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Article
Tree Species Classification in Temperate Forests Using
Formosat-2 Satellite Image Time Series
David Sheeren 1,*, Mathieu Fauvel 1, Veliborka Josipovi´c 1, Maïlys Lopes 1, Carole Planque 1,
Jérôme Willm 1and Jean-François Dejoux 2
1DYNAFOR, INP-ENSAT, INP-EI Purpan, INRA, University of Toulouse, Auzeville 31320, France;
mathieu.fauvel@ensat.fr (M.F.); vecajosipovic@gmail.com (V.J.); mailys.lopes@toulouse.inra.fr (M.L.);
carole.planque@free.fr (C.P.); jerome.willm@toulouse.inra.fr (J.W.)
2CESBIO, CNES, CNRS, IRD, UPS, University of Toulouse, Toulouse 31401 Cedex 9, France;
jean-francois.dejoux@cesbio.cnes.fr
*Correspondence: david.sheeren@ensat.fr; Tel.: +33-5-34-32-39-81
Academic Editors: Randolph H. Wynne and Prasad S. Thenkabail
Received: 30 June 2016; Accepted: 30 August 2016; Published: 7 September 2016
Abstract:
Mapping forest composition is a major concern for forest management, biodiversity
assessment and for understanding the potential impacts of climate change on tree species distribution.
In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image
time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date
SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified
in a study area of southwest France. The performance of parametric (GMM) and nonparametric
(k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of
the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed
cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates.
Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct
classifications show a very high suitability of the SITS to identify the forest tree species based on
phenological differences (average
κ=
0.93 estimated by cross-validation based on 1235 field-collected
plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear
benefit of removing noise by smoothing can be observed. Classification accuracy is even improved
using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series.
However conclusions of the results need to be considered with caution because of possible overfitting.
Disagreements also appear between the maps produced by the classifiers for complex mixed forests,
suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series
data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates
the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.
Keywords:
tree species; forest; time series; classification; smoothing; Whittaker; phenology; biodiversity
1. Introduction
Forest ecosystems provide essential services to human society. Beyond the production of multiple
resources (timber, energy, foods), these ecosystems play a major role in carbon sequestration, regulating
biogeochemical cycles and climate [
1
]. However, the provision of such ecosystem services may
depends on tree species diversity [
2
]. The complementarity among species can sustain multiple
services simultaneously. Mapping tree species is therefore crucial to assess forest ecosystems and their
services. Under the current context of climate change, it is also essential to build more accurate models
predicting future tree habitat shifts [
3
]. Information about tree species diversity is required to assess
forest resilience and vulnerability to drought and pathogens [4,5].
Remote Sens. 2016,8, 734; doi:10.3390/rs8090734 www.mdpi.com/journal/remotesensing
Remote Sens. 2016,8, 734 2 of 29
While important advances in the knowledge of global tree cover and its change have been reported
recently [
6
], little is known about the current composition of forests at large scale [
7
]. Existing maps
of tree species are often derived from species distribution models (SDM) which provide potential
geographic ranges of tree species but not the exact geo-location of the species [
8
,
9
]. These models
give projections of the ecological niches of species using a set of habitat indicators (such as climatic,
edaphic and topographic variables) and a set of species observations, often based on National Forest
Inventory plot data [10,11].
An alternative to the SDM-based approach for mapping tree species is the use of earth observation
imagery. Tree species identification is a classical topic in optical remote sensing of forests [
12
,
13
].
Many studies have already addressed this issue in different ways. However, obtaining an accurate
tree species classification is still a challenging task. A large number of factors influences the spectral
response of species such as leaf biochemical properties, canopy structure, forest density and maturity,
illumination and acquisition conditions. Thus, the spectral variability within species may be sometimes
higher than the variability between species.
In the past, much attention has been devoted to the spatial resolution of the data [
14
].
Aerial photography have been the most frequently used data for forest inventory including the
estimation of forest composition [
15
,
16
]. Discrimination of tree species was often based on visual
interpretation, relying on the fine spatial details provided by this data and on the stereoscopic vision.
Automated methods have been developed [
17
,
18
] including textural analysis [
19
,
20
] or canopy height
models from stereo-images [
21
] to improve classification accuracy. In general, results show a limited
success with potentially high confusion rates (>20%–30%).
The emergence of the very high spatial resolution (VHR) sensors has increased the number of
works on tree species classification. New studies focused on the spatial details to map individual trees
in temperate or tropical forest environments [
22
–
24
]. These studies are in line with the first attempts
based on the high-resolution multispectral sensors like Landsat TM [
25
,
26
]. Despite some encouraging
results, the reduced number of spectral bands in these images cannot allow an accurate species
discrimination when only one image is available.
Alternatively, several authors investigated the ability to identify tree species using airborne
hyperspectral imagery [
27
–
30
], LiDAR data [
31
] or a combination of multiple sources [
32
–
35
].
Spectral variability between species related to differences in biochemical properties are better
preserved using hyperspectral data which allow continuous sampling of the electromagnetic spectrum.
Incorporating LiDAR-derived information on height and canopy structure to hyperspectral responses
of vegetation can also improve the classification of tree species [
33
,
36
]. The simultaneous use of these
sources have demonstrated a high potential for species discrimination with accuracies higher than
90%. However, the operational use of these data remains difficult because of their limited availability
and high cost.
A third approach for the classification of individual tree species is the use of optical multitemporal
imagery. In this approach, phenological variations from green-up to senescence are assumed to increase
the spectral separability between species. The use of multi-seasonal images has been successfully
applied in several previous works. However, most of them were based on Landsat data composed
of a limited number of dates, sometimes acquired from different years, and covering partially the
key phenological periods [
25
,
37
–
40
]. Relatively few studies have attempted to assess the potential of
dense high spatial resolution satellite image time series (SITS) for mapping forest types. SITS with
very high frequency of observations such as MODIS have been already tested but the coarse spatial
resolution of the images makes the tree species identification difficult in the case of complex forest
environment [
41
]. The previous experiments were based on NDVI temporal profiles which help to
reduce the data dimensionality but can also limit the discrimination between species. Using high
resolution airborne images, [
42
,
43
] showed promising results at finer spatial resolution but they also
highlighted the influence of the number of spectral bands and the number of dates on classification
performance, in addition to the timing of image acquisition during the stages of leaf flush, leaf tinting
Remote Sens. 2016,8, 734 3 of 29
and fall. More recently, [
44
] investigated the ability of intra-annual image time series of RapidEye
(6.5 m pixel size) to identify the most frequent tree genera in the urban environment of Berlin, Germany.
The authors observed a decrease in quantity disagreement and allocation disagreement for data with
higher temporal and spectral resolution.
In this paper, we explore the benefits of using a very dense high spatial resolution multispectral
Formosat-2 image time-series for mapping 13 tree species in temperate forests. Compared to previous
studies, the available time-series data composed of 17 dates acquired across one year, with constant
viewing angles, is unique. The specific aims of the paper are:
•
Develop an optimal classification strategy for mapping tree species in natural forests and tree
plantations at three class hierarchy levels using dense Formosat-2 SITS.
•
Quantify the effect of removing noise (i.e., clouds and cloud shadows) in the time series on
classification accuracy.
