137
Key words: Biodiversity, data 
collection, Mahavir Swami Wildlife 
Sanctuary, DBH Height.
 
Ključne besede: biodiverziteta, 
zbiranje podatkov, Zavetišče za divje 
živali Mahavir Swami, prsni premer, 
višina.
Corresponding author:
Pavan Kumar
E-mail: pawan2607@gmail.com
Received: 2. 2. 2023
Accepted: 13. 6. 2024
Data Collection Survey on Forest 
Conservation in Mahavir Swami Wildlife 
Sanctuary for Addressing Climate Change
Abstract
Tropical forest serves as an important pivotal role in terrestrial biological 
diversity. The Data Collection Survey on Forest Conservation aims to provide a 
comprehensive analysis of the current state of forests and evaluate the effectiveness 
of conservation efforts. This survey integrates advanced data collection methods, 
including remote sensing and Geographic Information Systems (GIS), to 
monitor forest health, biodiversity, and threats. This study makes an attempt 
to data collection surveying methods to assess forest management in Mahavir 
Swami Wildlife Sanctuary (MSWS), Lalitpur, UP, India, using Sentinel-2A 
sensor data. We selected tree samples from thirty transects in MSWS through 
random sampling. A method of collecting tree measurement factors based on 
height and DBH with a variety of high-precision methods is proposed. The data 
collection survey used in this study can help forest managers, environmentalist, 
and conservationist for formulating policies for management of forest ecosystem at 
various scales.
Izvleček
Tropski gozd ima na kopnem pomembno vlogo v biotski pestrosti. Namen zbiranja 
podatkov ohranjenosti gozdov je zagotoviti celovito analizo trenutnega stanja 
gozdov in oceniti učinkovitost prizadevanj za njihovo ohranjanje. V raziskavi smo 
za spremljanje zdravja gozdov, biotske raznovrstnosti in njihove ogroženosti združili 
napredne metode zbiranja podatkov, vključno z daljinskim zaznavanjem in geo-
grafskimi informacijskimi sistemi (GIS). V raziskavi smo poskušali zbrati podatke 
geodetskih metod z uporabo podatkov senzorja Sentinel-2A in oceniti gospodar-
jenje z gozdovi v rezervatu za divje živali Mahavir Swami (MSWS), Lalitpur, UP, 
Indija. Z naključnim vzorčenjem smo izbrali vzorce dreves iz tridesetih transektov 
v MSWS. Predlagamo metodo zbiranja meritev drevesnih lastnosti na podlagi 
višine in DBH z različnimi, visoko natančnimi metodami. Analiza o zbiranju 
podatkov, uporabljena v tej študiji, lahko pomaga vsem, ki gospodarijo z gozdovi in 
okoljevarstvenikom ter naravovarstvenikom pri oblikovanju politik za upravljanje 
gozdnih ekosistemov na različnih ravneh.
Pankaj Lavania1, Garima Gupta1, Pavan Kumar2, K. K. Singh3, Prabhat 
Tiwari1, Manmohan Dobriyal4, A. K. Pandey5 & Manish Srivastav4
1 Department of Silviculture & Agroforestry, College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.
2 Department of Forest Biology & Tree Improvement, College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.
3 Addl PCCF, Forest Department, Uttar Pradesh, India.
4 College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.
5 College of Horticulture, Central Agricultural University, Bermiok, Sikkim, India.
DOI: 10.3986/hacq-2025-000124/1 • 2025, 137–144
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Data Collection Survey on Forest Conservation in Mahavir Swami Wildlife 
Sanctuary for Addressing Climate Change
Introduction
Forests play important roles for humans and the environ-
ment. The conservation of forests is an important global 
imperative, given the myriad environmental, social, and 
economic benefits that forests provide. From serving as 
vital carbon sinks and biodiversity reservoirs to offering 
livelihoods for millions, the health of forest ecosystems is 
intricately linked to the well-being of our planet and its 
inhabitants. However, forests face unprecedented threat 
from deforestation, illegal logging, climate change, and 
unsustainable land use practices. These challenges require 
comprehensive and systematic data collection to inform 
effective conservation strategies. Information on forest sta-
tus is seamlessly useful for sustainable forest management. 
