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MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION OF MADURU OYA AND HORTON PLAINS NATIONAL PARKS USING LANDSAT 8 (OLI) IMAGERY TO ASSIST THE ECOLOGICAL STUDIES

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Availability of accurate and detailed vegetation and habitat maps is an essential requirement in the modern ecological studies. Geo-referenced vegetation maps of Sri Lanka's protected areas are scarce and only a handful of maps are available with detailed vegetation patterns. Since majority of the current ecological research work takes place within the protected area network of the island, we identified that preparing updated maps to assist the ecologists as well as the park management is highly important. In this study we developed vegetation and land cover maps for Maduru Oya and Horton Plains national parks in Sri Lanka. The procedure was based on supervised classification of Landsat 8 multispectral image data. Classification was complemented by ground truth data obtained through field surveys. The present study generated accurate (overall accuracy-92-93%; Kappa-0.89) and detailed vegetation/land cover maps and habitat types were proposed based on vegetation patterns. The results provide accurate information for ecologists and decision-makers to assist future research work as well as conservation and management of protected areas concerned.
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WILDLANKA Vol.9, No.1, pp. 122 - 135, 2021.
Copyright 2021 Department of Wildlife Conservation, Sri Lanka.
,and




MAPPING THE VEGETATION COVER AND HABITAT
CATEGORIZATION OF MADURU OYA AND HORTON PLAINS
NATIONAL PARKS USING LANDSAT 8 (OLI) IMAGERY
TO ASSIST THE ECOLOGICAL STUDIES
ABSTRACT : Availability of accurate and detailed vegetation and habitat maps is an essential requirement
in the modern ecological studies. Geo-referenced vegetation maps of Sri Lanka’s protected areas are
scarce and only a handful of maps are available with detailed vegetation patterns. Since majority of
the current ecological research work takes place within the protected area network of the island, we
identied that preparing updated maps to assist the ecologists as well as the park management is highly
important. In this study we developed vegetation and land cover maps for Maduru Oya and Horton
Plains national parks in Sri Lanka. The procedure was based on supervised classication of Landsat 8
multispectral image data. Classication was complemented by ground truth data obtained through eld
surveys. The present study generated accurate (overall accuracy - 92-93%; Kappa – 0.89) and detailed
vegetation/land cover maps and habitat types were proposed based on vegetation patterns. The results
provide accurate information for ecologists and decision-makers to assist future research work as well as
conservation and management of protected areas concerned.
KEY WORDS:
cover

Availability of accurate and detailed
vegetation and habitat maps is an essential
requirement in the modern ecological studies.
A vegetation map includes critical information
      
from land management, detecting land cover
changes, understanding biodiversity patterns
and conservation planning (Dias  , 2004).
     (2019) vegetation
maps are based on two essential elements;
      
     
vegetation mapping group together similar plant
     
their arrangement pattern with spatial reference
    
ecological studies, the availability of vegetation
maps makes the task of the researchers easier.
      

Furthermore, availability of maps with detailed
vegetation patterns is scarce. As many ecological
researches are being conducted in the protected
areas of the island presently, we saw that there
is a crucial need of preparing updated maps to
assist ecologists as well as park management.
    
land cover mapping of forested areas of Sri
     
(1993) to the recent publications of Rathnayake
 
most of the previous work has been focused
on large scale patterns of general land cover
rather than localized rigorous assessments. We
     
        
 
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 123

(2006; 2007), Abayasinghe   (2014) and
Ranawana (2014) has generated vegetation
maps along with change detection for the years
 (2015) have
      
and during the procedure several vegetation
maps have been prepared using imagery from
    
vegetation maps for some of the Department of
  
Wilpattu (Sandamali and Welikanna, 2018),


Sanctuary (Khanh and Subasinghe, 2018).
       
    
   
We observed two main drawbacks in most of
these past maps which limit their applicability
      
of high resolution digitally accessible maps, 2)
      
to complement the remote sensing techniques.
      
needs of ecological research, management and
conservation of protected areas, we developed
   
      
      

literature, we propose habitat categorization for
the two protected areas considered.
     
multispectral satellite data has enabled
the analysts to obtain spectral signature of
     
bodies, soil types, rocky areas, roads/other
     
       
when forested landscapes are concerned,

calculation is the most widely used method
     
      
red and near-infrared (NIR) wavelengths, from
       
      
       
healthy and highly photosynthetic vegetation is



We incorporated remote sensing data with
       
    
characteristics were obtained at ground level
to complement the remote sensing data.
       
     . (2007)
     
to make the results understandable by a wider
     
     
of vegetation categories, including multiple
attributes of vegetation (rather than in depth
phytosociological descriptions) and building
upon the already available knowledge from
      
of a physiognomic nature which is based on
the basic physical structure of the vegetation
(forest, shrubland, grassland, etc.) and the main
growth forms (trees, shrubs, grasses, etc.) of
the dominant or co-dominant species in the
vegetation formation (Ichter , 2014).
   
which an ecologist would focus on were also
    
Ichter  , (2014) vegetation composition
        
     
form the vegetation categories can be used to
segregate the habitat types. We believe that the
output generated through our study would be
meaningful in a remote sensing as well as in an
ecological point of view.

