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ISSN 1995-4255, Contemporary Problems of Ecology, 2022, Vol. 15, No. 6, pp. 717–729. © Pleiades Publishing, Ltd., 2022.
Monitoring Conservation of Forest in Protected Areas
using Remote Sensing Change Detection Approach: a Review
Wendy Miranda-Castro (ORCID: 0000-0002-0536-6285)a, *,
Rosa Acevedo-Barrios (ORCID: 0000-0002-5593-0171)a, b, **,
and Milton Guerrero (ORCID: 0000-0001-9597-0103)b, ***
a Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas, Universidad Tecnológica de Bolívar,
Cartagena, 130010 Colombia
b Grupo de investigación en Sistemas Ambientales e Hidráulicos, Facultad de Ingeniería, Universidad Tecnológica de Bolívar,
Cartagena, 130010 Colombia
*e-mail: wmiranda@utb.edu.co
**e-mail: racevedo@utb.edu.co
***e-mail: mguerrero@utb.edu.co
Received June 14, 2022; revised June 20, 2022; accepted June 20, 2022
Abstract—Deforestation and fragmentation threaten biodiversity owing to their impacts on many species. To
prevent and minimise the problem, protected areas have been created with the aim of conserving biodiversity,
and parts of continental territories have been designated for this purpose. However, these areas are not exempt
from forest loss and can be directly and indirectly disturbed by surrounding territories, natural disasters, cli-
mate, and human actions. In addition, the management quality of many protected areas is unknown. Thus,
forest change detection using remote sensing data has been implemented as an approach to assess forest loss
in conservation areas, since it generates spatio-temporal information about the protected forest area, which
can then be used to improve forest management and decision making. This article reviews the approaches that
have been implemented to study forest changes in protected areas.
Keywords: vegetation change, landscape ecology, environment, natural resources, forest loss, forest resto-
ration
DOI: 10.1134/S1995425522060154
1. INTRODUCTION
Declines in forest areas and their biodiversity are
caused by the overexploitation of resources, forest
fires, logging, and changes in the climate and environ-
ment. Anthropogenic activity and the expansion of
urban centres have also contributed to the remaining
forests becoming smaller and more isolated because of
increasing deforestation (Crooks and Sanjayan, 2006;
Santos and Tellería 2006), resulting in the loss of a
quarter of global forests (Curtis et al., 2018; Jackson
et al., 2020; Morand and Lajaunie 2021). Further-
more, forests are increasingly being fragmented, with
areas transformed into smaller patches (Pereira et al.,
2010; Haddad et al., 2015; Wilson et al., 2016; Monti-
beller et al., 2020), thus altering the original ecosystem
and changing the composition and configuration of
the forest. This leads to more open areas and fewer
trees, and alters abiotic factors such as solar radiation,
temperature, and humidity, which influence the vege-
tation and animal species present in the area (Rut-
ledge, 2003; Serna-Chavez et al., 2018; Bologna and
Aquino, 2020).
Protected areas have been designated as a strategy
to prevent and minimise forest loss. These territories
represent 15% of the global land area and are selected
for the purpose of conserving species and ecosystems
(Juffe-Bignoli et al., 2014; Geldmann et al., 2015;
Montibeller et al., 2020), providing important social
and ecological services, and constituting a key strategy
for the preservation of forests (Pimm et al., 2014; Wat-
son et al., 2014; Geldmann et al., 2015; Venter et al.,
2018). However, these areas are not exempt from dis-
turbances and habitat loss (Butchart et al., 2010; Crai-
gie et al., 2010; Laurance et al., 2012; Geldmann et al.,
2013; Tittensor et al., 2014; Carlson et al., 2019; Singh
et al., 2021) as they can be affected by natural phe-
nomena and climate change (Tews et al., 2004; Boutin
et al., 2009; Desrochers et al., 2012) and can be
directly and indirectly threatened by human pressure
from surrounding areas (Dolman, 2000; Ma et al.,
2004; Hooper et al., 2012; Jones et al., 2018). Addi-
tionally, accessing and monitoring these areas is often
complex owing to their remoteness, resulting in forest
monitoring and management issues.
718
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MIRANDA-CASTRO et al.
Forest change detection using remote sensing data
can be employed to assess the forest status inside pro-
tected areas. The basis of detecting changes is the data
on the comparison of land scenarios obtained at two or
more different times (Mouat et al., 1993; Zhu, 2017;
Meli Fokeng et al., 2020). Detecting changes is possi-
ble since plants have the ability to absorb and reflect
electromagnetic radiation. Specifically, they absorb
part of the radiation in the visible spectrum, but reflect
a considerable amount of near-infrared radiation
(Gitelson and Merzlyak, 1996; Xue and Su, 2017;
Montibeller et al., 2020). The spectra reflected by veg-
etation canopies can provide information that is used
to estimate the green biomass and understand the dis-
tribution of the vegetation (Jackson and Huete, 1991;
Pettorelli et al., 2005; Glenn et al., 2008; Montibeller
et al., 2020). This information can be utilised as an
indicator of biodiversity (Ozanne et al., 2003; Linden-
mayer et al., 2006; Gao et al., 2014) because treetops
mediate the microclimate and provide shelter, nesting
sites, and habitats for many animal species. In addi-
tion, trees influence the quality, diversity, and accessi-
bility of food sources (Hamer and Herrero, 1987; Dol-
man, 2000; Halaj et al., 2000; Nagelkerken et al.,
2008; O’Connell and Keppel, 2016; Finlayson, 2018).
