Light detection and ranging and

hyperspectral data for estimation of

forest biomass: a review

Qixia Man
Pinliang Dong
Huadong Guo
Guang Liu
Runhe Shi

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 REVIEW
Light detection and ranging and hyperspectral data for
estimation of forest biomass: a review

Qixia Man,a,b,c Pinliang Dong,d,* Huadong Guo,b,c Guang Liu,c and

Runhe Shia,b
a
East China Normal University, Key Laboratory of Geographic Information Science (Ministry of
Educating), No. 500 Dongchuan Road, Minhang District, Shanghai 200241, China
b
East China Normal University and Institute of Remote Sensing and Digital Earth (RADI),
Joint Laboratory for Environmental Remote Sensing and Data Assimilation,
No. 500 Dongchuan Road, Minhang District, Shanghai 200241, China
c
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth (RADI),
Key Laboratory of Digital Earth, No. 9 Dengzhuang South Road,
Haidian District, Beijing 100094, China
d
University of North Texas, Department of Geography, 1155 Union Circle #305279,
Denton, Texas 76203, United States

Abstract. Forests are one of the most important sinks for carbon. Estimating the amount of
carbon stored in forests is a major task for understanding the global carbon cycle. From
local to global scales, remote sensing has been extensively used for forest biomass estimation.
With the availability of multisensor image data, fusion has become a valuable method in remote
sensing applications. Light detection and ranging (LiDAR) can provide information on the ver-
tical structure of forests, whereas hyperspectral images can provide detailed spectral information
of forests. Effective fusion of LiDAR and hyperspectral data is expected to help extract important
biophysical parameters of forests. However, it is still unclear as to how forest biophysical and
biochemical attributes derived from hyperspectral data relate to structural attributes derived from
LiDAR data. A summary of previous research on LiDAR-hyperspectral fusion for forest biomass
estimation is valuable for further improvement of biomass estimation methods. A review on the
status of hyperspectral data, LiDAR data, and the fusion of these two data sources for forest
biomass estimation in the last decade is provided. Some future research topics and major chal-
lenges are also discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0
Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of
the original publication, including its DOI. [DOI: 10.1117/1.JRS.8.081598]
Keywords: light detection and ranging; hyperspectral; data fusion; aboveground biomass.
Paper 14411V received Jul. 15, 2014; accepted for publication Nov. 13, 2014; published online
Dec. 18, 2014.

1 Introduction
The complex structure and diverse material resources of a forest make it a perfect resource pool
and biological gene bank for nature. Forest ecosystems have an irreplaceable role in improving
the ecological environment and maintaining ecological balance. In addition, forest ecosystems
are also an important component of the carbon cycle, in which forest ecosystems account for
80% of the aboveground carbon stocks and 40% of the underground carbon stocks.1,2 In recent
years, carbon sequestration has been a hot topic in climate change studies3 and carbon balance
estimation.4 From local to global scales, it is of increasing importance to quantify forest carbon
exchange and stocks because of international policies to reduce greenhouse gases, such as the
United Nations Framework Convention on Climate Change (UNFCCC),5 the Kyoto protocol,4
and the program for reducing emissions from deforestation and forest degradation (REDD).5–9

*Address all correspondence to: Pinliang Dong, E-mail: pdong@unt.edu

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

Forest biomass is one of the main biophysical parameters that describe the carbon content of
the forest.10,11 Therefore, accurate estimation and assessment of forest biomass are important to
quantify terrestrial carbon, control greenhouse gases, keep forest sustainably managed, and make
policies which can reduce CO2 mission.11,12 Field measurements are the traditional methods for
forest biomass estimation. However, it is destructive, labor intensive, costly, time consuming,
and sometimes inapplicable due to poor accessibility.13 Remote sensing has been frequently used
as a practical and economical means for forest biochemical and biophysical parameters estima-
tion, and a primary source for forest biomass estimation.14 At local to sub-regional scales, inter-
mediate and fine resolution remote sensors have been used for biomass estimation,15 such as
Landsat ETM+,16 SPOT,17 and WorldView-2.18 At regional to national scales, coarse resolution
sensors were used such as National Oceanic and Atmospheric Administration (NOAA),
Advanced Very High Resolution Radiometer (AVHRR),19 and Moderate Resolution Imaging
Spectroradiometer (MODIS).20
Hyperspectral sensors acquire hundreds of narrow bands of the electromagnetic spectrum
from visible to short-wave infrared wavelengths which could provide detailed and continuous
spectral information of forests,21 whereas light detection and ranging (LiDAR) has been known as
a vital method for characterizing forest vertical structures, including height, volume, and biomass.
With the availability of multisensor image data, effective fusion of the two complementary data
sources can improve the estimation of forest biomass and other forest structure parameters.22–25
Many previous papers have reviewed forest biomass estimation using remote sensing. Lu14
reviewed the potential and challenge of remote sensing-based biomass estimation and pointed
out that biomass estimation is still a challenging task, especially for areas with complex forest
structure and environmental conditions. Koch3 reviewed the status and future of three remote
sensing technologies (LiDAR, synthetic aperture radar, and hyperspectral remote sensing)
for forest biomass estimation. Treitz and Howarth26 published a review on hyperspectral remote
sensing for estimation of forest biophysical parameters. Govender et al.27 provided a review of
hyperspectral remote sensing and its application in vegetation and water resource studies.
Adam et al.28 reviewed multispectral and hyperspectral remote sensing for identification and
mapping of wetland vegetation. Lim et al.29 reviewed the recent research progresses of LiDAR
on forest structure extraction, including canopy height, volume, and biomass. Van Leeuwen and
Nieuwenhuis (2010)30 reviewed the methods and challenges in forest inventory using LiDAR.
Frolking et al.31 reviewed the impacts of forest disturbance and recovery on aboveground
biomass (AGB) and canopy structure in the context of space-borne remote sensing. Since
the review by Koch,3 many papers, which will be included in this review, have been published
on the fusion of hyperspectral and LiDAR data for forest biomass estimation.
Although research on biomass estimation using remote sensing has been investigated in the
past decades, a comprehensive review on the fusion of LiDAR and hyperspectral data for forest
biomass estimation is still lacking. It is unclear as to how forest biophysical and biochemical
parameters derived from hyperspectral data relate to the structural attributes from LiDAR data.
Many studies have involved the fusion of hyperspectral and LiDAR data in forest biomass appli-
cation, which makes it possible to provide an overview of the techniques that have been used and
to identify the aspects that still need further investigation. This review consists of the following
six major parts: (1) overview of LiDAR and hyperspectral remote sensing systems and concepts
of image fusion; (2) AGB estimation using LiDAR data; (3) AGB estimation using hyperspectral
data; (4) fusion of LiDAR and hyperspectral data for biomass estimation; (5) discussion of cur-
rent challenges and research needs; and (6) general conclusions.