Forests, especially tropical forests, are being the main car-
bon sinks and play a vital role in climate change analysis. 
Deforestation is responsible for about 10% of total global 
warming (Achard et al., 2002). The assessments of forest 
traits using allometric equations have gain momentum 
among ecologists. Natural variations can be found in the 
appearance and structure of plants. As plants grow, differ-
ent forms of plants emerge. Monitoring and analyzing of 
forests has become easier with the development of remote 
sensing data with fine resolutions. Crown, volume, mor-
phology and biomass of trees can be effectively identified 
and measured using satellite imagery (Barret, 2014; Bar-
rett & Christensen, 2011; Kumar et al., 2012, 2014, 2016, 
2018a, 2018b, 2022; Singh et al., 2021). It has made it 
possible to identify and measure the crown dimension, 
volume, morphology and biomass of individual trees from 
satellite imagery (Popescu et al., 2003). Advances in the 
field of the optical remote sensing has made the assess-
ing and monitoring of changes in forest cover easier. There 
are various applications of remote sensing for monitoring 
the earth’s surface, forest changes and the greenness level 
of vegetation cover. With the advances in remote sensing 
technology, the tedious process of collecting data through 
field survey, which requires a lot of time, labor and huge 
costs, has been simplified as the required data can be col-
lected by establishing a clear relationship between the field 
measurements and spectral data.
The concentration of carbon dioxide in the atmosphere 
is very low (0.03%). However, this meager amount can 
play an important role for living organisms on Earth 
(Beets et al., 2014; Berg & Lindholm, 2005; Blaschke, 
2010; Boudreau et al., 2008). This gas is converted into 
carbohydrates by plants during the process of photosyn-
thesis. The carbon is released into the atmosphere after 
the plants die. Forest ecosystems are the driving force of 
the carbon sink due to their age, ecological conditions 
and possible disturbance. The carbon emission into the 
atmosphere is the main cause of global warming, which 
was duly considered in the Kyoto Protocol of the 1997 
Earth Summit (Oberthür Ott, 1999). Later, it has at-
tracted the attention of the scientific community to stop 
the rising carbon dioxide concentration from forests in 
the global carbon cycle. Thus, biomass has been consid-
ered a significant variable for assessing the environmental 
implications, climate change and food security (Otukei & 
Emanuel, 2015). Both below- and above-ground biomass 
are used to estimate carbon stock. 
Therefore, monitoring above-ground biomass (AGB) 
is essential for forest carbon at different spatial scales 
(Mitchard et al., 2011). However, assessment of biomass 
is a major challenge, especially in areas characterized by 
complex forest structures. In areas affected by deforestation 
and forest degradation, there is therefore little information 
on carbon estimates. If carbon is not sequestered form the 
atmosphere and stored in soils, forests and oceans, the rate 
of heating the earth surface global warming would increase 
rapidly. The sequestration of carbon from the atmosphere 
helps to balance carbon dioxide levels and maintain the 
carbon cycle. On the contrary anthropogenic activities, 
especially land use change and the burning of fossil fuels, 
have significantly disturbed the balance.
Various researchers have estimated AGB in different 
forest ecosystems using allometric regression equations 
as a function of morphological traits (Brown et al., 1989; 
Glenn et al., 2008; Chave et al., 2005; Hossain et al., 
2016; Segura & Kanninen, 2005; Vashum & Jayakumar, 
2012). Quantification of forest biomass and estimation 
of carbon stocks are of great importance for forest man-
agement. Mapping the spatial pattern of forest biomass 
can help in assessing the carbon stock within a region 
for scientific management of the forest (Vargas-Larreta 
et al., 2017). 
Kumar et al. (2018) analyzed the suitability of allomet-
ric models to assess plant morphology using parameters 
such as diameter, height and stem volume of Anogeissus 
pendula Edgew. The suitability of allometric models for 
analyzing morphological traits and predicting the vol-
ume of an individual tree was investigated. The allomet-
ric relationship between volume and different degrees of 
stem’s diameter and height was determined using linear, 
logarithmic, quadratic and cubic equations. The regres-
sion analysis revealed that the product of diameter and 
height [(D]^(2) H) in the linear regression equation has 
a strong relationship with tree volume (R2=0.9996). The 
linear regression equation model proved to be a useful 
model for analyzing morphological traits and predicting 
stem volume using the product of diameter and height 
[(D]^(2) H). In their work, Clerici et al. (2016) calcu-
lated the biomass and carbon stock in the Andean forest. 
