EFFECTIVENESS OF UNMANNED VEHICLE SYSTEM LIDAR FOREST INVENTORY
By
OSCAR IVÁN RAIGOSA GARCÍA
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2019
1
©2019 Oscar Iván Raigosa García
2
To my mom, Gloria, for the incredible support, sacrifices and love along these years, and
Mark Milligan and Rick Davis, for putting in all their trust and energy to help me out
through this journey
3
ACKNOWLEDGMENTS
I would like to thank my committee. The doors were always open whenever I needed
advice. They allowed me to develop my own skills and use my criterion to conduct this research
but steered me in the direction whenever they thought I need it. I am gratefully indebted for their
valuable effort.
I would also like to thank F4 Tech for funding this research during these two years of
intense work.
Finally, I would like to thank my family for all the love and support along the way, even
in the most difficult moments they were always there to push me in the right direction.
4
TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ...............................................................................................................4
LIST OF TABLES ...........................................................................................................................6
LIST OF FIGURES .........................................................................................................................8
LIST OF ABBREVIATIONS ........................................................................................................11
ABSTRACT...................................................................................................................................12
CHAPTER
1
INTRODUCTION ..................................................................................................................14
2
RESEARCH QUESTIONS ....................................................................................................22
3
METHODS .............................................................................................................................23
Study Site ................................................................................................................................23
Stands ......................................................................................................................................23
Data Collection .......................................................................................................................23
Collection of Ground Inventory Data ..............................................................................24
Collection of Drone-Based LiDAR Data ........................................................................25
LiDAR Data Processing .........................................................................................................25
Individual Tree Detection ................................................................................................26
Tree Height and Diameter ...............................................................................................26
Experimental Design and Data Analysis .........................................................................27
4
RESULTS AND DISCUSSION .............................................................................................39
Ground Inventory....................................................................................................................39
UAV LiDAR Based Inventory ...............................................................................................41
5
CONCLUSIONS ..................................................................................................................103
LIST OF REFERENCES .............................................................................................................104
BIOGRAPHICAL SKETCH .......................................................................................................107
5
LIST OF TABLES
Table
page
4-1
Number of total trees and dead trees in plots within four stand types ...............................49
4-2
Mean and standard deviation (SD) of diameter at breast height (DBH) by tree species
determined in the March ground inventory .......................................................................50
4-3
Mean and standard deviation (SD) total live height determined in the March ground
inventory by tree species. ...................................................................................................51
4-4
Mean and standard deviation (SD) diameter at breast height (DBH) determined in the
June ground inventory by tree species. ..............................................................................52
4-5
Mean and standard deviation (SD) total live height determined in the June ground
inventory by tree species. ...................................................................................................53
4-6
Comparison of the difference between the values from the March and June
assessments of diameter breast height (DBH) and total height means ..............................54
4-7
Seasonal mean leaf area index (LAI) by sampling plot for the four stand types ...............55
4-8
Trials applied to individual tree detection with WS (window size), filter type, fixed
window size (FWS) and minimum height. ........................................................................56
4-9
4-10
Summary of tree detection statistics by trial2 at thinned pine plantation .........................57
4-11
Tree detection at the mixed pine-hardwood stand. ............................................................58
4-12
Tree detection at natural mature pine stand .......................................................................58
4-13
Comparison of mean distributions in LiDAR height measures versus field plot
assessment in March ..........................................................................................................59
4-14
Comparison of mean distributions in LiDAR height measures versus field plot
assessment in winter at the mixed pine-hardwood stand for pine trees only .....................60
4-15
Height assessment comparison between LiDAR and ground-based methods at the
thinned pine plantation.......................................................................................................61
4-16
Height assessment comparison between LiDAR and ground-based methods at the
non-thinned pine plantation ...............................................................................................61
4-17
Comparison of mean distributions in LiDAR assessments of DBH assessment using
various point quantities to sample RANSAC iteration (n) in the March assessment. .......62
Tree detection at non-thinned pine plantation ...................................................................57
6
4-18
Comparison of mean distributions in LiDAR assessments of DBH assessment using
various point quantities to sample RANSAC iteration (n) in the June assessment. ..........63
4-19
Comparison of DBH estimation using LiDAR assessments using various point
sampling intensity (n) versus the ground-based sampling in the March assessment .........64
4-20
Comparison of DBH estimation using LiDAR assessments using various point
sampling intensity (n) versus the ground-based sampling in the June assessment ............65
4-21
Percentage of trees with LiDAR DBH estimates ...............................................................66
7
LIST OF FIGURES
Figure
page
3-1
The pyramidal target is shown at the mixed pine-hardwood stand ...................................29
3-2
General location is shown for the study area .....................................................................30
3-3
Location for thinned pine plantation is shown...................................................................31
3-4
View of the thinned pine plantation ...................................................................................32
3-5
Location for non-thinned pine plantation is shown ...........................................................33
3-6
View of the non-thinned pine plantation ...........................................................................34
3-7
Location for the mixed pine-hardwood stand is shown .....................................................35
3-8
View of the mixed pine-hardwood stand ...........................................................................36
3-9
Location of the mature natural longleaf pine stand is shown ............................................37
3-10
View of the natural mature pine stand ...............................................................................38
4-1
Individual of Sabal palmetto within plots in the non-thinned pine plantation ...................67
4-2
Tree locations from ground-based field assessment and their relocation using LiDAR
data for the five plots at thinned pine plantation................................................................68
4-3
Tree locations from ground-based field assessment and their relocation using LiDAR
data for the five plots at non-thinned pine plantation ........................................................69
4-4
Tree locations from ground-based field assessment and their relocation using LiDAR
data for the five plots at mixed pine-hardwood stand ........................................................70
4-5
Tree locations from ground-based field assessment and their relocation using LiDAR
datafor the five plots at mature natural pine stand .............................................................71
4-6
Mean DBH and total height difference between the ground-based assessments in
March and June for each of the twenty plots.. ...................................................................72
4-7
DBH distribution for each plot during the March ground-based seasonal assessment......73
4-8
DBH distribution for each plot during the June inventory ground-based seasonal
assessment ..........................................................................................................................74
4-9
Total height distribution for each plot during the March inventory ground-based
seasonal assessment ...........................................................................................................75
8
4-10
Total height distribution for each plot during the June inventory ground-based
seasonal assessment ...........................................................................................................76
4-11
Example of tree detection output at the non-thinned pine plantation showing LiDAR
detection, ground-based location, and color-scaled detection strength value ....................77
4-12
Example of tree detection output at the mature natural pine stand showing LiDAR
detection, ground-based location, and color-scaled detection strength value ....................78
4-13
Thinned pine plantation total height determined by ground-based and LiDAR
assessment for the five plots at March ...............................................................................79
4-14
Non-thinned pine plantation total height determined by ground-based and LiDAR
assessment for the five plots at March ...............................................................................80
4-15
Mixed pine-hardwood stand total height determined by ground-based and LiDAR
assessment for the five plots at March ...............................................................................81
4-16
Mature natural pine stand total height determined by ground-based and LiDAR
assessment for the five plots at March ...............................................................................82
4-17
Mixed pine-hardwood stand total height determined by ground-based and LiDAR
assessment for the five plots at March showing only pines ...............................................83
4-18
Comparison of automatic LiDAR height (LH) and manual LiDAR height assessment
in the thinned and non-thinned pine plantations during the March assessment ................84
4-19
Comparison of LiDAR height (LH) and ground base height assessment in the
thinned and non-thinned pine plantations during the March assessment ..........................85
4-20
Example of DBH from ground-based assessment and LiDAR at the thinned pine
plantation during the March inventory season ...................................................................86
4-21
Example of DBH from ground-based assessment and LiDAR at the non-thinned pine
plantation during the March inventory season ...................................................................87
4-22
Example of DBH from ground-based assessment and LiDAR at the mixed pinehardwood stand during the March inventory season .........................................................88
4-23
Example of DBH from ground-based assessment and LiDAR at the mature natural
pine stand during the March inventory season ..................................................................89
4-24
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at the thinned pine plantation in the March seasonal assessment ....................90
4-25
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at the non-thinned pine plantation in the March seasonal assessment .............91
9
4-26
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at mixed pine-hardwood stand in the March seasonal assessment ..................92
4-27
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at matured natural pine stand in the March seasonal assessment ....................93
4-28
Example of DBH from ground-based assessment and LiDAR at the thinned pine
plantation during the June seasonal assessment.................................................................94
4-29
Example of DBH from ground-based assessment and LiDAR at the non-thinned pine
plantation during the June seasonal assessment.................................................................95
4-30
Example DBH from ground-based assessment and LiDAR at mixed pine-hardwood
stand during the June seasonal assessment. .......................................................................96
4-31
Example of DBH from ground-based assessment and LiDAR at mature natural pine
stand during the June seasonal assessment ........................................................................97
4-32
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at the thinned pine plantation in the June seasonal assessment .......................98
4-33
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at the non-thinned pine plantation in the June seasonal assessment ................99
4-34
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at mixed pine-hardwood stand in the June seasonal assessment ...................100
4-35
Relationship between ground-based assessment of tree DBH and DBH determined
by LiDAR at mature natural pine stand in the June seasonal assessment .......................101
4-36
Example of point cloud density at various point height showing an orthogonal view
of individual tree stems for different stand types assessed ..............................................102
10
LIST OF ABBREVIATIONS
CHMsmoothing
LiDAR-derived Canopy Height model Smoothing
CW
Continuous wave ranging
DBH
Diameter at breast height
DTM
Digital terrain model
FindTreesCHM
Individual tree detection within the LiDAR-Derived canopy height model
Fws
Dimension of window size for the FindTreeCH
GE-UFL
GatorEye Unmanned Flying Laboratory
LiDAR
Light detection and ranging.
minht
Height threshold
RANSAC
Ramdon sample consensus
SfM
Structure-from-motion photogrammetry facilitates the generation of a 3D
reconstruction model and a point cloud
TOF
Travel time of the roundtrip flight
UAS
unmanned aerial system
Ws
Dimension of a window size for the canopy height model
11
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
EFFECTIVENESS OF UNMANNED VEHICLE SYSTEM LIDAR FOREST INVENTORY
By
Oscar Ivan Raigosa Garcia
August 2019
Chair: Patrick Joseph Minogue
Cochair: Eben North Broadbent
Major: Forest Resources and Conservation
This research contributes to the development of new approaches for forest inventory and
stand characterization. Airborne light detection and ranging (LiDAR) and ground LiDAR
scanning have been used in the past, rather than unmanned aerial system (UAS) platforms, due to
technological problems such as load capacity and sensor resolutions. We have determined the
effectiveness and efficiency of UAS-based LiDAR forest inventory under different conditions of
density, distribution, species composition and measurement timing. Mean 82 and 93% individual
tree detection was be achieved at pine plantations of slash pine, as well as accurate height
measurements in both thinned and non-thinned stands with mean differences of 0.99 and 1.03 m
between ground-based and automatic LiDAR assessment at the thinned and non-thinned pine
plantations. Mean 88 and 93% tree detection rates were obtained for established trees in mature
natural pine stands. There was a significant effect of stand type on individual tree detection.
