Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests

Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.

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Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽

10.3390/f10100905 ◽

2019 ◽

Vol 10 (10) ◽

pp. 905 ◽

Cited By ~ 3

Author(s):

Guerra-Hernández ◽

Cosenza ◽

Cardil ◽

Silva ◽

Botequim ◽

...

Keyword(s):

Laser Scanning ◽

Basal Area ◽

Remote Sensing Data ◽

Tree Height ◽

Point Clouds ◽

Volume Estimation ◽

P Value ◽

Test Statistic ◽

Individual Tree ◽

Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

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Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status

Drones ◽

10.3390/drones4030036 ◽

2020 ◽

Vol 4 (3) ◽

pp. 36

Author(s):

Kellyn Montgomery ◽

Josh Henry ◽

Matthew Vann ◽

Brian E. Whipker ◽

Anders Huseth ◽

...

Keyword(s):

Spectral Index ◽

Precision Agriculture ◽

Low Cost ◽

Canopy Structure ◽

Nutrient Status ◽

Point Clouds ◽

Fertility Variation ◽

Structural Metrics ◽

Foliar Concentration ◽

Crop Health

Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.

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Contrasting Development of Canopy Structure and Primary Production in Planted and Naturally Regenerated Red Pine Forests

Forests ◽

10.3390/f10070566 ◽

2019 ◽

Vol 10 (7) ◽

pp. 566 ◽

Cited By ~ 2

Author(s):

Laura J. Hickey ◽

Jeff Atkins ◽

Robert T. Fahey ◽

Mark T. Kreider ◽

Shea B. Wales ◽

...

Keyword(s):

Primary Production ◽

Canopy Structure ◽

Structural Complexity ◽

Red Pine ◽

Stem Density ◽

Light Use Efficiency ◽

Planted Forests ◽

Area Index ◽

Use Efficiency ◽

Naturally Regenerated Stands

Globally, planted forests are rapidly replacing naturally regenerated stands but the implications for canopy structure, carbon (C) storage, and the linkages between the two are unclear. We investigated the successional dynamics, interlinkages and mechanistic relationships between wood net primary production (NPPw) and canopy structure in planted and naturally regenerated red pine (Pinus resinosa Sol. ex Aiton) stands spanning ≥ 45 years of development. We focused our canopy structural analysis on leaf area index (LAI) and a spatially integrative, terrestrial LiDAR-based complexity measure, canopy rugosity, which is positively correlated with NPPw in several naturally regenerated forests, but which has not been investigated in planted stands. We estimated stand NPPw using a dendrochronological approach and examined whether canopy rugosity relates to light absorption and light–use efficiency. We found that canopy rugosity increased similarly with age in planted and naturally regenerated stands, despite differences in other structural features including LAI and stem density. However, the relationship between canopy rugosity and NPPw was negative in planted and not significant in naturally regenerated stands, indicating structural complexity is not a globally positive driver of NPPw. Underlying the negative NPPw-canopy rugosity relationship in planted stands was a corresponding decline in light-use efficiency, which peaked in the youngest, densely stocked stand with high LAI and low structural complexity. Even with significant differences in the developmental trajectories of canopy structure, NPPw, and light use, planted and naturally regenerated stands stored similar amounts of C in wood over a 45-year period. We conclude that widespread increases in planted forests are likely to affect age-related patterns in canopy structure and NPPw, but planted and naturally regenerated forests may function as comparable long-term C sinks via different structural and mechanistic pathways.

