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Vol.:(0123456789)
Landscape and Ecological Engineering
https://doi.org/10.1007/s11355-024-00626-5
ORIGINAL PAPER
Projected trends inecosystem service valuation inresponse toland
use land cover dynamics inKishtwar High Altitude National Park, India
DurlovLahon1· GowharMeraj2 · ShizukaHashimoto2 · JatanDebnath10· AbidMuslimBaba3· MajidFarooq3·
Md.NazrulIslam4· SurajKumarSingh5· PankajKumar6 · ShrutiKanga7· PankajChandan8· SanjeevSharma9·
DhrubajyotiSahariah1
Received: 9 February 2024 / Revised: 17 August 2024 / Accepted: 30 September 2024
© The Author(s) 2024
Abstract
In an era where global biodiversity hotspots are under unprecedented threat, understanding the intricate balance between
land use land cover (LULC) changes and their implications on ecosystem services value (ESV) becomes paramount. The
region of Jammu and Kashmir, with its distinctive ecological importance, is well known for these challenges and opportuni-
ties. This region embodies various conservation reserves and national parks, and one of the most ecologically rich is called
Kishtwar High Altitude National Park. It is often considered an example of biodiversity richness in the Indian subcontinent,
as it protects a myriad of species and provides essential ecosystem services. However, despite its significance, it faces pres-
sures from both peripheral human activities, such as seasonal grazing by nomadic communities and broader climatic changes.
This study aims to investigate the complex relationship between these LULC shifts and their consequent effects on the park’s
ESV. We used the cellular automata (CA)–Markov model to simulate the LULC for the future. Using the LULC from 1992
to 2020 and projecting for 2030, 2040, and 2050, we employed the global value coefficient method to understand the ESV
contributions of different LULC types. Our results revealed a 7.43% increase in ESV from 1992 to 2020, largely due to the
increase of forests and waterbodies. In contrast, our projections for 2020 to 2050 intimate a 7.55% decline in ESV, even
amidst anticipated grassland expansion. These results highlight the role of forests in securing resilient ecosystem services.
These findings shall help offer informed conservation strategies, that are relevant both regionally and globally.
Keywords CA–Markov model· GIS and RS· Spatiotemporal change· Sustainable forest management· Mitigation
strategies· Landscape planning
* Gowhar Meraj
gowharmeraj@g.ecc.u-tokyo.ac.jp; gowharmeraj@gmail.com
1 Department ofGeography, Gauhati University, Jalukbari,
Assam, India
2 Department ofEcosystem Studies, Graduate School
ofAgricultural andLife Sciences, The University ofTokyo,
1-1-1 Yayoi, Tokyo113-8654, Japan
3 Department ofEcology, Environment andRemote
Sensing, Government ofJammu andKashmir, Srinagar,
Kashmir190018, India
4 Department ofGeography andEnvironment, Jahangirnagar
University, Savar,Dhaka1342, Bangladesh
5 Centre forClimate Change andWater Research, Suresh Gyan
Vihar University, Jaipur302017, India
6 Institute forGlobal Environmental Strategies,
Hayama240-0115, Japan
7 Department ofGeography, School ofEnvironment andEarth
Sciences, Central University ofPunjab, VPO-Ghudda,
Bathinda151401, India
8 National Development Foundation, N.D.F., Shakuntla
Bhawan, Udheywala, Jammu180018, India
9 Centre fortheStudy ofRegional Development, School
ofSocial Science, Jawaharlal Nehru University,
NewDelhi110067, India
10 Dhamma Dipa International Buddhist University, Sabroom,
SouthTripura799145, India
Landscape and Ecological Engineering
Introduction
Ecosystems, comprising plant and animal life, microor-
ganisms, and non-living environmental aspects, supply
a broad range of goods and services, either directly or
indirectly, which significantly benefit human beings. Such
services, which can be generally grouped into provision-
ing, supporting, regulating, and cultural categories, are
critical to human well-being (Costanza etal. 1997; Millen-
nium Ecosystem Services Assessment 2005). As pressure
on natural resources continues to rise globally, the concept
of ecosystem services (ES) has gained significant impor-
tance in policy-making and land conservation (Schirpke
etal. 2020). At the same time, a rapidly growing human
population, economic expansion, and increased demand
for natural resources has posed substantial threats to these
ecosystem services (Song 2018). Historically, factors such
as changes in land use land cover (LULC), climate change,
environmental degradation, and resource depletion have
significantly impacted the structure and functionality of
ecosystems, as well as the services they deliver (Hu etal.
2008; Das etal. 2021; Rather etal. 2022; Meraj etal.
2021, 2022a, b). Land, as a vital natural resource, holds
paramount importance, especially in rapidly developing
regions like India. This highlights the need for comprehen-
sive research that addresses the consequences of land use
changes on ecosystem services. Moreover, acknowledging
the multifaceted importance of ecosystems, assigning a
monetary value to ecosystem services serves as a criti-
cal instrument for decision-makers to rationalize resource
allocation and offer valuable perspectives on the relative
worth of existing ecological services (Song and Deng
2017; Farley 2008; Li etal. 2019a, b; Groot etal. 2012;
Lahon etal. 2023a, b). Hence, researchers have made sig-
nificant efforts to quantify the financial worth of ecosys-
tem goods and services.
Advancements in Remote Sensing and Geographic
Information Systems (GIS) have proven invaluable in
this domain. These technologies provide geographically
accurate data, enabling deeper insights into changes in
LULC and the projections of future LULC patterns (Lu
etal. 2004; Das etal. 2021; Rafi etal. 2022; Gupta etal.
2023). While technological advancements, especially in
remote sensing, have considerably advanced our ability to
understand and model LULC changes, they get improved
when combined with precise spatial allocation method-
ologies. One such powerful methodology is the use of
detailed planning zones (DPZ), which provides specific
information on the spatial allocation of different land
use classes and buildings, allowing for a comprehensive
understanding of how specific areas are being utilized and
transformed. The use of detailed planning zones (DPZ)
combined with satellite imagery and remote sensing has
become a pivotal methodology in detecting and assess-
ing LULC changes, offering insights into urban expan-
sion, agricultural decrease, and other LULC phenomena.
In a foundational study by Saidur Rehman (2013), DPZ
and satellite imaging were harnessed to examine the
LULC changes alongside the Dhaka By-Pass Road. This
approach showed how infrastructure projects can stimu-
late local economic growth, modifying settlements and
agricultural land use. Specifically, DPZ analysis identi-
fied areas where lands transitioned from fallow to indus-
trial, and where waterbodies became encroached. This
study emphasized the need for methodological applica-
tions, particularly in rapidly developing regions, where
LULC shifts can be both rapid and dynamic. Similarly, the
work of Hassan etal. (2022) applied DPZ in association
with satellite imagery to determine optimal locations for
urban green belts and green wedges in Chittagong city. By
analyzing the evolution of the urban built-up area over 3
decades, the research identified significant reductions in
dense vegetation and revealed how DPZ can play a role in
strategic urban planning, advocating for land use zoning
and the introduction of green belts to mitigate environ-
mental degradation. Further highlighting the use of DPZ
in LULC studies, Markéta Růžičková (2022) focused on
the Prague Metropolitan Region, creating a LULC change
geodatabase over nearly 3 decades. Integrating datasets
like the M11-Urban Atlas with satellite imagery, the study
mapped out shifts from agricultural lands to built-up areas,
transport networks, and recreational facilities. Importantly,
Růžičková also highlighted the challenges and importance
of harmonizing disparate classification systems to generate
a coherent and consistent LULC geodatabase. In essence,
the combined use of DPZ and satellite imagery, as evi-
denced by the above studies, provides a robust approach
to understanding and addressing the multifaceted LULC
changes in various urban contexts (Bera etal. 2022; Singh
etal. 2023). This methodology, offering both breadth and
precision, is crucial for urban planners, policymakers, and
researchers in shaping sustainable urban futures.
In protected areas, such as national parks and nature
reserves, there has been a growing emphasis on evaluat-
ing the economic value of forest ecosystem services (Meraj
2020; Meraj etal. 2022a, b). These evaluations aim to com-
prehend the ecological, societal, and economic benefits
derived from ecosystems (Meraj etal. 2021; Chen etal.
2022). For instance, the Kishtwar High Altitude National
Park (KHANP) in Jammu and Kashmir, India, serves as
an ideal case for understanding how changes in LULC
impact ecosystem services. Given its rich biodiversity and
diverse landscape, the park is an essential focal point for
such investigations. The valuation of ecosystem services
is a key aspect of understanding the benefits provided by
Landscape and Ecological Engineering
natural environments, which is increasingly important in the
context of global environmental changes. As land use and
land cover dynamics continue to impact ecosystems world-
wide, the findings from KHANP in India provide a signifi-
cant perspective that extends beyond regional boundaries.