Significant differences between the mixed pine-hardwood stand and the other stand types were
detected. It was not possible assess diameter at breast height (DBH) using UAS-based LiDAR
with the random sample consensus (RANSAC) algorithm at the mixed pine-hardwood stand and
the thinned and non-thinned pine plantations, but good assessments were obtained at the mature
natural pine stand. While these methods are not going to replace ground-based forest inventory
12
in the near future, they provide tools to assess some stand level characteristics, including site
index. More importantly, these methods may help forest managers estimate variability of natural
and planted stands to optimize traditional forest inventory and thus intensify sampling where
needed. Seasonal assessment timing did not affect DBH estimation.
13
CHAPTER 1
INTRODUCTION
Forest inventory information is necessary to guide forest management for economic and
natural resource conservation objectives. Forest inventory typically includes measures of tree
height, diameter, form and species composition to ascertain merchantable volume and values but
may include measurements regarding disease incidence or presence of invasive species.
Traditional inventory techniques are time-consuming, labor-intensive, and often are limited by
access difficulties (Zandoná, Lingnau, & Nakajima, 2007). Extensive work is needed to obtain
ground measurements or to physically mark trees. Height is particularly hard to measure
accurately, sometimes tree felling is done to accurately measure height of the fallen timber to
develop equations to estimate the volume of trees of the same species in a stand or on similar
sites (Aijazi, Checchin, Malaterre, & Trassoudaine, 2017).
LiDAR (Light Detection and Ranging) is an active remote sensing technique that use a
laser scanner to measure distances by computing the time required for a laser to travel to a target
in the space of interest and return to the sensor. There are two common methods used to measure
the travel time. One is pulsed ranging, which records the travel time of the roundtrip flight (TOF)
between the laser pulse source and the target. The other is continuous wave (CW) ranging, where
a continuous emitting laser is used and the phase change is converted to travel time (Wehr et al.
1999). Currently, most LiDAR systems use TOF method (Lim, Treitz, Wulder, St-Ongé, &
Flood, 2003). There are a variety of applications where LiDAR sensors can be used.
Applications include mapping of corridors for roads, railway tracks, pipelines and electrical
transmission lines. Digital terrain model (DTM) generation is used in forested areas for road
planning, the study of surface drainage and in measurements of coastal areas to determine
changes due to erosion. DTMs also have applications like road design, volume calculation in
14
open mines, rapid mapping after natural disasters, measurement of snow accumulation and
measurement of forest stands to obtain parameters such as diameter at breast height (DBH), tree
heights and biomass.
Previous research has demonstrated the application of LiDAR for measurement of tree
height and crown diameter. These crown structure variables may be included in regression
models utilizing forest height stratification to allocate plots in the field to reduce sample error
(Zandoná et al. 2007). Lim et al. (2003) describes how LiDAR systems applied in forestry can be
classified in two types, “discrete return” or “full waveform”. They differ in the way vertical
sampling is realized: “The vertical sampling of LiDAR systems relates to the number of range
samples recorded for each emitted laser pulse. The horizontal sampling is determined by the area
of the footprint and the number of such footprints or hits per unit area”. Discrete return LiDAR
has one or a few returns for every pulse emitted that are recorded, while the full waveform
LiDAR records the amount of energy returned to the sensor sampled over a time interval for
every emitted pulse; thus, the number of intervals determine the amount of detail. In forestry
applications, the ability of discrete return sensors to penetrate the first reflective surface of the
canopy allows us to record stem density and understory composition information. Modern
systems measure four or more echoes per pulse. However, for research where the true vertical
profile of the canopy is important, the full waveform should be considered as opposed to discrete
return measurements (Lim et al. 2003).
Estimation of forest metrics like height, basal area, volume and quality are not the only
derivable. LiDAR combined with GIS allows us to visualize the spatial stand distribution to
calculate production optimization models, in addition to providing more tools for vegetation
growth and yield, site index and genetic improvement (Zandoná et al. 2007). Depending on the
15
objectives, airborne LiDAR scanning can produce satisfactory results when monitoring large
ecosystems, by providing fairly accurate estimates of biomass in a short period of time (Chen et
al. 2016; Gregoire et al. 2011).
The potential of airborne LiDAR for forest inventory has not been fully applied due to
restrictions like high operation cost and limited flying seasons. There are also some potential
measurement variables such as forest health, degree of defoliation and canopy closure that are
not feasible from the current LiDAR sensors used by forest industry. However, improvements in
small scale technology bring the opportunity to use unmanned aerial systems (UAS) to provide
higher resolution data, potentially reducing the standard deviation in tree height, crown width
and tree location measurements (Wallace, Lucieer, Watson, & Turner, 2012). Thus, any increase
in point density may significantly improve tree metrics such as form, stem product determination
and quality class. Crown area has a correlation with sweep and DBH. This correlation can be
used as a log quality indicator in individual tree scaling, and this relationship can be better
estimated with higher density LiDAR scanning (Adams, 2011). Nevertheless, González-Ferreiro
et al. (2012) found that pulse density can be reduced for variable stand estimation, at least for
tree mean and dominant height, basal area, stand total volume and stand biomass.
UAS technology and data processing capability have been improving over recent years,
making it feasible to obtain data for forest monitoring and assessing tree attributes, with a lower
cost and faster performance than traditional methods. An example of that is the use of a low
consumer grade camera attached to a UAS for individual tree detection. In this case, the
integration of regular photography and structure-from-motion (SfM) photogrammetry facilitates
the generation of a 3D reconstruction model and a point cloud. Use of this technique proved to
be effective for detecting individual tress in open canopy forest; on the other hand, the proximity
16
between the trees and low contrast with understory vegetation may limit this technique’s
effectiveness (Mohan, 2017; White et al., 2013). Stand density does not seem to be a significant
limitation, at least in automatic tree detection, when using LiDAR scanning. Algorithms have
been able to successfully identify trees in non-thinned stands and stands with one or two thinning
treatments, with up to 93% to 100% accuracy (Kathuria, Turner, Stone, Duque-Lazo, & West,
2016).
Image-based point clouds present some advantages over airborne LiDAR scanning
(ALS). Steep terrains present a challenge for ALS due to flight altitude restrictions imposed by
the laser power where greater pulse densities are required (White et al., 2013). ALS image
platforms are able to fly faster and higher, so these platforms can cover larger areas in fewer
flight hours. However, the automatic tree detection method used in imaged based inventory is
restricted by light availability, a restriction not present when using LiDAR sensors. In addition,
data processing time is shorter for ALS. However, the greater advantage of imagery lies in the
ability for visual interpretations of species and attributes that cannot be derived from traditional
LiDAR sensors (Lin et al 2016; White et al. 2013). Thus, LiDAR is limited by sensor height
above ground and limitations in laser beam penetration to ground level due to dense canopy
cover. These factors result in insufficient point density within the understory layer (Aijazi et al.
2017).
When the LiDAR point density is low, the precision of stand metrics and biomass
measures can be improved by integration with hyperspectral data because the combination of
hyperspectral imagery and waveform LiDAR can increase the data reliability, enhancing the
coefficients of determination and measures of error (Anderson et al., 2008; Gregoire et al., 2011;
Jiménez et al., 2017; Yoga, Bégin, St-Onge, & Gatziolis, 2017). In the past, one of the biggest
17
concerns was the high volume of LiDAR data and this post processing implication, but modern
remote sensing tools and object segmentation approaches are making it possible to automate
many of the processes involved in forest structure mensuration and delineation using digital
canopy height models and clustering tools (Pascual, García-Abril, García-Montero, MartínFernández, & Cohen, 2008). However, formulating methods to increase cloud density and
optimizing tree detection algorithms can open new possibilities in UAS based LiDAR inventory
data analysis. Thus opening the window to measure the accuracy of variables like DBH and
crown area, which are important factors to estimate volume, timber quality and in species
identification (Lin et al., 2016; Mohan, 2017). It is possible to collect data regarding stem form
and volume, crown area and bark using terrestrial LiDAR scanning with effectiveness, but
effective species identification is still a problem. The identification of branching and leaves are
limitations to obtaining species information. It is necessary to use even higher resolution data to
collect such information, to improve the availability of data for specific species, and to develop
new algorithms to automatically identify tree species (Andrade, Leivas, & Gomes, 2013).
Due to the low-resolution data available with current methods, some studies have focused
on estimating volume and biomass in forest settings. Weinacker et al. (2004) found horizontal
and vertical crown delimitation was not accurate in dense coniferous stands because
subdominant trees partly combine their crowns with adjacent dominant trees and form a dense
and homogenous canopy that cannot be separated. They also found that the number of trees is
severely underestimated in deciduous stands because the crowns are usually merged. As
discussed above, approaches use a double sampling model, taking advantage of the strong
relationship between DBH and height. The combination of ground sampling and LiDAR
provides reliable volume estimates; however, species identification still presents challenges for
18
LiDAR. Research has been conducted with multispectral imagery, LiDAR for lager areas, and
for industrial applications to address this current limitation (Parker and Evans 2004).
Terrestrial LiDAR scanning proved to be useful to identify individual trees and measure
heights and diameters, even in dense understory conditions. Studies in Malaysian tropical forests
have been conducted successfully, but multiple scanning sessions may be needed; therefore,
manual data processing is a time consuming job (Prasada, Hussin, Weir, & Karna, 2016).
Methodologies have compared LiDAR to robust ground plot data, using statistics such as number
of trees per plot, average height per plot and average height difference per plot to verify results
(Zandoná et al. 2007). New sensor-based methods for evaluation of timber product class and
value are being developed to refine forest inventory and reduce data collection cost. The
collection of data without product class and quality variables can induce errors in timber value
estimations. Airborne LiDAR measurements are adequate for tree height and stand volume
estimation; nevertheless, product class and quality variables need to be linked to ground
measurements (Murphy et al. 2010). Yoga et al. (2017) studied the distribution of live and dead
trees in boreal forests using LiDAR and multispectral imagery to accurately filter dead trees to
decrease merchantable volume estimates in stands with high and low mortality.