2 Definitions

2.1 Overview of LiDAR Systems

LiDAR is an active remote sensing technology that determines distances based on the speed of
light and the time required for an emitted laser to reach a target object. It can simultaneously
capture vertical and horizontal forest structure and terrain morphology with high accuracy.32 The
components of LiDAR systems includes laser and scanning subsystems, a global position

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

system, and an inertial measurement unit. The basic ranging principle can be expressed with
the following equation:

R ¼ ct∕2; (1)

where R represents the distance from the sensor to the object; c is the speed of light; and t is
the round-trip transmission time from the sensor to the measured target.
Dubayah and Drake33 summarized three characteristics that can be used to classify LiDAR
systems for forestry applications, including: (1) the manner in which the return signal is recorded
as either discrete return LiDAR, which typically includes the first, last, and several intermediate
returns, versus the full-waveform LiDAR, which characterizes the returned energy in a continu-
ous manner;34 (2) footprint size—small (a few centimeters) or large (tens of meters); and (3) sam-
pling rate and scanning pattern.

2.2 Overview of Hyperspectral Systems

Hyperspectral remote sensing is a technology that acquires hundreds of narrow continuous
spectral bands between 400 and 2500 nm, throughout the visible (400 to 700 nm), near-infrared
(700 to 1000 nm), and short-wave infrared (1000 to 2500 nm) sections of the electromagnetic
spectrum.35 It is also known as imaging spectroscopy or imaging spectrometry.

2.3 Methods of Forest AGB Estimation

Biomass is the dry weight of living and dead organisms.36,37 In forests, aboveground living bio-
mass mainly includes the wood of canopy trees, vine, epiphyte, canopy leaf, understory, and
groundcover biomass and would exclude all aboveground dead material.38
In remote sensing applications, biomass normally refers to the AGB. Measurements of
biomass can be divided into direct and indirect methods. As for direct estimations, based on
the relationship between remote sensing response and the biomass, AGB can be estimated with
different methods, such as multiple regression analysis, K nearest neighbor classification, neural
network, and statistical ensemble methods.3,39–43 For indirect methods, the mean tree height or
diameter at breast height are derived from remote sensing images.44–47 Using these parameters,
biomass is obtained through allometric equations.48 For individual trees, biomass is added to
get the AGB for the whole plot.

2.4 Concepts of Image Fusion

Image fusion can be defined as the combination of two or more different images into a new
image using algorithms.49 Based on the stage at which fusion occurs, image fusion can be di-
vided into three levels: pixel level; feature level, and decision level.50,51 (1) Pixel level: After
coregistration, a series of raster data layers are directly added to an image which has more abun-
dant and reliable information. Pixel-level fusion is the lowest level of image fusion, in which
information synthesis and analysis based on the original information of various images are con-
ducted. Its merit is keeping most of the original information which could provide subtle infor-
mation, while the shortcomings are mainly reflected in the following four aspects: (a) large
amount of information, long processing time, and high cost; (b) because of the uncertainty, inse-
curity, and instability of the original information, it requires high error correction capability dur-
ing the fusion process; (c) low anti-interference ability; and (d) the fusion process requires a pixel
calibration accuracy. (2) Feature level: Using segmentation procedures, remote sensing images
are processed individually using feature extraction to generate unidentified features. The feature
extraction processes mainly depend on the image elements such as shape, extent, and neighbor-
hood.52 The extracted objects from multiple data sources are then fused for further assessment
using statistical approaches such as artificial neural networks (ANN).50 By combining the fea-
tures of the two datasets, the identification of features is carried out. Feature-level fusion could
keep sufficient important information, achieve objective data compression and guarantee a cer-
tain accuracy. (3) Decision level: datasets are processed completely separately, and only the final

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

Fig. 1 Levels of image fusion, from left to right: pixel-level, feature level, and decision level (take
hyperspectral-LiDAR fusion as an example), MNF: minimum noise fraction; NDVI: normalized
difference vegetation index; GLCM: gray level co-occurrence matrix; DSM: digital surface model;
CHM: canopy height model.

results are combined in geographic information systems. Apart from the three fusion levels,
image fusion can also be applied to various data types, e.g., single sensor and multisensor.50
Figure 1 shows the three levels of data fusion.