• Identify the best supervised learning classifier among parametric and nonparametric methods.
•
Evaluate the sensitivity of the classification accuracy to the dimensionality of the data and to
the feature space, by comparing the classification results based on different feature sets: spectral
bands, NDVI index or spectral bands and NDVI.
This study should demonstrate the potential contribution of the recently launched Sentinel-2
satellites for studying forest ecosystems. Formosat-2 and Sentinel-2 have close sensor characteristics
in the visible and near-infrared spectral domain (similar spatial and spectral resolutions with close
period of revisit).
2. Study Area and Data
2.1. Study Site
The study site is located in southwest France (Figure 1), in the Garonne river floodplain,
approximately 30 km west of Toulouse (1
◦
10
0
E, 43
◦
27
0
N). It is a rural landscape within a sub-Atlantic
climate characterized by mild and rainy winters with warm and dry summers (average annual
temperature
>
13
◦
C ; annual precipitation
=
656 mm). Woodlands cover less than 10% and are
dominated by Oak. Non-forest areas consist of a combination of crops (including wheat, sunflower
and maize) and grasslands.
15 km
Typical landscape
France
1°25' E
1° E
43°20' N
43°35' N
Study area
N
Figure 1. Location of the study area in southwest France.
Remote Sens. 2016,8, 734 4 of 29
2.2. Image Data and Forest Map
Formosat-2 optical imagery was used for this study. The time series has been acquired in the
framework of the VEN
µ
S mission preparation that results from a cooperation between the Israeli Space
Agency (ISA) and the French Centre National d’Etudes Spatiales (CNES) [
45
]. The Formosat-2 SITS
was acquired on 17 dates in 2013 (Figure 2). The multispectral images provide 4 spectral bands from
the visible (Blue: 0.45–0.52
µ
m, Green: 0.53–0.60
µ
m, Red: 0.63–0.69
µ
m) through the near-infrared
(NIR: 0.76–0.90
µ
m) at 8-m spatial resolution with a field of view of 24 km. The radiometric resolution
of the data is 8-bit. All the images were acquired with the same constant viewing angle which is likely
to reduce the within-species spectral variation.
An ancillary vector map was also used to mask the non-forest areas. These data were derived from
the French National Forest Inventory database (IGN BD Foret
®
, v.1) produced in 1996. This database
provides a forest/non-forest map with a minimum forest area (i.e., the minimum mapping unit) of
2.25 hectares (Figure 2). Because of a temporal mismatch between the SITS (year 2013) and the ancillary
map (year 1996), the forest data layer was manually updated, based on the SITS and aerial photographs.
Feb. 16th 2013 March 3rd 2013 May 6th 2013 May 26th 2013
June 6th 2013 July 6th 2013 July 20th 2013
July 30th 2013 Aug. 11th 2013
June 26th 2013
Oct. 12nd 2013
Aug. 22nd 2013 Sept. 1st 2013
Sept. 21st 2013 Nov. 28th 2013
Dec. 20th 2013
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Forest / non-forest mask (IGN BD Foret ®)
Formosat-2 time series (17 dates in 2013)
Oct. 27th 2013
Oct. 27th 2013
N
4km
1°25' E
1° E
43°35' N
43°20' N
Figure 2.
Dataset used in the study composed of a multispectral Formosat-2 time series of 17 dates
covering a 24 km
×
24 km area and a forest/non-forest mask derived from the French National Forest
Inventory database. The Formosat images with excerpts focusing on one forest are viewed in true
color composites.
Remote Sens. 2016,8, 734 5 of 29
2.3. Field Data
Four specific surveys were conducted across the study site in November 2013, January 2014,
May 2014 and October 2015 to collect sample points (
n=
1235) of the most dominant broadleaf and
conifer tree species. Each plot was recorded by two observers on pure stands covering approximately
an extent of 576 m
2
(i.e., nine contiguous Formosat pixels of 8 m
×
8 m). Plots were distributed over
the whole study site (51 distinct forests) and their location were assessed using a Garmin GPSMap
62st receiver (
±
3–5 m accuracy). The field-collected data did not include the young generation of trees
(age <15 years)
. Class errors in this reference dataset were estimated to be less than 2%. Because of
possible mix of species at the plot borders (due to the GPS inaccuracy), we only used the pixel at the
center of the nine contiguous pixels in the calibration and validation procedures of the supervised
classification. Thus, all the plots were converted into homogeneous polygons of one pixel size.
Three thematic levels were defined (Table 1). The first level split the forests into broadleaf and conifer
species (level 1). The second one makes distinction between four groups of species including deciduous
and evergreen broadleaves species, pines and the other conifer species (level 2). The last one includes
twelve distinct species and one group of three species of the same genus (level 3). This hierarchy
was inspired from the French National Forest Inventory database nomenclature adapted to visual
interpretation of aerial photographs [
46
]. Sample size per species varied from 51 pixels for Willow to
209 pixels for Aspen.
Table 1.
Class hierarchy composed of three levels with the sample size of collected reference data
(in pixels ;
n=
1235) for all the tree species. Each sample represents the pixel at the center of nine
contiguous pixels of the same species.
Level 1 Level 2 Level 3 Sample Size
Broadleaf Deciduous Silver birch (Betula pendula) 85
Broadleaf Deciduous Oak (Quercus robur/pubescens/petraea) 113
Broadleaf Deciduous Red oak (Quercus rubra) 145
Broadleaf Deciduous European ash (Fraxinus excelsior) 80
Broadleaf Deciduous Aspen (Populus tremula) 209
Broadleaf Deciduous Black locust (Robinia pseudoacacia) 59
Broadleaf Deciduous Willow (Salix spp.) 51
Broadleaf Evergreen Eucalyptus (Eucalyptus spp.) 148
Conifer Pine Corsican pine (Pinus nigra subsp. laricio) 62
Conifer Pine Maritime pine (Pinus pinaster) 87
Conifer Pine Black pine (Pinus nigra) 55
Conifer Other conifer Douglas fir (Pseudotsuga menziesii) 66
Conifer Other conifer Silver fir (Abies alba) 75
3. Methods
The processing chain defined to discriminate tree species using Formosat-2 SITS included several
steps (Figure 3). First, orthorectification, atmospheric correction and cloud detection was performed
using an operational pre-processing chain, called MACCS (Multi-sensor Atmospheric Correction and
Cloud Screening), resulting in (i) surface reflectance time-series data; and (ii) masks of clouds and
cloud shadows [
47
,
48
]. In this chain, atmopheric effects are corrected by combining multi-temporal
and multi-spectral criterion to estimate aerosol optical tickness [
48
] while clouds are detected by
analyzing the reflectance increase in the blue spectral band [
49
]. Then, NDVI index was computed
for each date (Figure 3). Three image datasets were derived: one including the spectral bands only
(68 image layers; dataset 1), another one including the NDVI index only (17 image layers; dataset 2)
and the last one including both the spectral bands and NDVI (85 image layers; dataset 3). In the
next step, each of these datasets was smoothed to deal with noisy pixels (i.e., cloudy and shady).
