Mapping Cork Oak Mortality Using Multitemporal High-Resolution Satellite Imagery
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remote sensing
Article
Mapping Cork Oak Mortality Using Multitemporal
High-Resolution Satellite Imagery
João Catalão * , Ana Navarro and João Calvão
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal;
acferreira@fc.ul.pt (A.N.); jmrodrigues@fc.ul.pt (J.C.)
* Correspondence: jcfernandes@fc.ul.pt; Tel.: +351-21-750-0833
Abstract: In the Mediterranean region, a significant decline in the vitality of vegetation has been
observed in the last two decades, with a high forest mortality rate for several species. The increase
in mortality has been attributed to water stress resulting from an increase in temperature and long
periods of drought. To detect and quantify the impact of these events on tree mortality, an efficient
and easy-to-use methodology for rapid damage assessment is required. Our study aims to assess
the potential of high spatial resolution multispectral images from the Pleiades constellation to detect
and map cork oak mortality in a pasture environment with multiple forest species. An approach
based on change detection and the use of an unsupervised classifier is proposed to detect mortality
at the cork oak level. The change in the values observed for three vegetation indices, NDVI, RGI,
and GNDVI, between two epochs is used in an unsupervised classification algorithm to estimate the
dead tree class. The classification results are accurate, with precision and recall values higher than
90%. Detailed cork oak mortality mapping is of significant use in comprehending ecosystem change
as a result of tree mortality and for the implementation of mitigation mechanisms for the ongoing
desertification process.
Keywords: Quercus suber L.; cork oak decline; Pleiades; time series; vegetation indices
Citation: Catalão, J.; Navarro, A.;
Calvão, J. Mapping Cork Oak
Mortality Using Multitemporal
High-Resolution Satellite Imagery. 1. Introduction
Remote Sens. 2022, 14, 2750. https:// Climate warming, which is associated with an increased frequency of drought and
doi.org/10.3390/rs14122750 heat events, is positively related to forest mortality in several species worldwide [1–4].
Academic Editor: Guangsheng Chen
Climate-induced tree mortality rapidly alters the ecosystem structure and function, having
an impact on the ecology and biosphere–atmosphere dynamics [5,6]. According to the
Received: 27 April 2022 latest assessment report of the International Panel on Climate Change (IPCC) [7], over
Accepted: 3 June 2022 the last two decades in the Mediterranean region, the mean annual temperature has been
Published: 8 June 2022
increasing while precipitation has been decreasing, predicting an increased frequency of
Publisher’s Note: MDPI stays neutral extreme drought and precipitation events [8,9]. The impact on water resources is causing
with regard to jurisdictional claims in groundwater levels to decrease, which consequently hinders cork oak growth since it
published maps and institutional affil- depends on deep-rooting to access the water table [10]. In Portugal, a significant decline in
iations. cork oak vitality has been observed in the last two decades, with trees having a high mor-
tality rate [11,12]. The increase in mortality has been attributed to water stress, of which the
cork oak is subject due to the increase in temperature and the long periods of drought [13].
Besides land-use disturbances, which are mainly associated with grazing intensity/type
Copyright: © 2022 by the authors.
and shrub encroachment, tree water stress is an additional cause of decline [14,15]. Cork
Licensee MDPI, Basel, Switzerland.
oak is a special tree with a long life span (more than one hundred years), reaching its
This article is an open access article
mature production of cork past 40 years. Cork oak was established as Portugal’s National
distributed under the terms and
Tree in 2011, mostly due to its economic, social, and environmental relevance. Besides
conditions of the Creative Commons
the importance of the cork industry, it also promotes local population employment and
Attribution (CC BY) license (https://
contributes to the preservation of a wide range of flora and fauna habitats. Legislation
creativecommons.org/licenses/by/
4.0/).
protecting cork oak stands forbids the felling of trees, meaning that only dead or diseased
Remote Sens. 2022, 14, 2750. https://doi.org/10.3390/rs14122750 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022, 14, 2750 2 of 19
trees can be cut down and only with permission from the National Nature Conservation
and Forest Authority (ICNF).
The decline of the cork oak is a slow process that can take several years, wherein the
first signs of vitality loss are expressed by a gradual loss of foliage [16]. The detection of
foliage loss is not always possible and depends on several factors. For example, reduced
foliage loss is undetected in the reflectance registered at the satellite level, or the spatial
resolution of the sensor does not allow the individualization of the trees [17]. In the case of
the cork oak, it should be noted that foliage may easily recover with the re-establishment of
the hydrological cycle. Also, sometimes the foliage loss is only partial, with the remaining
crown foliage intact. Detecting cork oak foliage decay is a complex process that can
take several years. Multitemporal image analysis enables us to overcome some of the
aforementioned limitations as long as some of the images are acquired in the tree’s healthy
stage, just before the onset of vitality loss, so that the pre-death conditions of the tree can
be quantified.
Given this, the goal is to identify, in the images time series, areas where a change in
the tree’s vitality condition occurred, and to characterize the spectral signature of dying
trees. As the cork oak is an evergreen tree, it performs photosynthesis for a longer period
during the year when compared to other broadleaf trees that lose their leaves during
winter. This makes the use of vegetation indices (VI) time series suitable for monitoring
the phenology of the cork oak and identifying leaf phenological events. The Normalized
Difference Vegetation Index (NDVI) has proved to be a good indicator of cork oak vitality,
allowing the characterization of cork oak mortality in a time series of Sentinel-2 satellite
images [18]. Other authors [17] have reported that the NDVI shows a good relationship
with cork production at the stand level, revealing that one-tenth of the area of cork and holm
oak stands in mainland Portugal has been significantly declining over the past 34 years.
This trend, which was identified using an NDVI time series with images acquired during
summer (July and August) by the Landsat and MODIS satellites, resulted not from short-
term events, such as droughts or debarking, but reflects a long-term evolution of forest
stand health.