3 ABG Estimation with LiDAR Data

LiDAR technology can provide horizontal and vertical information about the forest canopy
which makes it one of the most applicable technologies in forest monitoring. LiDAR data
has been used to derive tree height,53 estimate stem volume,54 and classify tree species.55
An overview of LiDAR for forest applications can be found in the papers by Lim et al.,29
Hyyppä et al.,56 and Mallet and Bretar57 These papers reviewed recent research progress on
the extraction of canopy height, estimation of ABG, and canopy volume from LiDAR as
well as the status of small footprint, multiple point or full-wave LiDAR data for forest
applications. Table 1 shows the previous studies of forest biomass estimation using LiDAR
data.
Compared with other sensors, Airborne LiDAR data is much more effective for forest bio-
mass estimation.66 Many studies have described the approaches of biomass estimation from
LiDAR data, including single regression between LiDAR-derived height metrics, tree crown
delineation and biomass, stochastic simulation and machine learning approaches.67 There are
two levels of forest biomass estimation: individual-tree level and plot-level.12

3.1 Individual Tree Level

With individual tree level forest biomass estimation, a crown height map (CHM) was produced
from raw LiDAR point cloud data; then, individual trees were identified by applying algorithms
to locate the maximum heights in CHM, such as local maxima filtering; finally, biomass was
calculated by using regression between the tree height and biomass. There were numerous meth-
ods for local maxima filtering based on different search window sizes.67 Popescu68 identified the
crown diameter from a CHM using local maxima filtering and quantified forest biomass by using
regression algorithms which included crown width as a parameter. His study managed to explain
93% of the biomass using individual tree metrics. Recently, some researchers have developed
advanced methods to identify individual trees. Bortolot and Wynne69 proposed a new individual
tree-based algorithm for forest biomass estimation using small footprint LiDAR data. Kwak
et al.70 proposed a watershed segmentation algorithm to identify individual trees. While biomass

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Table 1 Summary of the previous studies of LiDAR-based biomass.

References Parameters Regression Results

Nelson Average of the three greatest laser Two logarithmic R 2 ¼ 0.55

et al.58 heights, mean plot height (all pulses equations
and canopy pulses), distance between
the top of canopy and a point 2, 5 or
10 m aboveground

Means LiDAR canopy height, quadratic mean Allometric equations R 2 ¼ 0.96

et al.59 canopy height, canopy reflectance sum on DBH

Lefsky Max/min canopy height, canopy cover, Stepwise multiple R 2 ¼ 0.86

et al.60 variability in the upper canopy surface, regression
total volumes of foliage and empty
space in canopy

Drake LiDAR canopy height, height of The tropical wet R 2 ¼ 0.93

et al.61 median energy, height /median allometric equation
ratio, ground return ratio

Nelson Quadratic mean height of pulses Parametric linear R 2 ¼ 0.66

et al.62 in the forest canopy regression,
nonparametric linear
regression

Popescu Average/maximum crown diameter; Regression models R 2 ¼ 0.82ðPineÞ,

et al.63 maximum height R 2 ¼ 0.33ðhardwoodsÞ

Zhao et al. LiDAR-derived canopy height A linear functional R 2 ¼ 0.95

200964 distributions (CHD) canopy height model and an
quartile functions (CHQ) equivalent nonlinear
model

García LiDAR height, intensity or height A stepwise regression R 2 ¼ 0.85ðPineÞ,

et al.11 combined with intensity data R 2 ¼ 0.70ðSpanish juniperÞ,
R 2 ¼ 0.90ðHolmoakÞ

Zhao LiDAR composite metrics Support vector RMSE ¼ 21.4ð40.5Þ

et al.65 machine and Gaussian Mg∕ha
processes (GP).

estimation at the individual tree level is promising, there are several limitations: (1) for small
footprint LiDAR, the laser pulse may miss the tree top. Therefore, to accurately extract indi-
vidual trees, point density is an important factor. Previous studies have shown that a point density
lower than 4 m−2 might be insufficient for the identification of individual trees;59,69,71 (2) in
CHM applications, crown overlapping was the major issue for tree crown identification; (3) indi-
vidual tree level methods were usually tedious, time consuming, and expensive for field data
collection and validation; (4) subdominant trees could not be detected using LiDAR returns data,
and the aggregation of individual trees within a plot underestimated the entire plot biomass; and
(5) in mixed forest such as tropical forest, it would be difficult to acquire enough species-
allometric. The individual tree level methods for biomass estimation could be greatly improved
by fusion with spectral data to classify tree species. It is difficult to identify tree species using
LiDAR data alone because there are no published standards for radiometric calibration of LiDAR
intensity data. Few studies have showed classification of tree species using LiDAR intensity data
alone. Donoghue et al.72 studied tree species classification using LiDAR intensity data, but only
two species were included in the study area. Even with the disadvantages listed above, this
method could be greatly improved by fusion with other spectral remote sense data to identify
tree species. Many studies have already illustrated the combined performance of LiDAR-derived
parameters and spectral data for forest biomass estimation.73–75 Furthermore, an adaptable model
and LiDAR-derived parameters is needed to automatically identify trees and then calculate
the forest biomass based on tree height.63,68–70,76,77