Therefore, knowing the changes to the status of a for-
est in space and time can determine the effectiveness
of conserving protected areas. This article reviews the
approaches that have been implemented to study for-
est changes in protected areas.
2. MATERIALS AND METHODS
2.1. Literature Search Strategy
To identify the approaches used for measuring for-
est change within protected areas, a literature review
was conducted. The original article search was carried
out in the Google Scholar and Science Direct data-
bases during the period between December 2019
through to December 2021. The query was carried out
using the following search terms: protected area, forest
change, forest cover, forest, change detection, and
remote sensing. These keywords were combined using
the following search query: “forest cover” and
“change detection” and “protected area”, “forest
change” and “change detection” and “protected area”,
“forest change” and “protected area” and “remote
sensing”, and “forest” and “protected area” and
“change detection”. Keywords were searched in both
the titles and bodies of manuscripts in the Google
Scholar database using quotation marks for each word,
while the search in Science Direct was completed
using advanced search with the title, abstract, or
author-specified keywords without quotation marks.
Additionally, manual searches were performed to
obtain important information that may have been
missed by the search engines (Karlson and Ostwald,
2016). The search was culminated when 50 articles met
the selection criteria.
2.2. Inclusion and Exclusion Criteria
The abstracts of the selected articles were reviewed
and only those on forest cover change in protected
areas were chosen. Irrelevant articles, duplicates, and
articles without quantitative information about tem-
poral changes in protected forests (forests within pro-
tected areas) were excluded. The selected protected
areas were either categorised under the International
Union for Conser vation of Nature (IUCN) categories
(Table 1) or not categorised. Those that were not cat-
egorised required specification that they did indeed
occur in an established and recognised protected area.
3. RESULTS
Initially, 205 articles were retrieved by the com-
puter-assisted search. After a review of the article con-
tent, 50 publications were selected for the analysis.
These articles were published between 1990 and 2020,
with information from 312 protected areas over
35 countries (Fig. 1). In total, 119 these protected
areas were under IUCN categories, with most
reported as category III; 113 did not have categories
reported or the IUCN management categories were
not applicable (Table 2).
3.1. Data Sources
To implement forest change detection analysis of
protected areas, a detection system must first be
selected that will allow generation of the information
that is necessary to achieve the study objective (Turner
et al., 2003; Lu et al., 2004; Zhu, 2017). The time scale
and image resolution are factors that must be taken
into account in the selection of the appropriate satel-
lite data (Lu and Weng, 2007). There are three classi-
fications of spatial resolution, low, medium, and high,
corresponding to pixel sizes of approximately 250 to
500 m, 30 m, and <1 m, respectively. The IKONOS,
SPOT 5 HRG, QuickBird, WorldView-2 and Geo-
eyes, sensors generate data with high spatial resolu-
tion, which is applicable for local studies, while
medium-resolution sensors such as Landsat and Terra
ASTER are useful at a regional scale; for continental-
scale investigations, AVHRR, MODIS, and SPOT
vegetation data are commonly used (Lu and Weng,
2007; Willis, 2015).
In the articles reviewed, aerial photos and multi-
spectral remote sensing images were used to estimate
forest and land cover. Landsat images were the main
source of remote sensing image used owing to their
high sampling frequency, the availability of data dating
back to 1972 and free data (Willis, 2015). All types of
Landsat images were downloaded and used, including
images captured by Multispectral Scanner System
(MSS), Thematic Mapper (TM), Enhanced Thematic
Mapper Plus (ETM +), and Operational Land Imager
(OLI) sensors (Huang et al., 2007; Sieber et al., 2013;
Da Ponte et al., 2017). Other type of multispectral
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MONITORING CONSERVATION OF FOREST IN PROTECTED AREAS 719
images were also used such as SPOT, Sentinel-2A,
ASTER, QuickBird, WorldView-2 and Geoeyes
(Mugagga et al., 2012; Ghofrani et al., 2014; Tsai et al.,
2019; Lossou et al., 2019; Meli Fokeng et al., 2020).
Articles from India commonly used Indian Remote
Sensing data in their studies (Joseph et al., 2009;
Satish et al., 2014; Adhikari et al., 2015; Dutta et al.,
2016; Reddy et al., 2017; Ramachandra et al., 2018).