In studies where greater detail is required for cork oak mortality analysis, for example
at the tree level, a spatial resolution compatible with the size of the tree crown is required,
i.e., less than 1 m [16,19]. Although freely available low–medium resolution satellite data
have been successfully used to monitor cork oak decline on a national scale, high-resolution
images are required to develop an operational system to be used by the national forestry
associations to automatically detect unhealthy or dead trees on an annual basis, which
implies detailed information at the tree level. This automatic detection largely decreases the
efforts in the field by the forest owners for the identification of dead trees and quickens the
permission request to ICNF for cutting down cork oak trees. Currently, several observation
missions are available that fulfill these requirements of spatial and spectral resolution
(Pleiades, QuickBird, WorldView). However, since the images acquired by these space
missions are not freely available, their use is limited to a few images per year. High spatial
resolution images have been used to detect changes in forests, such as burnt areas due
to wildfires [20,21] and the occurrence of small patch-cutting (or isolated dead trees) in
dense forests [22]. Clear-cuts in dense forests represent a good indicator of vitality loss,
but may not be suitable for open forests, such as cork oak forests. In this case, studies
will be necessary to assess the usefulness of high-resolution images to quantify cork oak
mortality in areas of “montado” (cork oak in a pasture environment), where mortality is
due to different causes such as pests, pathogenesis, or plants water stress. It is important to
mention that low-medium resolution satellite data allow a national-scale analysis of cork
oak mortality whereas high-resolution satellite data provide detailed information at the
tree level.
This study aims to assess the potential of high spatial resolution multispectral images
from the Pleiades constellation to detect and map cork oak mortality in a pasture environ-
ment with multiple forest species. An approach based on change detection and the use of an
Remote Sens. 2022, 14, 2750 3 of 19
unsupervised classifier is proposed for the detection of mortality at the cork oak level. The
rationale for the development of this approach was that it should be as simple as possible to
be implemented as an operational system to be used by national forestry associations, and,
most importantly, to be independent of in-situ data and to rely on few annual commercial
high-resolution satellite data—due to its high cost. Three Pleiades images acquired in 2018,
2019, and 2020 in a cork oak forest area in central Portugal were used in this study. As
a first step, the images are pan-sharpened, geolocalised, and radiometrically normalized.
Then, the images are used to spectrally characterize dead and healthy trees to evaluate their
spectral separability. The change in the values observed for three vegetation indices, the
NDVI, the RGI (Simple Ratio Red/Green Index), and the GNDVI (Green Normalized Dif-
ference Vegetation Index), between 2018 and 2020 is used in an unsupervised classification
algorithm to estimate the dead tree class. Good classification results were achieved with
the developed approach, with both precision and recall exhibiting values higher than 90%.
Detailed cork oak mortality mapping is of significant use in comprehending the ecosystem
change as a result of tree mortality and for the implementation of mitigation mechanisms
for the ongoing desertification process.
2. Materials and Methods
2.1. Study Area
The study was undertaken in one agro–silvo–pastoral system in central Portugal,
namely Herdade da Machoqueira do Grou (HMG website) (Figure 1). Herdade da Ma-
choqueira do Grou is located at the left bank tributary of the Tagus River, approximately
100 km from Lisbon. This region is characterized by a mild Mediterranean climate with
Atlantic influences. The bioclimate is considered semi-arid to sub-humid, with a mean
annual rainfall of 662.5 mm and a mean annual temperature of 16.3 ◦ C.
Figure 1. The study area location is approximately 100 km northeast of Lisbon, Herdade da Ma-
choqueira do Grou (HMG), yellow marker on the right image. The Pleiades image is shown in a
real color combination (red, green, and blue). In this area, the dominant vegetation cover types are
cork oak (spread throughout the area), stone pine (southwest), and eucalyptus (north-center). The
vegetation cover map (yellow line) was provided by the property owners.
Herdade da Machoqueira do Grou is a private agro-silvo-pastoral system with 2423 ha.
Cork oak is the dominant species, occupying an area of 1017 ha and an additional 464 ha of
a mixed-tree stand with stone pine. In the study area, the cork oak mortality is attributed
to the lowering of groundwater levels, which consequently harms cork oak growth since it
relies on deep-rooting to access the groundwater table. The entire watershed is on deep
Miocene sands and the main soil types include fluvisols, leptosols, and podzols. The
landscape is mostly plain, with altitudes ranging from 79 to 173 m, and slopes between 0%
Remote Sens. 2022, 14, 2750 4 of 19
and 5%—with some exceptional examples being up to 35%. Cork oak’s average density is
90 trees/ha and the average production of dry weight cork is 1.3 ton/ha.
2.2. Pleiades Satellite Images
Pleiades 1A/1B are very high-resolution twin satellites offering 0.5 m spatial resolu-
tion products. The constellation is highly reactive and has been designed for daily revisit
anywhere on Earth with stereo and tri-stereo capacity. The available output bands of the
Pleiades block are: red (0.590–0.710 µm), green (0.500–0.620 µm), blue (0.430–0.550 µm),
NIR (0.740–0.940 µm), and panchromatic (0.470–0.830 µm). The spatial resolution is 0.5 m
for panchromatic and 2 m for multispectral bands. A single 100 km2 orthoimage in a
UTM/WGS84 projection covers the study area. For this study, three cloud-free images ac-
quired on 9 October 2018, 21 October 2019, and 7 October 2020 were used. In early autumn,
the humidity reaches its lower values, and, consequently, the trees’ activity is reduced until
the winter dormancy period, thereby allowing for better discrimination between health
and dead trees. Level-3 products provide orthorectified bottom-of-atmosphere reflectance
in a JPEG2000 format. Multispectral images were pan-sharpened with the panchromatic
band, resulting in three multispectral images with a 0.5 m spatial resolution [23]. The
pan-sharpened transformation was performed within Orpheo Toolbox (© Copyright 2020
CNES) (Figure 2) and co-registered to the 2018 image using the SURF algorithm [24]. All
the processed images have the same size: 14,301 columns, 10,869 lines, and 4 bands.
Figure 2. False-color composite of the multispectral image acquired in 2018 (left) and the correspond-
ing pan-sharpened image (right).
2.3. Radiometric Normalization
The ground reflectance recorded by Earth observation satellites is affected by the
atmosphere during the radiation path between the Sun, the surface, and towards the
satellite [25]. The correction of the atmospheric effects may be performed with atmospheric
models [26] provided that the atmosphere properties at the time of image acquisition are
known. When atmosphere properties are unknown, a simple radiometric normalization of
the time series may be applied as an alternative [27]. Relative radiometric normalization
generally assumes that the relationship between a pair of multispectral images, acquired
at two different epochs, is assumed to be a linear function. The critical aspect lies in the
identification of the time-invariant pixels to be used for the linear function parameter
estimation [28]. In Canty & Nielsen [29], an automatic algorithm is proposed to determine
the time-invariant pixels based on the invariance properties of the Multivariate Alteration
Detection (MAD) transformation [30]. In our study, we applied an improved version of
MAD called Iterative Reweighted MAD (IR-MAD) [31]. This new version of MAD performs
better in isolating invariant pixels in images with large seasonal radiometric changes.