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

3.2 Plot Level

When the scope of the study is the plot, regression models were always used for forest biomass
estimation based on LiDAR-derived statistics, such as height and canopy density metrics.
Regression models usually use a simple/multiple and stepwise linear regression.15,69,78 One
of the pioneering studies is Nelson et al.58 using two logarithmic equations in conjunction
with six LiDAR-derived canopy measurements to estimate canopy volume and biomass. The
model that used the mean plot height metric derived from all LiDAR pluses as an input parameter
was identified as the best model. Previous studies indicated that tree height and crown diameter
were highly correlated with biomass.63,79 Some recent studies focused on canopy height metrics
that take into account structural data at multiple heights throughout the whole forest canopy,76
such as quadratic mean canopy height, height of median energy (HOME), height/median ratio
(HTRT), simple ground return ratio (GRND), CH0.5-1.5 (as the proportion of laser hits above
0.5 m that belong to the height interval of 0.5–1.5 m), CH1.5-2.5, CH2.5-3.5, and CH3.5-4.5.71
Lefsky et al.78 employed simple linear models involving the parameters of maximum canopy
height, median canopy height, mean canopy eight, and quadratic mean canopy height and found
that these parameters account for 80%, 70%, 73% and 80% of the variation in the ABG esti-
mation, respectively. Drake et al.61 derived four metrics from LiDAR data: LiDAR canopy
height, HOME, HTRT and a GRND. The four metrics were then input into a stepwise regression
procedure to predict field-estimated AGB with r2 (correlation coefficient) up to 0.93. Means
et al.59 and Lefsky et al.78 employed almost the same data processing and analysis techniques
to demonstrate the capabilities of Scanning LiDAR Imager of Canopies by Echo Recovery
(SLICER) for AGB. Means et al.59 concluded that models using (1) mean canopy height,
(2) quadratic mean canopy height and mean canopy height, and (3) the sum of the portion
of waveform return from the canopy as predictors account for 90%, 94%, and 96% of variation
in AGB estimation. Riggins et al.76 calculated multiple height percentiles (5, 15, 25, 35, 45, 55,
65, 75, 85, 95 and 100th percentiles) from small-footprint aerial LiDAR data and used a regres-
sion-tree model to get the forest biomass. Lefsky et al.80 applied a single regression including the
parameters of LiDAR measured canopy structure in three different biomes and explained that
these parameters account for 84% of the variance in AGB estimation (P < 0.0001). Lim and
Treitz81 introduced quartile estimators (at 0, 25, 50, 75 and 100th percentiles) derived from air-
borne discrete return laser scanner data to estimate forest AGB. The coefficient of determination
(r2 ) for each model was >0.8. Zhao et al.64 proposed a scale-invariant forest biomass estimation
method and obtained promising results. Rowell et al.82 estimated conifer-mixed forest biomass
using both generalized and species-specific allometric models.
As far as data analysis is concerned, a wide variety of machine learning models have been
effectively used in forest applications such as ANN,42 support vector regressions (SVRs),65,67
random forest (RF),67 cubist,83 bagging,83 and various algorithms based on decision trees (DTs)
such as single and ensemble regression trees.71 Van Aardt et al.84 assessed a LiDAR-based,
object-oriented approach to forest AGB models. The results showed that the new method
was better than previous stand-based and plot-based approaches. Zhao et al.65 used two kernel
machines, a support vector machine (SVM), and Gaussian processes to relate canopy character-
istics to high-dimensional LiDAR metrics. Results illustrated that two machine learning models
in conjunction with LiDAR metrics outperformed traditional approaches such as maximum like-
lihood classifier (MLC) and linear regression models. Gleason and Im67 used machine learning
approaches to estimate forest biomass. In their study, four modeling techniques were used for
moderately dense forest biomass estimation, including linear mixed effects regression, RF, SVR,
and cubist. Results indicated that when estimating biomass at the plot level, the SVR modeling
technique produced the most accurate biomass, whereas at the individual tree level, similar
results were obtained by all models. The relationship between crown identification accuracy
and biomass estimation accuracy is complex and needs to be further investigated. Figure 2
is a diagram summarizing some existing methods for biomass estimation using LiDAR data.
The space-borne full-wave laser system Ice, Cloud, and land Elevation satellite (ICESat)/
geoscience laser altimeter system (GLAS) has been used to estimate vegetation height85–87
and forest biomass in large areas of the globe.73,88,89 These studies were mainly based on wave-
form decomposition. Duncanson et al.87 estimated forest canopy height from GLAS waveform

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

Fig. 2 Process of biomass estimation by LiDAR data (LHt: mean canopy height derived from
LiDAR data; QMCH: quadratic mean canopy height; CanRef: canopy reflection sum; HOME:
height of median energy; GRND: a simple ground return ratio; Ht: mean canopy height; BA:
basal area; CanCov: canopy cover (range 0 to 1); TotBio: total above ground stand biomass;
FolBio: foliage biomass).

metrics. Results indicated that GLAS waveforms could estimate forest height and AGB accu-
rately, especially in flat areas with homogeneous forest conditions. Sun et al.86 also demonstrated
that the vertical information derived from the GLAS waveform was very similar to that of the
laser vegetation imaging sensor (LVIS) waveform which has been successfully used for forest
structural parameters estimation. Although there are many studies on sub-boreal forest systems
using ICESat/GLAS data, hardly any research exists on temperate, dry or tropical forests. This
was mainly due to the sparse sampling density. Additionally, ICESat/GLAS data were often
integrated with imaging sensors.34,88 Boudreau et al.88 combined multiple data sources to esti-
mate biomass, including GLAS, SRTM, Landsat ETM+, airborne LiDAR, ground inventory
plots, and vegetation zone maps. Their study showed that space-borne remote sensing measure-
ments could be efficiently used for biomass estimation over large areas.
Increasingly, more studies focus on the detection of change in the AGB. By correlating
LiDAR to forest inventory data, Jubanski et al.90 attempted to estimate AGB and its variability
across large areas of tropical lowland forests. Huang et al.91 used NASA’s Laser Vegetation
Imaging Sensor data for mapping biomass change. Meyer et al.92 also researched detecting tropi-
cal forest biomass dynamics from repeat LiDAR measurements. Næsset et al.93 detected change
of forest biomass over an 11-year period using airborne LiDAR data. Englhart et al.94 quantified
changes of tropical peat swamp forest with multitemporal LiDAR datasets. In the future, the
fusion of LiDAR with other sensors is the tendency for biomass estimation. He95 fused
LiDAR with SPOT-5 data to estimate coniferous forest biomass. Tsui et al.96 conducted a
study to fuse multifrequency radar and discrete-return LiDAR measurements for AGB estima-
tion in a costal temperate forest. Tsui et al.97 focused on the fusion of LiDAR and radar for
forest biomass estimation.