3.2. Pre-processing Data
Before implementing change detection, the satel-
lites images were pre-processed to eliminate the noise
occasioned by instrument error and atmospheric
influences; as a result, radiometric correction, geo-
metric correction, orthorectification, image enhance-
ment, and masking were needed. An instrument error
Fig. 1. Countries where Protected areas implemented change detection to study forest cover.
Number of protected areas
0
1
2–4
5–10
11–50
> 50
Table 1. International Union for Conservation of Nature (IUCN) management categories (https://www.iucn.org)
IUCN Category Objective
Ia, Strict nature reserve An area strictly protected for biodiversity and serves for science or wilderness protection.
Also protects biodiversity and geological/geomorphological features
Ib, Wilderness area An unmodified or slightly modified area without permanent or significant human habita-
tion. Protected and managed to preserve its natural condition
II, National park A large natural or near-natural area managed mainly for species and ecosystem protection,
which also has environmentally and culturally compatible spiritual, scientific, educational,
recreational, and visitor opportunities
III, Natural monument or
feature
A protected area managed mainly for the conservation of specific natural features such as a
landform, sea mount, marine cavern, geological feature such as a cave, or a living feature
such as an ancient grove
IV, Habitat/species man-
agement area
An area to protect particular species or habitats. Managed mainly for conservation through
management intervention
V, Protected landscape or
seascape
Managed mainly for safeguarding the integrity of people and natural interactions over time;
this interaction has produced an area of distinct character with significant ecological, biolog-
ical, cultural, and scenic values
VI, Protected area with the
sustainable use of natural
resources
An area that conserves ecosystems, together with the associated cultural values and traditional
natural resource management systems. Generally large, mainly in a natural condition, with a pro-
portion under sustainable natural resource management and where low-level non-industrial nat-
ural resource use compatible with nature conservation is seen as one of the main aims
720
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MIRANDA-CASTRO et al.
Table 2. Number of protected area under the International
Union for Conservation of Nature (IUCN) management
categories (Dudley et al., 2013)
IUCN Category Number of protected
areas under category
Ia 5
Ib 58
II 1
III 127
IV 4
V4
VI 5
Not applicable or not reported 113
is found in all Landsat ETM + images collected after
May 31, 2003, which have data gaps resulting from the
failure of the Scan Line Corrector (SLC) (https://
www.usgs.gov/faqs/what-landsat-7-etm-slc-data) (Los-
sou et al., 2019). Thus, images were frequently
excluded or treated as clouds or cloud shadows (Zhu,
2017; Scharsich et al., 2017). However, the missing
data are not an impediment for using data from Land-
sat ETM +; Borelli et al., (2014) and Damnyag et al.,
(2013) overcame the issue by using other Landsat
scenes to gap-fill.
Geometric correction involved georeferencing and
registration, where an image is resampled by referenc-
ing to another image in order to correct for geometric
distortions. Jayanthi et al., (2018), Joseph et al.,
(2009), Satish et al., (2014), and Yen et al., (2005) used
ground control points and nearest-neighbour algo-
rithms to perform the resampling procedure and regis-
ter the images, and obtained an error of less than one
pixel for all satellite data. Most authors opted to use
Level 1T when using Landsat images, as these images
come intercalibrated and georegistered (Borrelli et al.,
2014; Kusimi, 2015; Young et al., 2020).
The temporal comparison of an area requires the
images to be calibrated to the surface reflectance since
the atmosphere can absorb and scatter parts of solar
radiation, attenuating the signal received by the sensor
(Richards, 2013). Hence, atmospheric correction is
needed to reduce or eliminate these distortions. Either
absolute or relative atmospheric corrections were per-
formed. Absolute corrections obtain top-of-atmo-
sphere radiance using an atmospheric radiative trans-
fer model and the metadata images (gains, offsets,
acquisition time, solar irradiance, and sun elevation),
as performed by Gilani et al., (2015) and Young et al.,
(2020). However, Phua et al., (2008) and Kamlun
et al., (2016) corrected atmospheric scattering using
the dark object subtraction method, which assumes
absent or minimal atmospheric effects on the surface
reflectance. On the other hand, relative normalisation
is performed by adjusting the radiometric properties of
uncorrected images to match a reference image (Jung
et al., 2005; Xie et al., 2008).