The Whittaker smoothing algorithm (see Section 3.1) was applied on the SITS in which, for each date,
pixels affected by cloud coverage and cloud shadows were marked as missing values, using the cloud
Remote Sens. 2016,8, 734 6 of 29
and shadow masks. Then, a part of field data on tree species was used to train several supervised
classifiers: Support Vector Machines (SVM), Random Forest (RF), Gaussian Mixture Model (GMM)
and k-Nearest Neighbors (k-NN). The remaining field data samples on tree species were used to assess
the classification performance of the models. In order to evaluate the effect of smoothing and noisy
data on classification accuracy, the procedure was repeated on (i) the non-smoothed image datasets
contaminated by clouds or cloud shadows (i.e., with the original reflectance values in the cloudy and
shady areas); and (ii) on image datasets with no cloud or cloud shadow on forests (14 dates instead of
17 dates). Finally, all the results were compared to conclude on the best classification strategy. The key
steps of the method are detailed below.
Field data Multi-temporal
FORMOSAT-2
images
Ancillary
vector data
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)
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Predicting
tree species
Maps of forest tree species (Level 1,2,3)
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$(2
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,
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Figure 3.
Tree species classification process of the Formosat-2 satellite image time series. Note that the
smoothing step is applied on the three datasets separately (*).
3.1. Smoothing of Temporal Profiles
Optical dense time series are often affected by noise due to clouds and shadows. In this study,
noisy pixels were marked as missing values and the resulting temporal profiles were corrected using
the Whittaker smoother [
50
]. This smoother has been selected for its fast execution, its limited number
of parameters (only one) and its ability to deal with missing values [51].
Remote Sens. 2016,8, 734 7 of 29
Let
z(ti)
, the value of one observed (noisy) pixel at time
i
for a given spectral band of the
SITS of length
T={t1
,
t1
, ...,
tn}
. The vector composed of each observed value of the pixel in
the SITS is denoted by
z={z(t1)
, ...,
z(tn)}
. The noise free (unobserved) version is denoted by
x={x(t1), ..., x(tn)}
. The related noise model is such as:
z=x+n
where
n
follows a zero mean
normal distribution.
The Whittaker smoother algorithm is based on penalized least squares with the basic principle
that smoothing must be a compromise between fidelity to the data (denoted by
S
in Equation (1)),
and roughness of the smoothed curve (denoted by
Rd
). More precisely, the objective of the smoother is to
find
ˆ
x
such as it minimizes, with respect to
x
, a function
Q(x)
which combines the two conflicting goals:
ˆ
x=arg min
x
Q(x)where Q(x) = S(x) + λRd(x)(1)
The fidelity
S
is expressed as the sum of the squared differences between observed pixels
z(ti)
and smoothed pixels x(ti):
S(x) =
n
∑
i=1
(z(ti)−x(ti))2(2)
The measure of roughness
Rd
is the norm of derivatives. It can be expressed, in its discrete
form, as the squared sum of the differences
∆dx(ti)
, where
∆dx(ti)
represents a
d
order difference of
a pixel x(ti):
Rd(x) =
n
∑
i=1
(∆dx(ti))2(3)
Regularization parameter
λ
in Equation (1) is a smoothing parameter defined by the user.
The larger it is, the smoother
ˆ
x
will be (increasing the lack of fit to the data). Here, the value of
λ
has been estimated by ordinary (OCV) and generalized (GCV) leave-one-out cross validation. OCV
and GCV were computed for a series of values of λ={100, ..., 1015} to search for a minimum of it.
Since we had missing values in the data at each date because of clouds and shadows, a vector
of weights
w
(with the same size of
z
) was introduced in
S
with
w(ti) =
0 for missing values and
w(ti) =
1 for non-missing values. This information was derived from the 17 cloud and shadow masks.
Expressed in matrix notation, it took the form:
S(x) =
n
∑
i=1
w(ti)(z(ti)−x(ti))2= (z−x)>W(z−x)(4)
where Wis a n×nmatrix with vector won its diagonal.
A version of Whittaker smoother adapted for unequally spaced data was used since the time
period between two consecutive images in the SITS was not constant. In this version,
S
was estimated
in the same way as for an equally spaced data with missing values. The measure of the roughness of
x
was given by:
Rd(x) =
n
∑
i=1
(∆dx(ti))2=
Ddx
2=x>Dd>Ddx(5)
where
Dd
is the derivative matrix of order
d
. Finding partial derivatives of the final function
Q
in
Equation (1) and setting them to zeros leads to the following solution:
ˆ
x= (W+λDd>Dd)−1Wz (6)
3.2. Training and Validating the Models
Classifications were carried out on nine datasets (Table 2) corresponding to the cross product
between the stacked images of spectral bands alone, ndvi alone, and both spectral bands and ndvi with
(i) the smoothed version of the SITS (17 dates, dataset W); (ii) the non-smoothed version of the SITS
Remote Sens. 2016,8, 734 8 of 29
with cloudy images (17 dates, dataset C); and (iii) the non-smoothed version of the SITS including only
the cloud-free and cloud shadow-free images on forests (14 dates, dataset R, a subset of C).
Table 2.
Datasets used to evaluate the influence of the spectral features and the smoothing on the
classification accuracy.
Dataset Composition
Wbands Smoothed time series based on Whittaker including spectral bands only (17 dates)
Wndvi Smoothed time series based on Whittaker including NDVI only (17 dates)
Wbands+ndvi
Smoothed time series based on Whittaker including spectral bands and NDVI (17 dates)
Cbands Non-smoothed (cloudy) time series including spectral bands only (17 dates)
Cndvi Non-smoothed (cloudy) time series including NDVI only (17 dates)
Cbands+ndvi Non-smoothed (cloudy) time series including spectral bands and NDVI (17 dates)
Rbands Non-smoothed time series with no cloud coverage or cloud shadows on forests
including spectral bands only (14 dates)
Rndvi Non-smoothed time series with no cloud coverage or cloud shadows on forests
including NDVI only (14 dates)
Rbands+ndvi Non-smoothed time series with no cloud coverage or cloud shadows on forests
including spectral bands and NDVI (14 dates)
All the classifiers were computed using the scikit-learn Python library [
52
]. SVM was used with
a gaussian Radial Basis Function (RBF) kernel given its superior performance compared to other
kernels [
53
]. Adequate values of the hyperparameters were selected after testing ranges of values using
a grid search. Ranges were specified as follows: the regularization parameter
C=
{1, 10, ..., 10
5
} and
the width of the RBF kernel function
γ=
{2
−5
, ..., 2
5
}. For RF, we varied the number of classification
trees from 10 to 500 (step = 50). For k-NN, the spectral similarity between the unlabeled pixels and
the reference pixels was measured as Euclidean distance in the feature space. The optimal value
of
k
, the number of nearest neighbors, was selected in the range from 1 to 50 (step = 5). For GMM,
a regularization was introduced in the inversion of the covariance matrix with a parameter varying
from 10
−8
to 10
8
(step = 1 for the power value). All the hyperparameters of each method have been
selected using a 5-fold cross validation.
The classifiers were trained using 2/3 randomly selected reference pixels per class (i.e., using
an equal sample rate for each class by stratified sampling), and the learning models were validated
using the remaining independent pixels (1/3). Classifications were carried out after masking
the non-forest areas using ancillary data. Confusion matrices with overall accuracy and kappa
statistics were computed and averaged over 25 repetitions (i.e., by applying the repeated random
data-splitting procedure). Finally, comparisons of performance between classifiers and datasets were
carried out statistically, based on the Wilcoxon rank-sum nonparametric test, and spatially, based on
map comparison.