4 AGB Estimation with Hyperspectral Data

Hyperspectral imagery can provide numerous narrow bands. Compared with traditional multi-
spectral imagery, hyperspectral can separate subtle changes of the biophysical parameters of
forest.98 Because of this strength, hyperspectral imagery has been used for classifying vegetation
species,23,99,100–108 extracting tree health information,109,110 deriving biophysical parame-
ters,26,109,111,112 and estimating biomass.110 Pu and Gong112 used EO-1 Hyperion data to map
forest crown closure (CC) and leaf area index (LAI). Three methods were used in their
study, including: band selection, principal component analysis (PCA), and wavelet transform
(WT). Results show that WT was the most effective method for mapping forest CC and
LAI (mapping accuracy for CC ¼ 84.9%, LAIMA ¼ 75.39%). Zhang et al.109 explored a proc-
ess-based method to retrieve leaf chlorophyll content from hyperspectral remote sensing imagery
in complex forest canopies. Dalponte et al.107 evaluated two high spectral and spatial resolution
hyperspectral sensors for tree species classification, where SVM, RF, and Gaussian maximum
likelihood (ML) classification methods were used. Their results suggest that there was no

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

significant difference between SVM and RF classifiers and that the image spatial resolution had a
strong effect on the classification accuracy.
Hyperspectral remote sensing data such as MODIS, Hyperion, AVHRR, AISA, HyMap,
AVIRIS, and DAIS have also been frequently used for quantifying biomass from local to global
scales.74,113–116 Among them, the spatial resolutions of MODIS and AVHRR are 250 m to 1 km
and 1.1 km, respectively, and are usually used for forest biomass estimation at a regional or
global scale. Dong et al.117 utilized normalized difference vegetation index (NDVI) derived
from AVHRR images for forest biomass estimation. Because MODIS is a hyperspectral sensor,
numerous band and index combinations can be used for regression modeling. At regional and
global scales, MODIS and AVHRR are promising for forest biomass estimation. However, there
are two major limitations of these sensors for forest biomass study: (1) because of low spatial
resolution, it was not appropriate for small-area forest research; (2) because of long lapses, it was
difficult to avoid the influence of cloud cover, especially the missing information of interesting
areas.12 To make up for these limitations, fusion with other sensors might provide more accurate
results in forest biomass estimation, especially the combination of spectral information and
structural information.
For airborne hyperspectral sensors, most of the existing studies utilized different ways for
forest biomass estimation, including raw spectral bands,99,100 regression analysis,115 and machine
learning methods such as SVMs,104 end-member methods,108,118,119 ML classification, spectral
angle mapper (SAM), RF, genetic algorithms, regression trees, ANN, and DT classifiers.107
Hansen and Schjoerring115 used NDVI in a linear regression analysis for estimating green bio-
mass. The results showed that partial least square regression analysis may provide a useful tool
when applied to hyperspectral data. Dong et al.117 used a multiple linear regression analysis to
investigate the relationship between field estimates of AGB and various vegetation indices (VIs)
acquired by hyperspectral data. Cho et al.116 used spectral indices and partial least squares regres-
sion to estimate green grass/herb biomass in a seminature landscape from airborne hyperspectral
imagery. Their results showed that partial least squares regression combined with airborne
hyperspectral imagery provides a better result than unvaried regression involving hyperspectral
indices for grass/herb biomass estimation in Majella National Park, Italy. Gong et al.120 utilized
Airborne Imaging Spectrometer Applications (AISA) hyperspectral imagery for forest biomass
estimations for three forest crops. In their study, VIs and red edge positions (REPs) were derived
from hyperspectral imagery and then regression models were used for the estimation of
forest biomass. Results indicated that both VIs and REPs were effective for forest biomass
estimation.
Some studies argued that a direct estimation of AGB cannot be achieved from hyperspectral
imagery alone due to the weak relationship between vegetation biomass and spectral indices,121
especially in dense forests. However, it seems that fusion of hyperspectral and other
types of remotely sensed data for biomass estimation is a promising area that needs further
investigation.

5 AGB Estimation with Fusion of Hyperspectral and LiDAR Data

As no single data type could fulfill all requirements in AGB estimation, the complementary
information of the fused data has obtained increasing interest. For tree-level biomass estimation,
the species type is needed for application of species-specific allometric equations. Compared
with multispectral data, hyperspectral data have shown to be promising in species classifica-
tion101 and spectral attributes.21 Therefore, species classification maps derived from hyperspec-
tral data could be used as an additional parameter to refine models based on LiDAR-derived
metrics and intensity information. To date, many studies have been focused on the fusion of
hyperspectral and LiDAR data for a variety of applications, including crown identification,23
AGB estimation,74 sagebrush distribution mapping,122 and fuel type mapping.123 Furthermore,
numerous studies have particularly investigated forest species classification,23,104,106,124–126,127,128
vegetation type classification,100 and species-level discrimination.23,74,128 Additionally, the
LiDAR/hyperspectral fusion has also been shown to increase the capacity of image segmentation
and object-based classification.100,128