Radiometric normalization was performed using the 2018 reflectance values as a refer-
ence, being that the 2018–2019 and 2018–2020 image pairs were normalized independently.
The green, red, and infrared bands were used. The normalization parameters of the 2020
invariant pixels are listed in Table 1. In the 2020 image, there is a scaling effect in the
visible bands, with values of 1.42 and 1.57, which is not observed in the infrared band.
Remote Sens. 2022, 14, 2750 5 of 19
The reduction of this scale effect, as well as the bias (−118.7 and −241.5), independent of
the band, will have a direct effect on the value of the vegetation indices. The radiometric
normalization of the 2020 image mitigated the unmodeled atmospheric effects and the
radiometric changes arising from the orthorectification and mosaicking process.
Table 1. Radiometric normalization parameters for the 2020 invariant pixels: interception, slope, and
correlation. Values are digital numbers (DN).
Channel Intercept Std.err. Slope Std.err. Correlation RMSE
Red −118.7 0.093 1.42 0.00035 0.99 9.60
Green −241.5 0.254 1.57 0.00079 0.98 13.91
NIR −51.5 0.200 1.08 0.00037 0.99 34.66
The Pleiades satellites, like most high spatial resolution satellites, are limited to four
spectral bands—generally blue, green, red, and infrared. These four bands allow the
calculation of multiple vegetation indices suitable for the detection of tree mortality. Some
authors highlighted that the NDVI decay is an indicator of decreased photosynthesis,
and therefore, is extremely useful in discriminating healthy from dying trees [32]. Other
greenness-based indices are also mentioned as good indicators of leaf greenness level, being
considered as differentiators of healthy and dead trees as well [33]. In this study, the indices
NDVI [34], RGI [35], and GNDVI [36] were used to detect tree mortality.
The study area includes other land cover elements such as agricultural land, lakes,
paths, and artificial areas that can be masked out from the images. As the objective is to
detect trees in loss of vitality or dead between 2018 and 2020, only pixels within forest areas
were considered for the analysis. A detailed land cover map, provided by the landowner
(Figure 1, delimitation in yellow), was used to subset the forest areas, which include all the
tree species present in the field (pine, holm oak, and cork oak). Pixels outside the forest
area were masked out in the 2018 image.
2.4. Individual Tree Crowns Identification
Tree detection and crown delineation algorithms based on high resolution (0.5 to 1 m)
multispectral images make use of the spectral reflectance properties of sparse forest areas
(or “montados”), which are characterized by high canopy intensities in contrast to low
surrounding ground intensities in all spectral bands. The approach consists of detecting the
outer limits of the canopy and then the local maxima as a representative of each crown’s
center. For this purpose, several methods have been proposed that combine image analysis
methods with probabilistic methods [37] or morphological analysis [38,39] or by defining
separability thresholds between trees and the ground (for a review, see [40]). The vast
majority of studies use the visible and near-infrared bands and aim to identify trees in
forests of moderate density [41]. With the increase in the spatial resolution of satellite
sensors, and with the massive use of UAVs (Unmanned Aerial Vehicles), new algorithms
for individual tree identification in open forests and orchards have emerged [18,38]. The
identification of individual trees depends on several factors, of which the spatial resolution
of the sensor or the distance between trees stands out—in particular when there is great
variability in the size and shape of the trees, as is the case with cork oak stands. Cork oak
stands are characterized by great diversity in the dimension and shape of the trees and by
the irregularity of their distribution, often with the presence of tangled branches, which
constrains the identification of individual trees.
The detection of trees in multispectral images is usually preceded by a spectral di-
mensionality reduction, transforming them into a grey-level image [42] or a vegetation
index like the NDVI—or any of the abovementioned vegetation indexes. The vegetation
indices have the advantage of implicitly promoting image segmentation into levels of
photosynthetic activity related to the structure of the leaves. In the case of cork oak forests,
it is expected that healthy trees with a higher leaf density will exhibit higher vegetation
Remote Sens. 2022, 14, 2750 6 of 19
index intensities than the surrounding soil. However, in high-density cork oak forests, the
reduced distance between trees (or groups of trees) results in the spectral mixing of soil and
canopy, compromising the detection of the trees. In this study, the NDVI index was used.
The NDVI is a vegetative index related to the leaf of the tree in the sense that it highlights
the difference in spectral reflectance between the pigment and the leaf structure. The
pigment, chlorophyll, strongly absorbs radiation in the red band while the leaf structure
reflects more than 50% of the radiation in the near-infrared band. The use of NDVI seems
to be suited to identify the contrast between trees and the surrounding soil, whether it
corresponds to areas of little or no vegetation or areas with sparse herbaceous vegetation.
However, in cork oak forests, the soil is often occupied by pasture or small shrubs with a
significant photosynthetic activity, which decreases the soil/tree contrast.
The NDVI index image is then subjected to an object-based segmentation according to
the local thresholding method of Sauvola & Pietikainen [43], in which a local separability
threshold is defined for each pixel and calculated with the value of the mean and standard
deviation of the vegetation index of the neighboring pixels. The choice of dimension is
critical in this method. Given the spatial resolution of the images (0.5 m) and the average
crown size (2 m), a 5 × 5 pixel window was adopted. To reduce the number of isolated
pixels or small clusters of pixels, a low-pass median filter is applied to the binary image.
The final result for Herdade da Machoqueira do Grou is presented in Figure 3b.
Figure 3. (a) False-color image (NIR, R, G); (b) Adaptative binarization; (c) Euclidean distance map;
(d) Individual tree location obtained using the developed algorithm (yellow cross).
The filtered binary image is composed of pixel clusters that correspond to single
trees, tree clusters, and noise. Here, noise is interpreted as clusters of pixels that do not
correspond to trees. At this step, no method for noise detection/reduction was applied,
except for the removal of pixel clusters with an area inferior to 8 m2 , assuming this value
as the minimum area of the crown of an adult cork oak. Next, the binary image was
vectorized, resulting in a set of polygons that are expected to correspond to trees or clusters
Remote Sens. 2022, 14, 2750 7 of 19
of trees. The polygons resulting from the binarization of the image are divided into two
groups according to their area and further processed distinctly. It was then assumed that
polygons with an area larger than 20 m2 , which likely correspond to clusters of trees, and
polygons related to individual trees should be dealt with differently. However, in both
cases, the same strategy was used to identify the centers of the trees. For each polygon, the
Euclidean distance map is calculated using the binarized image. The algorithm measures
the distance of each pixel inside the polygons to its border and considers the pixel with the
longest distance from the border as the polygon centroid. One can assume that the centroid
corresponds to the tree crowns centre. Figure 3c shows the Euclidean distance map for the
binary image. In this case, the Euclidean distance inside each polygon has a maximum
value of 25 pixels, which is about 0.50 × 25 = 12.5 m.