4. Results
Statistical results of the classifications are reported in Figure 4. For each classifier, the kappa index
is plotted for the three levels of the class hierarchy and the accuracy is provided for all the image
datasets (27 tests per classifier).
In general, the results revealed a very high potential of Formosat-2 image time series to be used to
discriminate forest tree species based on phenological differences. On average, a kappa (
κ
) value of 0.93
was obtained, all the classifiers, thematic levels and band combinations included (108 classifications).
In most of the cases, an increase in thematic accuracy (from level 1 to level 3) has lead to a decrease in
classification accuracy, with an average kappa value varying from 0.94 for level 1 to 0.91 for level 3.
Remote Sens. 2016,8, 734 9 of 29
However, the results exhibited a sensitivity to the spectral features, the smoothing and the classification
algorithms. This sensitivity is analyzed in the following sections. For all the comparisons, difference in
accuracy is declared significant when the p-value of the Wilcoxon rank test is less or equal to
α
= 0.05.
12 3
0.8
0.85
0.9
0.95
1
Class Level
Kappa
(a)GMM classifier
12 3
0.8
0.85
0.9
0.95
1
Class Level
Kappa
(b)SVM classifier
12 3
0.8
0.85
0.9
0.95
1
Class Level
Kappa
(c)RF classifier
12 3
0.8
0.85
0.9
0.95
1
Class Level
Kappa
(d)k-NN classifier
W bands W ndvi W bands ndvi C bands C ndvi C bands ndvi R bands R ndvi R bands ndvi
Figure 4.
Average Kappa coefficient
±
standard deviation obtained after 25 repetitions for each
classifier (GMM in (
a
), SVM in (
b
), RF in (
c
), k-NN in (
d
)) at the three levels of the class hierarchy
(2 classes at level 1; 4 classes at level 2; 13 classes at level 3). The smoothed and non-smoothed versions
of the SITS (17 dates) are denoted respectively by W(in blue) and C(in red). The non-smoothed
cloud-free version of the SITS (14 dates) is denoted by R(in orange). Accuracy is provided for each
group of spectral features: spectral bands alone (solid line), NDVI alone (dashed line), or spectral bands
and NDVI (dotted line).
4.1. Influence of the Classifier
On average, considering all the nine datasets, SVM was found to be the best classifier at the three
levels of the class hierarchy (Table 3). GMM performed the worst at level 1 (
κmean
= 0.91). RF was the
less efficient at level 3 (
κmean
= 0.90). However, the results are close between the classifiers with only
partial significant differences (16/27 between SVM and k-NN, 24/27 between SVM and RF and 20/27
between SVM and GMM; Figure 4). The highest
κ
values obtained with SVM were 0.98 using R
bands
at
level 1 (broadleaf vs conifer tree species), 0.97 using W
bands
at level 2 (4 classes of tree species), and 0.97
using Cbands at level 3 (13 classes of tree species) (Figure 4).
In most of the cases, as the classification becomes more specific from level 1 to level 3, the performance
decreases significantly, especially using NDVI alone (Figure 4). This is true for SVM, RF and k-NN,
excepting GMM. At level 1, the GMM average accuracy based on the W
ndvi
,C
ndvi
and R
ndvi
datasets
(
κmean
= 0.83
±
0.02; Figure 4) is lower than the ones at level 2 (
κmean
= 0.88
±
0.02) and level 3
(κmean = 0.85 ±0.02)
. For GMM, learning fewer heteregeneous classes which contain various tree
Remote Sens. 2016,8, 734 10 of 29
species at level 1 is more difficult than learning a greater number of more homogeneous classes at
levels 2 and 3, in particular, when only a subset of spectral features available is used.
Table 3.
Accuracy comparison between the classifiers based on the average kappa values
±
standard
deviation computed from the results of all the nine datasets.
GMM SVM RF k-NN
Level 1/all datasets 0.91 ±0.02 0.96 ±0.01 0.93 ±0.02 0.95 ±0.01
Level 2/all datasets 0.93 ±0.01 0.95 ±0.01 0.93 ±0.01 0.94 ±0.01
Level 3/all datasets 0.92 ±0.01 0.93 ±0.01 0.90 ±0.01 0.91 ±0.01
4.2. Influence of the Spectral Features
Except for RF, there is no significant benefit of adding NDVI to spectral bands to discriminate tree
species. The kappa values are very close whatever the classifier (GMM, SVM, k-NN) and the level of
the class hierarchy (Figure 4). In the case of RF, incorporating NDVI has only a slight but significant
positive effect on classification accuracy for levels 1 and 2 (
∆kmean
= 1%). By contrast, the classification
performance decrease significantly using NDVI alone for all the classifiers (e.g.,
∆kmean
= 6% between
bands-based and ndvi-based classifications of level 1 with SVM).
4.3. Influence of the Smoothing
Smoothing was performed to remove cloud contamination in the data. A large proportion of the
pixels of the SITS were impacted by clouds and cloud shadows (Figure 5). The most affected images
are those one acquired in 26 May (14.3% of the entire image), 20 July (4.9%) and 21 September (3.9%).
Considering only the reference plots, 397 pixels were affected by cloud contamination at least once
(i.e., 32% of the sample size). Twelve of the thirteen species were concerned. However, if we only focus
the analysis on forests, fourteen images of the SITS were non-cloudy and non-shady. Clouds and cloud
shadows on forests appear in 26 May, 1 and 21 September.
4km
4km
(a) (b)
(c)
(d)
N
Figure 5.
Resulting graylevel image of clouds and cloud shadows combining all the individual masks
of the SITS at each date (
a
); or detected only in 26 May (
b
); in 20 July (
c
); and in 21 September (
d
).
The grayscale intensity vary from black (cloud and shadow free) to white (maximum of clouds
and shadows).
Remote Sens. 2016,8, 734 11 of 29
The smoothing effect can be evaluated by comparing the performance of the three groups of
datasets W,Cand R(Figure 4). In the experiments, the optimal value for the regularization parameter
λ
of the Whittaker smoother was estimated to 10
5
from both Generalized and Ordinary Cross-validation.
This parameter value has been retained to smooth all the Wdatasets.
In a general way, there is no difference in accuracy between the classifications based on the
smoothed and non-smoothed datasets at level 1, except for RF. With this classifier, the non-smoothed
datasets Cor Rperform slightly but significantly better than W(
∆kmean
= 1%). This is also true for SVM
and k-NN using NDVI alone (
∆k≥
2% between W
ndvi
and C
ndvi
) . At level 2, a decrease in classification
accuracy is observed with the Rdataset. The non-smoothed Cdataset performs significantly better for
most of the classifiers, especially using NDVI alone. No significant differences appear between the C
and Wdatasets using the spectral bands alone or the spectral bands combined with NDVI, except for
RF. At level 3, differences in accuracy between the datasets increase. The Rdataset including only the
14 cloud-free and shadow-free images is the worst. Thus, the results are improved when all the dates
are added in the SITS, even if the time series contains noisy data, as the Cdataset. In some cases, the C
dataset performs significantly better than the smoothed Wdataset. This is true for all the classifiers
using NDVI alone. This also appears for RF and k-NN using spectral bands alone or spectral bands
with NDVI.