It contributes to the global discourse on sustainable land
management and conservation strategies. By examining
how these dynamics influence ecosystem service valuation,
this study not only highlights the importance of preserv-
ing high-altitude ecosystems but also provides a framework
that can be applied to similar environments globally. Vari-
ous studies have been conducted on this subject, previously
(Meraj 2021). Arowolo etal. (2018a, b) utilized the global
land cover dataset, GlobeLand 30, to assess how ecosys-
tem service values (ESV) in Nigeria evolved in response
to LULC dynamics. Similarly, Kindu et al. (2016a, b)
employed Landsat data to explore these dynamics in the
Munessa–Shashemene landscape of the Ethiopian high-
lands. Talukdar etal. (2020a, b) conducted their study in
the lower Gangetic plain of India, utilizing Landsat satellite
data and Costanza etal.’s global coefficient value to assess
the changes in ESV vis-à-vis LULC alterations. Meanwhile,
Das etal. (2021) evaluated these values as well as projected
future trends using the cellular automata (CA)–Markov
model. Despite these strides in the LULC–ecosystem service
change dynamics, there remains a lack of research focusing
on potential future shifts in ecosystem service valuations,
especially when accounting for projected LULC changes. As
a result of the ongoing environmental crisis, climate change,
pollution, increasing population, and rapid urbanization, it is
paramount to address this research gap. These global chal-
lenges might necessitate a broader spectrum of ecosystem
services to maintain a balance (Hepinstall etal. 2008; Das
etal. 2021; Singh etal. 2022). The valuation of ecosystem
services is pivotal for understanding the benefits provided by
natural environments, especially in the face of accelerating
global environmental changes. As LULC dynamics continue
to shape ecosystems worldwide, insights gained from this
study shall extend far beyond regional implications. This
research is aimed to contribute valuable perspectives to
the global discourse on sustainable land management and
conservation strategies. The findings from this study high-
light the necessity of preserving high-altitude ecosystems,
key components of global biodiversity, by examining the
interplay between LULC changes and ecosystem service
valuation.
Although numerous studies have evaluated ESV across
different global regions (Kindu 2016; Song 2017; Long
2022; Debnath etal. 2022a, b, c), the majority have
concentrated on current estimations, often overlooking
projections of future trends. This gap in research is sig-
nificant. Projecting ESV can provide pivotal insights for
policymakers aiming to design effective, forward-looking
strategies. Addressing this need, the primary objective
of our study is twofold: to analyze and predict the shifts
in LULC of KHANP, and to assess both the current and
projected future values of natural capital and ecosystem
services. A unique aspect of our research is the examina-
tion of the temporal change in the unit value of ecosystem
services for the region—an aspect often neglected in simi-
lar studies. The findings from this research aim to provide
a robust scientific foundation for assessing nature reserves.
Furthermore, they can steer policy decisions related to
ecological restoration, especially in areas with high envi-
ronmental quality. Ultimately, our overarching ambition
is to aid in providing sustainable management plans that
would recommend strategies to ensure the sustained and
enhanced value of ecosystem services, while preserving
the region’s ecological harmony.
Materials andmethods
Study area
Located in the Kishtwar district of Jammu Province, India,
the Kishtwar High Altitude National Park (KHANP)
encapsulates a myriad of natural wonders across its
expanse of approximately 2,713.78 sq km. Positioned
between 33° 27' and 33° 59' N latitudes and 75° 40' to 76°
17' East longitudes, this unique reserve lies roughly 60km
northeast of its namesake town, Kishtwar (Fig.1). Since its
establishment by the government of Jammu and Kashmir
in 1981, the park has served as a prominent site of ecologi-
cal preservation and exploration. KHANP is distinguished
by its diverse topography that ranges from high-altitude
alpine meadows and glaciers to snow-capped peaks. Such
altitude variations, coupled with climatic shifts, contrib-
ute to a diverse ecosystem that fosters a rich biodiversity,
particularly in the northern and eastern regions of the
park. The terrain is etched with four prominent streams
(nallas), Renai, Kiyar, Nanth, and Kiber, whose lower
catchment areas provide favorable conditions for the
growth of diverse tree species. Adding to its ecological
value, KHANP is also a sanctuary for numerous rare and
endangered species. Notably, the elusive snow leopard, the
Himalayan black bear, and the Himalayan blue sheep all
find refuge within its borders, contributing to the park’s
conservation significance on a national and international
scale. Beyond its ecological importance, KHANP is also
an epicenter for adventure tourism. The unique landscape
and untouched wilderness make it an ideal destination for
trekking and mountaineering, attracting outdoor enthusi-
asts from across the globe.
Landscape and Ecological Engineering
Datasets
This research used four Landsat images comprising the tem-
poral range of 1992, 2000, 2010, and 2020. These dates were
selected for assessing the decadal change of the LULC of the
region using the best available satellite imagery. The imagery
was downloaded through the USGS Earth Explorer platform
(accessible at https:// earth explo rer. usgs. gov), which served
as a valuable resource for evaluating land use and cover-
age. In pursuit of utmost precision and clarity, we selected
cloud-free images by gathering data during the post-monsoon
seasons, only. To maintain consistency within the dataset,
the Landsat Thematic Mapper (TM) imagery was employed
for year 1992, year 2000, and year 2010, while the Landsat
Operational Land Imager (OLI) was utilized for 2020 (Deb-
nath etal. 2023). The datasets utilized for this study have a
spatial resolution of 30m (Table1). This resolution is suit-
able for an in-depth examination of LULC patterns within
the defined study region. Notably, this dataset has been previ-
ously adopted and validated in numerous studies (Alam etal.
2020; Belay etal. 2022; Kumar etal. 2020). We also aimed
to capture certain environmental and policy shifts that have
influenced LULC in the KHANP and other alpine areas of
this region while selecting these specific time periods. For
Fig. 1 Location map: a the
location of the previously state
of Jammu and Kashmir (now
UT of Jammu and Kashmir and
Ladakh) in relation to India (red
square); b the location of Kisht-
war High Altitude National Park
in relation to Kashmir valley
(blue square); c the location
of Kishtwar High Altitude
National Park in relation to
Kashmir valley (blue square).
The map coordinates are in
the UTM 43 (north) World
Geodetic System (WGS-1984)
reference system
Landscape and Ecological Engineering
example, the 1990s saw increased enforcement of the For-
est (Conservation) Act1 and amendments to the Jammu and
Kashmir Wildlife (Protection) Act,2 which had enhanced for-
est conservation efforts in protected areas. The early 2000s
were marked by the implementation of the National Action
Plan on Climate Change,3 which included initiatives aimed at
reforestation and sustainable land management, particularly
in ecologically sensitive regions like Kishtwar. More recently,
the reorganization of Jammu & Kashmir in 20194 brought
about significant policy changes, potentially impacting land
use regulations and conservation strategies in the region.
These shifts provide a broader context for understanding the
LULC dynamics observed in this study.
Methods
Interpretation andimplications foridentifying thechanges
This research focused on five types of LULC: forest, grass-
land, rocky barren, snow cover, and waterbodies, based on
the rationale that the global ESV database has similar LULC
categories. During the image processing stage, several image
enhancements were applied to all images using ERDAS
Imagine 9. These enhancements included haze reduction,
noise reduction, and histogram equalization to aid in the sub-
sequent visual image interpretation process. Our approach
centered on visual image interpretation and digitization tech-
niques for LULC classification (Table2). Specifically, satel-
lite imagery from the years 1992, 2000, 2010, and 2020 was
analyzed through the manual interpretation method (Debnath
etal. 2022a, b, c; Nath etal. 2023). Following the interpreta-
tion, we derived LULC maps. To ensure the robustness and
Table 1 Datasets used in the present study
Sl. no. Satellite and
sensor
Row/path Date of acquisi-
tion
Spatial resolu-
tion
Band Cloud cover (%) Source
1 Landsat 5 TM 37/148 28–08–1992 30m 1–8 < 10 USGS Earth
Explore (https://
www. usgs. gov)
2 Landsat 5 TM 37/148 01–09–2000 30m 1–8 < 10
3 Landsat 5 TM 37/148 31–08–2010 30m 1–8 < 10
4 Landsat 8 OLI 37/148 04–09–2020 30m 1–11 < 10
Table 2 Details of different
LULC classes S. no. Class Description
1 Forest This class incorporates open forest, dense forest, and scrub land
2 Grassland Pastures area, natural grassland, and sparsely vegetated area
3 Rocky barren Open mountain cliffs and rocky areas that are under snow due
to which they are devoid of any vegetation cover
4 Snow cover Seasonal or perennial snow
5 Waterbody Area covered by lake, river, and other waterbodies
Table 3 Error matrix of LULC from LANDSAT OLI 2020 using ground truthing
Diagnonal values are standard way to show in bold
For forest, Gr grassland, RB rocky barren, Sc scrub, Wb waterbody
Classified LULC categories Grand total User’s accuracy
For Gr RB Sc Wb
Classified LULC categories For 32 32 100.00%
Gr 29 4 33 87.88%
RB 1 29 30 96.67%
Sc 15 15 100.00%
Wb 20 20 100.00%
Grand total 33 29 33 15 20 130
Producer’s accuracy 97% 100% 88% 100% 100%
1 https:// jkfor est. gov. in/
2 https:// www. jkwil dlife. com/ wild/ index. asp
3 https:// www. ncbi. nlm. nih. gov/ pmc/ artic les/ PMC28 22162/
4 https:// igr. jk. gov. in/ files/ J&K% 20Reo rgani sation% 20Act ,% 202019.
pdf
Landscape and Ecological Engineering
reliability of our classifications for the years 1992, 2000,
2010, and 2020, an accuracy assessment was carried out. This
assessment involved comparing the derived LULC maps with
ground-truth data collected in the field for 2020 and cross-
referencing with Google Earth’s historical imagery for the
year 2010. The results of this accuracy assessment, including
discrepancies and alignment between our interpretations and
the reference data, are presented in an error matrix shown in
Tables3 and 4. For the years 1992 and 2000, we relied on
historical archived maps for validation purposes. While this
method does not match the precision of ground truthing, it
still offers a satisfactory degree of accuracy, as reflected by
the Kappa coefficients and overall accuracy rates we noted
(Table5). After postclassification accuracy assessment, we
performed vector-to-raster conversion for the subsequent step
of LULC projections. Based on the observed trends and pat-
terns, projections for LULC changes were then made for the
years 2030, 2040, and 2050 (Kumar etal. 2014; Lillesand etal.