The center of each tree is determined as the maximum distance inside the correspond-
ing polygon. For polygons with multiple trees, the multiple tree centers are identified the
same way, but on the condition that the local maxima are at a greater distance than the
minimum distance between trees, i.e., 2 m. In this case, the process is executed recursively
for all the local maxima of the distance function, starting with the highest value of the local
maximum. Figure 3d shows the final results of the identification of the centers of the trees
in Herdade Machoqueira do Grou. The final result has the tree crown centers’ locations.
A tree is assumed to belong to the class “dead tree” whenever there is a significant
loss of photosynthetic activity, expressed by an abrupt reflectance reduction in the infrared
band, or when a tree has been cut down. In the 2020 image, 897 dead trees were identified
out of a total of 7572 trees automatically identified in 2018. The remaining 6675 trees were
considered healthy. The identification of dead trees was performed by visual interpretation
of the 2020 image in its false-color composite (IR, R, G) complemented with the visualization
of Microsoft’s Bing Maps RGB images (dated 2021). The two classes of trees (healthy/dead
trees) were used to characterize the spectral signature of each class and to validate the
algorithm by calculating accuracy metrics. It is important to emphasize that the knowledge
of the trees’ conditions is not incorporated into the image classification process once an
unsupervised classifier is applied. This is another advantage of the developed approach,
considering that the identification of dead trees is itself a source of error in the supervised
classification. Although the vitality of the trees was consistently evaluated by a trained
operator, the definition of a percentage of defoliation and discolored branches may be quite
challenging and can be a source of error in the tree class assignment.
2.5. Tree Mortality Detection
The reduction in cork oak vitality is related to crown structure and leaf loss. This change
can be detected in satellite images acquired at different stages of tree evolution [33,44]. The
temporal difference of a VI image, relatively to a reference VI image, was used to characterize
the temporal variation of the tree’s vitality [33]. The temporal difference of the indices was
calculated as:
∆VI (t) = VI (t = 0) − VI (t) (1)
where ∆VI is the difference between a VI image calculated at a time (t) and its corresponding
reference VI image (t = 0) so that the variation in the vegetation index is positive if the
index value increases in time. In this study, the 2020 VI images are compared to the 2018 VI
reference images.
The differences calculated for the three VI considered in this study are then combined
into a 3-band image. The 3-band image is used in an unsupervised classification process
that aimed to segment the image into classes. These algorithms aggregate the pixels into
clusters with similar spectral characteristics according to a given model. In the case of
k-means, pixels are aggregated according to their distance in the feature space from the
center of the cluster. The feature space in this case is defined as the three-dimensional space
of variation of the vegetation indices. The number of clusters is defined a priori. One of the
estimated classes is expected to be the “dead trees” class, which also includes cut trees and
Remote Sens. 2022, 14, 2750 8 of 19
trees with loss of vitality. The best model, defined by the combination of bands and the
number of clusters, is determined by the analysis of the classification accuracy metrics.
2.6. Rating Evaluation Metrics
The performance of the classification model was evaluated by the computation of
several accuracy metrics. For this purpose, confusion matrices (also referred to as double-
entry contingency tables) were generated, where the rows correspond to the reference data
(ground truth) and the columns refer to the predicted classes (retrieved by the classifier), to
quantify the agreement between the automatic classification and the reality observed on
the ground. In this study, the interest lies in the evaluation of the ability of the proposed
algorithm to correctly estimate the “dead trees”. The most appropriate metrics to evaluate
the classification accuracy in a binary classification (“dead trees” vs. “healthy trees”) are
precision, recall, and F1-score.
Precision, or user’s accuracy, is the ability of the model to return relevant instances,
i.e., the proportion of correctly classified plots relative to the total number of plots (correctly
or incorrectly) classified in that class (column’s total):
TP
precision =
TP + FP
where TP are true positives, i.e., trees correctly classified as dead, and FP are trees that are
classified as dead but are healthy in the reference. Precision is related to the commission
error (=1 − precision).
Recall, or producer’s accuracy, refers to the completeness of the model, i.e., it is the
proportion of correctly classified trees compared to the total amount of trees observed on
the ground for a certain class. For each class, the number of correctly classified trees is
divided by the total number of trees in that class (row’s total):
TP
recall =
TP + FN
where TP are the true positives, i.e., trees correctly classified as dead, and FN are the trees that
are classified as healthy but are dead trees. Recall is related to the omission error (=1 − recall).
The F1-score is a weighted average of precision and recall, with equal weight for both
values (it also varies between 0 and 1). The expression to calculate the F1-score is:
precision ∗ recall
F1 − score = 2
precision + recall
In most cases, the question of a possible trade-off between precision and recall is to be
considered. A generic model will maintain a balance between these two metrics that can be
evaluated with the F1-score metric.
3. Results
3.1. Analysis of the Time Series of Vegetation Indices
A comparison of the vegetation index values for healthy trees and trees with vitality
loss or those that are dead shows that healthy trees have higher NDVI and GNDVI values
and lower RGI values (Figure 4a–c).
Remote Sens. 2022, 14, 2750 9 of 19
Figure 4. Healthy (green) and dead (red) trees’ temporal variability is computed based on the NDVI
(a), RGI (b), and GNDVI (c) indices. Cumulative distribution function (CDF) applied to NDVI (d),
RGI (e), and GNDVI (f). Green lines show the cumulative probability of healthy trees while red lines
show the cumulative probability of dead trees.
One can notice that mortality occurred after 2019 with a significant decrease in the
NDVI and GNDVI values and a significant increase in the RGI values. The NDVI index
demonstrates a higher ability to discriminate healthy from dead cork oaks trees when
compared to the other two VI indices. According to the cumulative distribution function,
for an NDVI value below 0.45, the probability of detecting a dead tree is 90% (Figure 4d).