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

Although numerous previous studies used the fusion of hyperspectral and LiDAR data in
forest applications, most of the studies focused on forest species classification. For the fusion
of hyperspectral and LiDAR data, there were three general levels as introduced in Sec. 2.4: pixel-
level fusion, feature-level fusion, and decision-level fusion. Most of the existing studies used
pixel-level fusion, which includes the following major steps: (1) preprocessing of hyperspectral
and LiDAR data; (2) creating a combined dataset; (3) applying machine learning methods for
forest species classification, such as SVM,129 RF,106,125 object-based classification,127 and
Gaussian ML.103 Hill and Thomson100 fused Hymap images and airborne LiDAR data on
the pixel level to classify woodland species. In their study, a digital canopy height model
and the first two principal components from HyMap data were processed by a parcel-based
unsupervised classification approach. Naidoo et al.106 also conducted an experiment to accu-
rately classify and map individual trees at the species level in a savanna ecosystem.
Hyperspectral- and LiDAR-derived parameters were grouped into seven predictor datasets
and then an automated RF modeling approach was applied to classify eight common savanna
tree species in the Greater Kruger National Park region, South Africa. The results showed that
the most significant predictors were the NDVI, the chlorophyll b wavelength (chlorophyll
includes chlorophyll a, chlorophyll b, chlorophyll c, chlorophyll d, and chlorophyll f. The
range of chlorophyll b absorption wavelength is from 460 to 645 nm) and a selection of
raw, continuum removed and SAM bands. Naidoo et al.106 also concluded that RF modeling
with hybrid datasets yielded the highest accuracy for the eight tree species with an overall accu-
racy of 87.68%. There are other classification methods for information extraction from LiDAR–
hyperspectral datasets, for example, object-oriented classification methods,127 RF classifier,125
SVM, and Gaussian ML.103 Feature-level fusion is a little different from pixel-level fusion. The
major differences between them are in step (2) and step (3). In feature-level fusion, step (2) is
used to derive LiDAR and hyperspectral metrics, such as canopy height and NDVI, and step
(3) is used to form the combined dataset (combination of LiDAR and hyperspectral-derived
metrics). Puttonen et al.129 used datasets consisting of two reflectance and two shape parameters
to classify coniferous and deciduous trees and individual tree species through a SVM method,
and the best classification result was 95.8% for the separation of coniferous and deciduous trees.
Dalponte et al.125 extracted LiDAR (i.e., Hmin , Hmax ) and hyperspectral metrics, respectively, and
then a feature-selection technique was used to extract variables that have the most information.
Several fused bands were formed including all hyperspectral bands, spectral bands + max height
(LiDAR low density), spectral bands + max height (LiDAR high density), and spectral bands +
height features (LiDAR height density). A nonlinear SVM and an RF classifier were used to
classify tree species. When combined with either hyperspectral or multispectral data, high-den-
sity LiDAR data could provide more information for tree species than low density LiDAR data.
As few studies used decision-level fusion of multiple data, decision-level fusion will not be
discussed here. Figure 3 shows the preprocessing of LiDAR and hyperspectral data before
fusion. Figure 4 illustrates the pixel-level fusion of hyperspectral and LiDAR for forest species
distribution mapping. Table 2 shows the previous studies of forest biomass estimation using
the fusion of hyperspectral and LiDAR data.
In addition to species classification, hyperspectral and LiDAR fusion data were also used for
forest biomass estimation. Hyperspectral sensors were used for species classification, and then
LiDAR data were used to perform biomass estimation of each classified species. The fusion
of LiDAR and hyperspectral data included the following steps: (1) preprocessing of each

Fig. 3 Preprocessing of LiDAR and hyperspectral data.

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Fig. 4 Pixel-level fusion of hyperspectral and LiDAR for forest species distribution mapping,
CVPs: canopy volume profiles; PRI: photochemical reflectance index; CRI: carotenoid reflectance
index; PCA: principal component analysis; MNF: minimum noise fraction; NDVI: normalized
difference vegetation index.

data (i.e., removing clear outlier points from LiDAR data, atmospheric correction of hyperspec-
tral data, co-register LiDAR and hyperspectral data); (2) deriving LiDAR and hyperspectral
metrics (e.g., canopy height model from LiDAR data, height metrics from CHM, varieties of
vegetation index); (3) classifying forest species using hyperspectral data and creating the com-
bined dataset; and (4) estimating forest biomass using regression methods or a species-specific
allometric. In a complex forest, Lucas74 used airborne LiDAR data and CASI hyperspectral
image data to automatically identify trees and estimate their biomass. In their study, a
Jackknife linear regression was also used to estimate plot-level forest biomass using six
LiDAR strata heights and crown cover. Results showed that the Jackknife linear regression
method was more robust for forest biomass estimation and showed a closer relationship
with plot-sale ground data (r2 ¼ 0.90, RSE ¼ 11.8 Mg∕ha, n ¼ 31). The fusion study also
required methods which could deal with high-density LiDAR data and complex forests.
Clark et al.8 estimated tropical forest biomass using the fusion of hyperspectral and small-foot-
print, discrete-return LiDAR data. LiDAR metrics (i.e., mean height, maximum height) and
hyperspectral metrics (i.e., NDVI) were retrieved, respectively. Then single and two-variable
linear regression analyses were used to relate plot-scale LiDAR and hyperspectral metrics to
field-derived biomass from all plots. The results showed that the best model was created
using all 83 biomass plots including two LiDAR height metrics, plot-level mean height, and
maximum height with an r2 of 0.90 and RMSE of 38.3 Mg∕ha. However, analysis combined
for plantation plots had the most accuracy field data with r2 increased to 0.96 and RMSE of
10.8 Mg∕ha (n ¼ 32). Swatantran et al.25 used LVIS metrics, AVIRIS spectral indices, multiple
endmember spectral mixture analysis fraction, and linear and stepwise regressions to map spe-
cies-specific biomass and stress at landscape scale. The results showed that the accuracy of
prediction by LVIS after species stratification of the field data was up to r2 ¼ 0.77, and
RMSE ¼ 70.12 Mg∕ha. The results also suggest that LiDAR data were better for biomass esti-
mation, whereas the hyperspectral data were used to adjust the LiDAR predictive models for
species. Latifi et al.132 fused LiDAR-hyperspectral data for forest structure modeling, including
models of stem density and aboveground total biomass. Their results indicated that LiDAR

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Table 2 List of references on the fusion of hyperspectral/LiDAR for biomass estimation and forest
species classification.

References Parameters Methods Goals

Hill et al.100 PCs 1 and 2 of HyMap and Digital Segmentation and Mapping of woodland
Canopy Height Model (DCHM) ISODATA species composition and
structure

Geerling Pixel-level fusion of 10 CASI bands MLC Classification of floodplain

et al.51 and 6 LiDAR texture bands (min, max, vegetation
mean, median, SD, and range)

Sugumaran 4 band Quickbird image with and Object-oriented Tree species identification
et al.127 without LiDAR; 24-band AISA classification in an urban environment
hyperspectral image with and without
LiDAR; 63-band AISA Eagle
hyperspectral image with and without
LiDAR.

Dalponte 25 hyperspectral bands, elevation and SVM classifier; Classification of complex

et al.103 intensity of the first LiDAR return; 40 GML-LOOC; K-NN forest
hyperspectral bands, elevation and
intensity of the first LiDAR return; 126
hyperspectral bands, elevation and
intensity of the first LiDAR return.