2015; Debnath etal. 2022a, b, c). Table2 illustrates the vari-
ous LULC classes. Figure2 shows the overall flowchart of the
methodology used in this study.
For this study, ArcGIS 10.8 software was used for change
detection analysis, estimating the scope of LULC change dur-
ing each period from 1990 to 2020 using the change matrix
(raster polygon). Projections were also created for anticipated
LULC changes in 2030, 2040, and 2050, to understand future
land transformations in the area. The following equation deter-
mined the magnitude and percentage of change for each LULC
class:
Using the following equation, the % change in LULC
class was determined:
where “Ci” depicts the magnitude of class ‘i’; “Pi” depicts
the percentage of class ‘i’; “Bi” refers to the base image, and
“Li” is the latest image.
Futuristic changes inLULC withthecellular automata (CA)–
Markov chain approach
The CA–Markov model was utilized to simulate LULC
changes over time, in this research (Tadese etal. 2021;
Sinha etal. 2022; Weng 2002; Debnath etal. 2022a, b, c).
This model combines the spatio-temporal characteristics
of cellular automata (CA) with the probabilistic transitions
of Markov chains, enabling the analysis of LULC change
patterns and trends over time (Hamad etal. 2018). Policy-
makers can leverage this approach to respond effectively
to vegetation change processes and develop sustainable
LULC management strategies (Yang etal. 2014). Terrset
software was utilized to predict future LULC changes in
the KHANP. The process involved generating the transi-
tion potential image (TPI) using the base map (BP), transi-
tion suitability image (TSI), and LULC images from 1992,
2000, 2010, and 2020 (Fitzsimmons and Getoor 2003).
The Markovian transition estimator was used to deter-
mine transition probabilities from 2000 and 2010, which
were then used to simulate the LULC map for 2020 (for
(1)
Ci=Li−Bi
(2)
P
i=
L
i
−B
i
Bi
×
100
Table 4 Error matrix of LULC from LANDSAT ETM + 2010 using Google Historical Imagery
Diagnonal values are standard way to show in bold
For forest, Gr grassland, RB rocky barren, Sc scrub, Wb waterbody
Error matrix of LULC from LANDSAT ETM + 2010 using Google Historical Imagery
Classified LULC categories Grand total User’s accuracy
For Gr RB Sc Wb
Classified LULC categories For 31 2 33 93.94%
Gr 31 4 35 88.57%
RB 2 28 30 93.33%
Sc 38 38 100.00%
Wb 15 15 100.00%
Grand total 31 33 32 40 15 151
Producer’s accuracy 100% 94% 88% 95% 100%
Table 5 Accuracy assessment indices of classified images during
1992–2020
Year Overall accuracy (%) Kappa coefficient
1992 89.15 0.801
2000 91.33 0.838
2010 94.70 0.930
2020 96.15 0.950
Landscape and Ecological Engineering
validation purposes). The period from 2000 to 2010 was
selected for determining the transition probabilities over
1992 to 2000 due to the longer timeframe offering a more
detailed understanding of LULC changes and the availabil-
ity of higher resolution imagery by 2010, which was used
for validation of the 2010 dataset. This approach ensured
a balance between capturing significant land cover transi-
tions and utilizing reliable data available for modeling.
The transition probabilities from 2010 and 2020 were sub-
sequently used to model the 2030, 2040, and 2050 LULC
data products. The TSI was computed based on variables
and constraints obtained from the MCE module (Singh
etal. 2015). Finally, future LULC simulations for 2030,
2040, and 2050 were created by merging the BP, TSM, and
TPM. The CA model equation is represented as follows
(Tadese etal. 2021):
where S (t + 1) indicates the system status at the time of (t,
t + 1), as a function of the state probability at any time (N).
This model is popularly used for LULC monitoring, eco-
logical modeling, change simulation, and predicting land
use change and the stability of future land development in
(3)
S(t,t+1)=f(S(t),N)
the area of interest. Equation(4) illustrates the calculation
of LULC change projection:
where S (t) is the system status at time t, S (t + 1) is the
system status of time t + 1; Pij is the transition probability
matrix in a state which is calculated as follows (Kumar etal.
2014; Wangyel etal. 2021):
In the CA–Markov model, the TBs are represented by
P, where the likelihood of a state i moving to another state
j in the following timeframe is signified by Pij. Any given
instance’s state probability is indicated by PN. The analysis
of the Markov chain involves associating near-zero prob-
abilities with minimal transitions, while probabilities close
to one suggest significant transitions (Kumar etal. 2014;
Wangyel etal. 2021). This technique efficiently estimates
(4)
S(t,t+1)=Pij ×S(t)
(5)
=
‖Pij‖=‖
P
1,1
P
1,2
P
1,N
P1,1P1,2 P2,N
………
P
N,1
P
N,2
P
N,N
‖
(
0≤P
ij
≤1
),
Fig. 2 Details of the data, methods and processes involved in the overall methodology utilized in this study
Landscape and Ecological Engineering
the scale of land alterations from a preceding year to an
anticipated future year.
Determination ofESV
The determination of the ESV necessitated examining the
LULC of the KHANP for the years 1992, 2000, 2010, and
2020, and making projections for the years 2030, 2040,
and 2050 (Table7). For each respective year, LULC data
sets, serving as substitutes for ESV measurement, were
compiled and their corresponding areas were combined
using GIS (Kindu etal. 2016a, b). The valuation of eco-
system services incorporated the evaluation of concepts
and methodologies proposed by Costanza etal. (2014),
utilizing an adjusted coefficient approach (Table6). This
methodology accords a value to each LULC classification
based on its contribution to ecosystem services. The ESV
for every LULC category was determined by multiply-
ing its unique value coefficient by its size in hectares. By
summing the values of all the existing LULC classifica-
tions, the total landscape’s ESV was calculated for each
year. The coefficient values suggested by Costanza etal.
(2014) were chosen for this study due to their extensive
applicability. These coefficients offered evaluations for 16
significant biomes and 17 ecosystem service functions,
thus making them the most apt set of valuation coefficients
for our analysis (Table7).
To calculate the ESV of different LULCs, LULC fn, and
the ESVtotal, these mathematical equations were utilized:
(6)
ESV
k=
∑
f
Ak×VCkf
Table 6 Specific coefficient
values recommended by
Costanza etal. (2014)
LULC types Similar biomes as per Costanza
etal. (2014)
Coefficient value of ecosystem services (US
$ ha−1 year−1) as per Costanza etal. (2014)
Forest Forest 3800
Grassland Grassland/rangeland 4166
Rocky barren Ice/rock 0
Snow Ice/rock 0
Waterbody Lake/river 12,512
Table 7 The yearly value
coefficients for ES (in USD
ha−1 year−1)
Sl. no Ecosystem services types Forest Grassland Rocky barren Snow cover Waterbody
Provisioning
1 Food production 270 1192 – – 106
2 Raw materials 152 54 – – –
3 Genetic resource 448 1214 – – –
4 Water supply 143 60 – – 1808
Regulating
5 Gas regulation 4 9 – – –
6 Disturbance regulation 19 0 – – –
7 Erosion control 100 44 – – –
8 Pollination 9 35 – – –
9 Climate regulation 710 40 – – –
10 Biological control 169 31 – – –
11 Water regulation 3 3 – – 7514.1
12 Waste-treatment 120 75 – – 917.7
Supporting
13 Nutrient cycling 66 0 – – –
14 Soil formation 14 2 – – –
15 Habitat/refugia 619 1214 – – –
Cultural
16 Cultural 1 167 – – –
17 Recreation 953 26 – – 2166
Total ESV 3800 4166 – – 12,512
Landscape and Ecological Engineering
In this context, ESVk, ESVf, and ESVt signify the ESV of
the LULC category k, LULC function f, and the comprehen-
sive ESV, respectively. The symbol Ak stands for the area
(measured in hectares) of LULC category k, while VCkf indi-
cates the value coefficient (expressed in US dollars per hectare
annually) that correlates to the specific land use category ‘k’
and the ecosystem service function ‘f’. The value coefficients
were computed using unit values from 2007 to assess the spa-
tio-temporal patterns of ESV during the test periods (Kindu
etal. 2016a, b; Sannigrahi etal. 2018a, b; Arowolo etal.
2018a, b). The fluctuating dynamics of ESVs were evaluated
by computing the percentage variance between the projected
values in each baseline year using the ensuing formula:
In Eq.(9), ESVc is indicative of the ESV change rate, while
ESVend and ESVstart signify the projected ESV at the end and
the start of the investigation duration, respectively, and ‘t’ is a
symbol for the period. Regarding the unpredictability related
to the utilization of certain biomes as substitutes for LULC
categories in the ESV model offered by Costanza etal. (2014),
a sensitivity examination was carried out to scrutinize the
reaction of ESV to modifications in the value coefficient (He
etal. 2021). This process encompassed a 50% adjustment of
the value coefficient for several entities such as forest, grass-
land, rocky barren, snow cover, and waterbody, followed by
the calculation of the associated CS (coefficient of sensitivity)
through the utilization of the formula:
In Eq.10, ESV and VC represent the estimated ESV
and the coefficient value, respectively. ‘i’ depicts the ini-
tial value, ‘j’ depicts the adjusted values, and ‘k’ represents
the land use types. CS values greater than 1 indicate elastic
ESV estimates with respect to the coefficient, while CS val-
ues less than 1 indicate inelastic ESV estimates, indicating
reliable results even with the relatively low accuracy of the
value coefficient (Kreuter etal. 2001; Liu etal. 2012). The
spatial variation of ESV was assessed by dividing the study
area into equal grids and calculating ESVs for each LULC
class within each grid. The kriging spatial analysis tool was
employed to assess the ESV spatial variations across the
study area. ESV values were estimated for 1992, 2000, 2010,
and 2020 based on observed LULC maps, and projected
(7)
ESV
f=
∑
k
Ak×VCkf
(8)
ESV
t=
∑
k
∑
f
Ak×VCkf
(9)
ESV
c=
ESV
end
−ESV
start
ESVstart
×1
t
×100
%
(10)
CS
=
(ESV
j
−ESV
i
)∕ESV
i
(VC
jk
−VC
ik
)∕VC
ik
ESV values were determined for 2030, 2040, and 2050 based
on projected LULC maps.