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Data Collection Survey on Forest Conservation in Mahavir Swami Wildlife 
Sanctuary for Addressing Climate Change
Field measurements, GeoEye-I and Pleiades-1A images 
were used to estimate above-ground biomass and total 
carbon stock. Six vegetation indices, namely ratio vegeta-
tion index (RVI), normalized difference vegetation index 
(NDVI), normalized difference greenness index (NDGI), 
vegetation index number (VIN) and transformed vegeta-
tion index (TVI) were used to estimate the carbon stock 
and biomass of the forest. Log-log, linear and semi log 
models were used to determine the appropriate indices 
for biomass estimation. They found that the applied 
methodology is suitable for accurate AGB estimation in a 
small region. Previous work on carbon assessment is based 
on elementary statistical studies and is less precise. Multi-
temporal and spatial satellite databases with improved 
analytical algorithms have led to a reliable assessment of 
carbon and land use. Assessing the biomass of tree spe-
cies is important to determine the structural aspect and 
conditions for their growth. The sustainability of forests 
also depends on the estimation of biomass. Therefore, al-
lometric equations that consider morphological traits of 
forest species (tree height, crown diameter and DBH) 
are constructive in estimating forest biomass (Kebede & 
 Soromessa, 2018). In this study, an attempt is made to 
collect data collection methods to assess forest manage-
ment in Mahavir Swami Wildlife Sanctuary (MSWS), 
Lalitpur, UP, India using Sentinel-2A sensor data. 
Figure 1: Location map of study area.
Slika 1: Lokacija preučevanega območja
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Data Collection Survey on Forest Conservation in Mahavir Swami Wildlife 
Sanctuary for Addressing Climate Change
Study Area
The Mahavir Swami Wildlife Sanctuary (MSWS) in Lal-
itpur is located in the state of Uttar Pradesh. Its latitudi-
nal and longitudinal extent is 24°29’ N to 24°32’ N and 
78°14’ E to 78°17’ E (Figure 1) and is situated at an eleva-
tion of 300 feet above the Betwa river, near the Vindyan 
Range. It is 125 km away from Jhansi. It spreads over an 
area of 5.4 km2. The annual dry months can range from 
3 mm to 7 mm. Summer temperatures range from 27°C 
to 45°C and can exceed 49°C and winter temperatures 
range from 6°C to 26°C. The Sanctuary is less known for 
its wildlife, but it is an important site for the critically 
endangered vulture species and focuses on their popula-
tions and conservation strategy. It is an important breed-
ing site for the Long-billed vultures (Gyps indicus) and has 
been reularly monitored since 2007. The high cliffs are an 
ideal habitat for the vultures, owls, eagles and other bird 
species. The forest is comprised of Sagwan (Tectona gran-
dis) butother tree species like Arjun (Terminalia arjuna), 
Tendu (Diospyros melanoxylon), Gunja (Abrus precatorius) 
etc., are also found.
Selection of samples and 
plot layout design
Dimension of samples and unpredictability in species 
affect the accuracy of their estimation. Population is di-
vided into homogenous groups and samples are selected 
randomly from such group independently for reducing 
sampling discrepancy (Chako, 1965). Effectiveness of 
stratification can be assessed through variance analysis. 