Jones AISA data (40 bands); 40 AISA bands A multiclass SVM Mapping species
et al.104 and CHM; 40 AISA bands and 4 CVPs; classifier distribution
40 AISA bands, and 2 CVPs

Onojeghuo Spectral data only; spectrally Maximum likelihood Optimize the use of LiDAR
et al.130 compressed data: PCA transformed classifier (MLC) and hyperspectral data for
data and MNF transformed data; reedbed habitats mapping
SSPCA transformed data; texture
combined data: MNF 1-15-GLCM45;
(Optical MNF + texture) and LiDAR
derived measures; Optical SSPCA
image and LiDAR derived measures.

Naidoo et Height only dataset; Hyperspectral Random forest module Classification of savanna
al.106 indices only dataset (CRI, NDVI, PRI, tree species
red edge NDVI); Height and indices
dataset (Height, CRI, NDVI, PRI, red
edge NDVI); Raw bands datasets (all 72
CAO raw bands); SAM selected bands
dataset (Band Add-On); Nutrient and
Leaf Mass Bands Dataset

Anderson 24 AVIRIS MNF bands and 4 LVIS Stepwise mixed linear LiDAR-hyperspectral
et al.131 metrics (RH25, RH75, RH50 and regression techniques fusion for inventory of a
RH 100) northern temperate forest

Note: CVPs, canopy volume profiles; PRI, photochemical reflectance index; CRI, carotenoid reflectance
index.

provided most of the information for the combination, whereas hyperspectral data only made
a modest contribution. Figure 5 shows two methods of AGB estimation by fusion of LiDAR-
hyperspectral dataset.
The above studies showed good results of data fusion for forest biomass estimation, yet there
are still many aspects which need refinement for improving biomass estimation through fusion of
hyperspectral and LiDAR data. The major limitations are: (1) a very high LiDAR sampling rate
is required to correctly identify treetops;133,134 (2) forest biomass estimation was affected by the
plot size for some sensors; (3) at the individual tree level, if the spatial resolution is coarser than
that of individual crown areas, it would be difficult to identify individual trees, especially in
complex forests; and (4) in complex forests, subcanopy trees and stems are usually omitted
from the traditional canopy height models, which would underestimate the biomass.

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Fig. 5 Two methods of above-ground biomass (AGB) estimation by fusion of LiDAR-hyperspec-

tral dataset.

6 Challenges and Future Research

LiDAR provides precise height information which could extract vertical and horizontal infor-
mation of the forest135 and is less sensitive to saturation when compared with other sensors.
Hyperspectral data can provide detailed spectral information which can be used to effectively
classify forest species. The findings on forest biomass estimation in the last decades are encour-
aging, and there is a growing interest for hyperspectral, LiDAR, and fusion of LiDAR and hyper-
spectral data for forest biomass estimation. However, there are still many limitations that should
taken into account: (1) because a forest is a complex ecosystem, many factors may impact the
estimation of forest biomass when using LiDAR and hyperspectral imagery, such as spectral and
spatial resolution, co-registration of data from different sources (data from different platforms,
different dates or with different resolutions), LiDAR point density, fusion framework, classifi-
cation algorithm, allometric equations, plot-size, study area, stem density, canopy volume, and
height; (2) although the Global Climate Observing System has suggested some accuracy levels,
there is still no clear standard for forest biomass estimation. In addition, it is still unclear as to
how forest biophysical and biochemical attributes derived from hyperspectral data relate to
structural attributes from LiDAR data. The current methods normally add LiDAR elevation
information as an extra band of hyperspectral data on the pixel level, or extract metrics,
respectively, and classify fused images using different algorithms. In some other studies, hyper-
spectral and LiDAR data were processed separately. For example, hyperspectral data were used
to classify forest species, and LiDAR data were used to identify individual trees, then specific
allometric equations were used for forest biomass; (3) it is difficult to estimate forest biomass in
forest with complex structures, especially tropical forests. In addition, high-quality ground truth
data for validation is still lacking. For example, it is unrealistic to classify all tree species with
hyperspectral data101 and classify all species-specific allometric equations; (4) for tree-level bio-
mass estimation, although hyperspectral and LiDAR data have been successfully used, it is still
problematic, because it is difficult to allocate individual trees to the proper species. Using a
spatial resolution coarser than that of individual tree crown size, it is difficult to classify and
attribute individual trees. In this case, tone can assume that there are several species within
a pixel area, especially in heterogeneous forests. Using a spatial resolution finer than that of
individual tree crown size, it is also difficult to classify and attribute individual trees. As in
this case, an individual crown may contain several pixels with different illumination.
Although there are limitations as described above, the fusion of hyperspectral and LiDAR is
still promising for forest biomass estimation, especially with the emergence of new sensors and
new fusion methods.

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

6.1 New Sensors

Many dedicated missions will be launched.136,137 For example, forthcoming hyperspectral mis-
sions will also be oriented toward image fusion, such as Environmental Mapping and Analysis
Program (EnMap), PRecursore IperSpettrale of the application mission (PRISMA), Medium
Resolution Imaging Spectrometer (MERIS), and Hyperspectral Infrared Imager (HyspIRI).
A new processing and fusion framework will also need to deal with these new sensors.

6.2 New Fusion Methods

More advanced modeling methods are needed to quantify the biophysical characteristics of for-
est.120 Compared with pixel-level classification, an object-based classification method is more
accurate when segmenting tree crown.74,138–140 Therefore, it is a reasonable step to implement
simple crown segmentation algorithms to generate crown objects for future analysis, especially
in a low density forest. However, the selection of optimal parameters remains a challenge in the
case of a high-density forest area. The new fusion methods include the fusion of different sensors
and the fusion of different modeling methods: (1) fusion of different sensors, including electro-
optical, radar and LiDAR sensors; (2) fusion of direct and indirect methods. For example, bio-
mass extracted from high resolution remotely sensed images over some study areas can be used
in the less computationally demanding indirect regression methods; and (3) fusion of different
modeling methods, such as SVM, RF, and object-based classification. The combination of these
methods might improve the classification accuracy.
There are several questions that need further investigation: (1) can alteration in the fusion
framework (pixel-level, feature-level and decision-level) of hyperspectral and LiDAR data lead
to different findings? (2) Can feature selection methods be further improved to increase the sta-
bility and accuracy of tree species classification from hyperspectral data? (3) Can additional
LiDAR measures be connected to forest structure and canopy architecture?