As described above, in this study, the CA–Markov model
was utilized to project future LULC patterns in the study
area. A key step in validating the effectiveness of this model
was to assess its performance in accurately projecting LULC
changes. For this purpose, we employed a Chi-square test, a
statistical method commonly used to determine the goodness
of fit between observed and projected data. The Chi-square
test compares the percentage share of individual LULC
classes, observed in the field (denoted as “A” in our data),
with the corresponding percentages estimated by the Markov
chain model (denoted as “P”) (Table8). This comparison
helps in quantifying the degree to which the model’s projec-
tions align with the actual observed data. A lower Chi-square
value indicates a closer fit between the model predictions
and the observed data, suggesting a higher accuracy of the
model in reflecting the real-world LULC distribution. In our
analysis, the Chi-square test yielded a value of 0.135. To
interpret this result, we compared it against the critical value
for the Chi-square distribution at a 95% confidence level
(denoted as
X2
0.05
) with 4 degrees of freedom (df), which
is 9.49 (Eq.11). The computed Chi-square value is signifi-
cantly lower than the critical value indicating a good fit. This
outcome demonstrates that the Markov chain model has a
high level of accuracy in predicting LULC changes for our
study area, thus validating its suitability as a tool for project-
ing future LULC patterns:
Results
Dynamics ofLULC change
Investigating the LULC changes from 1992 to 2050
revealed significant variations across five categories,
(11)
X
2=
∑
(P−A)
A
2
=0.135; df =4 and X2
0.05(4)=
9.49
Table 8 Validation of change prediction based on actual (2020) and
projected (2020) LULC map using the Chi-square test (X2)
LULC classes Actual area
(A) (2020)
Projected area (P) (P-A)2/A
Forest 27.47 27.47 0.016
Grassland 5.09 5.09 0.078
Rocky barren 37.59 37.59 0.012
Snow 29.41 29.41 0.014
Waterbody 0.44 0.44 0.015
Total 100 100 0.135
Landscape and Ecological Engineering
namely, forest, grassland, rocky barren, snow cover, and
waterbody. Table9 shows the area-specific variations
within these categories, providing insights into the tem-
poral changes. Figures3 and 4 depict the spatial LULC
distribution, explaining the future progression of these
changes. The forest region, originally covering 666.75
km2 in 1992, underwent a decline of 9.74%, shrinking to
601.84 km2 in 2050. Concurrently, the grassland region
exhibited a growth of 34.14%, expanding from 152.71
km2 in 1992 to 204.85 km2 in 2050. Most noticeably,
the rocky barren class experienced a dramatic 343.83%
increase, expanding from 304.82 km2 in 1992 to 1352.91
km2 by 2050. In contrast, the snow-covered region expe-
rienced a 65.63% decline, contracting from 1578.29 km2
in 1992 to 542.40 km2 by 2050. The area of the waterbody
remained relatively constant throughout the period, exhib-
iting a minor increase from 11.22 km2 in 1992 to 11.79
km2 by 2050. Table10 presents decade-wise changes in
LULC categories. The forest area experienced phases of
expansion and contraction, initially expanding by 21.35%
Table 9 Extent of LULC types
in the KHANP from 1992 to
2050
LULC types 1992 2000 2010 2020 2030 2040 2050
Area in sq km
Forest 666.7 809.1 825.0 745.4 684.1 637.2 601.8
Grassland 152.7 100.5 91.8 138.2 170.0 191.1 204.8
Rocky barren 304.8 813.8 862.6 1020.0 1151.3 1261.1 1352.9
Snow 1578.2 978.8 922.9 798.1 696.4 612.5 542.4
Waterbody 11.2 11.3 11.3 11.8 11.8 11.8 11.7
Fig. 3 LULC maps of study area for 1992, 2000, 2010, and 2020
Landscape and Ecological Engineering
from 1992 to 2000, then contracting during the subse-
quent decades, with decreases of 9.64%, 8.23%, 6.86%,
and 5.55% during 2010–2020, 2020–2030, 2030–2040,
and 2040–2050, respectively. Similarly, the grassland area
initially contracted by 34.14% between 1992 and 2000,
before reversing the trend with continuous expansion
in the ensuing decades. The rocky barren area consist-
ently grew over the studied periods, whereas the snow-
covered region and the waterbody area underwent con-
tinual decreases and marginal increases, respectively.
Table11 presents the LULC transformations across dif-
ferent classes during the observed period (1992–2020),
the projected period (2020–2050), and the total changes
covering from 1992 to 2050. To enhance the credibility of
our model, we assessed the accuracy of the LULC maps
derived from LANDSAT OLI 2020 (Table3) and LAND-
SAT ETM + 2010 (Table4), which formed the basis for
the transition potential projections. The 2020 LULC map,
assessed using ground truthing, demonstrated high reli-
ability with user accuracies of 100% for forest, 87.88%
Fig. 4 Maps of LULC of the study area for 2030, 2040 and 2050
Table 10 Period-wise LULC
changes in the study site
between 1992 and 2050
LULC types 1992–2000 2000–2010 2010–2020 2020–2030 2030–2040 2040–2050 1992–2050
Area in percentage
Forest 21.3 1.9 – 9.6 – 8.2 – 6.8 – 5.5 – 9.7
Grassland – 34.1 – 8.6 50.5 23.0 12.4 7.1 34.1
Rocky barren 167.0 5.9 18.2 12.8 9.5 7.2 343.8
Snow – 37.9 – 5.7 – 13.5 – 12.7 – 12.0 – 11.4 – 65.6
Waterbody 1.1 0 4.0 0 – 0.08 – 0.08 5.0
Landscape and Ecological Engineering
for grassland, 96.67% for rocky barren, 100% for scrub,
and 100% for waterbody. The corresponding producer’s
accuracies were equally robust at 97% for forest, 100%
for grassland, 88% for rocky barren, 100% for scrub, and
100% for waterbody. Similarly, the 2010 LULC map, vali-
dated using google historical imagery, showed user accu-
racies of 93.94% for forest, 88.57% for grassland, 93.33%
for rocky barren, 100% for scrub, and 100% for waterbody.
Producer’s accuracies for this map were 100% for forest,
94% for grassland, 88% for rocky barren, 95% for scrub,
and 100% for waterbody.
The LULC change analysis depicted a dynamic shift in
the park’s landscape over nearly 6 decades. From 1992 to
2020, significant changes included the conversion of forest
to grassland and rocky barren, totaling 80.13 km2 and 38.46
km2, respectively. Notably, there was forest regeneration
from rocky barren, accounting for 18.79 km2. This transi-
tion, while seemingly counterintuitive, may partly reflect
minor classification errors due to shadow effects in satellite
imagery, where rocky areas could have been misclassified
as forests and vice versa. In addition, a modest increase
from snow cover to forest (2.43 km2) was observed. Grass-
lands experienced a conversion of 17.30 km2 into forest,
indicating a dynamic interplay between these categories.
The period was marked by a substantial transformation of
snow cover (697.86 km2) into rocky barren, with smaller
proportions transitioning to forest and grassland. Waterbody
areas, although minimal, underwent changes into various
other land use classes. The 2020–2050, projections suggest
further forest conversion into rocky barren and grassland by
78.29 km2 and 66.95 km2, respectively. A significant por-
tion of the snow-covered area (254.45 km2) is expected to
transition to rocky barren. However, other LULC categories
are expected to exhibit relative stability, with no significant
transformations projected. Over the extended period from
1992 to 2050, the total changes highlight a continuous
change in the landscape. The overall trend shows a persistent
decrease in forest cover, with a total reduction of 118.36 km2
transitioning to grassland and rocky barren. Grassland areas
have fluctuated, with a net change of 75.61 km2 transforming
into other LULC categories. The rocky barren class has seen
a notable increase, primarily due to transitions from snow
cover, totaling 675.42 km2. It is important to note that some
of these transitions, especially between forest and rocky bar-
ren, could be attributed to minor classification errors as a
result of shadow effects in the satellite imagery as described
above. Figures5 (a-c) depict these LULC transitions, pro-
viding a clear representation of the magnitude and direction
of changes between categories. They effectively illustrate
the flow of landscape alterations over time, highlighting the
dynamic and evolving nature of the region’s land cover. The
acknowledgment of potential classification errors due to
shadow effects reinforces the need for cautious interpretation
of transitions, especially in complex terrains where LULC
categories like forests and rocky barrens intersect.