3.3. Change Detection
The most frequently used change detection tech-
nique was image differencing, with the spectral/index
or post classification method (Jung et al., 2005; Joseph
et al., 2009; Bayarsaikhan et al., 2009; Rasuly et al.,
2010; Lung and Schaab, 2010; Meliadis et al., 2010;
Mugagga et al., 2012; Twongyirwe et al., 2015; Kusimi,
2015; Kamlun et al., 2016; Islam et al., 2018; Jayanthi
et al., 2018; Lossou et al., 2019; Meli Fokeng et al.,
2020). Other methods used were machine learning
algorithms such as support vector machine (Knorn
et al., 2012; Sieber et al., 2013), decision trees (Souza
et al., 2013), and random forests (Griffiths et al., 2014;
Scharsich et al., 2017; Tsai et al., 2019; Young et al.,
2020). Segmentation and object-based image analysis
were also used to detect forest changes within pro-
tected areas (Gilani et al., 2015; Sánchez-Reyes et al.,
2017; Gambo et al., 2018; Cheţan et al., 2018).These
detection techniques presented accuracy above 70%
(Huang et al., 2007; Valožić and Cvitanović 2011;
Souza et al., 2013; Sieber et al., 2013; Sánchez-Reyes
et al., 2017; Gambo et al., 2018), demonstrating their
applicability as good methods to estimate forest cover.
The major overall accuracy was obtained by Bozkaya
et al., (2015), with a range of 97–98% using a maxi-
mum likelihood classification algorithm.
3.4. Forest Change
Forest change evaluations consisted of compari-
sons between an area in an initial year and in a later
year. Deforestation and forest loss were reported in 210
out of the 312 protected areas included in the review,
which were the main type of change identified within
the protected areas. The most major loss occurred in
the Amazon Forest, Brazil, with 169.074 km2 defor-
ested in ten years, followed by Agasthyamalai Bio-
sphere Reserve, India, which reported 747.1 km
2 of
forest lost in nearly one hundred years (Souza et al.,
2013; Dutta et al., 2016). In contrast, Tati Yupi and
Monument Moises had the least amount of forest loss
inside their areas, with 0.07 and 0.02 km2 lost, respec-
tively (Huang et al., 2009). Reddy et al., (2017) carried
out a study in various protected areas of India; 96 of
these sites lost a total of 1985 km2 of forest cover from
1973 to 2013. In Italy, 472.46 km2 of forest was changed
to other land covers within conservation areas during
2002–2011 (Borrelli et al., 2014). Huang et al., (2009)
estimated a reduction of 230.66 km2 of Paraguayan
protected forests during 1990–2000. Detailed infor-
mation about forest loss within protected areas can be
found in the Supplementary Material.
Gains in cover forest were present in 54 protected
areas. The largest gains were in Romanian and
Ukrainian Carpathian forest from 1985 to 2010, with
an increase of 11310.60 and 3115.52 km2, respectively.
This increase was owing to abandonment and natural
forest expansion (Griffiths et al., 2014) (Table 3).
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MONITORING CONSERVATION OF FOREST IN PROTECTED AREAS 721
Moreover, there were cases where both gains and
losses of forest area occurred in the same protected
area, such as in Bonsambepo Forest Reserve, Ghana
(Lossou et al., 2019), where the closed forest area was
reduced from 81.80 to 4.06 km2 while the open forest
area increased from 48.66 to 108.90 km2 from 1990–
2017. Forest cover can also change over different peri-
ods. This occurred in Klias Forest Reserve, Malaysia,
where the peat swamp forest was deforested during
1985 to 1998 but reforested during 2004 to 2013 (Kam-
lun et al., 2016). Lastly, many protected areas had no
forest change; their conserved areas and the monitor-
ing periods are presented in Table 4.
3.5. Acurracy Assessment
Validation of data is an important step in change
detection due to any map and data derived by remote
sensing can be assumed to contain some errors and an
accuracy assessment identifies the errors of the classifi-
cation and give and idea of how well of estimated areas
correspond to the actual area of change (Foody 2002;
Olofsson et al., 2013, 2014; Chughtai et al., 2021). Most
forest change studies include an accuracy assessment,
thus 45 of the articles included this review presented this
type of assessment. Typically, areas of changes are esti-
mated by an image classification analysis and the accu-
racy is performed for individual classifications. The
accuracies of the maps produced at each date typically
include and matrix of error, the overall accuracy value of
the classification, the Kappa index, the user accuracy,
and the producer accuracy (Foody 2002; Da Ponte
et al., 2017) However, this type of accuracy assessment
do not include confidence interval for the forest area of
change and the uncertainty of the estimation.
Olofsson et al., (2013) and Olofsson et al., (2014)
recommend the estimation of area of change imple-
menting an accuracy assessment based on change
maps and reference sample data (using stratified esti-
mator) as a good practice. Through this methodology
an error-adjusted estimator of area can be obtained
once an accuracy assessment has been carried out and
the error matrix constructed. These error-adjusted
area are rarely included in forest change studies, as
evidenced in this review where for the forest cover
change in protected areas only 5 studies included this
accuracy assessment that calculate this adjusted area
for forest changes.