One of the primary objectives of this study is to determine the effectiveness of UASbased LiDAR forest inventory compared to traditional ground inventory. For this reason,
scanning productivity becomes an important factor for comparison. Murphy et al. (2010) found
scanning productivity between 15 and 30 minutes per plot using terrestrial LiDAR scanning,
depending on site preparation method, plot size and number of scans. This author also found that
between 4% and 9% of the stems were not detected with a single scan, but when they used
multiples scans detection failure fell to less than 1%. The differences in the precision due to scan
19
number are probably due to plot characteristics, such as the presence of undergrowth and small
limbs on trees or likely the occlusion of trees by other trees.
Forest inventory is a time-consuming and expensive activity needed to provide
information about species composition, health, size, location, growth and yield of trees in natural
and managed forest. Inventory data provides information on conditions present to guide
management activities in reaching specific goals, for both conservation and timber production
objectives. Current methods require the work of field technicians, with sampling inefficiencies
due to factors such as inventory methodology, the weather, understory composition and
accessibility. Remote sensing techniques may be used to collect highly accurate data over
relatively large land areas in a short period of time. One relatively new method is LiDAR
combined with low altitude platforms to collect point clouds for forest inventory.
Methods for LiDAR forest inventory measurements have been developed recently, but
limitations in ground-based LiDAR measures and the high cost of airborne LiDAR have been
limiting this application. The development of stable UAS platforms provides new opportunities
to explore capabilities in forest inventory.
The objective of this research was to compare UAS-borne measurements derived from
LiDAR those acquired using ground measurement techniques and to determine opportunities and
limitations to the use of LiDAR in forest Inventory sampling. We examined the effects of
sampling timing (early leaf-out and spring full leaf), stand type (pine plantations vs. mixed pinehardwood stands), and the effect of stem density. The early spring timing was chosen to facilitate
the precision of species identification of deciduous woody plants; whereas, the full leaf timing
was chosen to evaluate measurement precision when leaf reflectivity is high. Stand type is a
basic classification of the forest structure; including species composition, spatial distribution of
20
individuals, height and other common strata characteristics. Density refers to the number of trees
per area. The basal area of a stand is a common measure of stocking, including measures of tree
density and their diameter at breast height (DBH) distribution. These variables affect the quality
of point cloud data, since they influence the effectiveness of the laser to penetrate the vegetation
to their lower stratums reducing the reflectance and the amount of detail captured. We compare
the use of traditional forest inventory measurements in fixed-radius plots to UAS (Unmanned
Aerial System) measurements in the same area. Forests are diverse and complex ecosystems, and
optimum sampling methods are likely to vary due to specific conditions.
21
CHAPTER 2
RESEARCH QUESTIONS
The primary research objective was to determine the effectiveness and limitations of
LiDAR equipped UAS platforms in forest inventory and to compare the precision of metrics
obtained with this method to conventional ground-based inventory compared the precision of
LiDAR measurements in stands of four forest types to examine the limitations of LiDAR in
various stand conditions: (1) non-thinned 18-year-old slash pine plantation (high stand density),
(2) recently 4th-row thinned 18-yearold slash pine plantation (low stand density), (3) mixed pinehardwood forest of natural origin, and (4) mature natural longleaf pine at wide tree spacing, with
open understory conditions. Each of the stands were measured by LiDAR and conventional
ground inventory methods at two timings to contrast foliage reflectance. The first assessment
was in mid-March 2018. The first timing was chosen to facilitate identification of deciduous
woody plants and to provide conditions of low leaf reflectance. The June assessment was done in
June when conditions were at full leaf and cuticle wax development providing conditions of high
potential reflectance.
To explore the commercial viability of using UAS-based LiDAR in forest inventory we
will examine these specific questions:
1.
Can UAS-based LiDAR inventory be as accurate as traditional ground inventory?
2.
What is the effect of stand density (thinned vs. non-thinned pine plantations) on UASbased LiDAR inventory in slash pine plantations at harvest age?
3.
What is the effect of stand type (pine plantation, mature natural longleaf, and mixed pinehardwood) on parameters of UAS-based LiDAR inventory?
4.
What is the effect of seasonal crown density and reflectance (early spring vs. full leaf) on
the precision of UAS-based LiDAR inventory?
22
CHAPTER 3
METHODS
Study Site
The Longleaf Flatwoods Preserve is managed by the St. John’s River Management
District and includes approximately 1,160 acres in eastern Alachua County Florida. The study
site is located 11.4 miles southeast of the University of Florida campus in Gainesville, at latitude
29° 33’ north and longitude 82° 11’west, approximately 160 feet above sea level. Mean annual
precipitation is 44. 44 inches, January, February, October, November and December are the
driest months in the year, and mean temperature is 61° F (NWS 2018)The study site is accessed
by dirt roads west of County Road 325 (Figure 3-2). Recreation opportunities like hiking, biking,
camping, wildlife viewing, photography and equestrian activities are allowed for the public
within the conservation area boundaries.
Stands
We assessed four stands with different structural characteristics. The first stand was a
thinned slash pine (Pinus elliottii Engelm.) plantation established in 1998 (Figure 3-3Figure 3-4).
The second stand was a non-thinned slash pine plantation planted in 2003 (Figure 3-5Figure 36). The third stand was a mixed pine-hardwood forest, composed predominately of laurel oak
(Quercus hemisphaerica W. Bartram ex Willd.), slash pine and longleaf pine (Pinus palustris
Mill.) (Figure 3-7Figure 3-8). The forth stand was a relatively very open mature natural pine
stand comprised primary of longleaf pine, with some slash pine and loblolly pine (Pinus taeda
L.) (Figure 3-9Figure 3-10).
Data Collection
To compare ground and UAS-based LiDAR inventory, we initiated five permanent fixedradius plots, using precision ground survey methods from known benchmarks, in each of four
23
stands of varying conditions. The five 500 m2 fixed-radius plots in each stand were assessed by
both methods at two seasonal timings, the first was at early leaf-out (middle March 2018) when
leaf reflectiveness was low and deciduous species were readily identified. The second timing was
in late spring (May 2018) when trees are at full-leaf and when reflectance is high. The location of
the center of the tree stem determined whether individuals were in or out of the sample. We also
compared the effect of stand basal area on measured parameters among the stand types.
Collection of Ground Inventory Data
The five permanent fixed-radius plots in each stand type were placed where
environmental conditions were similar and away from stand edges. Field measurements where
made in the company of experienced forestry consultants to ensure that selected permanent
fixed-radius plots within the stand type had uniformity in initial stand and site conditions. After
uniform sample plots were determined, permanent markers were placed at plot centers and their
location was surveyed by precision ground survey methods from known benchmarks or corners,
or alternatively from RTK GPS positions taken from multiple points in open areas (such as a
road). Each measurement tree within the fixed-radius plot will be identified by a uniquely
numbered metal tag placed on an aluminum nail two inches above diameter breast height (DBH),
4.5 ft (1.37 m) above average ground level . For each measurement tree, the azimuth bearing and
radial distance from the plot center will be recorded to calculate individual tree coordinates
(Equations 4-1 and 4-2). Ground tree inventory assessments will include species, total live
height, total merchantable height, DBH, Dead trees were also noted in the ground inventory.
Measurements were taken and recorded by a crew of 2 experienced cruisers. Individual stems
under 3 inches in DBH and palm trees were not included in the sample.
Tree′ s X Coordinat = Plot center X projection + Distance ∗ sin (θ)
24
(4-1)
Tree′ s Y Coordinate = Plot center Y projection + Distance ∗ Cos (θ)
(4-2)
We generated a spreadsheet to collect volume parameters, diameter outside bark at DBH
was collected using a diameter tape to the nearest millimeter. Total live height was collected for
every tree in the plot to the nearest decimeter using a laser hypsometer. Leaf Area Index (LAI)
was collected with the CI-110 Plant Canopy Imager (Bio-Science, WA, USA). The sensor was
placed in the center plot to characterize the canopy density of every plot in the study site. The
measurements were collected in the mid-morning to avoid direct sun interference.
Collection of Drone-Based LiDAR Data
The GatorEye Unmanned Flying Laboratory (GE-UFL) was used in collection of the
LiDAR georeferenced point cloud. The characteristics of the LiDAR sensor and the UAV are
listed in Tables 1 and 2 . The UAV platform was piloted by Dr. Eben Broadbent of the School of
Forestry of Forest Resources and Conservation, The University of Florida. The flight height was
45 meters above the ground while the flight speed was 6 𝑚𝑚/𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 to provide a point density
of 140 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝/𝑚𝑚2 . The flight routes for every stand type are shown in Figures 3, 5, 7, and 9.
Pyramidal targets were placed over the plot centers to facilitate center plot location and
individual tree location during the initial assessment in March (Figure 3-1).
LiDAR Data Processing
The LiDAR point clouds were preprocessed and normalized using LASTools applications
(Reference needed). The first normalization tool used was lasground to compute height followed
by lasheight to replace the original absolute height with relative height of the cloud points to the
ground. Finally, individual plot point clouds were delimited with a radius of 14.5 meters to make
sure that we were including borderline trees inside individual plots. Due to relative tree position
uncertainty, and to make sure that we had the most accurate location of all the trees within the
25
study, we relocated every tree using the LiDAR cloud as true reference. To asses such goal, we
filtered the cloud points by height, keeping only the points between 1 and 4 meters above the
ground. With that being said, we were able to see trees’ stems and field trees’ positions at the
same time using ArcMap, ArcScene and CloudCompare to effectively relocate the trees to a most
accurate position.
Individual Tree Detection
Individual tree detection is important in order to assess individual stem characteristics of
height and DBH. We used the software Cloudcompare to export every individual plot as a TIF
file with cell size (step) of 0.20 and 0.25, and height grid values as active layer. After this step,
we used RStudio version 3.5.0 and the packages RLiDAR (LiDAR Data Processing and
Visualization) (Silva, Crookston, Hudak, & Vierling, 2017) and Raster to detect individual trees.
In the first place, we used the function CHMsmoothing (LiDAR-derived Canopy Height model
Smoothing) to eliminate branches effect. We defined the parameters of the function
FindTreesCHM (individual tree detection within the LiDAR-Derived canopy height model) ws
(dimension of a window size for the canopy height model), filter (filter type: Mean, median,
Maximum or Gaussian), fws (dimension of window size for the FindTreeCHM), minht (height
threshold). Analyses using various combinations of these factors were called trials. False
positives and false negatives detections were recorded once the automatically detected trees were
compared with the slightly relocated trees mapped during the fieldwork in order to analyze
success rates for every plot. Individual tree detection was performed for the LiDAR data taken in
the March assessment only.