Only 10% of the dead trees have NDVI values higher than 0.5, which can be related to a
misinterpretation of the trees’ conditions in the field or to the existence of shrub vegetation
that has replaced the former tree. The results are consistent for the three dates under
analysis, although there is a greater variability for the 2019 indices, thereby resulting in
a lower ability to discriminate between dead and healthy trees. This is likely due to the
presence of clouds in the 2019 image, in particular the existence of thin, non-perceptibleRemote Sens. 2022, 14, 2750 10 of 19
clouds over the study area. The GNDVI index behavior is very similar to the NDVI index,
which is justified by the strong relationship between the vegetation vitality and the infrared
reflectance. There is, however, a lower ability to discriminate between healthy and dead
trees. For a GNDVI value below 0.40, the probability of occurrence of dead cork oak is 82%
(Figure 4f). The RGI index has a lower ability to discriminate between dead and healthy
trees, with a confusion region between the two classes that equals an error of at least 15%
(probability of finding a dead tree with an RGI value below 0.9).
It is expected that radiometric differences in the image remain due to atmospheric
variability and lighting conditions (shadows) as well as changes in land cover resulting in
reflectance variations.
For each pair of indices, the relationship between the vegetation indices differences
between the reference image (2018) and the 2020 image was analyzed (Figure 5). For the
∆NDVI-∆RGI pair (Figure 5a), a negative relationship was observed in which pixels with
an ∆NDVI positive variation have an ∆RGI negative variation, and vice versa. The two
classes are logically distributed along the correlation axis, with dead trees concentrated
in the upper left quadrant, while healthy trees are mainly concentrated in the lower left
quadrant yet spreading to the center of the graph. The highest density is concentrated at
the origin of the frame, that is, around zero. The level of separability between the two
classes is defined by the confidence ellipse at a 90% level, which is represented in black
in Figure 5a. The confidence ellipse defines the region that contains 90% of the data for
each class, assuming that the variation of the vegetation indices has a normal distribution
with a mean and a standard deviation. The correlation coefficient is identical for dead trees
and healthy trees, −0.59 and −0.54, respectively. Curiously, the two classes have a distinct
distribution pattern in the feature space. Unhealthy and dead trees have a larger dispersion
than healthy trees, as shown by the area of the confidence ellipse, which is twice as large for
dead trees. Unhealthy trees start to lose leaves, which leads to an increase in the spectral
response from the ground surrounding the trees, resulting in omission errors that can only
be minimized by increasing the commission error. From the analysis of the graph, it is clear
that the two classes are disjointed in the ∆NDVI versus ∆RGI indices feature space.
Figure 5. Distribution of dead trees (red) and healthy trees (green) in the indices feature space:
(a) ∆NDVI versus ∆RGI and (b) ∆NDVI versus ∆GNDVI. The 90% confidence ellipse for each of the
trees’ classes is drawn in black.
Contrarily, for the ∆NDVI-∆GNDVI pair, a positive relationship is observed between
dead and healthy trees, with a correlation coefficient of 0.85 and 0.81, respectively. As in the
previous pair, the two classes are distributed along the correlation axis, with healthy trees
occupying the positive quadrant and dead trees occupying the negative quadrant. Likewise,
a high level of separability is observed between the two classes for a 90% confidence level,
which is represented in black in Figure 5b.Remote Sens. 2022, 14, 2750 11 of 19
3.2. Detection of Tree Mortality
The former three vegetation indices proved to be suitable for the detection of dead
trees and were then used as inputs for an unsupervised classification process. To decide
on the optimal vegetation indices combination for cork oak mortality detection, three
combinations were evaluated: (∆NDVI, ∆RGI, ∆GNDVI), (∆NDVI, ∆RGI), and (∆NDVI,
∆GNDVI). For each combination, tests were carried out with different numbers of clusters:
3, 4, 5, and 6. The tree mortality detection performance was assessed using the following
accuracy metrics: precision, recall, and F1-score, assuming that true positives were the
correct estimation of dead trees.
For all three datasets, an increase in the number of clusters is positively related to
precision, while the opposite is verified for recall (Figure 6). F1-scores do not seem to
be related to the number of clusters. An increase in the number of clusters results in the
segmentation of the image into more homogeneous regions, which is expected to minimize
the area of each class in the feature space. Thus, increasing the number of clusters will
allow the reduction of the cluster area corresponding to dead trees. Consequently, a smaller
number of trees will be classified as dead trees, decreasing the number of false positives
and therefore increasing accuracy. For the same reason, a greater number of false negatives
is observed, and therefore there is a decrease in recall.
Figure 6. Classification accuracy as a function of the number of clusters used in the k-means
classification for the three-band combinations: (a) ∆NDVI, ∆RGI, ∆GNDVI, (b) ∆NDVI, ∆RGI and
(c) ∆NDVI, ∆GNDVI.
The behavior of the midpoint (center) of clusters corresponding to the dead tree class
was analyzed as a function of the increase in the number of clusters. We verified that, with
the increase in the number of clusters, the dead tree cluster centers shift away from the
origin of the feature space (Figure 7), leading to an increase in precision, and, consequently,
to a decrease in the number of healthy trees misclassified as dead trees. For the same reason,
dead trees located near the separability threshold between the two classes are likely to be
erroneously classified as healthy trees.
Figure 7. Graphical representation of the location of the centers of the clusters in the feature space for
the “dead trees” class adopting different numbers of clusters (3, 4, 5, and 6). The error ellipses for the
two classes are shown for comparison with ellipses in Figure 5.Remote Sens. 2022, 14, 2750 12 of 19
In this study, the main objective is to develop a methodology for dead tree detection
from the producer’s perspective, i.e., maximizing the estimated number of dead trees
while keeping a low commission error. The appropriate metric to evaluate the accuracy
of the estimate against the ground truth is the recall, or, equivalently, the omission error.
According to the results in Figure 6, an increase in the recall is achieved by reducing the
number of clusters as long as the accuracy is not significantly reduced. The F1-score metric,
which combines both the precision and recall, was used to define the optimal number of
clusters. For all indices’ combinations, the F1-score was maximal for four clusters, so that
was the number of clusters adopted for the k-means classification.
Accordingly, the class “dead tree” is associated with the cluster centered in the upper
left quadrant, and, in case there is more than one, with the cluster with a center in the
upper left quadrant farthest from the origin of the feature space. The remaining trees are
associated with the class “healthy trees”.
