 100 open-grown and mature trees of 10 different species were scanned in an urban park in Cracow, Poland. To generate models, we used the PBH (perimeter at breast height) and TFD variables and simple linear regression procedures. We also tested machine learning algorithms. In general, the TFD value corresponded to two times the size of a given DBH (diameter at breast height) for both methods of point cloud acquisition. Linearized models showed similar statistics to machine learning techniques. The random forest algorithm showed the best fit for the TFD estimation, R2 = 0.8780 (iPad Pro), 0.8961 (TLS FARO), RMSE (m) = 0.0872 (iPad Pro), 0.0702 (TLS FARO). Point clouds generated from iPad Pro imageries (matching approach) promoted similar results as TLS FARO for the TFD estimations. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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29 pages, 5648 KiB  
Article
Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.
by Todd A. Schroeder, Shingo Obata, Monica Papeş and Benjamin Branoff
Remote Sens. 2022, 14(17), 4386; https://doi.org/10.3390/rs14174386 - 3 Sep 2022
Cited by 2 | Viewed by 2076
Abstract
The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to [...] Read more.
The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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17 pages, 3028 KiB  
Article
Identification of Old-Growth Mediterranean Forests Using Airborne Laser Scanning and Geostatistical Analysis
by Andrea Hevia, Anabel Calzado, Reyes Alejano and Javier Vázquez-Piqué
Remote Sens. 2022, 14(16), 4040; https://doi.org/10.3390/rs14164040 - 18 Aug 2022
Cited by 2 | Viewed by 2077
Abstract
The protection and conservation of old-growth forests (OGFs) are becoming a global concern due to their irreplaceability and high biodiversity. Nonetheless, there has been little research into the identification and characterization of OGFs of the oldest tree species in Mediterranean areas. We used [...] Read more.
The protection and conservation of old-growth forests (OGFs) are becoming a global concern due to their irreplaceability and high biodiversity. Nonetheless, there has been little research into the identification and characterization of OGFs of the oldest tree species in Mediterranean areas. We used forest inventory data, low-density airborne laser scanning (ALS) metrics, and geostatistical analysis to estimate old-growth indices (OGIs) as indicators of old-growth forest conditions. We selected a pilot area in European black pine (Pinus nigra subsp. salzmannii) ecosystems where the oldest known living trees in the Iberian Peninsula are found. A total of 756 inventory plots were established to characterize standard live tree and stand attributes. We estimated several structural attributes that discriminate old growth from younger age classes and calculated different types of OGI for each plot. The best OGI was based on mean tree diameter, standard deviation of tree diameter, and stand density of large trees (diameter > 50 cm). This index is useful for assessing old-growthness at different successional stages (young and OGFs) in Mediterranean black pine forests. Our results confirm that the estimation of OGIs based on a combination of forest inventory data, geostatistical analysis, and ALS is useful for identifying OGFs. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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18 pages, 2880 KiB  
Article
Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients
by Jacob L. Strunk, David M. Bell and Matthew J. Gregory
Remote Sens. 2022, 14(14), 3433; https://doi.org/10.3390/rs14143433 - 17 Jul 2022
Cited by 3 | Viewed by 1485
Abstract
We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements [...] Read more.
We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23/km2 when acquired for entire states), repeatedly (2–3 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (≈1 plot/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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23 pages, 5870 KiB  
Article
A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds
by Zhenyang Hui, Zhaochen Cai, Bo Liu, Dajun Li, Hua Liu and Zhuoxuan Li
Remote Sens. 2022, 14(11), 2545; https://doi.org/10.3390/rs14112545 - 26 May 2022
Cited by 7 | Viewed by 2111
Abstract
Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint [...] Read more.
Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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30 pages, 5705 KiB  
Article
What Is the Most Suitable Height Range of ALS Point Cloud and LiDAR Metric for Understorey Analysis? A Study Case in a Mixed Deciduous Forest, Pokupsko Basin, Croatia
by Saray Martín-García, Ivan Balenović, Luka Jurjević, Iñigo Lizarralde, Sandra Buján and Rafael Alonso Ponce
Remote Sens. 2022, 14(9), 2095; https://doi.org/10.3390/rs14092095 - 27 Apr 2022
Cited by 1 | Viewed by 2032
Abstract
Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to [...] Read more.
Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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26 pages, 2191 KiB  
Article
Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data
by Cecilia Alonso-Rego, Stéfano Arellano-Pérez, Juan Guerra-Hernández, Juan Alberto Molina-Valero, Adela Martínez-Calvo, César Pérez-Cruzado, Fernando Castedo-Dorado, Eduardo González-Ferreiro, Juan Gabriel Álvarez-González and Ana Daría Ruiz-González
Remote Sens. 2021, 13(24), 5170; https://doi.org/10.3390/rs13245170 - 20 Dec 2021
Cited by 21 | Viewed by 3579
Abstract
In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and [...] Read more.
In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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2 pages, 193 KiB  
Correction
Correction: Lowe, T.; Pinskier, J. Tree Reconstruction Using Topology Optimisation. Remote Sens. 2023, 15, 172
by Thomas Lowe and Joshua Pinskier
Remote Sens. 2023, 15(11), 2739; https://doi.org/10.3390/rs15112739 - 25 May 2023
Viewed by 655
Abstract
Text Correction [...] Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
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