Tree Height and Diameter
To estimate tree height and diameter, LiDAR clouds where filtered using the coordinates
of the trees relocated as mentioned before, we used the tool las2las from LASTools to clip a
26
circle with a radius of 1.5 m around the tree coordinate to get individual tree clouds. As before,
we used the FindTreesCHM tool with every individual LiDAR tree cloud to estimate the tree
Height. To estimate diameter we used the package TreeLS (Tree Terrestrial LiDAR Scanning
Processing) (De Conto, 2017) and the Tool fit RANSAC circle. RANSAC (random sample
consensus) algorithm is useful to fit models in presence of many data outliers. The algorithm
automatically fits circles using the LiDAR cloud of isolated stems and selects the circle with
least squares, we repeated the process 200 times keeping the trials within two standard deviations
from the mean and using the median as final diameter to avoid extreme fittings. In the thinned
and non-thinned pine plantations 30 trees were randomly selected to compare the heights
automatically assessed by the FindTreesCHM with the heights of the same trees measured
manually using a LiDAR point cloud clip, for every tree and their heights measured in the field
ground assessment. The tree clips where generated using the coordinates of the relocated trees
with a radius of 1.2 m and then compared to field ground measurements and automatic tree using
a T-test.
Experimental Design and Data Analysis
We compared the results from traditional ground inventory with those from the UASbased LiDAR data, considering each measurement tree within the five 500 𝑚𝑚2 fixed-radius plots
as an experimental unit, with each of the two assessment methods (traditional ground or LiDAR
inventory) replicated five times in each of the four stand conditions and at two assessment
timings. Measured variables included tree height, DBH, parameters of every tree collected from
the cloud point of LiDAR inventory. Plot means for measured variables were compared using
mean, range and standard deviation statistics. In the case of DBH estimations with the LiDAR
cloud and the RANSAC algorithm, data filtering was done excluding the observations outside of
the range of the mean plus or minus three standard deviations to exclude extreme values. We
27
also used these results from the two assessment methods to determine the effect of stand
conditions, in particular, stand density and stand type (non-thinned pine plantation, thinned pine
plantation, mixed pine-hardwood, and natural mature pine stand). To understand the precision of
UAS-based LiDAR inventory, we compared these results with those using traditional inventory
methods. Additionally, we compared the results from the two assessment seasons to examine the
effect of crown density on the quality of the data obtained in the four stand conditions.
28
Figure 3-1. The pyramidal target is shown at the mixed pine-hardwood stand. Photo courtesy of
author.
29
Figure 3-2. General location is shown for the study area. Photo courtesy of author.
30
Figure 3-3. Location for thinned pine plantation is shown. Photo courtesy of author.
31
Figure 3-4. View of the thinned pine plantation. Photo courtesy of author.
32
Figure 3-5. Location for non-thinned pine plantation is shown. Photo courtesy of author.
33
Figure 3-6. View of the non-thinned pine plantation. Photo courtesy of author.
34
Figure 3-7. Location for the mixed pine-hardwood stand is shown. Photo courtesy of author.
35
Figure 3-8. View of the mixed pine-hardwood stand. Photo courtesy of author.
36
Figure 3-9. Location of the mature natural longleaf pine stand is shown. Photo courtesy of
author.
37
Figure 3-10. View of the natural mature pine stand. Photo courtesy of author.
38
CHAPTER 4
RESULTS AND DISCUSSION
Ground Inventory
Plots in the thinned pine plantation did not have tree species other than slash pine and the
non-thinned pine plantation plots had only slash pine and some Sabal palmetto Walter (Figure 41). Within the plots in the mixed pine-hardwood stand, slash pine, longleaf pine, loblolly pine,
laurel oak, live oak (Quercus virginiana Mill.) and post oak (Quercus stellate Wangenh.) were
recorded. Plots in the mature natural pine stand had individuals of longleaf and slash pine. A
total of 690 trees were measured within plots across the stand types. A summary of the tree
inventory can be found on Tables 4-1, 4-2, and 4-3 for the March field assessment and on Tables
4-5, 4-6, and 4-7 for the June assessment. At the June assessment, two of the tagged stems were
found dead. The relative location and distribution of the trees are shown in Figures. The thinned
pine plantation plots had an average of 28.2 trees, while the non-thinned pine plantation plots
had an average of 76.6 trees. Natural stands had 22.6 trees per plot in the mixed pine-hardwood
stand and 10.6 trees in the mature natural pine stand (Table 4-1). During the March field
assessment the smallest plot average DBH was found at the non-thinned pine plantation, with
values between 15.9 and 16.5 cm; whereas, the plot average DBH at the thinned pine plantation
was larger, with values between 17.5 and 20.9 cm, a significant difference was observed between
these stand types. The largest average DBH was found at the mature natural pine stand with
values between 32.0 and 38.1 cm. The mixed pine-hardwood stand had average DBH values
between 21.9 and 24.9 cm (Table 4-2). In contrast to the March assessment, at the June
assessment the smallest average DBHs were found at the non-thinned pine plantation with values
between 16.0 and 16.7 cm; whereas, larger DBH was found at the thinned pine plantation, with
values between 17.6 and 20.0 cm. This result underscores the variability associated with ground39
based results. The older, natural stands had the highest average DBH with values between 20.4
and 25.3 cm at the mixed pine-hardwood stand and values between 32.5 and 38.0 cm at the
mature natural pine stand (Table 4-4).
The plots at the mixed pine-hardwood natural stand and the mature natural pine stand are
from natural populations that often not follow a Gaussian shape distribution, as is characteristic
for even aged pine plantations (Figures 4-7 and 4-8). For that reason, only stems with diameters
adequate for chip-and-saw product and saw timber class (DBH larger than 22.7 cm) were used
for plots 12, 16 and 19 to adjust DBH to a normal distribution because when all individuals of
these plots analyzed did not follow a Gaussian distribution. Most of the plots had statically
different mean DBH between seasonal assessments, with exception of plots 7, 12, 17, and 19 (p
values 0.46, 0.95, 0.81, and 0.25, respectively), when analysis was performed using trees having
DBH above 12.7 cm. Means from plots 6 and 18 were not normally distributed for either of the
assessments. The average DBH was larger during the June sampling for all the plots except plots
3, 13 and 18 (Table 4-6). For these the average difference was smaller, with values of 0.3, 1.5,
0.1 cm with no significant difference. For the other plots the difference between sampling
seasons was not larger than 0.5 cm (Table 4-6 and Figure 4-7).
Total tree height during the March field seasonal assessment had average values between
15.0 and 17.5 m at the thinned pine plantation and 14.1 and 15.1 m at the non-thinned pine
plantation. The tallest trees were found at the mature natural pine stand with heights between
23.8 and 27.0 m, followed by the mixed pine-hardwood stand with heights between 14.3 and
19.4 m (Table 4-3). The June assessment had similar results, with average height values between
14.4 and 18.1 m at the thinned pine plantation, and between 14.4 and 15.4 m at the non-thinned
pine plantation. The mixed pine-hardwood stands had average heights between 15.9 and 20.2 m,
40
whereas the mature natural pine stand had average heights between 23.3 and 25.6 m (Table 4-5).
T-tests showed a significate difference in height for all the plots within the thinned pine
plantations, with exception of plot 2 (P= 0.38). In the non-thinned plantation only plot 6 had
significant different height between assessments. Average differences between the March and
June assessments varied between 0.2 and 0.9 m in the thinned pine plantation, whereas at the
non-thinned pine plantation average differences varied between 0.4 and 0.7 meters. The mixed
pine-hardwood stand heights did not have significate differences between assessments but
significate differences in height were observed at the mature natural pine stand in plots 16, 18
and 20 (Table 4-6).
UAV LiDAR Based Inventory
In the non-thinned pine plantation, 93% of trees were detected with only 3% false
negative detections, when only trees above 12.7 cm in DBH were considered using trial number
5. Trial 13 had 1% false negative detections and 87% positive detections. At the thinned
plantation 92% detection was obtained with 18% false positives in trial 13, whereas 3% false
positive and 87% positive detections were observed with trial 14 (Tables 4-10 and 4-11).
Window sizes 5 and 7 m worked better at this stand, when Gaussian filter was used with a
default value of 0.67. The greater positive detection at the non-thinned pine plantation may be
explained by the greater tree homogeneity than in the thinned stand (Tables 4-2, 4-3, 4-4 and 45) Limited light availability and competition do not allow trees to achieve their potential crown
area, DBH and height, characteristics associated with more heterogeneous stands. False tree
detections were lower at the non-thinned stand compared to the thinned pine plantation (Table 49 Table 4-10). The thinned stand, where fourth-row cutting reduced density, may have had
greater false positives due to a difference in reflectance/scattering of light. Also, pines likely
41
responded to this thinning by generating more crown, extending branches that may have been
mistaken as individual trees.
Because trees in the pine plantations were in rows, it was relatively easy to recognize
which trees are positive detections and which ones are not by noting the individual tree location
on the planting row, compared to the automatic tree LiDAR detection (Figure 21). Trees were
relocated from the ground-based coordinates using the LiDAR clouds to have more precise
locations (Figures 12, 14 and 15). Spatial distribution of tree stems and their crowns in natural
stands is very different than in plantations. Crown location may not correspond to stem location
and the stem can be a few meters away from the coordinates where the crown was detected
(Figure 22), Also, a tree can have multiple separated crowns, in particular where oaks are
present, such as in the pine-hardwood stand. The mixed pine-hardwood stand had the lowest
positive detection observed, with 74% and 52%, and false positive detections were 107% and 5%
for trials 3 and 5, respectively (Table 4-11). The natural mature pine stand had positive
detections of 93% and 88%, and false positive detections were 121% and 12% for trial 5 and 1,
respectively (Table 4-12). The ANOVA showed a significant effect of stand type on positive
detection, whereas the natural mature pine stand and the thinned and non-thinned pine plantation
did not differ using Tukey’s test. However, the mixed pine-hardwood stand was different from
the other three stand types. Some improvements can be proposed to refine individual tree
detection. Mixing tree detection with cluster analysis could be a way to achieve this goal, by
filtering the cloud points at low stem heights and setting up a small search radius of a few
decimeters in the plantations and a larger search radius in natural stands. However, the low point
density at the mixed pine-hardwood stand may represent a challenge since the higher crown
density reduced the number of points at lower heights of the forest.