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Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries

Remote Sensing ◽

10.3390/rs11070800 ◽

2019 ◽

Vol 11 (7) ◽

pp. 800 ◽

Cited By ~ 14

Author(s):

Xin Shen ◽

Lin Cao ◽

Bisheng Yang ◽

Zhong Xu ◽

Guibin Wang

Keyword(s):

Point Cloud ◽

Growth Models ◽

Wide Band ◽

Point Clouds ◽

Forest Growth ◽

Stem Density ◽

Spectral Indices ◽

Aerial Photogrammetry ◽

Highly Correlated ◽

Structural Metrics

Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral and RGB imageries were compared and assessed. The selected spectral and structural metrics were used to fit partial least squares (PLS) regression models individually and in combination to estimate forest structural attributes (i.e., Lorey’s mean height (HL) and volume(V)), and the capabilities of multispectral- and RGB-derived spectral and structural metrics in predicting forest structural attributes in various stem density forests were assessed and compared. The results indicated that the derived DAP point clouds had perfect visual effects and that most of the structural metrics extracted from the multispectral DAP point cloud were highly correlated with the metrics derived from the RGB DAP point cloud (R2 > 0.75). Although the models including only spectral indices had the capability to predict forest structural attributes with relatively high accuracies (R2 = 0.56–0.69, relative Root-Mean-Square-Error (RMSE) = 10.88–21.92%), the models with spectral and structural metrics had higher accuracies (R2 = 0.82–0.93, relative RMSE = 4.60–14.17%). Moreover, the models fitted using multispectral- and RGB-derived metrics had similar accuracies (∆R2 = 0–0.02, ∆ relative RMSE = 0.18–0.44%). In addition, the combo models fitted with stratified sample plots had relatively higher accuracies than those fitted with all of the sample plots (∆R2 = 0–0.07, ∆ relative RMSE = 0.49–3.08%), and the accuracies increased with increasing stem density.

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VERTICAL ORIENTATION CORRECTION OF UAV IMAGE-BASED POINT CLOUDS USING STATISTICAL MODELING OF GABLE ROOF GEOMETRY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-519-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 519-523 ◽

Cited By ~ 1

Author(s):

P. Polewski ◽

W. Yao ◽

L. Fang

Keyword(s):

Rural Areas ◽

Laser Scanning ◽

Low Cost ◽

Remote Sensing Data ◽

Point Clouds ◽

Density Estimator ◽

Vertical Orientation ◽

Gable Roof ◽

The World ◽

Uav Image

<p><strong>Abstract.</strong> Coregistration of point clouds obtained from various sensors is an important part of workflows for automatic building reconstruction from remote sensing data. Many approaches assume a common Z axis between the coordinate systems, and perform coregistration in 2D. While this assumption is usually valid for laser scanning (LS) data, for photogrammetric point clouds the Z axis is in general different from the world Z axis, and requires correction e.g. by manually measured ground control points (GCP). In this paper, we propose a fully automatic, GCP-free procedure for finding the world Z axis in rural areas, based on the relationships of planar surfaces in building gable roofs. Instead of performing direct gable line detection, we derive these lines as theoretical intersections between adjacent roof planes from 3D shape fitting. Each gable roof then casts a vote for both the Z axis direction and sign based on roof convexity constraints, and the votes are aggregated through a non-parametric kernel density estimator model. Experiments on two real world UAV image-based point clouds show that the Z axis recovered by our method leads to high-accuracy planimetric coregistration, with a median distance over 89 as well as 149 matched linear feature pairs (respectively for dataset 1 and 2) lying below 1&thinsp;cm. Our results indicate that a high-quality vertical orientation can be achieved without using any GNSS or IMU hardware, which enables the use of low-cost UAV platforms for suburban and rural mapping tasks.</p>

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Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

Forest Systems ◽

10.5424/fs/2016253-08682 ◽

2016 ◽

Vol 25 (3) ◽

pp. e074 ◽

Cited By ~ 5

Author(s):

Noureddin Nourian ◽

Shaban Shataee-Joibary ◽

Jahangir Mohammadi

Keyword(s):

Forest Structure ◽

Basal Area ◽

Remote Sensing Data ◽

Cluster Sampling ◽

Stem Density ◽

Stand Volume ◽

Hyrcanian Forests ◽

Medium Resolution ◽

Cart Algorithm ◽

Tree Stem

Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results. Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests.