Estimation ofESV
The value of ecosystem services provided by the LULC cat-
egories has been estimated and modeled using the global
coefficient value of Costanza etal. (2014) for the years 1992,
2000, 2010, 2020, 2030, 2040, and 2050. Table12 provides
the estimated values of the ESV in US$ for different LULC
types in different years. The data show that the highest ESV
is recorded for forestland, followed by grassland and water-
bodies. On the other hand, rocky barren and snow-covered
areas have no recorded ESV because Costanza etal. did not
provide any coefficient values against these land use classes.
In 1992, the ESV of forestland was recorded at 253.37 mil-
lion US$, which was the highest among all LULC types.
It was observed an increasing trend up to 2010, which was
313.5 million US$. However, by 2020, there was a decrease
in the ESV of forestland, reaching 283.28 million US$ and
Table 11 LULC transition matrix depicting area in sq km that tran-
sitioned between various LULC classes during the observed period
from 1992 to 2020, the projected changes for the period from 2020 to
2050, and the total changes from 1992 to 2050
LULC class transformation 1992–2020 2020–2050 1992–2050
Area in sq km
Forest–forest 548.11 600.14 440.21
Forest–grassland 80.13 66.95 139.44
Forest–rocky barren 38.46 78.29 87.03
Forest–snow cover 0.02 – 0.01
Forest–waterbody 0.12 0.1 0.15
Grassland–forest 17.3 – 92.33
Grassland–grassland 134.47 138.15 52.67
Grassland–rocky barren 0.96 0.07 7.72
Grassland–snow cover – – 0
Grassland–waterbody 0.01 0.03 0.02
Rocky barren–forest 18.79 0.46 12.24
Rocky barren–grassland 0.07 0.05 0.27
Rocky barren–rocky barren 283 1020.01 290.38
Rocky barren–snow cover 2.43 0.09 1.38
Rocky barren–waterbody 0.44 0.01 0.45
Snow cover–forest 74.02 0.83 56.56
Snow cover–grassland 10.71 – 12.78
Snow cover–rocky barren 697.86 254.45 967.36
Snow cover–snow cover 795.22 542.4 541.1
Snow cover–waterbody 0.42 – 0.42
Waterbody–forest 0.09 0.02 0.08
Waterbody–grassland 0.03 0.1 0.07
Waterbody–rocky barren 0.29 0.04 0.33
Waterbody–snow cover 0.01 – –
Waterbody–waterbody 10.55 11.4 10.49
Landscape and Ecological Engineering
Fig. 5 Sankey diagram visual-
izing LULC transitions. a 1992
to 2020, b 2020 to 2050, c 1992
to 2050
Landscape and Ecological Engineering
this trend continued until 2050. By 2050, the ESV of forest-
land is expected to reach 228.7 million US$. In 1992, the
ESV of grassland was recorded at 63.62 million US$, which
was much lower compared to the ESV of forestland. How-
ever, by 2000 and 2010, it was found 41.9 million US$ and
38.26 million US$, respectively. In 2020, there was a notice-
able increase in the ESV of grassland, with a value of 57.58
million US$, and this trend continued up to 2050, when it
was expected to reach 85.34 million US$. Costanza and his
team did not provide any coefficient value for rocky barren
and snow cover areas and as a result, this study found no
ESV for these particular land use classes. The ESV of water-
bodies remained consistent over the years, with 14.04 mil-
lion US$ recorded in 1992 and 14.2 million US$ recorded in
2000 and 2010. From 2020 to 2050, the ESV of waterbodies
remained nearly unchanged, with values ranging from 14.78
to 14.75 million US$.
The data in Table12 also shows the total ESV from 1992
to 2050. In 1992, the total ESV was recorded at 331.03 mil-
lion US$, and this value increased to 363.57 million US$
in 2000. The total ESV reached its highest value in 2010,
with 365.96 million US$. From 2010 to 2020, there was a
decrease in the total ESV, with a value of 355.64 million
US$ recorded in 2020. This trend continued from 2020 to
2050. By 2050, the total ESV is expected to reach 328.79
million US$. Hence, the data indicate a significant decrease
in the ESV of forestland and an increase in the ESV of grass-
land. Table13 displays the change rate in estimated ESV for
different LULC types from 1992 to 2020 and the projected
period from 2020 to 2050. The change rate in the ESV of
forests from 1992 to 2020 was 11.8%; however, it is expected
to decrease by 19.27% from 2020 to 2050, depicting an over-
all decrease of 9.74% from 1992 to 2050. The grassland has
experienced negative changes from 1992 to 2020 at a rate of
9.49%. However, it is expected to increase by 48.21% from
2020 to 2050, resulting in an overall increase of 34.14%
from 1992 to 2050. No change rate values are available for
rocky barren and snow cover areas due to their nil ESV. The
change rate in the ESV of waterbodies from 1992 to 2020
was 5.27%, and it is expected to decrease by 0.2% from 2020
to 2050, resulting in an overall increase of 5.06% from 1992
to 2050. The total change rate in the total ESV from 1992
to 2020 was 7.43%, but it is expected to decrease by 7.55%
from 2020 to 2050. The study observed an overall decrease
in ESV of 0.68% from 1992 to 2050.
Table14 presents the estimated values of various eco-
system functions within the study area from 1992 to 2050.
The ecosystem services are categorized into four groups:
provisioning, regulating, supporting, and cultural services.
Provisioning services cover activities such as food produc-
tion, raw material provision, genetic resource availability,
and water supply. The value of food production increased
from 36.32 million US$ in 1992 to 40.79 million US$ in
2050. The value of raw materials decreased from 10.96 mil-
lion US$ in 1992 to 10.25 million US$ in 2050. The value of
genetic resources remained relatively constant with a slight
increase from 48.41 million US$ in 1992 to 51.83 million
US$ in 2050. The value of water supply decreased from
12.48 million US$ in 1992 to 11.97 million US$ in 2050.
Regulating services include gas regulation, disturbance reg-
ulation, erosion control, pollination, climate regulation, bio-
logical control, water regulation, and waste-treatment. The
value of these services increased from 88.70 million US$
in 1992 to 83.05 million US$ in 2050. Supporting services
include nutrient cycling, soil formation, and habitat/refugia.
The value of these services remained relatively constant,
with a slight decrease from 65.17 million US$ in 1992 to
66.97 million US$ in 2050. Cultural services include cul-
tural and recreation services. The value of cultural services
increased from 2.62 million US$ in 1992 to 3.48 million
Table 12 ESV provided by
various LULC types from the
year 1992 to 2050
LULC types ESV in US$ 106
1992 2000 2010 2020 2030 2040 2050
Forest 253.37 307.47 313.5 283.28 259.97 242.14 228.7
Grassland 63.62 41.9 38.26 57.58 70.85 79.64 85.34
Rocky barren 0 0 0 0 0 0 0
Snow 0000000
Waterbody 14.04 14.2 14.2 14.78 14.77 14.76 14.75
Total 331.03 363.57 365.96 355.64 345.59 336.54 328.79
Table 13 ESV change rate during the observation period (1992–
2021) and the projected period (2021–2050)
LULC types 1992–2020 2020–2050 1992–2050
Change rate in percentage
Forest 11.8 – 19.27 – 9.74
Grassland – 9.49 48.21 34.14
Rocky barren – – –
Snow – – –
Waterbody 5.27 – 0.2 5.06
Total 7.43 – 7.55 − 0.68
Landscape and Ecological Engineering
US$ in 2050. The value of recreation increased from 66.37
million US$ in 1992 to 60.44 million US$ in 2050.
Spatiotemporal variation inESV categories
The ESV spatio-temporal variation has been investigated in
relation to changes in LULC. Figures6 and 7, and Table15
illustrate the spatial and temporal patterns of ESV based
on coefficient values provided by Costanza etal. (2014) for
the period spanning from 1992 to 2050. The ESV catego-
ries considered in the study include very low (< $70,000
per sq. km.), low ($70,000–140,000 per sq. km.), moderate
($140,000–210,00 per sq.km.), high ($210,000–280,000
per sq. km), and very high ($280,000–350,000 per sq.