The result of the mortality classification using the three indices (∆NDVI, ∆RGI,
∆GNDVI) and the k-means algorithm with four clusters is presented in Figure 8. Out
of a total of 7572 trees identified in the study area, 882 trees were classified as dead. The
accuracy metrics were calculated from the confusion matrix data in Table 2. According
to the data in Table 2, dead trees were identified with a precision of 94%, a recall of 92%,
and an F1-score of 93%, meaning that only 6% of trees were incorrectly classified as dead
trees (commission error) and only 8% of dead trees were omitted from the classification
(omission error).
Figure 8. Results of the multitemporal tree classification for the study area. Red triangles represent
dead trees and green triangles represent healthy trees, superimposed on the Pleiades image.Remote Sens. 2022, 14, 2750 13 of 19
Table 2. Confusion matrix for the result of the k-means classifier using the three vegetation indices.
Columns represent the classification results and rows represent the reference data.
Classification
Dead Healthy Recall
Reference Dead 825 72 0.92
Healthy 57 6618 0.99
Precision 0.94 0.99
F1-score 0.93 0.99 OA = 0.98
Macro F1-score 0.96
3.3. Spatial Pattern of Mortality
Mortality has an irregular spatial distribution pattern characterized by high concentra-
tion areas of dead trees, with 21 dead trees per ha, and scattered dead trees throughout the
study area, with a density of about 2.2 dead trees per ha (Figure 8). The spatial distribution
of dead trees is related to forest management activities and loss of tree vitality.
The highest density of dead trees is observed to the west of the property in an area
of pine trees (Figure 8). Other smaller concentrations of dead trees are also observed on
the perimeter of the study area, also corresponding to areas of pine trees. The comparative
analysis of areas with high dead trees density in the 2018 and 2020 images revealed that
those are areas where logging took place. Figure 9a,b show, respectively, the 2018 and
2020 images in a false-color composite (NIR, R, G) for the westernmost area of pine trees
in Figure 8. Comparing the two images, a reduced number of trees are notorious in the
2020 image due to tree felling. Figure 9c presents the results of the k-means classification
for the class “dead tree” in light blue overlaid on the 2020 image. The visual analysis of
the classification results demonstrates the good performance of the classification approach
in detecting land cover changes. It is perceptible that the detection of changes in pixels
adjacent to the crown of trees results from differences in the geometry of image acquisition
due to—albeit slight—differences in the tree crown’s position. One can also observe the
detection of changes resulting from variations in vegetation cover as a consequence of
annual rainfall variability.
Dispersed mortality is characterized by trees with a significant change in the spectral
response resulting from a reduction in the tree’s photosynthetic activity. Cork oak is
predominant in this group. Figure 9d,e show images for 2018 and 2020, respectively, in
a false-color composite (NIR, R, G) for a cork oak trees area. In Figure 9f the results of
the k-means classification for the class “dead tree” is presented in light blue overlaid on
the 2020 image. The cork oak trees have an irregular distribution pattern, with great
spatial variability in density, and can vary between isolated trees to clusters of trees. This
spatial variability, associated with its small leaf size, may constrain the characterization of
the spectral response of healthy (or dead) trees and the subsequent detection of spectral
changes over time. Figure 9d shows three trees where a decrease in the spectral response
was observed between 2018 and 2020, which were correctly assigned to the class ‘dead trees’
by the classifier assigned (see Figure 9f). The trees, still visible in the 2020 image, exhibit a
significant reduction in the NIR band reflectance and an increase in the red band reflectance,
and therefore reveal a brownish color. Also, in Figure 9f, a fair agreement between the
spectral response changes detected by the classifier in light blue and the identification of
dead trees is evident.Remote Sens. 2022, 14, 2750 14 of 19
Figure 9. Two examples of the dead tree detection approach: tree felling (a–c) and cork oak decline
(d–f). False-color composite of the 2018 image (a,d)—and of the 2020 image (b,e). The light blue mask
(c,f) is the result of the “death tree” classification overlaid on the 2020 image.
4. Discussion
We aimed to evaluate the potential of high spatial resolution multispectral images for
the detection of cork oak trees for loss of vitality or trees that were dead. For this purpose,
we used the temporal variation of three vegetation indicators (NDVI, RGI, and GNDVI)
calculated from two Pleiades images acquired in 2018 and 2020 to classify areas with the
presence of dead trees or trees with decreased foliar density, and also areas where trees
have been cut down. The results show that the proposed methodology accurately maps the
trees’ vitality, enabling the identification of the dead trees’ location and their quantification,
and, consequently, making the request for permission from the national forestry entity
to cut down dead cork oak trees easier and promoting more efficient forest management.
However, in the proposed approach, some issues are essential to achieve high performance
for tree mortality detection.
First, the identification of the trees’ location is of extreme relevance. The knowledge
of the trees’ positions limits the analysis of the vegetation indices variability to the corre-
sponding canopies. This is critical in multispectral studies where herbaceous vegetation
and soil can introduce noise into the process of evaluating the spectral separability between
dead and healthy trees [45]. The process of tree identification can become difficult or
even impossible in areas of low contrast between the tree crowns and the surrounding
vegetation [12,46]. To increase the contrast between the tree crowns and the surrounding
herbaceous vegetation, one should use the images acquired at the end of the dry season,
before the first rains of the season (in the Mediterranean, between the second half of Septem-
ber and the first half of October). At this time of the year, the shrub vegetation is dry, with
reduced photosynthetic activity, and therefore, exhibiting a high red radiation reflectance
in contrast to a high red radiation absorption for the healthy trees [18].
In the case of cork oak forests, where the density is generally low and the tree crowns
are individualized, the adaptive binarization algorithm proved to be efficient in identifying
the canopy of isolated trees [43]. However, in areas of dense forest or where groups of cork
oaks canopies occur, the algorithm fails to identify the individual trees. A new algorithm
was proposed in which the center of each crown is identified as a local maximum ofRemote Sens. 2022, 14, 2750 15 of 19
the Euclidean distance of the binary mask resulting from the adaptive binarization. The
segmentation of tree crowns clusters into a set of separate trees enables the detection and
analysis of vitality at the tree level.
The second crucial issue is the radiometric calibration of the images. In multi-temporal
studies, the meteorological conditions at the time of image acquisition differ and may
interfere with the radiance recorded at the sensor level. The MAD algorithm identifies a set
of pixels based on which a linear relation is established for each band [29]. The radiometric
calibration of the image has a direct effect on the intensity of the spectral bands and an
indirect effect on the vegetation indices calculation. Vegetation indices are band ratios
or ratios of algebraic band operations in which the effect of global linear calibration is
mitigated or even eliminated. The calibration of individual bands has the effect of changing
the vegetation indices, as was verified for the 2019 image calibration (see Figure 4). It
should be noted that small variations in the vegetation indices can disguise the trees’
vitality trend in the classification process. Therefore, the images’ radiometric calibration
and their co-registration are critical in the process of change detection and subsequent
mortality classification.