4. DISCUSSION
The use of change detection in protected areas is a
good strategy to assess changes in the forest cover over
time and determine the effectiveness of management
strategies to protect forested areas. Multispectral
image data were demonstrated as a good source of data
to use in change detection assessments. By using sen-
sors with medium spatial and spectral resolutions such
as Landsat and Sentinel, it was possible estimate
changes in vegetation cover (Huang et al., 2007; Sieber
et al., 2013). However, this type of satellite data is not
adequate when the classification and identification of
tree species are needed; for these purposes, hyper-
spectral images are more suitable (Nagendra et al.,
2013). The disadvantage of hyperspectral data are their
availability and cost; as a result, these type of data are
less commonly used (He et al., 2011).
The application of change detection methods on
protected areas can help to improve conservation
efforts, as information on the status of vegetation and
forest cover is generated without disrupting the study
area (Knorn et al., 2012; Sieber et al., 2013; Willis,
2015). Change detection also allows the effectiveness
of protected area management to be determined. For
example, Gilani et al., (2015) reported an increase in
the forest cover inside Bhutan protected areas. More-
over, Adhikari et al., (2015) considered Bannerghatta
National Park, India as successful due to forest recov-
ery as a result of their management policy, while Sie-
ber et al., (2013) assessed the effectiveness of Oksky
State Nature Reserve, Russia and Mordovsky State
Nature Re ser ve, Russia. Th ey fou nd that both res erves
successfully protected the forests within them, with
the annual forest disturbance rates higher in areas sur-
rounding the nature reserves than inside the reserves.
Many protected areas exhibited a loss of forest
cover. In some cases, this decrease was comparatively
low inside the protected area in contrast to the sur-
rounding non-protected forest (Phua et al., 2008;
Potapov et al., 2012). Da Ponte et al., (2017) reported
a significant increase in deforestation outside of Para-
guayan protected areas, with rates ranging between 13
and 35%, whereas inside these areas deforestation was
only 3.3%. This trend was also observed along the buf-
fer zones of the Itabó, Limoy, Mbaracayú San Rafel,
Morombi and Caazapá protected areas. However,
conservation management goals are not always
achieved in the areas designated for conservation, as
was the case for the Special Protection Area of the
Apuseni-Vlădeasa Mountains, where deciduous and
coniferous forests deteriorated after the site was estab-
lished as a protected area in 2007 (Cheţan et al., 2018).
Although this loss was not attributed to anthropogenic
intervention, some of areas are under serious pressure
from various human activities (Jung et al., 2005; Dam-
nyag et al., 2013; Kusimi, 2015).
Protected forests are disturbed by fire, wood har-
vesting, agriculture, and the expansion of population
centres. However, since forests are naturally dynamic
ecosystems that undergo continuous change, ongoing
and subtle variations (such as growth and succession
processes) are more difficult to detect by the methods
commonly used to detect forest changes, including
remote sensing technology, which assess forest distur-
bances by analysing pre- and post-disturbance images
(Gómez et al., 2016). More recently, the fusion of
multispectral data and data from other technologies,
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MIRANDA-CASTRO et al.
Table 3. Forest cover gains in protected areas
Forest name Country Initial
year
Initial
area, km2
Final
year
Final
area, km2
Forest
gain, km2
Forest
gain, % Reference
Parque Luro Argentina 1987 – 1999 – – 0.30 (González-Roglich
et al., 2012)
Carpathian Austria 1985 7266.02 2010 7340.86 74.84 1.03 (Griffiths et al., 2014)
Khadimnagar National
Park Bangladesh 1988 1.55 2010 3.17 1.62 51.10 (Redowan et al., 2014)
Buthan protected
areas* Buthan 1990 – 2010 – – 1.49 (Gilani et al., 2015)
Metchie-Ngoum Pro-
tection Forest Reserve Cameroon 2000 – 2015 – 4.91 – (Meli Fokeng et al.,
2020)
Fanjingshan National
Nature Reserve China 1995 – 2016 – 2.78 – (Tsai et al., 2019)