4.4. Confusions between Species
A confusion matrix summarizes the results for the best classification at level 3 based on the SVM
classifier and the smoothed W
bands
dataset (Table 4) (see Appendix Afor values in pixels). Among
the broadleaf tree species, Silver birch, Red oak, Eucalyptus, European Ash, Aspen and Willow
showed the highest accuracies (confusion rate
<
1%). Oak and Black locust were the most difficult
to discriminate (confusion rates equal to 5.03% and 3.05% respectively). Oak is mainly confused
with Silver birch (1.64%) and Eucalyptus (1.44%). For Black locust, confusions were observed with
Oak (2.29%). Considering conifer species, the best agreement was obtained for Silver fir (96.77%).
Douglas fir showed the lowest accuracy (87.13%). The main confusions appeared with Silver fir (6.78%).
The other important classification errors were observed between the three Pine species and Pines
with Oak. In a general way, more confusions were observed among conifer tree species compared to
broadleaf tree species which is consistent with the fact that phenology is less informative for conifers
than deciduous tree species (see Appendix B).
The final results of forest type mapping are shown in Figure 6. According to our field observations,
the dominant species in the small forests is Oak. Conifer species are located in the biggest forests
including some deciduous species. Red oak, Eucalyptus and Aspen (tree plantations) are mainly
present in isolated homogeneous patches.
4.5. Classification Stability
In spite of the optimal accuracy estimated from the confusion matrix, an in-depth analysis of the
cartographic results revealed a high instability between the classifiers, especially at level 3. When the
class value predicted by each classifier was compared, for each pixel, classification consistency was
found at level 1 for most of the pixels (96.5% of agreement between 3 or 4 classifiers). Similar results
were computed at level 2 (94.5% of agreement). However, a high ambiguity was observed at level 3
with only 11.1% of pixels in agreement between all the classifiers and 55.6% of agreement between
3 classifiers (mainly SVM, RF and k-NN). The highest instability was observed in the core of complex
forests including various deciduous or conifer species. Disagreements between the classifiers were
also found in forest edges. By contrast, a high consistency appeared within monospecific broadleaf
plantations at the three levels of the class hierarchy, in particular, for Red Oak, Aspen and Eucalyptus.
Remote Sens. 2016,8, 734 12 of 29
Table 4.
Confusion matrix between the 13 tree species (level 3) computed in % and averaged over
25 repetitions. The matrix is based on the best classification obtained using the smoothed W
bands
dataset and the SVM classifier. Rows and columns represent pixels in the classification and the
reference respectively. The class labels are: 1 (Silver birch), 2 (Oak), 3 (Red oak), 4 (Douglas fir),
5 (Eucalyptus), 6 (European Ash), 7 (Aspen), 8 (Corsican pine), 9 (Maritime pine), 10 (Black pine),
11 (Black locust), 12 (Silver fir), 13 (Willow).
Reference Class
Predicted Class 1 2 3 4 5 6 7 8 9 10 11 12 13
1 100 1.64 0.08 0 0 0 0 0 0 0 0 0 0
2 0 94.97 0.08 0.52 0 0.43 0.22 1.45 2.27 3.80 2.29 0 0
3 0 0.51 99.84 0 0 0 0 0 0 0 0 0 0
4 0 0 0 87.13 0 0 0 1.45 0 0 0 1.54 0
5 0 1.44 0 0.52 99.76 0 0 0 0 0 0 0 0
6 0 0.62 0 0 0 99.00 0.06 0 0 0 0.76 0 0
7 0 0.10 0 0 0 0.57 99.72 0 0 0 0 0 0
8 0 0.62 0 0.35 0 0 0 93.64 3.07 2.11 0 0 0
9 0 0.10 0 2.43 0.24 0 0 2.91 91.73 0.63 0 0 0
10 0 0 0 2.26 0 0 0 0.55 1.87 93.46 0 1.69 0
11 0 0 0 0 0 0 0 0 0 0 96.95 0 0
12 0 0 0 6.78 0 0 0 0 0 0 0 96.77 0
13 0 0 0 0 0 0 0 0 1.07 0 0 0 100
2 km
Level 3
Silver birch
Oak
Red oak
Doulgas r
Eucalyptus
European ash
Aspen
Corsican pine
Maritime pine
Black pine
Black locust
Silver r
Willow
1° E 1°20' E
43°30' N
43°25' N
N
Figure 6.
Map of forest tree species at level 3 based on the smoothed W
bands
dataset and the SVM
classifier. Excerpts of image in false color infrared are derived from the winter Formosat-2 image
acquired in 16 Februrary 2013. At this time, a clear distinction between conifer and deciduous tree
species is observed.
Remote Sens. 2016,8, 734 13 of 29
Examples of classification stability are given in Figure 7. The first one shows tree plantations
composed of Aspen (Figure 7a, in the southwest) and Eucalyptus (Figure 7a, in the center and the
north). A good agreement is observed between the classifiers at the three thematic levels. The other
two examples show mixed forests composed of conifers (Douglas fir and Corsican pine in Figure 7b;
Maritime pine and Douglas fir in Figure 7c) with deciduous species (Oak in both examples with
Silver birch in Figure 7b). In these two cases, instability between the classifications is high at level
3, especially for mixed pixels and heterogeneous forest edges. These patterns were found for all the
datasets used.
This comparison between the classifiers suggested overfitting of the models at level 3. Indeed,
when the same similarity analysis was performed using only the reference pixels (
n=
1235),
97.6% of agreement between 3 or 4 classifiers was found. In order words, the models had a high
explanatory power at level 3 but a weak predictive performance. Except for broadleaf tree plantations,
an overestimation of the classification accuracy is assumed for complex forests, regarding the
dissimilarities between the maps of forest types.
Stability at class level 3Stability at class level 2Stability at class level 1
Formosat, Feb. 16th 2013
0 agreement 2 agreements 3 agreements 4 agreements
300m
(a)
(b)
(c)
N
Figure 7.
Degree of relative agreement between the SVM, RF, k-NN and GMM classifiers at the
three classification levels using W
bands
dataset. Stability is observed at level 3 for monospecific tree
plantations in (a) compared to complex forests with mixed pixels in (b) and (c).
5. Discussion
Globally, the results showed a high suitability of Formosat-2 multispectral SITS for mapping
detailed forest types in temperate woodlands. High classification accuracies were obtained from
several classifiers highlighting the importance of phenological information for discriminating various
broadleaf and coniferous tree species. To the best of our knowledge, this is the first study examining
the potential contribution of dense (17 dates) high spatial resolution (
<
10 m) image time series to
classify several tree species in temperate forests within a complete seasonal cycle of vegetation.
Regarding the classifiers, SVM outperforms RF, k-NN and GMM. This finding is consistent with
previous studies focusing on forest applications [
33
,
54
]. SVM often produces higher classification
Remote Sens. 2016,8, 734 14 of 29
accuracy than the traditional methods, especially when small training samples are available and when
the data are of high dimensionality [
55
,
56
]. However, to obtain the best accuracy, SVM supposes to
select the adequate kernel with the optimal values of the hyperparameters. The number of user-defined
parameters is higher than the other classifiers. In terms of computational time, the method is also
slower, especially for predictions. For GMM based on NDVI datasets, when the number of classes
increases from level 1 to level 2, an increase in accuracy is observed, contrary to the other classifiers.