Another significant issue is the selection of the vegetation indices. Although band
differences might be used for change detection, it is recognized that vegetation indices
are more suitable for vegetation vitality detection/monitoring [33,47]. The four spectral
bands in the Pleiades images: blue, green, red, and infrared, which are characteristic of
all high spatial resolution Earth observation satellites and also of the UAVs, were used to
compute the NDVI, RGI, and GNDVI. These three indices proved to be good indicators
of tree mortality (Figure 5). The NDVI and GNDVI indices are the ones that showed a
higher ability to discriminate between dead and healthy trees with a 90% probability for
the detection of dead trees for a given threshold [33]. For 2020 the separability is around
10%. The relationship between the vegetation indices behavior and the evolution of the
evergreen trees’ vitality is well-founded theoretically. The chlorophyll present in the leaves
absorbs essentially the radiation in the red band, to carry out photosynthesis, and strongly
reflects the radiation in the infrared band (60%) and to a lesser extent in the green band
(about 20%) [48]. When the tree loses vitality, the reduced photosynthetic activity causes
an increase in the red band reflectance and a decrease in the infrared band reflectance.
Therefore, the RGI index increases, and the NDVI and GNDVI decrease, for trees in loss of
vitality relatively to healthy trees. During the process of cork oak vitality loss, trees lose
their leaves leading to a reduction of the canopy area and consequently to a decrease in the
latter two vegetation indices values. It is important to emphasize that cork oak vitality loss
is a slow process that can last for at least 5 up to 10 years [17]. Small variations in vegetation
indices may indicate the beginning of cork oak vitality loss, which should be confirmed
for subsequent years. According to the previous rationale, it is expected that in case of
consistent trees’ vitality loss over the years, the RGI values will increase and the NDVI
and GNDI values will decrease. There is another important aspect. As tree crown area
decreases due to leaf loss, the ground reflectance over time becomes dominant generating
misclassifications. The use of the GNDVI index is critical in distinguishing green leaves
from shrub vegetation. Also critical is the choice of the images’ date, which should be
acquired in the period between the end of the dry season and the beginning of the rainy
season to minimize the influence of the surrounding vegetation [49].
Finally, the fourth and last issue is the choice of the classifier. The k-means algorithm is
an intuitive unsupervised classifier that is easy to interpret and analyze and is available on
most computer platforms. The reason for choosing an unsupervised classification algorithm
is that it does not require data for training the classifier [50]. This aspect is important for
the operationalization of the classification process and its use by technicians not familiar
with supervised classification processes. Supervised classification processes have been
widely and successfully used in the classification of tree mortality [51]. However, it requires
the acquisition of training data, which is always a time-consuming and expensive process
and not always susceptible to spatial and temporal transferability. That is, the modelRemote Sens. 2022, 14, 2750 16 of 19
trained for a given location and date may not apply to another location or other dates. It
is essential to underline that the proposed methodology does not rely specifically on the
k-means algorithm, but on any unsupervised classification algorithm. Other unsupervised
classification algorithms such as the maximum expectation [44,52] or Isodata [53] might
have been used, though some preliminary evaluations did not recognize any advantages
over the k-means algorithm for detecting cork oak mortality. Although no experiments
were performed, we strongly assume that the proposed methodology can be applied to
very high-resolution images acquired by UAVs [54]. In this case, the issues regarding
different acquisition geometries, different illumination conditions, and co-registration are
of particular relevance.
The methodology presented here can be automated for the annual monitoring of cork
oak forests with high spatial resolution satellite images or UAV images, either as a sole
monitoring tool or as a support to other field data-collecting instruments, or even to other
techniques using lower resolution images.
5. Conclusions
In this study, we present a methodology for cork oak tree mortality detection using
multitemporal high-resolution Pleiades images. The methodology combines unsupervised
classification with temporal differences for three vegetation indices, NDVI, RGI, and
GNDVI, for the detection of dead or unhealthy trees. The analysis of the vegetation indices
showed that healthy and unhealthy trees can be discriminated with a 90% probability using
the NDVI vegetation index. Also, the behavior expressed by the NDVI and the GNDVI
is similar, while the RGI has an opposite behavior. Regarding the bi-temporal differences
in the vegetation indices, the results showed a negative correlation between the ∆NDVI
and the ∆RGI, whereas a positive correlation was observed between the ∆NDVI and the
∆GNDVI. The correlation coefficients are −0.54 and 0.85, respectively. The three vegetation
indices were considered relevant for the detection of dead trees and therefore used in
unsupervised classification. The experiments pointed out that an increase in the number of
clusters in the unsupervised classification has the effect of increasing the precision (lower
commission errors), at the expense of decreasing the recall (higher omission errors). The
F1-score does not appear to be related to the number of clusters. The best classification
performance was achieved from the combination of the three bi-temporal vegetation indices
using 4 clusters, with 92% and 94% for recall and precision, respectively, and an F1-score
of 93%. The proposed methodology proved to be efficient in areas that have experienced
a natural cork oak vitality decline, including areas where trees have been cut down. This
is of utmost importance, especially for dead cork oak tree detection in mixed-species
woodlands, given that it provides forestry owners/managers an operational monitoring
tool for the quick detection of cork oak vitality loss/tree mortality and allows for a timely
forest management adaptation.
Author Contributions: J.C. (João Catalão) and A.N. designed the methodology and conducted
the research and investigation process. J.C. (João Catalão) implemented the computer code and
supporting algorithms. In-situ data was collected at the field by J.C. (João Catalão), A.N. and J.C.
(João Calvão). Manuscript preparation was performed by J.C. (João Catalão) with inputs on analysis
of results and content review from A.N. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was developed in the scope of the GEO SUBER project (PDR2020-101-031261)
under the EU’s rural development policy that is funded through the European Agricultural Fund
for Rural Development (EAFRD) from 2014 to 2020. The project is coordinated by the Portuguese
Union of the Mediterranean Forest (UNAC)—PDR2020-101-031259. The publication is supported by
FCT-project UID/GEO/50019/2019—Instituto Dom Luiz.Remote Sens. 2022, 14, 2750 17 of 19
Acknowledgments: Authors acknowledge Herdade da Machoqueira do Grou for allowing access
to the test sites and the use of the facilities. The authors would also like to thank Conceição S. Silva
(UNAC) and Systerra for providing data collected in the field within the scope of the GEO SUBER
project. This research was developed in the scope of the GEO SUBER project, funded by the Financing
Institute for Agriculture and Fisheries (IFAP) through the European Agricultural Fund for Rural
Development (EAFRD).