The Medvednica
Nature Park Croatia 1991 – 2007 – – 8.33 (Valožić and Cvitanović
2011)
Carpathian Czech Rep. 1985 26400.00 2010 26624.40 224.40 0.85 (Griff iths et al., 2014)
The Munessa-Shasha-
mane Forest Enterprise Ethiopia 1987 65.4 2015 73.8 8.40 12.90 (Young et al., 2020)
Besmena-Odo Bulu Ethiopia 1987 144.22 2015 163.4 19.18 13.30 (Young et al., 2020)
Bia Shelterbelt Forest
Reserve
Ghana 2013 – 2017 – 10.78 – (Lossou et al., 2019)
Bia-Tano Forest
Reserve Ghana 1990 133.98 2017 149.15 15.17 11.32 (Lossou et al., 2019)
Bonsambepo Forest
Reserve Ghana 1990 48.66 2017 108.9 60.24 – (Lossou et al., 2019)
Antichasia—Meteora Greece 1989 11.43 1999 15.414 3.98 34.86 (Meliadis et al., 2010)
Carpathian Hungary 1985 34770.07 2010 36640.70 1870.63 5.38 (Griffiths et al., 2014)
Andhari India 1975 420 2013 421 1.00 0.24 (Reddy et al., 2017)
Badrama India 1975 354 2013 355 1.00 0.28 (Reddy et al., 2017)
Barda India 1975 165 2013 166 1.00 0.61 (Reddy et al., 2017)
Buxa India 1975 118 2013 119 1.00 0.85 (Reddy et al., 2017)
Dampa India 1975 271 2013 276 5.00 1.85 (Reddy et al., 2017)
Dhauladhar India 1975 149 2013 150 1.00 0.67 (Reddy et al., 2017)
Kailadevi India 1975 278 2013 279 1.00 0.36 (Reddy et al., 2017)
Kothagarh India 1975 425 2013 426 1.00 0.24 (Reddy et al., 2017)
Madei India 1975 189 2013 191 2.00 1.06 (Reddy et al., 2017)
Pachmarhi India 1975 399 2013 400 1.00 0.25 (Reddy et al., 2017)
Painganga India 1975 272 2013 273 1.00 0.37 (Reddy et al., 2017)
Ranipur India 1975 221 2013 222 1.00 0.45 (Reddy et al., 2017)
Ratapani India 1975 729 2013 730 1.00 0.14 (Reddy et al., 2017)
Sajnakhali India 1975 319 2013 320 1.00 0.31 (Reddy et al., 2017)
Sundarbans India 1989 – 2013 – 1805.3 – (Jayanthi et al., 2018)
Bhitarkanika India 1989 – 2013 – 127.3 – (Jayanthi et al., 2018)
Mahanadi India 1989 – 2013 – 33.10 – (Jayanthi et al., 2018)
Devimouth India 1989 – 2013 – 1.80 – (Jayanthi et al., 2018)
Godavari India 1989 – 2013 – 141.70 – (Jayanthi et al., 2018)
Krishna India 1989 – 2013 – 70.90 – (Jayanthi et al., 2018)
Muthupet India 1989 – 2013 – 13.70 – (Jayanthi et al., 2018)
Pichavaram India 1989 – 2013 – 5.40 – (Jayanthi et al., 2018)
Gulf of Khambhat India 1989 – 2013 – 0.50 – (Jayanthi et al., 2018)
Bannerghatta India 1973 84.29 2007 81.1 10.50 9.90 (Adhikari et al., 2015)
Krau Wildlife Reserve Malaysia 1989 3196.52 2016 3436.20 239.68 7.50 (Gambo et al., 2018)
Klias Forest Reserve Malaysia 2004 – 2013 – 5.85 – (Kamlun et al., 2016)
Rio Abiseo National
Park Peru 1987 105.22 2004 142.07 36.85 35.03 (Kintz et al., 200 6)
Carpathian Poland 1985 45479.28 2010 47257.52 1778.24 3.91 (Griffiths et al., 2014)
Carpathian Romania 1985 169828.83 2010 181139.43 11310.60 6.66 (Griffiths et al., 2014)
Apuseni-Vlădeasa
Mountains Romania 1976 – 2015 – 35.11 – (Cheţan et al., 2018)
Apuseni-Vlădeasa
Mountains Romania 1976 – 2015 – 11.62 – (Cheţan et al., 2018)
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MONITORING CONSERVATION OF FOREST IN PROTECTED AREAS 723
such as Airborne Laser Scanner (ALS), Digital Stereo
Imagery (DSI), and Airborne Light Detection and
Ranging (LiDAR), have been implemented to study
vegetation in protected areas (Wulder et al., 2007;
Shaharum et al., 2018; Marinelli et al., 2019). With
ALS, DSI, and LiDAR data, various changes in the
forest structure (3D change detection) can be deter-
mined; however, the use of these technologies has
been limited due to their high cost and the need to use
novel and computationally efficient processing algo-
rithms (Vastaranta et al., 2013; Marinelli et al., 2018;
Noordermeer et al., 2019; Okyay et al., 2019).
Recently, advanced algorithms in computer vision,
such as deep learning and fusion algorithms, have
gained widespread attention due to their ability to
automatically learn abstract and complicated contex-
tual features, and multilevel expressions from raw data
(Zhang et al., 2016; Li et al., 2019). D eep learning has
promising applications in environmental conserva-
tion, since this type of algorithm can differentiate veg-
etation types and forest disturbances; therefore, there
is increasing interest to apply these algorithms to envi-
ronmental problems (Lamba et al., 2019; Wagner
et al., 2019; Kislov and Korznikov, 2020).