km.), which are given as a percentage of the total area. The
data are presented for seven different years: 1992, 2000,
2010, 2020, 2030, 2040, and 2050. In the present study,
the very low ESV category remained relatively stable over
the years, with a slight increase from 31.68% in 1992 to
32.23% in 2050. The low category showed a similar trend,
with a slight decrease from 29.99% in 1992 to 28.65%
in 2050. The moderate category showed a considerable
increase from 16.36% in 1992 to 19.82% in 2050. The high
category experienced a slight decrease over the years, from
16.57% in 1992 to 14.93% in 2050. The very high category
remained relatively stable, with a slight decrease from
5.40% in 1992 to 4.37% in 2050. Table16 shows the sen-
sitivity coefficient (CS) values for various LULC classes
over an extensive period, from 1992 to 2050. While the
primary factors driving LULC changes are environmen-
tal shifts, such as climate change, it is important to note
that human-driven activities, including seasonal grazing
by nomadic communities and peripheral resource extrac-
tion, may also have influenced the observed trends. These
activities could contribute to the fluctuations in the CS
values of different LULC types over time. These CS val-
ues are derived from the ESV framework as proposed by
Costanza etal. (2014). Using CS, we can gain insights into
the responsiveness of each LULC type to environmental
shifts and human-driven activities over the years. Table16
highlights that the forested areas started with a CS value of
0.76 in 1992, experienced a mild peak in the early 2000s
and then embarked on a gradual decline to reach 0.70 by
2050. This pattern potentially suggests that the sensitivity
Table 14 Calculated values for
various ecosystem services in
KHANP in 1992–2050
S. no. ESV functions 1992 2000 2010 2020 2030 2040 2050
Ecosystem service values (in US$ 106)
Provisioning
1 Food production 36.32 33.95 33.34 36.73 38.87 40.12 40.79
2 Raw materials 10.96 12.84 13.04 12.08 11.32 10.72 10.25
3 Genetic resource 48.41 48.46 48.11 50.18 51.29 51.75 51.83
4 Water supply 12.48 14.23 14.4 13.63 12.94 12.39 11.97
Total 108.17 109.48 108.89 112.62 114.42 114.98 114.84
Regulating
5 Gas regulation 0.4 0.41 0.41 0.42 0.43 0.43 0.43
6 Disturbance regulation 1.27 1.54 1.57 1.42 1.3 1.21 1.14
7 Erosion control 7.34 8.53 8.65 8.06 7.59 7.21 6.92
8 Pollination 1.13 1.08 1.06 1.14 1.21 1.24 1.26
9 Climate regulation 47.95 57.85 58.94 53.48 49.25 46.01 43.55
10 Biological control 11.75 13.99 14.23 13.03 12.09 11.36 10.81
11 Water regulation 8.68 8.8 8.81 9.14 9.13 9.11 9.1
12 Waste-treatment 10.18 11.51 11.63 11.07 10.57 10.16 9.84
Total 88.7 103.71 105.3 97.76 91.57 86.73 83.05
Supporting 0 0 0
13 Nutrient cycling 4.4 5.34 5.45 4.92 4.52 4.21 3.97
14 Soil formation 0.96 1.15 1.17 1.07 0.99 0.93 0.88
15 Habitat/refugia 59.81 62.29 62.22 62.93 62.99 62.65 62.12
Total 65.17 68.78 68.84 68.92 68.5 67.79 66.97
Cultural 0 0 0
16 Cultural 2.62 1.76 1.62 2.38 2.91 3.26 3.48
17 Recreation 66.37 79.83 81.32 73.96 68.2 63.78 60.44
Total 68.99 81.59 82.94 76.34 71.11 67.04 63.92
Total ESV 331.03 363.57 365.96 355.64 345.59 336.54 328.79
Landscape and Ecological Engineering
of forests has diminished over the years, possibly due to
evolving environmental conditions. In contrast, grasslands
present an interesting trajectory. Initiating at a CS value of
0.19 in 1992, they recorded their lowest sensitivity at 0.10
in 2010. However, from this point, there is a discernible
upward trend, with projections placing the coefficient at
0.26 by 2050. This rise indicates a heightened sensitivity
of grasslands, which is expected to increase in the com-
ing decades. Waterbodies display remarkable stability in
their CS, consistently remaining around 0.04 throughout
the observed years. This constancy implies that the sensi-
tivity of waterbodies to external alterations has remained
largely unaltered.
Discussion
KHANP’s ecological diversity, ecosystem service pro-
visioning, and natural beauty extend its importance
beyond regional boundaries, making it a crucial region for
conservation. This study presents a unique opportunity to
examine the impacts of land use changes on the value of
ecosystem services, significantly enhancing our understand-
ing of how these changes holistically affect the environ-
ment, biodiversity, and human communities. Notably, this
park is regarded as the least impacted by human activities
in the region concerning LULC change dynamics. However,
despite the park’s remote location, peripheral human activi-
ties, particularly seasonal grazing by nomadic communities
such as the Gujjar and Bakerwal, have contributed to local-
ized land degradation in the park’s buffer zones (Kumar and
Hamal 2009; Kumar and Sharma 2014; Kichloo etal. 2023,
2024). These activities, while limited, can lead to soil ero-
sion and vegetation loss. However, the significant expan-
sion of rocky barren areas observed in the park is primarily
driven by the reduction in snow cover and glacial retreat due
to climate change (Rai etal. 2024). While the core areas of
KHANP are largely undisturbed, these peripheral interac-
tions demonstrate the subtle yet impactful role of human-
driven activities on the park’s ecosystems. This aspect
Fig. 6 Spatial and temporal variations of the ESV in 1992, 2000, 2010, and 2020
Landscape and Ecological Engineering
Fig. 7 Spatial and temporal variations of the ESV in 2030, 2040, and 2050
Table 15 ESV spatio-temporal
variation as per Costanza etal.
(2014)
ESV categories 1992 2000 2010 2020 2030 2040 2050
Area in percentage
Very low 31.68 27.73 27.88 28.39 30.43 31.33 32.23
Low 29.99 28.02 27.94 28.1 30.94 28.6 28.65
Moderate 16.36 20.07 20.06 20.91 16.82 19.54 19.82
High 16.57 16.93 17.06 17.47 16.85 16.09 14.93
Very high 5.4 7.26 7.06 5.13 4.96 4.45 4.37
Table 16 Sensitivity coefficient
(CS) values for various LULC
classes based on ESV values
according to Costanza etal.
(2014)
Land use land cover types CS (coefficient of sensitivity)
1992 2000 2010 2020 2030 2040 2050
Forest 0.76 0.84 0.85 0.80 0.75 0.72 0.70
Grassland 0.19 0.11 0.10 0.16 0.20 0.23 0.26
Rocky Barren – – – – – – –
Snow –––––––
Waterbody 0.04 0.04 0.03 0.04 0.04 0.04 0.04
Landscape and Ecological Engineering
provides a critical context for our research. The insights
gained from this study are expected to inform proactive
biodiversity conservation strategies for this and other con-
servation reserves of the region. These strategies will aim to
strengthen ecosystem resilience, thereby enabling sustain-
able management of such reserves, particularly in the face of
significant natural forces compared to the impact of human-
driven land degradation. This study’s objective to assess
the current value of KHANP’s ES and predict future trends
based on potential changes in LULC offers valuable contri-
butions to conservation planning, ecological compensation
decisions, and sustainable management of this remarkable
natural reserve (Wani etal. 2022).
Our study revealed significant changes in ESV across a
range of LULC categories. Analysis of data from Table4
indicated a net decrease in forest cover from 1992 to 2050.
This decline in forest cover is linked to a decrease in the
ESV of forestlands over this period, raising concerns about
the sustainability of KHANP’s forest ecosystems and their
future ability to provide essential ecosystem services (Koo
etal. 2019; Chatterjee etal. 2022a, b). This decline in forest
cover within KHANP is not an isolated phenomenon but
is reflective of a broader global trend. Globally, forests are
under significant pressure due to a combination of deforesta-
tion, land conversion for agriculture, and climate change.
Studies have shown that forest ecosystems worldwide are
experiencing similar declines, leading to a reduction in
their ability to provide critical ecosystem services, such as
carbon sequestration and biodiversity conservation (FAO
2020). This aligns with global reports indicating that the
world’s forests have been decreasing at an alarming rate,
contributing to a loss of biodiversity and increased vulner-
ability to climate change (IPCC 2021). The trends observed
in KHANP are thus a microcosm of these larger global
patterns, emphasizing the need for concerted conservation
efforts both locally and globally.
In contrast, an increase in grassland extent from 1992 to
2050 can be attributed to a combination of natural events
and human activities, such as seasonal grazing by nomadic
communities, which have contributed to the conversion of
peripheral forested areas into grasslands (Kumar and Hamal
2009; Kumar and Sharma 2014). This shift is reflected in the
increasing ESV trend for grasslands as these areas expand.
This may be indicative of successful grassland management
and restoration efforts in the park. The substantial decrease
in forest ESV by 19.27% from 2020 to 2050, along with an
anticipated increase in grassland ESV by 48.21% during the
same period, signals a shift in the park’s ecosystem dynam-
ics (Yang etal. 2022a). Such trends underscore the need
for adaptive management strategies that can accommodate
these changing values and prioritize necessary conserva-
tion and restoration efforts (Peng etal. 2021; Lahon etal.
2023a, b). The consistent expansion of rocky barren land
could be attributed to soil degradation caused by factors like
deforestation, overgrazing, climate change, and geological
processes (Li etal. 2009). The reduction in snow cover due
to rising temperatures has also contributed significantly to
the expansion of rocky barren land in KHANP. As snow and
glaciers retreat, the underlying rocky surfaces are exposed,
adding to the area classified as rocky barren. While sea-
sonal overgrazing by nomadic communities accelerates land
degradation in some regions, the reduction in snow cover
driven by climate change is a major factor contributing to
the large-scale increase in rocky barren land observed in
the park, as discussed above and is a prominent process in
other Himalayan regions as well (Bajracharya etal. 2010;
Chand etal. 2022).
The region experienced a steady decline in snow cover
area during the study period, which can be largely attributed
to the continuous increase in both regional and global annual
temperatures. Climate change is indeed the main driver
behind this trend, as supported by several climatological
studies (Singh etal. 2018; Khan etal. 2024). Notably, the
warming trend in the Himalayas has been more pronounced
than the global average, with temperature increases ranging
between ~ 0.9 and 1.6 ℃ over the past century, compared
to the global mean increase of 0.85 ℃ (Li etal. 2019a, b;
Banerjee etal. 2021). The rate of temperature increase in
the region has also accelerated, as revealed by long-term
data. For example, while the trend between 1971 and 1994
showed an increase of + 0.12 ℃ per decade (Shrestha etal.
1999), more recent studies indicate that the Himalayan
region has been warming at an average rate of + 0.28 ℃ per
decade between 1951 and 2020 (Wester etal. 2023). This
accelerating warming trend suggests that the rate of snow
cover reduction is likely to continue or even intensify in the
coming decades, further impacting the region’s ecosystems
and hydrology.