This is due in large part to the Gaussian assumption of the Maximum Likelihood estimation. At level 1,
since the normal distributions of the two classes are defined from the pixels of all the conifer and
broadleaf species, they are characterized by a high variability in reflectance that may not handle
multimodal distributions well. In this case, GMM requires to define more classes (i.e., the subclasses)
to better estimate the data distributions [
57
]. The non-parametric methods such as SVM or RF are more
flexible since they do not require any assumptions about data distribution.
Regarding the spectral features, adding NDVI to the spectral bands has not significant effect on
the classification performance. This is probably due to the fact that NDVI is a function of the red and
near-infrared spectral bands and thus, is highly correlated to these bands. Conversely, using NDVI
alone decreases significantly the classification accuracy, even though a lower number of features is used
(i.e., the data dimensionality is reduced). This suggests that the differences in the seasonal dynamic of
NDVI are too slight among some tree species. Larger differences are found using all the spectral bands
(see Appendices Band C).
Regarding the smoothing, we did not found any clear benefit of removing noise in the data
using the Whittaker smoother. When NDVI is used alone, the non-smoothed dataset outperforms
the smoothed one. These findings contradict other previous studies that evaluated the effect of
noise reduction in time series data before classification. Some authors found improved inter-class
separability after smoothing using Whittaker algorithm [
51
,
58
]. Nevertheless, smoothing has not
always a positive effect. As revealed by [
58
] for crop-specific classification, smoothing may also
reduce classification accuracy. If the smoothing process is excessive, removing all the roughness of
the raw data, the inter-class separability may be affected. In our study, the smoothing parameter
λ
has been adjusted automatically by cross-validation (CV) in a fixed range of values (from 10
0
to 10
15
). As suggested by a visual inspection of the temporal profiles before and after smoothing
(see Appendix D), the optimal value we computed by CV may be adapted in some cases (i.e., producing
a stronger smoothing).
Another important point highlighted by our results is the fact that the classification performance
is improved using all the 17 dates in the SITS instead of including only the 14 cloud-free images,
even if the full SITS contains noisy data (i.e., non-smoothed images with cloud contaminated pixels).
One explanation for this is the small number of dates affected by clouds and cloud shadows in the
SITS. More than 32% of the reference plots was found to be related to noisy pixels but for most of them,
in only one image (26 May). Thus, the negative effect of noisy data is lower than the benefit of adding
more dates in the SITS. The inclusion of the three cloudy images on forests (26 May, 1 September,
21 September) improves the separability between the classes. This is especially true for conifer tree
species, as revealed by the comparison of the confusion matrix. The additional dates enable the
confusions to be reduced between Douglas fir and Corsican pine (
−
5%) as well as between the three
classes of pines. For deciduous species, no notable changes were observed in classification accuracy,
suggesting that no additional phenological information was provided with the supplementary images.
Despite the very high performance we found statistically, the classification stability analysis
between the classifiers has shown uncertainties at level 3, in particular, for non-plantations in mixed
forests. An overestimation of accuracy is assumed for these cases. This assumption is supported by
the important difference in accuracy between our analysis and the previous studies focusing on tree
species mapping using multitemporal images. A difference of 17% of kappa is observed with the best
results obtained by [
43
] to classify six deciduous tree species (Ash, Aspen, Birch, Elm, Maple, Oak)
using a combination of five Airborne Thematic Mapper images. Our results also outperform the ones
Remote Sens. 2016,8, 734 15 of 29
reported by [
42
] to identify four species (Yellow poplar, White oak, Red oak and Red maple). These
species were mapped at 76% overall accuracy (
κ
= 0.41) using a combination of four spectral bands and
five of the nine dates available from aerial photographs. In another study, [
38
] discriminated 33 forest
classes (20 dominant types with 13 sub-classes) with an overall accuracy of 79% based on multiseasonal
Landsat TM data. More recently, an intra-annual time series of RapidEye data including five dates was
used to classify 8 tree genus in an urban environment [
44
]. Several classification scenarios were tested
to evaluate the impact of phenological information and the RapidEye’s red edge band on the accuracy.
The best classification they achieved using SVM was estimated to 83% of kappa.
Although these previous studies are not directly comparable because they were conducted with
different methods and imagery, for different tree species, the strong classification performance we
obtained for 13 species (average kappa of 93% at level 3 with SVM) is surprisingly much higher,
suggesting a potential bias in the learning procedure and the accuracy assessment. This bias could be
explained by several reasons: (1) overfitting; (2) classification of data with imbalanced class distribution;
(3) the use of only pure pixels for training and validating the classifications; (4) a discrimination
restricted to the most dominant tree species; (5) a partial spatial dependence between training and
validation data.
Overfitting is suspected in our experiments regarding the high number of features relative to the
small number of learning samples. For some classes, the number of features is greater than the sample
size. In this case, overfitting is likely to occur, providing a model with a high explanatory power but
a poor predictive performance (as suggested by the spatial comparison of the four classifier results).
The sample size is known to be one of the most important factors affecting the quality of a model and
its ability to generate accurate predictions. In the remote sensing community, several recommandations
have been proposed to estimate the appropriate sample size although no clear consensus exists [
59
,
60
].
The required sample size depends on several factors including the classification algorithm but also
the complexity of the learning problem. As a rule of thumb, [
59
] suggests to collect a minimum of
50 samples for each category. In this study, eight categories contain less than 85 pixels for training
and validation. Because of the high dimensionality of our image datasets (when all the spectral
bands are included), our sample sizes were probably too small to avoid overfitting and evaluate the
classification accuracy properly, especially at level 3. As recommended with small reference datasets,
cross-validation was retained to estimate the accuracy. Regularization was also introduced, but these
efforts were probably insufficient. In order to prevent overfitting, one option may be to augment the
ground-based reference dataset and to analyze the impact of larger training subsets on classification
error (plotting the learning curve of the classifiers [
57
]). However, gathering additional reference
data is costly and highly time-consuming. Adapted classification approaches enabling training set
size to be reduced [
61
], or enabling training samples to be added iteratively, including unlabeled
pixels [
40
], could be another option. Beyond these strategies to control sample size, overfitting could
be also moderated using fewer features. The dimensionality of the data could be reduced from feature
extraction or selection techniques [
62
,
63
]. The use of NDVI instead of the spectral bands is also
an alternative, as we did. In this case, a decrease in classification accuracy should be observed with the
decrease of overfitting. This may be another explanation of the lower performance we obtained for the
classifications produced using NDVI alone.
The variation in the sample size across classes may also influence the rate of classification
errors [
64
]. Samples of smaller classes can be misclassified more often than those belonging to prevalent
classes. This could lead to an accuracy paradox: a less accurate model (in terms of overall accuracy)
may be more useful than a more accurate one. Indeed, an excellent accuracy may be obtained by always
predicting the prevalent classes with any prediction of the minority classes. In our study, the class
imbalance is weak (maximum class size ratio of 4:1). In addition, we selected the kappa coefficient as a
performance metric since kappa is more appropriate than the overall accuracy with imbalanced class
distributions. Nevertheless, the minority classes were often the most confused (e.g., Black locust and
Black pine). A part of the confusions appeared with the prevalent classes (e.g., Oak). This variation
Remote Sens. 2016,8, 734 16 of 29
in the sample size across classes is rather common for tree species classification [
65
], reflecting the
uneven distribution of species abundances on landscape. Several solutions exist to overcome this
problem, both at data level and algorithm level [
64
]. These include many different forms of resampling
(oversampling the small classes, undersampling the prevalent classes, generating new synthetic data).