Multitemporal comparisons and the detection of
forest changes using multispectral data result in good
applications for identifying forest cover status, forest
biomass, and carbon stocks (Mutanga et al., 2012;
Main-Knorn et al., 2013). This information is useful
for analysing protected forests and assessing the effec-
tiveness of areas designated for conservation in achiev-
ing their goals. However, multitemporal comparisons
have limitations due to the fact that the sources of any
forest disturbance are usually not identified (Kislov
and Korznikov, 2020). Thus, it is important to incor-
porate airborne technologies into assessments and
explore the use of deep learning for conservation pur-
poses to obtain more specific and detailed information
about forests, causes of disturbances, reforestation,
and the protection status of forests.
The implementation of change detection in pro-
tected areas allows the analysis of forest cover and the
assessment of the effectiveness of protected areas in
conserving forests. Information about the canopy of
protected forests is generated, indicating whether the
forest area is stable, or has decreased or increased over
time. Moreover, quantitative information regarding
forest cover changes, the transition matrix, and multi-
temporal mapping is also generated as a result of the
change detection analysis. This information contrib-
utes towards the improvement of conservation efforts
and decision making in protected areas. However,
multitemporal change detection has limitations as it
only provides information from the perspective of for-
est cover, disregarding the causes of disturbance and
reforestation. Therefore, multispectral, hyperspectral,
airborne scans, and other remote sensing data are
being used in conjunction with advanced computa-
tional algorithms to improve change detection,
including forest structural changes. Finally, protected
areas are sites designated for conservation; however,
many of these sites experience the loss of forest despite
their protected status, providing evidence of the need
to reinforce management strategies.
5. CONCLUSION
The implementation of change detection in pro-
tected areas allow the analysis of forest cover and the
effectivity assessment of protected areas. Information
about canopy of protected forest is generated, either sta-
bility, loss or gain status. Moreover, quantitative infor-
mation of forest cover changes, transition matrix and
–, not reported.
Defileul Mureşului
Inferior – Dealurile
Lipovei
Romania 1976 – 2015 – 16.95 – (Cheţan et al., 2018)
Drocea-Zarand Romania 1976 – 2015 – 15.32 – (Cheţan et al., 2018)
Oksky State Nature
Reserve and Mordo-
vsky State Nature
Reserve (Total forest
area)
Russia 1984 – 2010 – – 46.19 (Sieber et al., 2013)
Carpathian Slovakia 1985 48943.44 2010 49540.55 597.11 1.22 (Griffiths et al., 2014)
Igneada Turkey 1990 193.19 2010 195.92 2.73 1.41 (Bozkaya et al., 2015)
Budongo Uganda 1972/
1973 449.34 1995 462.44 13.10 2.92 (Lung and Schaab 2010)
Mabira Uganda 1986 274.21 2003 209.77 13.61 6.49 (Lung and Schaab 2010)
Budongo Uganda 1985 – 2014 – – 0.8 (Twongyirwe et al.,
2015)
Carpathian Ukraine 1985 56543.01 2010 59 658.53 3,115.52 5.51 (Griffiths et al., 2014)
Forest name Country Initial
year
Initial
area, km2
Final
year
Final
area, km2
Forest
gain, km2
Forest
gain, % Reference
Table 3. (Contd.)
724
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MIRANDA-CASTRO et al.
Table 4. Protected forest areas that remained without change over their survey period
Forest name Country Period Area, km2Reference
Wolong Nature Reserve China 1965 1997 – (Liu et al., 2001)
Bonkoni Forest Reserve Ghana 1990 2017 40.58 (Lossou et al., 2019)
Ans hi India 1975 2013 339 (Reddy et al., 2017)
Askot Musk Deer India 1975 2013 252 (Reddy et al., 2017)
Badalkh ol India 1975 2013 101 (Reddy et al., 2017)
Balaram Ambaji India 1975 2013 477 (Reddy et al., 2017)
Bandhavgarh India 1975 2013 424 (Reddy et al., 2017)
Barsey Rhododendron India 1975 2013 110 (Reddy et al., 2017)
Bha dra India 1975 2013 419 (Reddy et al., 2017)
Bhimashankar India 1975 2013 118 (Reddy et al., 2017)
Cor inga India 1975 2013 103 (Reddy et al., 2017)
Dhyanganga India 1975 2013 116 (Reddy et al., 2017)
Dib a n g In d i a 19 7 5 2 013 117 7 ( Re d d y e t a l . , 2017)
Dudhwa India 1975 2013 492 (Reddy et al., 2017)