42
Height estimation with LiDAR clouds were always normally distributed, homoscedastic
and higher at pine plantations, with significant differences between 0.5 and 1.1 m when trees
above 12.7 cm were analyzed at the pine plantations and the mature natural pine stand (Table 413). The total heights at the mixed pine-hardwood stand were not normal for any plot, for that
reason, the analysis was done only with individuals of pine species on plots, where the heights
were normally distributed, heteroscedastic and not significantly different from field
measurements on plots 12, 13 and 14 (Table 4-14 and Figure 25). In the case of the pine
plantations, the heights estimated with LIDAR were higher and all the plots presented significant
differences between the field measurements and the LiDAR estimates (Figures 23 and 24). The
mature natural pine stand had average differences smaller than 0.5 m between the field
measurements and LiDAR estimates with no significant differences (Table 4-13 and Figure 26).
The difference between the methods across all the pine plantation plots can be attributed to many
factors, such as the consistent underestimation of height measurement in ground-based
assessment and software error. Ground-based measurement error is an expected error considering
the crown closeness of these stands, making it difficult to distinguish the total height of a specific
tree. Software procedure error may occur at two main steps RLiDAR uses raster files with crown
height information derived from LiDAR clouds to detect individual trees, and such raster has a
determined pixel size that in our case varied between 0.20 and 0.25 m. For this reason, some
information identifying the top of the tree can be missed. In a similar way, the detection of
individual trees used a canopy height model smoothing step to avoid false tree detection;
however, this may affect tree height as well. To avoid this problem it may be better to run the
tree detection process using the raster without the smoothing filter to get better heights as was
done bySilva et al., 2016), while selecting the right tree location and avoiding false positives
43
using a smoothed model and a spatial join. Variation in height of planting beds were also
considered as a possible source of error, but this effect was not detected during the fieldwork or
post analysis of LiDAR cloud.
The manually and automatically LiDAR heights assessed were normally distributed and
had homoscedastic variance. Nevertheless, the height manually measured using the point cloud
was always higher than the automatic LiDAR assessment. In the case of the thinned pine
plantation, the average difference between both measurements was 0.28 meters while the
standard deviation was 0.10 m. Results were even better in the non-thinned pine plantation with
an average difference of 0.10 m and a standard deviation of 0.07 m (Figure 28 and Table 4-15).
In the two pine plantations, the T-test showed significant differences between the manual and
automatic LiDAR assessment heights. However, there is a clear relationship in the height
estimations (Figure 28 and Table 4-15 and 4-16). Ground-based height measurements were
statistically different from heights manually assessed using the LiDAR cloud. At the thinned pine
plantation, the height measured in the automatic LiDAR assessment was higher than the groundbased height with an average difference of 1.2 m and standard deviation 0.7 m (Figure 29 and
Table 4-15). The non-thinned pine plantation had a similar result, where the ground-based
heights measured were lower than the heights estimated in the automatic LiDAR assessment.
The average difference and standard deviation between both measurements were 1.0 and 0.68 m,
respectively (Figure 29 and Table 4-16). Nevertheless, heights assessed with LiDAR clouds
trough either an automatic or a manual procedure showed to be more precise and accurate than
ground-based height estimations using lasers. This finding confirms the knowledge that LiDAR
tree height assessments are more accurate than current ground-based measurements (Næsset, E.
et al.2013) used by foresters, ecologists, engineers, and others. These ground measurements are
44
subject to the limits of their technical proficiency. Tree height is one of the most expensive
variables to collect in ground-based assessments. For that reason, just a subsample of a few tree
heights within the fixed area plot are taken in some tree volume estimates. LiDAR could play an
important role by supplying accurate tree height information when used in combination with
ground-based measurements of tree diameters, thus improving the precision of volume estimates.
LiDAR DBH estimates were done for the two inventory seasons with 3, 10, or 15
iteration points each time. In the case of the thinned pine plantations, non-thinned pine plantation
and mixed pine-hardwood stands the best results were generated by the iterations with 3 points,
where the DBH was always underestimated. However, the LiDAR DBH estimation had a normal
distribution and a similar dispersion to the DBH measured in the field in both seasons excluding
plot 10. T-tests showed significant differences between the field measurements and DBH
estimations with three points in all the plots in the pine plantations during the March and the
June assessment (Tables 4-17 and 4-18). This test was performed only for the estimations that
met the assumptions (Snedecor. 1946). The mixed pine-hardwood stand had non-significant
differences in plots 13, 14, and 15 during the March assessment and in plots 11, 12, 13, 14, and
15 during June when only pine species were analyzed to meet the T-test assumptions (Tables 417 and 4-18). The standard deviation of the difference in DBH between the LiDAR estimation
and field measurements were greater in the mixed pine-hardwood stand than in the thinned and
non-thinned pine plantations during both seasons (Tables 4-17 and 4-18). At these stands, the
iterations with 10 and 15 points had more dispersion with a large number of outliers that lead to
data filtering (Figures 30, 31, 38 and 39) where the data were never normally distributed or
homoscedastic. Field measurements where done during the onset of spring leaf-out, but the
LiDAR data were collected when trees were in a more advanced state of foliar development. At
45
the first LiDAR assessment trees were at about two-thirds full leaf, and at full leaf at the second
LiDAR assessment. The effect of the seasonal assessment is most of interest at the mixed pinehardwood stand. All the plots in the natural mature pine stand, in contrast to the other stand
types, had better results when the number of points in the iteration was 10 and 15 during both
seasons (Figures 33 and 41). All the plots in the natural mature pine stand had normally
distributed estimations when the number of iterations were 10 and 15, with exception plot 19
during both seasons. However, a non-significant difference between the field measurements and
LiDAR DBH estimates were found on plots 16, 17, 18 and 20 when 10 and 15 iterations were
used, with average differences smaller than 3 cm in four of the five plots (Tables 4-19 and 4-20).
As it can be observed from Figures 34 to 37 and Figures 42 to 45 where field DBH and
LiDAR DBH estimates were plotted against each other, there is not an obvious visual
relationship between these measurements at the pine plantations, whereas the natural mature pine
stand shows a clear tendency between field and LiDAR measurements. The regressions showing
the relationship of these variables at the natural mature pine stand had an r2 of 0.24 and 0.19 for
March and June, respectively, which were significant at α-0.05 (Snedecor 1946). However, the
seasonal assessment change can be used as a quantification of LiDAR penetration to lower forest
stratums. The results show that LiDAR assessments in either March or June are not effective in
pine plantations (Figures 34 and 35). For this reason, we do not examine seasonality in LiDAR
DBH estimations. The relationship is less obvious at the mixed pine-hardwood stand. It is
important to note that not all the trees had DBH LiDAR estimates because the number of stem
cloud points between 1.4 and 4 m were not adequate to assess the DBH using the RANSAC
algorithm with a minimum of 3, 10 or 15 points. When the number of cloud points are small,
DBH assessments are not accurate, generating extreme values that are not close to the real DBH
46
in many cases. Due to the lack of points necessary for the iteration or at stem heights or extreme
values of this process. Figure 46 shows examples of point cloud distribution at stem heights
between 1.5 and 4 meters above the ground. Canopy closeness plays an important role in this
aspect by restricting the laser penetration into the lower strata of the forest where the natural
mature pine stand has the highest point density. Larger leaf area index (LAI) values were
measured during June assessment. The mixed pine-hardwood stand had values up to 2.35,
whereas the maximum value at the non-thinned plantation was 1.97. The thinned pine plantation
and the natural mature pine stand had the smallest LAI, with means of 1.0 and 0.9, respectively
(Table 4-7). Whereas, in the pine plantations and mixed pine-hardwood stands the reflectance of
the laser and the point cloud density is smaller. A set of 3 points generates better results by
allowing more interactions of the RANSAC algorithm to run, thus providing a more likely DBH
estimation. On the other hand, the greater cloud point density at the mature pine stand facilitates
runs of the RANSAC algorithm with 10 and 15 points in each iteration to yield a more likely
DBH estimation (Figures 33, 37, 41, 45 and Table 4-21). The thinned pine plantation had similar
numbers of trees with DBH estimates in March and June assessments, with values of 100% in
four plots. The non-thinned stands had estimates between 98 and 95% in March and fewer
estimates in June in plots 6, 7 and 8, with values of 93, 87 and 89%, respectively. Whereas, the
mixed pine-hardwood stand had the greatest difference between assessments. March estimates
included 95% of the trees in plots 11, 12 and 15, while plots 13 and 14 had values of 79 and
65%, respectively. These estimates were smaller during the June assessment, with values of 50,
48, 55, 30 and 58% in the plots 11, 12, 13, 14, 15, respectively. Season (p≤0.00) and stand type
(p≤0.00) had significant effects on the percentage of trees with DBH estimations (Table 4-21).
The thinned and non-thinned pine plantations did not have significant differences in the
47
percentage of LiDAR DBH estimations, and estimates for the mature natural pine type did not
differ from the thinned and non-thinned pine plantations using Tukey’s test. The mixed pinehardwood stand had fewer DBH estimates than the mature natural pine stand and the thinned and
non-thinned pine plantations using Tukey’s test. The success of LiDAR DBH estimation in the
mixed pine-hardwood stand may have been limited by stand characteristics different than the
other stand types, such as the un-even age structure, the multi-storied canopy and the use of only
pine trees that have a straight stem with a larger area and free of branches to return laser pulses
back to the sensor allowing better LiDAR DBH estimates. With that being said, it is more
effective to use UAV based LiDAR to estimate DBH in open stands, such as in the natural
mature pine stand, using more points for the RANSAC iterations. In the case of the mixed pine
hardwood stand, better DBHs can be assessed during the leaf out period when the cloud point is
denser in the lower stratums of the forest. Using 3 points for the RANSAC iterations produces
DBH estimates with non-significant differences with field-based assessments. However, since
the LiDAR-based assessments were done when hardwood leaves were already emerging, the leaf
out effect may not be fully understood or estimated.
LiDAR density is not as important for main variables like tree height, dominant height,
and basal area. Laser pulse density can be reduced to low densities without significant loss of
information but it is a key factor when DBH estimations are assessed by this method. Wilkes et
al. 2017 used terrestrial LiDAR to classify vegetation and estimate attributes such as DBH and
basal area with good accuracies. Other methods can be combined to achieve DBH estimation
using stand level information including age and trees per hectare, and individual tree variables
such as tree height and crown area, which can be estimated with LiDAR and used later in
equations as is those developed by Gonzalez-Benecke et al.(2014).