7 Conclusions
In this paper, the application of LiDAR data, hyperspectral data, and LiDAR/hyperspectral data
fusion for forest biomass estimation and the current difficulties and prospects were reviewed.
Generally, using remote sensing data, forest biomass could be directly estimated with different
methods, including multiple regression analysis, K nearest-neighbor and neural network. Forest
biomass could also be indirectly estimated. For example, canopy parameters such as crown
diameter are derived from remotely sensed data and finally. allometric equations were used
for forest biomass estimation.
Data from LiDAR systems such as ICESat/GLAS, SLICER, LVIS and discrete return LiDAR
have been used for forest biomass estimation. Biomass estimation from airborne LiDAR metrics
is more accurate than that from a satellite-borne GLAS instrument. The sparse sampling density
of GLAS and the limited spatial extent of airborne LiDAR can be integrated with data from other
imaging sensors. The methods of biomass estimation using LiDAR data include single regres-
sion between LiDAR-derived height metrics, tree crown delineation and biomass, stochastic
simulation, and machine learning approaches. It is expected that more studies will focus on
the variability of AGB estimated from LiDAR data and will multisensor measurements to esti-
mate biomass, including LiDAR-radar fusion and LiDAR-hyperspectral fusion.
Hyperspectral data have been used for classifying vegetation species, monitoring health
status, and deriving biophysical parameters. The main methods for information extraction
from hyperspectral data include SVMs, ML, ANN, RF, DT, and end-member methods. Due to
the loss of correlation, a few studies have used hyperspectral data for biomass estimation, espe-
cially in forests with high biomass and complex environmental conditions. Hyperspectral data
fused with other types of remotely sensed data will be a promising research area for forest bio-
mass estimation.
Since LiDAR data can characterize the vertical structure of forests with high accuracy and
hyperspectral data can provide detailed spectral information on biophysical parameters, the
fusion of LiDAR and hyperspectral data has received increased attention. There are three levels

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

of data fusion using LiDAR and hyperspectral data, including pixel level, feature level, and
decision level. LiDAR/hyperspectral data fusion has been applied to AGB estimation and
tree species classification. However, most of the studies were focused on forest species classi-
fication. As forest is a complex ecosystem, many factors may impact the estimation of forest
biomass when using LiDAR and hyperspectral imagery, such as spectral and spatial resolution,
co-registration of data from different sources (data from different platforms, different dates or
with different resolutions), LiDAR point density, fusion framework, classification algorithm,
allometric equations, plot-size, study area, stem density, canopy volume, and height. In addition,
new systems which occupy complementary sensors on the same platform are emerging, such as
Goddard’s LiDAR, Hyperspectral and Thermal (GLiHT) airborne imager which shows prom-
ising prospects for the fusion of hyperspectral and LiDAR data.141 In the near future, new sat-
ellite-based sensors will be launched, including the Medium Resolution Imaging Spectrometer
(MERIS). These developments will provide more opportunities for multisensor fusion. Besides
the fusion of hyperspectral-LiDAR data for forest biomass, there will be more fusions between
multiple sensors and methods, including the fusion of different sensors (fusion of space-borne
LiDAR, airborne LiDAR data, and hyperspectral data for upscaling forest biomass estimation),
the fusion of direct and indirect methods for forest biomass (biomass extracted from high res-
olution remote sensing can be used in the less computationally demanding indirect regression
methods), and the fusion of different machine learning methods.

Acknowledgments
This research was funded by the ABCC Program of the National Natural Science Foundation of
China (Project 41120114001), and the National Key Technology R&D Program (Projects
2012BAC16B01 and 2012BAH27B05). The first author would like to thank the China
Scholarship Council for the support of two years’ study in the Department of Geography,
University of North Texas. The authors would like to thank anonymous reviewers for their
helpful comments and suggestions.

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Qixia Man is a PhD student at the East China Normal University. She studied at the University
of North Texas as a visiting student from 2012 to 2014. Her research interests include digital
image processing, LiDAR, and hyperspectral remote sensing.

Pinliang Dong is an associate professor in the Department of Geography, University of North

Texas (UNT). He received his BSc degree from Peking University, China, MSc degree from the
Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences, and PhD
degree from the University of New Brunswick, Fredericton, New Brunswick, Canada. His
research interests are mainly in GIScience and remote sensing.

Huadong Guo is an academician and research professor at the Chinese Academy of Sciences.
He graduated from the Geology Department at Nanjing University in 1977 and received his MSc
degree from the Graduate University of the Chinese Academy of Science (CAS) in 1981. He is
a guest professor of eight universities in China. His research interests include radar remote sens-
ing, applications of Earth-observing technologies to global change, and Digital Earth.

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 Man et al.: Light detection and ranging and hyperspectral data for estimation of forest biomass. . .

Guang Liu received his BS/MS in physics from TsingHua University China in 1999/2002 and
received his PhD degree from the Institute of Remote Sensing Applications of the CAS in 2008.
He has been working with the Mathematic Geodesy and Positioning, Delft University of
Technology of the Netherlands from 2006 to 2007 as a visiting researcher. He is an associate
professor of Institute of Remote Sensing and Digital Earth, CAS. His work is focused on the
study of the feasibility and potential applications of SAR image time series analysis.

Runhe Shi received his BS in East China Normal University in 2001, and received his PhD
degree from Institute of Geographic Sciences and Natural Resources Research of the CAS.
His research mainly focused on research and application of quantitative remote sensing
algorithm.

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