Gan dhi Sag ar India 1975 2013 165 (Reddy et al., 2017)
Ghatigaon India 1975 2013 225 (Reddy et al., 2017)
Gir India 1975 2013 1235 (Reddy et al., 2017)
Girnar India 1975 2013 145 (Reddy et al., 2017)
Govind Pashu Vihar India 1975 2013 120 (Reddy et al., 2017)
Gulmarg India 1975 2013 114 (Reddy et al., 2017)
Hazaribagh India 1975 2013 148 (Reddy et al., 2017)
Indira Gandhi (Annamalai) India 1975 2013 746 (Reddy et al., 2017)
Itanagar India 1975 2013 150 (Reddy et al., 2017)
Jaldapara India 1975 2013 145 (Reddy et al., 2017)
Jam b u g ho d a In d i a 19 7 5 2 013 111 ( R e ddy e t a l ., 2 0 17 )
Jamwa Ramgarh India 1975 2013 110 (Reddy et al., 2017)
Jessore India 1975 2013 153 (Reddy et al., 2017)
Kalakad India 1975 2013 225 (Reddy et al., 2017)
Kanger Valley India 1975 2013 204 (Reddy et al., 2017)
Kan ha India 1975 2013 843 (Reddy et al., 2017)
Kedarnath India 1975 2013 430 (Reddy et al., 2017)
Khalasuni India 1975 2013 170 (Reddy et al., 2017)
Khangchendzonga India 1975 2013 1021 (Reddy et al., 2017)
Kishanpur India 1975 2013 146 (Reddy et al., 2017)
Koderma India 1975 2013 140 (Reddy et al., 2017)
Koyna India 1975 2013 277 (Reddy et al., 2017)
Kudremukh India 1975 2013 647 (Reddy et al., 2017)
Lawalong India 1975 2013 338 (Reddy et al., 2017)
Mah anan da In dia 1975 2013 137 (Reddy et al., 2017)
Mookambika India 1975 2013 274 (Reddy et al., 2017)
Mouling India 1975 2013 484 (Reddy et al., 2017)
Mudumal ai In dia 1975 2013 241 (Reddy et al., 2 017)
Nagzira India 1975 2013 149 (Reddy et al., 2017)
Nargu India 1975 2013 211 (Reddy et al., 2017)
Nawegaon India 1975 2013 127 (Reddy et al., 2017)
Overa-Aru India 1975 2013 136 (Reddy et al., 2017)
Pakke (Pakhui) India 1975 2013 598 (Reddy et al., 2017)
Palamau India 1975 2013 213 (Reddy et al., 2017)
Phen India 1975 2013 103 (Reddy et al., 2017)
Pushpagiri India 1975 2013 102 (Reddy et al., 2017)
Ramgarh Vishdhari India 1975 2013 117 (Reddy et al., 2017)
Ran thambore India 1975 2013 207 (Reddy et al., 2017)
Rupi Bhaba India 1975 2013 186 (Reddy et al., 2017)
Sanjay Dubri India 1975 2013 260 (Reddy et al., 2017)
Satyamangalam India 1975 2013 483 (Reddy et al., 2017)
Sessa Orchid India 1975 2013 233 (Reddy et al., 2017)
Shettihalli India 1975 2013 481 (Reddy et al., 2017)
Silent Valley India 1975 2013 143 (Reddy et al., 2017)
Sohelwa India 1975 2013 372 (Reddy et al., 2017)
CONTEMPORARY PROBLEMS OF ECOLOGY Vol. 15 No. 6 2022
MONITORING CONSERVATION OF FOREST IN PROTECTED AREAS 725
–, not reported.
Sri Lankamalleswara India 1975 2013 395 (Reddy et al., 2017)
Tadoba India 1975 2013 112 (Reddy et al., 2017)
Talakaveri India 1975 2013 102 (Reddy et al., 2017)
Tale Valley India 1975 2013 306 (Reddy et al., 2017)
Tansa India 1975 2013 285 (Reddy et al., 2017)
Wayanad India 1975 2013 173 (Reddy et al., 2017)
Yawal India 1975 2013 187 (Reddy et al., 2017)
Yorde-Rabe-Supse India 1975 2013 384 (Reddy et al., 2017)
Gulf of Kutch India 1989 2013 – (Jayanthi et al., 2018)
Mukurthi National Park India 1920 2012 12.7 (Satish et al., 2014)
Matobo National Park Zimbabwe 1989 2014 – (Scharsich et al., 2017)
Forest name Country Period Area, km2Reference
Table 4. (Contd.)
multitemporal mapping are also generated as a result of
change detection analysis. This information contributes
to improve the conservation efforts and decisions mak-
ing in protected areas. However, multitemporal change
detection has limitations due to only offers perspective
of forest cover, disregarding disturbances and reforesta-
tion causes, therefore multispectral, hyperspectral, air-
borne scan, and others remote sensing data are being
used in conjunction with advanced computational algo-
rithms to improve change detection, including struc-
tural forest changes. Finally, protected areas are site
destinated for conservation, however many of these ter-
ritories presented forest loss, evidencing the need to
reinforce management strategies.
ACKNOWLEDGMENTS
The authors thank the Universidad Tecnológica de
Bolívar for their support and funding.
FUNDING
This work was supported by the Universidad Tecnológica
de Bolivar (Colombia) (grant number C2018P009).
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
SUPPLEMENTARY INFORMATION
Authors supply the supplementary materials.
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