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1. Taccoen, A.; Piedallu, C.; Seynave, I.; Perez, V.; Gégout-Petit, A.; Nageleisen, L.M.; Bontemps, J.D.; Gégout, J.C. Background
Mortality Drivers of European Tree Species: Climate Change Matters. Proc. R. Soc. B Biol. Sci. 2019, 286, 20190386. [CrossRef]
[PubMed]
2. Hu, H.; Wang, G.G.; Bauerle, W.L.; Klos, R.J. Drought Impact on Forest Regeneration in the Southeast USA. Ecosphere 2017, 8, e01772.
[CrossRef]
3. Allen, C.D.; Breshears, D.D.; McDowell, N.G. On Underestimation of Global Vulnerability to Tree Mortality and Forest Die-off
from Hotter Drought in the Anthropocene. Ecosphere 2015, 6, 1–55. [CrossRef]
4. Liang, E.; Leuschner, C.; Dulamsuren, C.; Wagner, B.; Hauck, M. Global Warming-Related Tree Growth Decline and Mortality on
the North-Eastern Tibetan Plateau. Clim. Chang. 2016, 134, 163–176. [CrossRef]
5. Anderegg, W.R.L.; Kane, J.M.; Anderegg, L.D.L. Consequences of Widespread Tree Mortality Triggered by Drought and
Temperature Stress. Nat. Clim. Chang. 2013, 3, 30–36. [CrossRef]
6. Restaino, C.; Young, D.J.N.; Estes, B.; Gross, S.; Wuenschel, A.; Meyer, M.; Safford, H. Forest Structure and Climate Mediate
Drought-Induced Tree Mortality in Forests of the Sierra Nevada, USA. Ecol. Appl. 2019, 29, 1–14. [CrossRef]
7. IPCC. Climate Change 2014 Part A: Global and Sectoral Aspects; Cambridge University Press: Cambridge, UK, 2014.
8. Vicente-Serrano, S.M.; Lopez-Moreno, J.I.; Beguería, S.; Lorenzo-Lacruz, J.; Sanchez-Lorenzo, A.; García-Ruiz, J.M.; Azorin-Molina,
C.; Morán-Tejeda, E.; Revuelto, J.; Trigo, R.; et al. Evidence of Increasing Drought Severity Caused by Temperature Rise in
Southern Europe. Environ. Res. Lett. 2014, 9, 044001. [CrossRef]
9. Sousa, P.M.; Trigo, R.M.; Aizpurua, P.; Nieto, R.; Gimeno, L.; Garcia-Herrera, R. Trends and Extremes of Drought Indices
throughout the 20th Century in the Mediterranean. Nat. Hazards Earth Syst. Sci. 2011, 11, 33–51. [CrossRef]
10. Ogaya, R.; Peñuelas, J. Climate Change Effects in a Mediterranean Forest Following 21 Consecutive Years of Experimental
Drought. Forests 2021, 12, 306. [CrossRef]
11. Tiberi, R.; Branco, M.; Bracalini, M.; Croci, F.; Panzavolta, T. Cork Oak Pests: A Review of Insect Damage and Management. Ann.
For. Sci. 2016, 73, 219–232. [CrossRef]
12. Godinho, S.; Guiomar, N.; Machado, R.; Santos, P.; Sá-Sousa, P.; Fernandes, J.P.; Neves, N.; Pinto-Correia, T. Assessment of
Environment, Land Management, and Spatial Variables on Recent Changes in Montado Land Cover in Southern Portugal. Agrofor.
Syst. 2016, 90, 177–192. [CrossRef]
13. Besson, C.K.; Lobo-do-Vale, R.; Rodrigues, M.L.; Almeida, P.; Herd, A.; Grant, O.M.; David, T.S.; Schmidt, M.; Otieno, D.; Keenan,
T.F.; et al. Cork Oak Physiological Responses to Manipulated Water Availability in a Mediterranean Woodland. Agric. For.
Meteorol. 2014, 184, 230–242. [CrossRef]
14. Costa, A.; Pereira, H.; Madeira, M. Analysis of Spatial Patterns of Oak Decline in Cork Oak Woodlands in Mediterranean
Conditions. Ann. For. Sci. 2010, 67, 204. [CrossRef]
15. Acácio, V.; Holmgren, M.; Moreira, F.; Mohren, G.M.J. Oak Persistence in Mediterranean Landscapes: The Combined Role of
Management, Topography, and Wildfires. Ecol. Soc. 2010, 15, 40. [CrossRef]
16. De la Orden, M.S.; Cerrillo, R.M.; García-Ferrer, A.; Cano, F. Evaluación de La Defoliación Mediante Imágenes IKONOS En Masas
de Quercus Suber L. En El Sur de España. Investig. Agrar. Sist. Recur. 2005, 14, 242–252.
17. Aubard, V.; Paulo, J.A.; Silva, J.M.N. Long-Term Monitoring of Cork and Holm Oak Stands Productivity in Portugal with Landsat
Imagery. Remote Sens. 2019, 11, 525. [CrossRef]
18. Navarro, A.; Catalao, J.; Calvao, J. Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal.
Remote Sens. 2019, 11, 2525. [CrossRef]
19. Tilly, N.; Reddig, F.; Lussem, U.; Bareth, G. First Investigation of Mediterranean Oak Tree Vitality with High-Resolution
Worldview-3 Satellite Data: Comparing Ten Vegetation Indices and Three Machine Learning Classifiers. Int. Arch. Photogramm.
Remote Sens. Spat. Inf. Sci.-ISPRS Arch. 2020, 43, 1069–1076. [CrossRef]
20. Amraoui, M.; DaCamara, C.C.; Pereira, J.M.C. Detection and Monitoring of African Vegetation Fires Using MSG-SEVIRI Imagery.
Remote Sens. Environ. 2010, 114, 1038–1052. [CrossRef]
21. Filipponi, F. Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy
Wildfires. Remote Sens. 2019, 11, 622. [CrossRef]You can also read