Cost-sensitive learning approaches may be also adopted, assuming higher misclassification costs with
samples in the minority class.
As revealed by classification instability at forest edges (often composed of a high variability of
species), another cause of uncertainty may be the discrimination restricted to the most dominant
tree species. In the studied forests, the number of species is higher than the 13 detected species.
However, all the pixels were assigned to one of the dominant species. Thus, according to the
classifier, pixels composed of non-sampled tree species may be assigned to different tree species
classes. The non-sampled species (such as Hornbeam or Hazel tree) were too few to collect field
data on them. Most of them were mixed with other species and spatially interspersed. Instability
is also due to the presence of understory vegetation and the existence of harvesting operations in
the forests during the year. Understory vegetation is visible in some images, after the leaf fall of the
dominant trees. Clear cuts also appear in some forests (as observed in the excerpts of Figure 2, from
30 July). This may also lead to differences in classification between the classifiers. The comparison of
the classifiers should not be viewed as an accuracy assessment but rather as a similarity assessment
that may be affected by various factors.
Since we processed a high spatial resolution SITS, we adopted a hard classification approach,
assuming that all the pixels were pure in the forests. The results showed that this assumption was
consistent in monospecific tree plantations. However, in the case of complex forest environments
(Figure 8), the proportion of mixed pixels was too high, generating dissimilarities between the classifiers
at level 3. This result was not necessarily expected, the pixel size (8 m) being small enough to detect
individual tree and thus, the variety of species within a single stand. Alternative approaches based
on soft classification [
66
], spectral unmixing [
67
] or the use of small training sets containing mixed
pixels [61] could be used to account for uncertainty in these forests.
(a) (b)
Figure 8.
Examples of homogeneous and heterogeneous forest areas of the study site. At level 3,
mixed forests show a high classification instability between the classifiers. (
a
) Monospecific forest
composed of Silver birch; (b) Mixed forest composed of various deciduous and conifer species.
Finally, our evaluation method is independent, in the sense that pixels used to train and validate
the classifications are strictly distinct. However, our random sampling design to select training
and validation data (based on repeated split-sample) is not spatially constrained. Samples may
come from the same geographic area or the same forests. Some species exist only in one forest.
Other species appear in several forests but in various proportions. Thus, reference samples of tree
species are not distributed equally over the forests. Consequently, the selection of reference samples
for training and validation did not consider the effect of spatial autocorrelation among neighboring
Remote Sens. 2016,8, 734 17 of 29
pixels. Because close samples tend to have similar spectral values, this may influence the outcome of
the supervised classification [68–71].
Despite these potential optimistic bias in accuracy assessment, the qualitative validation of our
classifications, based on a visual interpretation of the maps and our field observations, confirmed the
high level of classification accuracy computed for the most tree species. This high accuracy can be
explained by the combination of the high spatial and temporal resolution of the Formosat-2 SITS, as well
as the image acquisition at constant viewing angles which may reduce the within-species spectral
variation. Phenological differences among the broadleaf tree species is also another factor explaining
the performance obtained in this study (see the temporal profiles of each species in Appendices Band C).
The possible discrimination between Eucalyptus and the other broadleaf species is obvious, mainly
due to the evergreen phenology of Eucalyptus. Red Oak is also unique, characterized by red leaves in
spring (leaf out in May) and in autumn (leaf fall in November). The European ash phenology differs
from the other broadleaf tree species by a shorter period with leaves (last tree to leaf out in May after
flowering, first tree to drop leaves in late September). Between the other broadleaf species of the
study, the leaf development starts with Aspen (early-mid of April), followed by Oak, Silver birch,
Black locust and Willow (mid-late April until May). In autumn, the species senescence start with
Aspen
(late October)
, Black locust and Silver birch (early-mid of November), followed by Willow
(yellow leaves) and Oak (late November, brown leaves).
6. Conclusions
Our study demonstrates the high suitability of dense optical high spatial resolution SITS for
mapping dominant tree species in temperate woodlands. High classification accuracies (kappa
>
0.9)
were obtained from several classifiers verifying the assumption that multispectral imagery makes tree
species recognition possible when phenological information is taken into consideration. Specifically,
we conclude from our findings that:
•
The classification performance is slighlty influenced by the classifier. RBF-SVM classifier
demonstrated the best accuracy at the three levels of the class hierarchy. GMM performed
the worst.
•
There is any clear benefit of removing cloudy and shady pixels using the Whittaker smoother
in our context, even if 32% of the reference pixels were contaminated at least once. By contrast,
adding all the dates in the SITS instead of only the cloud-free and shadow-free images enables
the classification accuracy to be increased.
•
There is no benefit of adding NDVI to spectral bands to discriminate tree species. By contrast,
using NDVI alone led to a significant decrease in classification performance, even if the
dimensionality of the data is reduced.
•
Classification uncertainty exists for complex mixed forests, regarding the spatial disagreements
that appear between the maps produced by all the classifiers. By contrast, a high consistency is
observed within monospecific broadleaf plantations.
•
Among the broadleaf tree species, Oak and Black locust are the most difficult to discriminate.
For conifers, the lowest accuracy is observed for Douglas fir.
The conclusions of the study have to be taken with caution. Because of small sample size for some
species, overfitting is suspected. Classification errors might also be affected by the imbalanced class
distributions. Additional works should be carried out to confirm that the approach is transferable
to other sites. Despite a possible overestimation of the predictive performance, we believe that these
results provide the basis to map forest tree species across a broad spatial extent, using the forthcoming
Sentinel-2 image time series. Further developments will be needed to better control the sampling and
learning procedures. The spatial and temporal variability of tree species phenology at a regional or
national scale should also be considered.
Our study is a first step towards the production of detailed and accurate maps of species
composition for forest managers. These maps would also permit a variety of ecological applications.
Remote Sens. 2016,8, 734 18 of 29
Supplementary Materials: Full classification map is available online at http://goo.gl/bbzwZC.
Acknowledgments:
This work was partially supported by the French Space Agency CNES (TOSCA OSO project).
A special thanks to M. Huc and O. Hagolle from CESBIO for providing the pre-processed Formosat-2 time series
with masks of clouds and cloud shadows.
Author Contributions:
D.S. and M.F. conceived and designed the experiments; D.S., C.P., J.W. and J.-P.D. carried
out the field work; V.J. and M.F. performed the experiments; M.L. implemented the smoothing method; D.S. and
M.F. analyzed the data; D.S. wrote the paper with feedback from M.F., M.L. and J.-P.D.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Confusion Matrix in Pixels from the Smoothed Wbands Dataset and the
SVM Classifier
Table A1.