Given the significant proportion of snow cover and
rocky barren areas in KHANP, their exclusion from the
ESV assessment presents a notable limitation. These areas,
although less biologically productive according to traditional
valuation models, play crucial ecological roles. Snow cover
is essential for water regulation and maintaining down-
stream water resources, while rocky barren areas contribute
to habitat provision, particularly for species like the snow
leopard. As such, their ecosystem functions are critical for
both biodiversity conservation and the stability of regional
hydrological systems. While Costanza etal. (2014) did not
provide specific coefficient values for these LULC types due
to their lower biological productivity, their ecological impor-
tance cannot be ignored. The lack of monetary valuation for
these areas in our analysis highlights a gap in the ecosystem
service valuation methodology. Nevertheless, this study
provides a strong foundation for understanding the broader
ecosystem services in KHANP and paves way for future
Landscape and Ecological Engineering
assessments that need to explore alternative approaches,
such as scenario-based modeling, to hypothesize ESV values
for snow cover and rocky barren areas, thereby to achieve
a more comprehensive understanding of the ecosystem ser-
vices and disservices associated with these land cover types.
In contrast, the consistent ESV of waterbodies through-
out the study period highlights their resilience and ongo-
ing importance in the ecosystem, serving as a stabilizing
factor amid the fluctuating values of other LULC types
(Roy etal. 2024). The varying ESV change rates for forests,
grasslands, and waterbodies from 1992 to 2050 reflect the
diverse impacts of both natural and anthropogenic factors
on these ecosystems (Yin etal. 2023). The overall decrease
in total ESV from 1992 to 2050, despite fluctuations in
individual LULC categories, is a concerning indicator of
broader ecosystem service degradation, calling for urgent
policy actions (Eguiguren etal. 2019; Kertész etal. 2019;
Hossu etal. 2019; Hasan etal. 2020; Li etal. 2022). Notably,
the projected reduction in overall ESV, driven primarily by
declining forest cover, emphasizes the critical role of forest
ecosystems (Fenta etal. 2020). This trend highlights the
urgency of prioritizing forest conservation and implement-
ing effective management practices to sustain and enhance
the ecosystem services they offer. The shifts in ESV within
KHANP, driven by changes in LULC, also mirror global
challenges associated with climate change. As climate
change continues to alter ecosystems worldwide, the capac-
ity of these systems to provide essential services is being
compromised. The reductions in forest and snow cover, and
the corresponding ESV declines in KHANP, are emblematic
of broader global trends where climate change exacerbates
ecosystem degradation, thereby threatening the well-being
of human and natural systems (MEA, 2005).
The robustness of our ESV estimates was confirmed by
the CS being less than 1 across all land use types, signifying
the reliability of our estimations despite inherent uncertain-
ties in value coefficients (Yang etal. 2022b). This level of
validation is critical for policymakers and researchers who
depend on these estimates for environmental planning and
conservation initiatives. The CS values for different LULC
types provide important insights into the responsiveness of
these ecosystems to changes (Kindu etal. 2016a, b). For
instance, the reduced CS observed in forests reflects either
a degradation process or an adaptation to environmental
stresses (Tolessa etal. 2017). Conversely, the increased sen-
sitivity in grasslands could indicate a heightened response
to both environmental and anthropogenic changes (Hu etal.
2019). Moreover, the consistent CS values for waterbod-
ies underscore their resilience to external changes, rein-
forcing the need to preserve these stable ecosystems (Chen
etal. 2021). Our spatial analysis, categorizing ESV into
five classes based on a grid-wise assessment, revealed that
areas with dense forest cover and grasslands correspond to
higher ESV classes, while snow-covered and rocky barren
landscapes fall into lower ESV classes. This categorization
underscores the need for a region-specific approach to man-
age and conserve these diverse landscapes (Roy etal. 2024).
The stability observed in the very low ESV category and
the significant increase in the moderate ESV category from
1992 to 2050 indicate a dynamic shift in land utilization
patterns. These shifts, potentially resulting from evolving
land management practices, urbanization, or natural habi-
tat transformation, have substantial impacts on ecosystem
services across different regions. This finding aligns with
the trends reported by Sannigrahi etal. (2018a, b), Assefa
etal. (2021), and Chen etal. (2020), highlighting the need
for adaptive strategies in response to these changing land
utilization patterns.
Ecological impacts of LULC changes, such as habitat
fragmentation, biodiversity loss, and alterations in ecosys-
tem functions, directly influence the resilience and sustain-
ability of natural systems (Nath etal. 2023). These changes
can lead to the decline of key species, disruption of eco-
logical processes, and degradation of ecosystem services
that are vital for maintaining ecological balance (Chand
etal. 2024). Moreover, changes in land use alters liveli-
hoods, affect food security, and influence social dynamics.
For instance, deforestation for agriculture or development
can lead to soil erosion and reduced water quality, impact-
ing agriculture and local economies (Meraj etal., 2023). In
addition, the loss of culturally significant landscapes and
resources can affect the social fabric and cultural heritage
of communities. Understanding the broader ecological and
socio-economic implications of these changes is essential
for devising comprehensive conservation strategies and sus-
tainable land management practices. KHANP stands as a
pivotal case study in this regard, offering insights into the
impact of LULC changes on vital ecological processes and
regional ecosystem services. The park’s significant role in
water cycle regulation, soil conservation, and biodiversity
preservation highlights the necessity of its rigorous man-
agement and protection (Pisani etal. 2021; Ali etal. 2023),
and the dynamic nature of ecosystem services in response
to LULC changes (Arowolo etal. 2018a, b). The trends we
observed in KHANP serve as a microcosm of broader envi-
ronmental challenges, mirroring global concerns discussed
in works such as Asadolahi etal. (2018), Balkanlou etal.
(2020), Chatterjee etal. (2022a, b), and Munthali etal.,
(2023), that further augment the urgency of integrated con-
servation efforts and the importance of strategic land use
planning to address these pervasive environmental issues.
The expected biodiversity changes observed in KHANP
due to LULC alterations are indicative of a global biodi-
versity crisis. Habitat loss and fragmentation, driven by
land use changes, are among the leading causes of biodi-
versity decline worldwide (Yuan etal. 2024). The findings
Landscape and Ecological Engineering
in KHANP are in line with global assessments, such as the
IPBES Global Assessment Report, which warns that nearly
one million species are at risk of extinction due to human
activities, primarily land use change (IPBES 2019). This
highlights the importance of integrating local conservation
efforts within a global framework to mitigate biodiversity
loss and promote ecosystem resilience.
In this context, the integration of emerging technologies
such as AI, drones, and remote sensing presents a trans-
formative opportunity for conservation (Jiménez López and
Mulero-Pázmány 2019). These innovative tools are revo-
lutionizing the way natural ecosystems are monitored and
managed, by offering capabilities that were previously unat-
tainable. In KHANP, employing drones could enable effi-
cient monitoring of LULC changes across large and inacces-
sible areas, thereby providing high-resolution, real-time data
that could inform more precise and effective management
strategies (Chisom etal. 2024). Furthermore, AI, particu-
larly through machine learning algorithms, holds the poten-
tial to analyze complex datasets, predict future changes,
and identify patterns that may not be immediately visible
through traditional methods. Although these technologies
were not utilized in our study due to logistical constraints,
their potential application in future research could provide
invaluable insights into the dynamics of LULC changes and
their impacts on ecosystem services. By implementing drone
surveys and AI-based analysis in KHANP, more detailed
and frequent monitoring of changes could be achieved, ena-
bling proactive conservation measures and more informed
decision-making processes.
Mitigation strategies forsustainable forest
management
These findings hold significant policy implications for the
preservation and conservation of the ecosystem services in
KHANP. We suggest policymakers prioritize sustainable for-
est management and reforestation, implement sustainable
land management practices (SLMP) in rocky barren areas,
and develop strategies for the conservation and sustainable
use of grasslands and waterbodies. In the context of rocky
barren areas, SLMP here, refers to those practices that aim
to minimize further land degradation, improve soil stabil-
ity, and prevent erosion while maintaining the ecological
balance of these fragile environments, and could involve
measures such as controlled grazing, erosion control through
physical barriers or vegetation cover, and careful manage-
ment of human activities that might accelerate land degrada-
tion (Salesa and Cerdà, 2020; Richards etal. 2022). Climate
change adaptation and mitigation strategies should also be
incorporated into local and regional planning efforts. In the
context of the national parks of the Himalayas, sustainable
forest management is paramount for preserving the delicate
ecological balance and ensuring long-term benefits for the
local communities and the environment. Given the unique
challenges and opportunities in KHANP, specific adapta-
tion and mitigation strategies are required. As seen in other
regions, community involvement in forest management also
referred as community-based forest management (CBM) is
a viable option as a means of adaptation strategy (Nugroho
2021). Building on the success of CBM strategies in other
high-altitude regions, similar approaches could be effectively
adapted for KHANP. Involving local communities in conser-
vation efforts can enhance the sustainability of these initia-
tives by fostering a sense of ownership and responsibility
among local residents. For instance, in Nepal’s Annapurna
Conservation Area Project (ACAP), local involvement has
led to improved conservation outcomes and the development
of sustainable tourism, which could be a viable model for
KHANP, given its potential for eco-tourism. Similarly, Bhu-
tan’s community forest management groups (Pretzsch 2022)
could inspire the establishment of local forest management
cooperatives in KHANP, aiming to reduce illegal logging
and promote sustainable forest use. The success of com-
munity-led eco-tourism in Peru’s Huascarán National Park,
which provides alternative livelihoods (Rasmussen 2019), is
particularly relevant for KHANP, where economic pressures
may lead to unsustainable resource extraction. Implement-
ing CBM strategies in KHANP would require careful con-
sideration of the local socio-cultural context, including the
integration of traditional knowledge systems with modern
conservation practices. Challenges such as ensuring equi-
table participation, preventing resource-use conflicts, and
building local capacity can be addressed by drawing on the
experiences of these international examples. Tailoring these
strategies to the unique environmental and social dynamics
of KHANP can help mitigate forest degradation, conserve
biodiversity, and enhance the resilience of local communi-
ties to climate change impacts.