Stratified random sampling technique was utilized for 
collecting samples from the homogeneous groups having 
high normalized difference vegetation index (NDVI) val-
ues. Size of the sample was determined as:
Where:
N = Number of samples needed
t = Student’s ‘t’ at a given level of probability (2 for 95% 
confidence limit)
e = Desired sampling error expressed as % of mean
CV = co-efficient of variation (%)
The total required samples were determined to be 37 
assuming that E% is equal to 10%. Four samples were 
located in inaccessible areas. Hence, 32 samples were col-
lected from the study area. Topographic sheet on a scale of 
1 : 50,000 were used for preparing base map and design-
ing plot. For this, the topographic map was divided into 
number of grids of 2½’ × 2½’. Four sub grids of 1¼’ × 
1¼’ size were made within each such grid of topographic 
map. Sufficient care is taken in laying of plots. The aspect 
of all the plots should be the same. The plot should be 
laid in a frame of 31.62*31.62 meter on one side of the 
stream avoiding any stream. Uniform direction either left 
or right should be followed throughout the plots. All the 
materials should be kept outside the plot to avoid tram-
pling of the flora. A square plot of 0.1 ha was selected 
from grid center in two sub grids randomly selected from 
each grid. Within each quadrant, girth at breast height 
(1.37 m above ground) was measured and using measur-
ing tape. All the measurements like tree height, DBH and 
branch length were properly recorded in the individual 
field performa prepared before field work. Similarly, the 
mean stand height was obtained by measuring the indi-
vidual height of various trees in each quadrant using Hyp-
someter. The field work was carried out in the month of 
July, 2021. 
Figure 2: Plot layout design.
Slika 2: Zasnova postavitve raziskovalne ploskve.
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Sanctuary for Addressing Climate Change
Data assembly and basal 
area
The basal area/genera and species/hectare were analyzed 
for each plot. Diversity indices of all the sampled spe-
cies in STR were calculated and their significance was de-
termined using the analysis of variance (ANOVA). Basal 
area (BA) was calculated as:
A=πr2
Where:
BA = Basal area of the tree (m2)
r = Radius (cm)
Tree species were collected from each sampled transect 
plot. For each species total numbers of individual species 
were determined. Natural log of species was calculated us-
ing total number of individual species. Mean number of 
genera/ha was calculated from total no. of species.
Segmentation forest and 
non-forest area
Masking of non-forest areas (built up areas, agriculture, 
plantations and water bodies) is essential before field 
sampling. This is done by detecting pixels of small near 
infrared radiances than in the visible radiances (Blaschke, 
2010). The near infrared band is sensitive to illumination 
variation and responds well to vegetation (Kumar, 2013). 
The near infrared and visible channels were normalized 
by non-vegetation comparison. Of the delineated tree 
crowns, 70% crowns were used as training sets and the 
30% for data validation. A knowledge base was built for 
making class hierarchy (Baatz, 2004). The dominant spe-
cies were separated using reflectance curve in the study 
area. Texture analysis (3*3 window) in ERDAS Imagine 
(version 2014) was used for smoothening of multispectral 
imagery. The tree crown edges were demarcated by zoom-
ing in at a scale of 1 : 250. The pan sharpened images al-
low a fine visual interpretation of overlapped tree crowns. 
Green, white and blue colors in the filtered image show 
the non-forest areas. Healthy vegetation types can also be 
identified in the smoothed image.
Global Moran’s index of 
autocorrelation sampling
The geostatistics (spatial statistics) tool was used for auto-
correlation sampling. It determines the degree to which 
the spatial features are either clustered or dispersed in 
space. This tool in ESRI’s ARC/INFO (v.10.2) software 
contains various indices like Moran’s I index, variance, Z-
score and p-value (Figure 3). The positive and negative 
Moran’s I values represent cluster of data and disperse pat-
terns respectively. The p-value is calculated from the Z- 
score and determines the statistical significance between 
the data sets and the acceptance/rejection of the null hy-
pothesis (). Here, refers that the trees in the study area are 
randomly distributed. 
Figure 3: Output of Global Moran’s I.
Slika 3: Rezultat Global Moran’s I.
Allometric analysis
The graphical exploration of the data enables to get an 
idea about the suitable type of model to be used and to 
analyze the relationship between the variables. Allometric 
equations were developed for data fitting of the sample 
trees. Thirty representative tree-groups (Table 1) of six 
major types of tree species in MSWS consist of Sagwan 
(Tectona grandis) and other tree species like Arjun (Ter-
minalia arjuna), Tendu (Diospyros melanoxylon), Gunja 
(Abrus precatorius
The field work was carried out in July 2021, as the 
canopy was completely closed at the beginning of the 
monsoon for the measurement of plant morphology. Five 
mean values were randomly taken from each type of tree 
species. Table 1 shows the description of the sample tree 
species.