48
Table 4-1. Number of total trees and dead trees in plots within four stand types
Plot mean
Treatment
Total number Mean number number of
mean number
Stand type
Plot
of trees
of trees
dead trees
of dead trees
Thinned pine 1
28
4
plantation
2
29
4
3
28
1
4
33
3
5
23
28.2
0
2.4
Non-thinned
6
80
0
pine
7
72
0
plantation
8
72
0
9
76
0
10
83
76.6
1
0.2
Mixed pine- 11
22
0
hardwood
12
28
3
stand
13
21
1
14
22
2
15
20
22.6
1
1.4
Mature
16
11
0
natural pine 17
8
0
stand
18
9
0
19
12
0
20
13
10.6
2
0.4
Mean
34.5
Total
690
49
Table 4-2. Mean and standard deviation (SD) of diameter at breast height (DBH) by tree species determined in the March ground
inventory
Laurel oak
Stand
type
Thinned
pine
plantation
Nonthinned
pine
plantation
Mixed
pinehardwood
stand
Natural
mature
pine stand
Mean
of
Plot
DBH
(cm)
1
2
3
4
5
6
7
8
9
10
11
21.3
12
18.3
13
20.3
14
20.0
15
23.3
16
17
18
19
20
SD
of
DBH
(cm)
Live oak
Loblolly pine
Longleaf
pine
Post oak
Mean
of
DBH
(cm)
Mean
of
DBH
(cm)
Mean
of
DBH
(cm)
SD
of
DBH
(cm)
Mean
of
DBH
(cm)
26.1
37.4
29.3
6.9
11.7
7.4
17.0
37.9
31.8
37.9
31.5
29.5
5.5
2.9
9.5
8.6
2.9
SD
of
DBH
(cm)
SD
of
DBH
(cm)
23.4
3.6
4.5
5.5
5.7
8.0
21.3
14.2
50
Slash pine
SD
of
DBH
(cm)
Mean
of
DBH
(cm)
19.8
18.6
18.7
17.8
20.9
16.4
16.4
15.8
16.1
15.9
31.6
27.0
4.3
33.4
30.3
29.5
26.7
38.7
38.4
37.3
38.2
SD
of
DBH
(cm)
4.4
4.3
4.7
5.6
4.4
3.1
2.9
2.6
3.1
3.7
8.4
10.6
5.2
4.1
3.5
13.1
11.8
5.3
12.5
Global
Mean
DBH
(cm)
19.8
18.6
18.7
17.8
20.9
16.4
16.4
15.9
16.1
15.9
24.9
23.2
22.1
24.2
24.6
34.8
37.0
38.1
32.0
34.2
Global
SD
DBH
(cm)
4.4
4.3
4.7
5.6
4.4
3.1
2.9
2.7
3.1
3.7
6.7
10.0
7.0
7.2
7.4
9.1
10.5
7.9
8.4
10.1
Table 4-3. Mean and standard deviation (SD) total live height determined in the March ground inventory by tree species.
Stand type
Thinned
pine
plantation
Nonthinned
pine
plantation
Mixed
pinehardwood
stand
Natural
mature
pine stand
Plot
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Laurel oak
Live oak
Mean
total
height
(m)
Mean
total
height
(m)
SD
total
height
(m)
SD
total
height
(m)
Loblolly pine
Longleaf pine
Post oak
Mean
total
height
(m)
Mean
total
height
(m)
Mean
total
height
(m)
SD
total
height
(m)
SD
total
height
(m)
Slash pine
SD
total
height
(m)
15.8
15.8
16.3
15.1
14.4
13.0
1.8
1.8
2.3
3.3
4.2
21.6
21.5
21.3
3.5
6.1
4.4
14.4
16.8
11.9
25.1
24.8
26.0
23.5
22.0
51
1.9
0.7
4.3
4.9
2.9
3.7
Mean
total
height
(m)
17.2
17.3
16.5
15.0
17.1
14.7
15.1
14.5
14.6
14.1
22.8
17.3
17.8
23.3
17.3
25.7
28.9
27.1
26.8
SD
total
height
(m)
3.1
3.2
3.8
4.5
3.1
1.7
1.6
2.1
1.8
2.1
1.7
7.3
8.7
2.1
10.3
7.8
3.7
2.1
4.0
Overall
mean
total
height
(m)
17.2
17.3
16.5
15.0
17.1
14.7
15.1
14.5
14.6
14.1
19.4
17.3
16.1
18.0
14.3
24.4
25.5
27.0
23.8
24.6
Global
SD
Total
Height
(m)
3.1
3.2
3.8
4.5
3.1
1.7
1.6
2.1
1.8
2.1
4.0
4.6
3.6
5.3
6.5
4.0
3.1
3.9
4.8
4.2
Table 4-4. Mean and standard deviation (SD) diameter at breast height (DBH) determined in the June ground inventory by tree
species.
Stand type
Thinned
pine
plantation
Nonthinned
pine
plantation
Mixed
pinehardwood
stand
Natural
mature pine
stand
Plot
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Laurel oak
Live oak
Mean
DBH
(cm)
Mean
DBH
(cm)
SD
DBH
(cm)
SD
DBH
(cm)
Loblolly pine
Longleaf pine
Post oak
Mean
DBH
(cm)
Mean
DBH
(cm)
Mean
DBH
(cm)
SD
DBH
(cm)
SD
DBH
(cm)
Slash pine
SD
DBH
(cm)
23.6
21.8
18.3
19.7
20.0
21.1
3.7
4.6
3.0
5.7
5.9
22.4
22.1
25.7
26.5
37.4
29.5
9.6
8.6
8.9
7.0
11.5
7.5
17.4
14.5
38.0
31.8
37.7
32.0
29.6
52
5.4
2.9
9.2
9.0
2.9
3.8
Mean
DBH
(cm)
SD
DBH
(cm)
Global
Mean
DBH
(cm)
Global
SD
DBH
(cm)
20.1
18.8
18.4
17.6
21.0
16.7
16.5
16.2
16.3
16.0
31.8
27.0
14.9
30.5
29.6
26.9
38.8
38.5
37.6
38.4
4.5
4.4
6.1
5.9
5.0
3.2
3.0
2.8
3.2
3.8
8.4
10.5
21.0
4.3
3.5
13.3
11.6
5.4
12.4
20.1
18.8
18.4
17.6
21.0
16.7
16.5
16.3
16.3
16.0
25.3
23.2
20.4
24.5
24.9
35.0
37.0
38.0
32.5
34.3
4.5
4.4
6.1
5.9
5.0
3.2
3.0
2.9
3.2
3.8
6.7
10.0
7.3
7.2
7.4
9.1
10.4
7.8
8.7
10.1
Table 4-5. Mean and standard deviation (SD) total live height determined in the June ground inventory by tree species.
Stand type
Thinned
pine
plantation
Nonthinned
pine
plantation
Mixed
pinehardwood
stand
Natural
mature pine
stand
Plot
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Laurel oak
Mean
SD of
total
total
height
height
(m)
(m)
Live oak
Mean
total
height
(m)
SD of
total
height
(m)
Loblolly pine
Mean
SD of
total
total
height
height
(m)
(m)
Longleaf pine
Mean
SD of
total
total
height
height
(m)
(m)
Post oak
Mean
total
height
(m)
SD of
total
height
(m)
18.4
17.4
16.7
15.5
15.3
15.9
2.9
2.3
2.1
2.1
1.6
16.4
10.5
13.0
21.7
20.9
21.1
3.4
6.2
2.8
3.5
5.9
4.8
16.1
12.0
24.6
23.9
24.2
23.2
22.3
53
2.1
0.5
4.0
4.3
3.3
2.6
Slash pine
Mean
SD of
total
total
height
height
(m)
(m)
18.1
3.3
17.7
3.3
16.7
4.2
15.4
4.9
17.4
3.3
15.4
1.7
15.4
1.6
15.0
1.7
15.1
1.8
14.4
2.2
23.1
2.0
17.3
7.4
12.1
17.2
23.2
2.0
20.9
8.0
21.9
7.8
24.9
2.1
28.4
3.0
24.7
26.4
4.1
Mean
total
height
(m)
18.1
17.7
16.7
15.4
17.4
15.4
15.4
15.1
15.1
14.4
20.2
17.4
15.9
18.1
16.2
23.9
24.6
25.6
23.3
24.5
SD of
total
height
(m)
3.3
3.3
4.2
4.9
3.3
1.7
1.6
1.8
1.8
2.2
3.8
4.6
4.9
5.1
5.6
4.1
1.8
4.1
4.1
4.2
Table 4-6. Comparison of the difference between the values from the March and June
assessments of diameter breast height (DBH) and total height means and their
standard deviations (SD) for four stand types
Stand type
1
Thinned
pine
plantation
1
2
3
4
5
Nonthinned
pine
plantation
6
7
8
9
10
Mixed
pinehardwood
stand
11
12
13
14
15
Natural
mature
pine stand
16
17
18
19
20
Mean
DBH
difference
Plot 1 (cm)
0.3
0.2
-0.3
0.1
0.5
0.2
0.0
0.3
0.2
0.1
0.5
0.0
-1.5
0.3
0.3
0.1
0.0
-0.1
0.5
0.2
Mean
height
difference
(m)
0.9
0.4
-0.2
0.5
0.3
0.5
0.4
0.7
0.6
0.4
0.8
0.2
-0.1
0.1
0
-0.6
-0.9
-1.4
-0.5
-0.1
SD of
DBH
difference
(cm)
0.4
0.3
0.5
0.6
0.3
0.5
0.5
0.7
0.4
0.4
0.2
0.3
0.4
0.5
0.3
0.2
0.3
0.7
1.4
0.2
SD of
height
difference
(m)
1.15
0.86
1.84
0.7
0.69
2.09
1.08
1.83
0.89
0.68
1.99
1.21
3.34
1.11
1.35
0.87
1.39
1.09
1.38
1.61
The difference was calculated as March minus June assessment values.