Confusion matrix between the 13 tree species (level 3) computed in pixels and averaged
over 25 repetitions (explaining why there are decimal fractions). The matrix is based on the best
classification obtained using the smoothed W
bands
dataset and the SVM classifier. Rows and columns
represent pixels in the classification and the reference respectively. The class labels are: 1 (Silver birch),
2 (Oak), 3 (Red oak), 4 (Douglas fir), 5 (Eucalyptus), 6 (European Ash), 7 (Aspen), 8 (Corsican pine),
9 (Maritime pine), 10 (Black pine), 11 (Black locust), 12 (Silver fir), 13 (Willow).
Reference Class
Predicted Class 1 2 3 4 5 6 7 8 9 10 11 12 13
1 29.00 0.64 0.04 0 0 0 0 0 0 0 0 0 0
2 0 37.04 0.04 0.12 0 0.12 0.16 0.32 0.68 0.72 0.48 0 0
3 0 0.20 49.92 0 0 0 0 0 0 0 0 0 0
4 0 0 0 20.04 0 0 0 0.32 0 0 0 0.40 0
5 0 0.56 0 0.12 49.88 0 0 0 0 0 0 0 0
6 0 0.24 0 0 0 27.72 0.04 0 0 0 0.16 0 0
7 0 0.04 0 0 0 0.16 71.80 0 0 0 0 0 0
8 0 0.24 0 0.08 0 0 0 20.60 0.92 0.40 0 0 0
9 0 0.04 0 0.56 0.12 0 0 0.64 27.52 0.12 0 0 0
10 0 0 0 0.52 0 0 0 0.12 0.56 17.72 0 0.44 0
11 0 0 0 0 0 0 0 0 0 0 20.36 0 0
12 0 0 0 1.56 0 0 0 0 0 0 0 25.16 0
13 0 0 0 0 0 0 0 0 0.32 0 0 0 16.00
Appendix B. Temporal Signatures in Each Spectral Band of the SITS for Each Broadleaf and
Conifer Tree Species
Broadleaf Conifer
0
20
40
60
100 200 300 100 200 300
Day of the year (DOY)
Reflectance (mean value in % * 10)
Tree species
Aspen
Black locust
Eucalyptus
European ash
Oak
Red oak
Silver birch
Willow
Corsican pine
Maritime pine
Black pine
Douglas fir
Silver fir
Blue
(a) Blue band
Figure B1. Cont.
Remote Sens. 2016,8, 734 19 of 29
Broadleaf Conifer
0
20
40
60
100 200 300 100 200 300
Day of the year (DOY)
Reflectance (mean value in % * 10)
Tree species
Aspen
Black locust
Eucalyptus
European ash
Oak
Red oak
Silver birch
Willow
Corsican pine
Maritime pine
Black pine
Douglas fir
Silver fir
Green
(b) Green band
Broadleaf Conifer
0
20
40
60
100 200 300 100 200 300
Day of the year (DOY)
Reflectance (mean value in % * 10)
Tree species
Aspen
Black locust
Eucalyptus
European ash
Oak
Red oak
Silver birch
Willow
Corsican pine
Maritime pine
Black pine
Douglas fir
Silver fir
Red
(c) Red band
Broadleaf Conifer
100
200
300
400
100 200 300 100 200 300
Day of the year (DOY)
Reflectance (mean value in % * 10)
Tree species
Aspen
Black locust
Eucalyptus
European ash
Oak
Red oak
Silver birch
Willow
Corsican pine
Maritime pine
Black pine
Douglas fir
Silver fir
Near Infrared
(d) Near infrared band
Figure B1.
Average spectral signatures of the thirteen tree species in the blue (
a
), green (
b
), red (
c
) and
near infrared (
d
) spectral bands of the SITS. Reflectance values (y-axis) are given for each day of the
year (x-axis) in percent, multiplied by a factor of 10.
Remote Sens. 2016,8, 734 20 of 29
Appendix C. Boxplots of the NDVI Index of the SITS for Each Broadleaf and Conifer
Tree Species
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(a)Eucalyptus
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(b)Silver birch
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(c)Oak
Figure C1. Cont.
Remote Sens. 2016,8, 734 21 of 29
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(d)Red oak
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(e)European ash
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(f)Black locust
Figure C1. Cont.
Remote Sens. 2016,8, 734 22 of 29
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(g)Aspen
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(h)Willow
Figure C1.
Seasonal dynamic of NDVI distribution among broadleaf tree species: Eucalyptus (
a
),
Silver birch (
b
), Oak (
c
), Red oak (
d
), European ash (
e
), Black locust (
f
), Aspen (
g
), Willow (
h
). Boxplots
(medians and the interquartile range from 1st to 3rd quartiles) are defined for each date of the year
(x-axis) of the Formosat-2 time series of 2013.
Remote Sens. 2016,8, 734 23 of 29
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(a)Douglas fir
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(b)Silver fir
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(c)Corsican pine
Figure C2. Cont.
Remote Sens. 2016,8, 734 24 of 29
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(d)Maritime pine
39 50 126 146 157 177 187 201 211 223 234 244 264 285 300 332 354
0.0
0.2
0.4
0.6
0.8
1.0
(e)Black pine
Figure C2.
Seasonal dynamic of NDVI distribution among conifer tree species: Douglas fir (
a
), Silver
fir (
b
), Corsican pine (
c
), Maritime pine (
d
), Black pine (
e
). Boxplots (medians and the interquartile
range from 1st to 3rd quartiles) are defined for each date of the year (x-axis) of the Formosat-2 time
series of 2013.
Remote Sens. 2016,8, 734 25 of 29
Appendix D. Smoothing of Temporal Profiles Using Whittaker
50 100 150 200 250 300 350
0
50
100
150
200
250
300
350
400
DN - Bl ue
50 100 150 200 250 300 350
0
50
100
150
200
250
300
350
DN - Green
50 100 150 200 250 300 350
0
50
100
150
200
250
300
350
400
DN - Red
50 100 150 200 250 300 350
0
100
200
300
400
500
600
700
800
DN - Near I nf ra- Red
50 100 150 200 250 300 350
Day of t he year
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
0. 8
0. 9
1. 0
DN - NDVI
(a)Pixel values affected by clouds in 26 May (DOY 146) and 20 July (DOY 201)
Figure D1. Cont.
Remote Sens. 2016,8, 734 26 of 29
50 100 150 200 250 300 350
0
5
10
15
20
25
30
35
40
45
DN - Bl ue
50 100 150 200 250 300 350
10
20
30
40
50
60
70
80
DN - Green
50 100 150 200 250 300 350
0
10
20
30
40
50
60
70
80
90
DN - Red
50 100 150 200 250 300 350
50
100
150
200
250
300
350
400
DN - Near I nf ra- Red
50 100 150 200 250 300 350
Day of t he year
0. 50
0. 55
0. 60
0. 65
0. 70
0. 75
0. 80
0. 85
0. 90
DN - NDVI
(b)Pixel values affected by cloud shadows in 26 May (DOY 146) and 26 June (DOY 177)
Figure D1.
Temporal profiles of spectral bands and NDVI for pixels affected by clouds (
a
) and cloud
shadows (
b
) at several days of the year (DOY) in 2013. The noisy values of the pixels within the SITS
are labeled in red. The cloud-free and shadow-free values of the pixels are represented in blue. The line
represents the temporal profiles after application of the Whittaker smoother.
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