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Tree No. (m) (m) (cm3) NDVI
1 0.21021 7.0104 337320.8 0.401
2 0.2183 7.3152 585221.9 0.333
3 0.22638 6.4008 687294.9 0.314
4 0.26681 6.096 452061.3 0.324
5 0.25872 7.62 486964.5 0.345
6 0.39779 4.572 581750.2 0.355
7 0.4463 4.8768 593550.4 0.364
8 0.30076 4.572 603916.1 0.348
9 0.32017 5.1816 643651.8 0.365
10 0.17787 9.4488 661400.3 0.365
11 0.18596 9.7536 671479.8 0.379
12 0.20213 10.0584 696268.8 0.381
13 0.2501 10.9728 719971.6 0.388
14 0.2838 10.3632 798965.9 0.385
15 0.254 10.668 896763.6 0.392
Source: Based on filed survey, 2021.
Table 1: Description of sample trees species: Tree height; stem diameter at a height of; stem diameter at 1.3 m height (DBH); 
stem volume.
Tabela 1: Opis vzorčenih dreves: višina drevesa; premer debla na višini; premer stebla na višini 1,3 m (DBH); prostornina debla.
Tree No. (m) (m) (cm3) NDVI
16 0.18596 9.7536 938555.9 0.389
17 0.20213 10.0584 977464.2 0.399
18 0.21021 10.9728 1007197 0.401
19 0.2183 10.3632 1041474 0.415
20 0.22638 10.668 1035246 0.418
21 0.26681 9.7536 1177554 0.441
22 0.25872 11.2776 1233747 0.465
23 0.27489 11.5824 1283568 0.475
24 0.28298 10.0584 1274948 0.469
25 0.25872 9.4488 1365358 0.457
26 0.18596 9.7536 1453835 0.478
27 0.25872 11.2776 1444289 0.498
28 0.27489 11.5824 1589435 0.512
29 0.28298 10.0584 1794537 0.524
30 0.25872 9.4488 2398411 0.621
Figure 4: Relationship 
among various morpho-
logical traits.
Slika 4: Korelacija med 
različnimi morfološkimi 
znaki.
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Data Collection Survey on Forest Conservation in Mahavir Swami Wildlife 
Sanctuary for Addressing Climate Change
Relationship between 
ecological indices and 
morphological trails
Allometry in forestry as a statistical relationship between 
the dimensions of trees and their other properties is a use-
ful tool to obtain information about the species in rela-
tion to the forest (Figure 4). 
With the advances in remote sensing technology, the 
tedious process of data collection through field surveys, 
which consumes time, labour and huge costs, has been 
simplified as remote sensing technology allows the re-
quired data to be collected by establishing explicit rela-
tionship between the field measurements and the spectral 
data. Various morphological traits such as the like num-
ber of trees, tree height, diameter at breast height, stem 
volume and height, and the ecological index NDVI were 
recorded during the field sampling to analyse the relation-
ship between them (Figure 5).
Figure 5: 3D surface plot of NDVI against height and DBH.
Slika 5: 3D površinski grafikon NDVI, višine dreves in prsnega 
premera (DBH).
Conclusion
The findings indicate that mixed-species diversity con-
tributes more to diversity in MSWS than that of mono-
culture plantations. The present study makes an attempt 
to identify the species concentration among tree diver-
sity in MSWS. This survey also provides a comprehen-
sive overview of the current forest conservation landscape 
and identifies critical areas for improvement. The findings 
and recommendations of this study will serve as a founda-
tion for policymakers, conservationists and stakeholders 
working to preserve our forests for future generations. By 
addressing the identified challenges and building on the 
successes, we can ensure a sustainable and resilient forest 
ecosystem.
Funding 
This research did not receive any specific grant from fund-
ing agencies in the public, commercial, or not-for-profit 
sectors
ORCID iD
Pavan Kumar  https://orcid.org/0000-0003-3653-8163
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