54
p value of
t -test of
DBH
≤0.00
≤0.00
≤0.00
0.02
≤0.00
≤0.00
0.45
≤0.00
≤0.00
≤0.00
≤0.00
≤0.00
0.02
≤0.00
0.03
0.81
0.64
0.25
0.01
p value of
t-test of
height
≤0.00
0.38
0.03
≤0.00
≤0.00
≤0.00
0.08
0.46
0.29
0.86
0.86
0.07
0.11
0.01
0.21
0.89
≤0.00
0.38
0.03
≤0.00
Table 4-7. Seasonal mean leaf area index (LAI) by sampling plot for the four stand types
LAI March
LAI June
Mean March Mean June
Stand type
Plot
assessment
assessment
assessment
assessment
Thinned pine
0.98
1
0.85
plantation
0.90
2
0.84
0.99
3
0.98
1.06
4
1.00
1.07
5
1.25
0.98
1.00
Overall mean
Non-thinned pine 6
1.82
1.63
plantation
1.87
7
1.95
1.97
8
1.98
1.81
9
1.67
1.82
10
1.66
1.78
1.86
Overall mean
Mixed pineNA
11
2.71
hardwood stand
2.25
12
2.00
1.98
13
1.96
2.32
14
2.16
2.35
15
1.48
2.06
2.23
Overall mean
Natural mature
0.97
16
0.45
pine stand
0.82
17
0.58
0.84
18
0.65
1.08
19
0.83
0.94
20
0.75
0.65
0.93
Overall mean
55
Table 4-8. Trials applied to individual tree detection with WS (window size), filter type, fixed
window size (FWS) and minimum height.
WS
FWS
Minimum height
Trial
(m)
Filter
Sigma
(m)
(m)
1
5 Gaussian
default 7
12
2
5 mean
5
1.4
3
5 mean
7
5
4
7 mean
7
8
5
7 mean
7
12
6
5 median
5
5
7
7 median
7
8
8
7 Gaussian
1.5
5
5
9
7 Gaussian
1.5
7
1.4
10
7 Gaussian
1.5
7
5
11
5 Gaussian
5
5
12
5 Gaussian
5
1.4
13
5 Gaussian
1.5
5
1.4
14
7 Gaussian
7
1.4
15
7 Mean
7
1.4
16
5 Mean
5
8
17
5 Mean
5
1.4
18
3 Mean
5
1.4
19
3 Mean
5
8
20
3 Mean
7
5
21
3 Mean
5
5
56
Table 4-9. Summary of tree detection statistics by trial2 at thinned pine plantation
Trial
Positive detection (%) False positives (%) False negatives (%)
All
6
85
22
16
4
82
9
20
17
79
3
22
without dead
6
89
24
13
trees
2
87
14
14
16
81
2
20
3
DBH > 12.7 cm
13
92
18
8
14
86
3
12
21
76
0
23
2
Trial is defined as a combination of factors
3
Diameter at breast height
Table 4-10. Tree detection at non-thinned pine plantation
Trial
Detection (%)
False positives (%)
All
5
84
3
13
80
1
1
79
1
Without dead trees 5
84
3
13
80
1
12
79
2
DBH> 12.7 cm
5
93
3
13
87
1
12
87
2
57
False negatives (%)
19
20
21
19
20
20
10
13
13
Table 4-11. Tree detection at the mixed pine-hardwood stand.
Trial Detection (%)
False positives (%)
All
3
62
27
5
51
6
11
72
106
without Dead trees 3
63
28
5
51
7
11
73
111
DBH> 12.7 cm
3
64
24
5
52
5
11
74
107
False negatives (%)
38
48
28
36
47
26
35
46
25
Table 4-12. Tree detection at natural mature pine stand
Trial
Detection (%)
False positives (%)
All
2
86
12
1
91
118
without Dead trees 5
86
12
1
91
118
DBH> 12.7 cm
5
88
12
1
93
121
False negatives (%)
14
9
10
9
9
7
58
Table 4-13. Comparison of mean distributions in LiDAR height measures versus field plot
assessment in March
Homogeneity T-test ( pMean difference
Stand
Plot Normality⁴
of variances value)⁵
(m)⁶
Thinned
1
Y
Y
≤0.00
1.1
pine
2
Y
Y
≤0.00
0.5
plantation 3
Y
Y
≤0.00
0.7
4
Y
Y
≤0.00
0.8
5
Y
Y
≤0.00
0.8
Non6
Y
Y
≤0.00
0.7
thinned
7
Y
Y
≤0.00
0.8
pine
8
Y
Y
≤0.00
0.7
plantation
9
Y
Y
≤0.00
0.6
10
Y
Y
≤0.00
0.7
11
Y
Y
0.01
1.7
Mixed
12
N
Y
2.4
pine13
N
Y
1.9
hardwood
14
N
Y
2.4
stand
15
N
Y
4.2
Mature
16
Y
Y
0.15
0.3
natural
17
Y
Y
0.79
-0.2
pine stand 18
Y
Y
0.32
-0.4
19
Y
Y
0.16
0.54
20
Y
Y
0.65
0.3
4
+
Y and N stands for meeting or not the assumptions respectively
5
Empty fields for not meeting the T-test assumptions
6
The difference was calculated as LiDAR minus June assessment values.
59
Table 4-14. Comparison of mean distributions in LiDAR height measures versus field plot
assessment in winter at the mixed pine-hardwood stand for pine trees only
Homogeneity of
Mean difference
Plot
Normality
variances
T-test (p value)
(m)
11
Y
Y
0.03
1.5
12
Y
Y
0.98
≤0.0
13
Y
Y
0.07
0.5
14
Y
Y
0.19
1.9
15
Y
Y
0.01
≤0.0
60
Table 4-15. Height assessment comparison between LiDAR and ground-based methods at the thinned pine plantation
Ground-based assessment
Automatic LiDAR assessment
Manual LiDAR assessment
Standard
Standard
Standard
Mean difference
deviation Mean difference
deviation Mean difference
deviation
(m)
(m)
(m)
(m)
(m)
(m)
Ground-based assessment
0.99
0.67
Automatic LiDAR
assessment
0.29
0.1
Manual LiDAR assessment
1.25
0.66
Table 4-16. Height assessment comparison between LiDAR and ground-based methods at the non-thinned pine plantation
Ground-based assessment
Automatic LiDAR assessment
Manual LiDAR assessment
Standard
Standard
Standard
Mean difference
deviation Mean difference
deviation Mean difference
deviation
(m)
(m)
(m)
(m)
(m)
(m)
Ground-based assessment
1.03
0.68
Automatic LiDAR
assessment
0.1
0.07
Manual LiDAR assessment
0.99
0.86
61
Table 4-17. Comparison of mean distributions in LiDAR assessments of diameter at breast
height (DBH) assessment using various point quantities to sample every RANSAC
iteration (n) in the March assessment.
Homogeneity of
Stand type
Normality⁷
variances
T test (p value)⁸
Plot n3 n10 n15 n3
n10
n15
n3
n10 n15
Thinned pine
N
≤0.00
1
N N
Y
Y
N
plantation
N
≤0.00 ≤0.00
2
Y Y
Y
Y
N
≤0.00
3
Y N
N
Y
N
N
N
≤0.00
4
Y N
Y
N
N
N
≤0.00
5
Y N
Y
N
N
Non-thinned pine
N
≤0.00
6
Y N
Y
N
Y
plantation
N
≤0.00
7
Y N
N
N
N
≤0.00
8
Y Y
Y
N
N
N
N
≤0.00
9
Y N
Y
N
N
N
≤0.00
10
N N
Y
N
N
Y
≤0.00
Y Y
Y
N
N
Mixed pine-hardwood 11
Y
≤0.00
12
Y Y
N
N
Y
stand
Y
0.08
13
Y Y
Y
N
N
Y
0.24
14
N Y
Y
N
N
Y
0.08
15
Y Y
Y
Y
N
Mature natural pine
≤0.00 0.05 0.1
16
Y Y
Y
Y
Y
Y
stand
0.31 0.5
17
N Y
Y
Y
Y
Y
≤0.00 0.22 0.5
18
Y Y
Y
Y
Y
Y
N
19
N N
Y
Y
Y
≤0.00 0.06 0.1
20
Y Y
Y
Y
Y
Y
7
+
Y and N stands for meeting or not the assumptions respectively
8
Empty fields for not meeting the T-test assumptions
62
Table 4-18. Comparison of mean distributions in LiDAR assessments of diameter at breast
height (DBH) assessment using various point quantities to sample every RANSAC
iteration (n) in the June assessment.
Homogeneity of
Stand type
Normality⁹
variances
T test (p value)¹⁰
Plot n3 n10 n15 n3
n10
n15
n3
n10 n15
Thinned pine
N
≤0.00
1
Y N
Y
N
N
plantation
N
≤0.00
2
Y N
Y
N
Y
≤0.00
3
Y N
N
Y
N
N
N
≤0.00
4
Y N
Y
N
N
≤0.00
5
Y Y
N
Y
N
Y
Non-thinned pine
≤0.00
6
Y N
N
Y
N
N
plantation
7
Y N
N
N
N
N
8
Y N
N
N
N
N
≤0.00
9
Y N
N
Y
N
N
10
N N
N
N
N
N
0.11
11
Y Y
N
Y
Y
N
Mixed pine-hardwood
0.68 0.35
12
Y N
Y
Y
N
N
stand
0.74
13
Y N
N
Y
N
N
0.96 0.17
14
Y N
N
Y
Y
N
0.74 0.15
15
Y Y
Y
Y
Y
N
Mature natural pine
≤0.00 0.13 0.60
16
Y Y
Y
Y
Y
Y
stand
17
Y Y
Y
Y
Y
Y
≤0.00 0.15 0.46
≤0.00
18
N Y
Y
Y
Y
Y
19
N Y
N
Y
Y
Y
≤0.00 0.38 0.41
20
Y Y
Y
Y
Y
Y
9
+
Y and N stands for meeting or not the assumptions respectively
10
Empty fields for not meeting the T-test assumption
63
Table 4-19. Comparison of diameter breast height (DBH) estimation using LiDAR assessments
using various point sampling intensity (n) versus the ground-based sampling in the
March assessment¹¹
Mean difference in
Standard deviation
Stand type
Mean DBH (cm)
DBH(cm)¹²
(cm)
Plot
n3
n10
n15
n3
n10
n15
n3
n10
n15
Thinned pine
3.7
1
15.4
5.3
plantation
3.7
2
15.0
4.2
4.5
3
16.2
4.1
4.2
4
16.0
4.4
5.5
5
18.9
2.4
Non-thinned pine 6
11.9
5.2
6.2
plantation
7
13.0
3.3
3.0
8
12.3
4.1
5.6
9
11.7
5.1
4.3
10
12.8
4.6
3.8
11
18.8
6.5
6.0
Mixed pine12
17.0
5.3
13.0
hardwood stand
13
18.2
3.6
3.7
14
20.8
4.3
10.0
15
19.4
5.2
9.7
Mature natural
3.0
2.2
5.3
16
40.2 39.3
4.3
pine stand
2.1
1.2
7.9
17
34.8 35.8
8.8
1.9
1.6
8.5
18
36.1 36.4
9.1
3.0
2.2
22.0 37.0
19
40.2 39.3
14.9
8.9
8.1
20
46.9 40.0
8.0
11
Values shown for best assessments
12
The difference was calculated as the absolute difference between March June assessment
64
Table 4-20. Comparison of diameter breast height (DBH) estimation using LiDAR assessments
using various point sampling intensity (n) versus the ground-based sampling in the
June assessment¹³
Mean difference in
Standard deviation
Stand type
Mean DBH (cm)
DBH(cm)
(cm)
Plot n3 n10 n15
n3
n10
n15
n3
n10
n15
Thinned pine
1
14.8
6.2
2.9
plantation
2
14.7
4.8
3.6
3
13.9
6.9
3.1
4
14.7
5.9
5.2
5
17.1
4.7
6.8
Non-thinned pine
6
12.4
4.9
6.2
plantation
7
14.1
2.3
8.7
8
11.6
5.2
3.9
9
12.6
4.4
4.8
14.4
10
3.1
9.5
20.7
11
4.5
11.0
Mixed pine21.7
1.6
12
10.9
hardwood stand
23.2
1.8
13
11.7
24.6
0.9
14
15.5
24.8
0.0
15
13.9
Mature natural pine 16
7.9
36.0 38.7
1.3
1.4
4.5
stand
9.3
17
34.3 35.4
2.7
1.5
8.7
5.1
18
36.8 36.6
3.3
3.5
5.4
9.1
19
36.7 37.8
0.9
2.0
6.9
20
49.3 44.1
12.5
7.2
41.5 27.5
¹³ Values shown for best assessments
65
Table 4-21. Percentage of trees with LiDAR DBH estimates¹⁴
Trees with DBH estimates
Trees with DBH estimates
Stand type
during March inventory (%)
during June inventory (%)
Plot
n3
n10
n15
n3
n10
n15
Thinned pine
100.0
100.0
1
plantation
100.0
100.0
2
100.0
100.0
3
96.0
100.0
4
100.0
95.4
5
Non-thinned
6
98.5
93.0
pine plantation 7
95.8
87.5
8
95.3
95.3
9
95.5
95.5
10
95.4
89.3
11
95.4
50.0
Mixed pine95.2
47.8
hardwood stand 12
13
78.9
55.0
14
65.0
30.0
15
94.7
57.9
Mature natural 16
100
100
100
100
pine stand
17
100
100
100
100
18
100
100
100
100
19
100
91.6
100
100
20
90.9
90.9
90
90
¹⁴ Values shown for best assessments
66
Figure 4-1. Individual of Sabal palmetto within plots in the non-thinned pine plantation. Photo
courtesy of author.