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Applicability of Structure-from-Motion Photogrammetry on Forest Measurement in the Northern Ethiopian Highlands

Sustainability ◽

10.3390/su13095282 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5282

Author(s):

Toru Sakai ◽

Emiru Birhane ◽

Buruh Abebe ◽

Destaalem Gebremeskel

Keyword(s):

Forest Management ◽

Structure From Motion ◽

Sustainable Forest Management ◽

Structural Parameters ◽

Low Cost ◽

Three Dimensional ◽

Tree Height ◽

Point Clouds ◽

Point Of View ◽

Ethiopian Highlands

Ethiopia is one of the countries with the most degraded forest resources. Information on tree structure is needed at some points in the process to assess the appropriateness of forest management. The objectives are to examine whether the Structure from Motion (SfM)-based photogrammetry can be used to derive the forest structural parameters, and how the tree structural parameters can vary by location. In this study, the possible applicability of low-cost SfM-based photogrammetry was evaluated for forest management and conservation purposes in the Adi Zaboy watershed of the Northern Ethiopian highlands. In the watershed, dwarf Acacia etbaica was sparsely distributed. Consequently, the full three-dimensional point clouds of the individual trees were generated, which provided a wide variety of tree structural parameters in a non-destructive manner. The R2 values for tree height, canopy width, and stump diameter were 0.936, 0.891, and 0.808, respectively, and the corresponding RMSE values were 0.128 m, 0.331 m, and 0.886 cm. In addition, differences in forest structure and composition were caused by differences in the environment. The SfM-based photogrammetry would provide fundamental information to meet the demand of sustainable forest management from a morphological point of view, especially in forests of Ethiopian highlands.

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Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽

10.3390/f12030327 ◽

2021 ◽

Vol 12 (3) ◽

pp. 327 ◽

Cited By ~ 1

Author(s):

Riccardo Dainelli ◽

Piero Toscano ◽

Salvatore Filippo Di Gennaro ◽

Alessandro Matese

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Relevant Information ◽

Forest Monitoring ◽

Machine Learning Techniques ◽

Planted Forests ◽

Systematic Analysis ◽

Aerial Photogrammetry ◽

Wide Range ◽

Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

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Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽

10.3390/f12081020 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1020

Author(s):

Yanqi Dong ◽

Guangpeng Fan ◽

Zhiwu Zhou ◽

Jincheng Liu ◽

Yongguo Wang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Low Cost ◽

Reference Value ◽

Tree Height ◽

Point Clouds ◽

Tree Structure ◽

Mean Square ◽

Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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Development patterns in two hemlock–hardwood stands in southern New England

Canadian Journal of Forest Research ◽

10.1139/x86-158 ◽

1986 ◽

Vol 16 (5) ◽

pp. 885-891 ◽

Cited By ~ 40

Author(s):

Matthew J. Kelty

Keyword(s):

New England ◽

Canopy Structure ◽

Basal Area ◽

Height Growth ◽

Northern Red Oak ◽

Red Maple ◽

Clear Cutting ◽

Southern New England ◽

Hardwood Species ◽

Constant Rate

Two forest stands, composed primarily of northern red oak (Quercusrubra L.), red maple (Acerrubrum L.), and eastern hemlock (Tsugacanadensis (L.) Carr.), were studied by stand-reconstruction techniques to determine the pattern of development of canopy structure. One stand had originated following clear-cutting 87 years ago; the other, following catastrophic windthrow 44 years ago. Juvenile height growth of the hardwood species was much greater than that of hemlock and a stratified canopy developed by age 30 years, with hardwoods forming an overstory canopy above hemlock. Hemlocks maintained overstory positions only if they were 3 m or more in height immediately following canopy disturbance. In the older stand, hardwood height growth was about twice that of the tallest understory hemlocks during the first 30 years. The hardwood overstory slowed after that and grew at the same rate as the tallest understory hemlocks, which maintained a constant rate of height growth, and a constant to accelerating rate of basal area growth for much of the 87-year measurement period. The height growth of the tallest understory hemlocks was apparently limited in part by breakage of terminal shoots, caused by abrasion against branches of overstory hardwood crowns.

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