In addition, the development of an eco-tourism sustain-
ability maximization model (ESMM) in the Indian Hima-
layas highlights the potential of integrating ecological and
socio-economic factors for sustainable forest management
(Ashok etal. 2022). ESMM approach can be replicated in
the national parks of this region, focusing on sustainable
tourism that supports forest conservation while benefiting
local communities. Further, a proactive approach in forest
management under climate change, considering reactive,
active, and robust strategies, is essential (Yousefpour etal.
2017), which involves assessing the costs and benefits of
adaptation, climate change uncertainty, and the attitudes of
decision-makers towards anticipated risk. As far as the cli-
mate mitigation strategies are concerned, implementing cli-
mate-smart forestry practices, which include selecting indi-
cators such as forest damage, tree species composition, and
carbon stock, is essential for effective forest management in
Landscape and Ecological Engineering
the face of climate change. These practices help in adapting
to climate change impacts as well as contribute to mitiga-
tion by enhancing carbon sequestration. Furthermore, utiliz-
ing technologies such as LiDAR for operational tree mark-
ing can support long-term sustainable forest management,
although its effectiveness in detecting small trees in dense
forests, like those in the KHANP, may be limited (Georgo-
poulos etal. 2023). In addition, addressing the challenges
related to fodder production and sustainable livestock hus-
bandry is crucial, as highlighted by the issues in high-alti-
tude pasturelands in the Kashmir Himalayas (Ahmad etal.
2018). Effective management of pasturelands and integrating
livestock sector needs into forest management can mitigate
adverse impacts on forest ecosystems. Hence, the adaptation
and mitigation strategies for sustainable forest management
in the KAHNP should encompass a combination of commu-
nity-based approaches, sustainable use of resources, climate-
smart forestry practices, technological advancements, and
integrated livestock and pastureland management. These
strategies need to be tailored to the unique environmental
and socio-economic contexts of the region, ensuring a bal-
anced approach to conservation and development.
Limitations ofthis study andfuture scope
We acknowledge some limitations of our study. While the
global value coefficient method provided a generalized pic-
ture of ESV dynamics, it could potentially oversimplify
complex ecological relationships. In addition, given the sig-
nificant proportion of snow cover and rocky barren areas in
KHANP, their exclusion from the ESV assessment presents
a notable limitation. These areas, although less biologically
productive according to traditional valuation models, play
crucial ecological roles. Snow cover is essential for water
regulation and maintaining downstream water resources,
while rocky barren areas contribute to habitat provision,
particularly for species like the snow leopard. As such, their
ecosystem functions are critical for both biodiversity con-
servation and the stability of regional hydrological systems.
As discussed above as well that while Costanza etal. (2014)
did not provide specific coefficient values for these LULC
types due to their lower biological productivity, their eco-
logical importance cannot be ignored. The lack of monetary
valuation for these areas in our analysis highlights a gap
in the current ecosystem service valuation methodologies.
Future studies should explore alternative approaches, such
as scenario-based modeling, to hypothesize ESV values for
snow cover and rocky barren areas.
Further, this study only employed a benefit transfer
approach to estimate ESV in the study area. In addition to
this method, there are various other approaches available
for estimating ESV. For example, the InVEST (Integrated
Valuation of Ecosystem Services and Tradeoffs) model can
deliver spatially explicit valuations by integrating ecological
and economic data (Liu etal. 2020; Huang etal. 2021). The
contingent valuation method enables direct estimation of
ESV through surveys, capturing individuals’ willingness to
pay for specific ecosystem services (Kubiszewski etal. 2022;
Suzuki and Kohsaka 2022). Combining multiple methods
may yield more accurate results than relying on a single
one. Furthermore, our LULC change projections assumed a
continuation of historical trends, which might not account
for unpredictable factors such as policy changes, socio-eco-
nomic developments, and climate events. To address this,
future studies could benefit from adopting scenario-based
modeling approaches, such as the land change modeler
(LCM) within the Clark Labs software suite. LCM is par-
ticularly effective in integrating localized policy scenarios,
including land use regulations, conservation initiatives, and
community-based management practices, into its modeling
framework. By simulating the effects of specific policy
interventions, LCM can help predict how different policy
scenarios might influence land cover changes and ecosystem
service values over time. This would enable more targeted
conservation planning, allowing policymakers to assess
potential outcomes under various strategies and make more
informed decisions.
Moreover, our grid-wise analysis for ESV distribution
might not capture specific variations across the national
park. The grid-based approach, although effective for
broader assessments, could overlook localized nuances in
ecosystem services, particularly in regions with complex
topography and diverse microhabitats. Future research could
enhance this by employing finer scale spatial analyses or
integrating high-resolution data, which would allow for
a more detailed and accurate mapping of ESV variations
across the park’s varied landscapes. This would help iden-
tify critical areas that require focused conservation efforts
and provide a more nuanced understanding of the ecosystem
dynamics within KHANP.
One more limitation of this study is the absence of
advanced technological tools such as drones and AI, which
have the potential to greatly enhance data accuracy and
monitoring efficiency. The large area and rugged terrain
of KHANP, combined with limited funding, posed signifi-
cant challenges to the deployment of drone technology for
LULC monitoring. In addition, the integration of AI for data
analysis could have provided more sophisticated predictive
models and deeper insights into ecosystem changes. Future
studies that incorporate these technologies could overcome
these limitations, thereby offering more precise and compre-
hensive assessments of conservation needs and the effective-
ness of management strategies in high-altitude ecosystems.
Landscape and Ecological Engineering
Conclusions
Ecosystem services valuation (ESV) highlights the criti-
cal economic importance of our natural assets, promoting
a deeper public appreciation for these resources beyond just
environmental significance. This appreciation, in turn, fos-
ters a stronger call for conservation and offers policymak-
ers concrete economic reasons for judicious land use and
its management. Our investigation into KHANP’s LULC
dynamics from 1992 through projections to 2050 has shown
critical shifts in the landscape. Specifically, significant
decreases in the forest (9.74%) and snow cover (65.63%)
were offset by rises in rocky barren land (343.84%), grass-
lands (34.14%), and waterbodies (5.08%). Interestingly,
from 1992 to 2020, the region experienced an ESV increase,
contrasting the global decline reported by Costanza etal.
(2014). This increase was largely driven by forest expansion.
However, projections from 2020 to 2050 suggest a potential
dip in ESV, as forest cover and waterbodies are expected to
decline. This brings to light the indispensable role of for-
ests and waterbodies in ESV dynamics and highlights the
urgency for conservation initiatives. There is a pressing need
for strategic land use and proactive conservation efforts to
maintain the region’s ecological balance. While our study
sheds light on the LULC need to aim for sustainability
goals for ecosystem services, it does have its constraints.
Its focus on one region means findings might not be uni-
versally applicable. Projections made are based on current
trends and might not factor in unforeseen disruptive natural
or anthropogenic events. Furthermore, while ESV is instru-
mental, it might not cover an ecosystem’s intangible values,
be they cultural or spiritual. Despite these constraints, our
research offers invaluable insights into LULC changes vital
for ecosystem service sustainability. We consider that our
findings shall help steer policy directives aimed at envi-
ronmental improvement in the study area. Broader studies
across India, building on our groundwork, are also hoped
to get initiated. Subsequent research should aim to expand
the study’s geographical scope, refine ESV techniques, and
incorporate non-material ecosystem values. Enhancing ESV
approaches and methodologies will, in turn, provide more
improved land use decisions, paving the way for a more sus-
tainable future.
Acknowledgements The authors acknowledge the support of the
Jammu and Kashmir Wildlife Department for all the logistic support
during the field survey conducted for this research. We would also
like to thank the two anonymous reviewers and the handling editor for
their valuable comments during the peer review process, which greatly
enhanced the quality and rigor of our manuscript.
Author contributions All the authors contributed to the conception
and design of the study. Durlov Lahon, Gowhar Meraj, Shizuka Hashi-
moto, Jatan Debnath, Majid Abid Muslim Baba, Farooq, and Dhruba-
jyoti Sahariah: they collectively contributed to the conceptualization,
methodology, software utilization, data handling, and the drafting of
the original manuscript. Majid Farooq, Pankaj Kumar, Md. Nazrul
Islam, Pankaj Chandan, and Gowhar Meraj: they were instrumental in
the reviewing and editing process of the manuscript. Shizuka Hashi-
moto, Shruti Kanga, Pankaj Kumar, Sanjeev Sharma, Suraj Kumar
Singh, and Gowhar Meraj: their contributions comprised methodology,
conceptualization, and the review and editing stages of the writing
process.
Funding Open Access funding provided by The University of
Tokyo. This work was supported by JSPS KAKENHI Grant Number
23KF0024.
Availability of data and materials Data are available based on the
request to the corresponding author.
Declarations
Conflict of interest The authors declare no competing interests.
Ethical approval All the authors have read, understood, and complied
as applicable with the statement on ethical responsibilities of authors.
Consent to participate All subjects gave their informed consent for
inclusion before they participated in the study.
Consent to publication Not applicable.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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