67
Figure 4-2. Tree locations from ground-based field assessment (shown in green) and their
relocation using LiDAR data (shown in red) for the five plots at thinned pine
plantation. Photo courtesy of author.
68
Figure 4-3. Tree locations from ground-based field assessment (shown in green) and their
relocation using LiDAR data (shown in red) for the five plots at non-thinned pine
plantation. Photo courtesy of author.
69
Figure 4-4. Tree locations from ground-based field assessment (shown in green) and their
relocation using LiDAR data (shown in red) for the five plots at mixed pinehardwood stand. Photo courtesy of author.
70
Figure 4-5. Tree locations from ground-based field assessment (shown in green) and their
relocation using LiDAR data (shown in red) for the five plots at mature natural pine
stand. Photo courtesy of author.
71
DBH difference in blue (cm), total Height difference in orange (m)
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Plot
Figure 4-6. Mean diameter at breast height (DBH) and total height difference between the
ground-based assessments in March and June for each of the twenty plots. The
difference was calculated as March minus June mean values. Photo courtesy of
author.
72
Figure 4-7. Diameter at breast height (DBH) distribution for each plot during the March ground-based seasonal assessment. The
thinned pine plantation is shown in orange, non-thinned pine plantation is shown in blue, mixed pine-hardwood stand is
shown in green, and mature natural pine stand is shown in red. Photo courtesy of author.
73
Figure 4-8. Diameter at breast height DBH distribution for each plot during the June inventory ground-based seasonal assessment. The
thinned pine plantation is shown in orange, non-thinned pine plantation is shown in blue, mixed pine-hardwood stand is
shown in green and mature natural pine stand is shown in red. Photo courtesy of author.
74
Figure 4-9. Total height distribution for each plot during the March inventory ground-based seasonal assessment. The thinned pine
plantation is shown in orange, non-thinned pine plantation is shown in blue, mixed pine-hardwood stand is shown in green
and mature natural pine stand is shown in red. Photo courtesy of author.
75
Figure 4-10. Total height distribution for each plot during the June inventory ground-based seasonal assessment. The thinned pine
plantation is shown in orange, non-thinned pine plantation is shown in blue, mixed pine-hardwood stand is shown in green
and mature natural pine stand is shown in red. Photo courtesy of author.
76
Figure 4-11. Example of tree detection output at the non-thinned pine plantation showing LiDAR
detection (blue triangles), ground-based location (green circles), and color-scaled
detection strength value. Photo courtesy of author.
77
Figure 4-12. Example of tree detection output at the mature natural pine stand showing LiDAR
detection (blue triangles), ground-based location (green circles), and color-scaled
detection strength value. Photo courtesy of author.
78
Figure 4-13. Thinned pine plantation total height determined by ground-based and LiDAR
assessment for the five plots at March. Photo courtesy of author.
79
Figure 4-14. Non-thinned pine plantation total height determined by ground-based and LiDAR
assessment for the five plots at March. Photo courtesy of author.
80
Figure 4-15. Mixed pine-hardwood stand total height determined by ground-based and LiDAR
assessment for the five plots at March. Photo courtesy of author.
81
Figure 4-16. Mature natural pine stand total height determined by ground-based and LiDAR
assessment for the five plots at March. Photo courtesy of author.
82
Figure 4-17. Mixed pine-hardwood stand total height determined by ground-based and LiDAR
assessment for the five plots at March showing only pines. Photo courtesy of author.
83
Figure 4-18. Comparison of automatic LiDAR height (LH) and manual LiDAR height
assessment in the thinned and non-thinned pine plantations during the March
assessment. Photo courtesy of author.
84
Figure 4-19. Comparison of LiDAR height (LH) and ground base height assessment in the
thinned and non-thinned pine plantations during the March assessment. Photo
courtesy of author.
85
Figure 4-20. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 1 of the
thinned pine plantation during the March inventory season. Photo courtesy of author.
86
Figure 4-21. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 6 of the
non-thinned pine plantation during the March inventory season. Photo courtesy of
author.
87
Figure 4-22. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 11 of the
mixed pine-hardwood stand during the March inventory season. Photo courtesy of
author.
88
Figure 4-23. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 16 of the
mature natural pine stand during the March inventory season. Photo courtesy of
author.
89
Figure 4-24. Relationship between ground-based assessment of tree diameter at breast
height(DBH) and DBH determined by LiDAR using three points (n) at the thinned
pine plantation in the March seasonal assessment. (r2=0.08). Photo courtesy of author.
90
Figure 4-25. Relationship between ground-based assessment of tree diameter at breast
height(DBH) and DBH determined by LiDAR using three points (n) at the nonthinned pine plantation in the March seasonal assessment. (r2=0.001). Photo courtesy
of author.
91
Figure 4-26. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at mixed pinehardwood stand in the March seasonal assessment (r2=0.07). Photo courtesy of
author.
.
92
Figure 4-27. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at matured natural pine
stand in the March seasonal assessment. (r2=0.24). Photo courtesy of author.
93
Figure 4-28. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 1 of the
thinned pine plantation during the June seasonal assessment. Photo courtesy of
author.
94
.
Figure 4-29. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 6 of the
non-thinned pine plantation during the June seasonal assessment. Photo courtesy of
author.
95
Figure 4-30. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 12 of
mixed pine-hardwood stand during the June seasonal assessment. Photo courtesy of
author.
96
Figure 4-31. Example of diameter at breast height (DBH) from ground-based assessment and
LiDAR using different numbers of points (n) and outlier filtering (F) at plot 16 of
mature natural pine stand during the June seasonal assessment. Photo courtesy of
author.
97
Figure 4-32. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at the thinned pine
plantation in the June seasonal assessment (r2=0.07). Photo courtesy of author.
98
Figure 4-33. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at the non-thinned pine
plantation in the June seasonal assessment (r2=0.002). Photo courtesy of author.
99
Figure 4-34. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at mixed pinehardwood stand in the June seasonal assessment(r2=0.06). Photo courtesy of author.
100
Figure 4-35. Relationship between ground-based assessment of tree diameter at breast height
(DBH) and DBH determined by LiDAR using three points (n) at mature natural pine
stand in the June seasonal assessment (r2=0.19). Photo courtesy of author.
101
Figure 4-36. Example of point cloud density at various point height (scaled colors) showing an
orthogonal view of individual tree stems (green circle) for different stand types
assessed. Photo courtesy of author.
102
CHAPTER 5
CONCLUSIONS
This research contributes to the development of new approaches for forest inventory and
stand characterization. Airborne LiDAR and ground LiDAR scanning have been used in the past,
rather than UAS platforms, due to technological problems such as load capacity and sensor
resolutions. We have determined the effectiveness and efficiency of UAS-based LiDAR forest
inventory under different conditions of stand density, stem distribution, species composition and
measurement timing. 82 and 93% can be achieved at pine plantations of slash pine, as well as
height in both thinned and non-thinned stands. Mature natural pine stands had 88 and 93% tree
detection rates among the established trees. The effectiveness of LiDAR assessments was poor
for the mixed pine-hardwood stand in comparison to the other stand types, and further research is
needed to examine the effect of differential age and canopy structure. It was not possible to
assess DBH using UAS-based LiDAR with the RANSAC algorithm at the mixed pine-hardwood
stand and the thinned and non-thinned pine plantations, but good estimates were obtained at the
mature natural pine stand, perhaps because of the larger mean DBH, lower stem density and
open understory maintained by prescribed burning. While these methods are not going to replace
ground-based forest inventory in the near future, they provide tools to assess some stand level
characteristics, including site index. Even more importantly, these methods could help forest
managers estimate variability of natural and planted stands to optimize traditional forest
inventory and intensify sampling where needed.
103
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106
BIOGRAPHICAL SKETCH
Oscar Ivan Raigosa Garcia got his forestry engineering degree from the National
University of Colombia, campus Medellin in 2017. He also received his MS degree in forest
resources and conservation from University of Florida in 2019. He has experience and passion
for geographic information systems, logging, forest inventory, modeling, economics, forest
management, sustainable development, soil sciences and hydrology.
107