Study site
Horton Plains National Park
      
located on the southern plateau of the central
 
     
highland plateau, the altitude ranging from
      
WILDLANKA [Vol. 9 No. 1124
by Kirigalpoththa (2,389 m) to the west and
      
are respectively the second and third highest
      
        
upgraded to national park status on 11th
1988, having previously been declared a nature
     
in natural habitats comprises upper montane
      
grasslands, with a narrow ecotone of shrubs and
   

cause of which is uncertain but may be related
to water stress, soil conditions, air pollution
    , 2009) or
natural phenomena.
FIGURE 01: 

by Dulan Jayasekara, originally published in Jayasekara  2020)
Maduru Oya National Park
    
(588 km) lies in the districts of Ampara and
     
   

       
      
      
      
cultivation resulting in secondary forests and
vast stretches of open plains dominated by

  


Determination of vegetation and habitat
categories
Initially we referred to available literature
and prepared priory vegetation/habitat categories
      
the main vegetation types were established, on
a physiognomic basis (Dias  ., 2004) with

maps available online. We established a grid
of 1×1 km plots covering the area of available


protected areas to remove some visible errors to
 
     
    
and study generated map areas and perimeters.
We randomly selected sampling plots and
established sampling quadrates of 10×10 m
within the larger 1×1 km plots. A total of 77
and 55 quadrates were sampled respectively
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 125
TABLE 01: Summary of vegetation and environmental parameters and standard methods used (X
indicates whether each parameter was obtained at the selected site)
      
    
1 were obtained using standard methods. We


clusters of similar vegetation/land cover types.
Environmental Abbreviation Method followed MONP HPNP
parameter
Stem density of SD1 Average distance to the X X
plants <10cm dbh nearest woody plant with
(dbh-diameter at dbh <10cm calculated as
breast height) 1/mean area [distance]
Stem density of SD2 Average distance to the X X
plants >10cm dbh nearest woody plant with
dbh >10cm calculated as
1/mean area [distance]
   
   
(ocular estimation)
   
   
visibility (%)
   
cover (%) (ocular estimation)
Rock availability RA Evaluated on a scale of 1-10 X X
  
 
 
   X
  
  
 
indicate June)
Pre-analysis for supervised classication
We utilized multi-spectral images from

     
     

      
database (http://glovis.usgs.gov/). Images
       
   

datasets with low cloud cover were considered

       
     
       
  
         
    
WILDLANKA [Vol. 9 No. 1126
and 7 were to discriminate between water and
       
was calculated for each image according to the

      

2016).
      
       
determined the number of vegetation classes
      
      
       

Supervised classication using Landsat 8
images
   
    
   
samples were selected from the data obtained
     
2016 to January 2021. We supplemented the

    
for inaccessible terrain and some known
locations.
Accuracy assessment of classied maps
Accuracy assessment was conducted to
    
   
the possibility of bias, in addition to the actual
ground survey points, we generated random
    
     
   



PCA clustering of vegetation and environ-
mental characteristics

      
shrublands, grasslands and rocky outcrops

     
cluster was overlapping with grasslands and

loading plot (Figure 2a) the important factors

       
with >10cm dbh), litter cover and litter depth.
Rocky outcrops were clearly separated based
     
      
     

     

     
    
low canopy, pigmy cloud forest, carpet grass,
tussock grass, marshes/dwarf bamboo, rocky
       
      
score plot indicated overlapping between the
two grass types and marshes/dwarf bamboo.
      
canopy cover, litter cover, litter depth and stem
     
high horizontal visibility and ground vegetation
     
easily categorized based on soil moisture
content (Figure 3a).
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 127
FIGURE 02: 
WILDLANKA [Vol. 9 No. 1128
FIGURE 03: 
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 129
TABLE 02: 
FIGURE 04: 
represent the legislated park boundary)
Spatial extents of land cover classes
     
     
with 37% accounting for 215,415ha of land
area (Figure 4). It was followed by dense
     
      
was the cloud forest (50%) covering an area of
  
areas repesents 25% (778ha) of park area.
      

Vegetation/Land cover type Percentage land cover (%) Area (ha)
MONP
Dense forest (DF) 29 170,171
  
  
  
  
  
WILDLANKA [Vol. 9 No. 1130
HPNP
  

  
  
  
  
  
  
  
  
FIGURE 05: 
represent the legislated park boundary)
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 131
TABLE 04: 
TABLE 03: 
Accuracy assessment of results
     
      
      
     

Class DF SL GL RO/BL R/WL Total U_Acc Kappa
Dense Forest 56 2 0 0 0 58 0.97
Shrubland 0 67 3 2 1 73 0.92
       
Rocky outcrops 0 3 1 17 1 22 0.77
Reservoir/Wetland 0 0 0 0 18 18 1
      
      0.92
Kappa 0.89
Class CF CFD PCF TG CG M/DB OV RO/BL Total U_Acc Kappa
          
          
          
          
          
          
          
          
          
          0.93
Kappa 0.89

    
and landcover maps for two of the prominent

remote sensing based approches are common
     

WILDLANKA [Vol. 9 No. 1132
provide more accurate results. It can be clearly
seen in the results of the accuracy assessments.
In our clustering process, we selected
vegetation and environmental parameters that
   
sophesticated phytosociological investigations.
    

audience and even the general management
decision making.
     
     
seperated based solely on ground survey
parameters while the shrubland habitat was
     
this result was actually related to the nature
of the shrublands which can be considered
a sucessional stage between the grassland
      
      

detailed and accurate vegetation and land cover
.,

plantation which currently consist of many dead/
dying teak plants. Since we did not observe any

and due to the limited distribution, it was not



highly important to conserve this area with high
priority in order to facilitate the biodiversity to
       
    
      
    
      
results of the present study and considering the
    
   
    
4. Rocky outcrops, 5. Reservoir/Wetlands.

vegetation features and landscape features along

     
     
topographical and climatic conditions of the

      
     
       
together a summarized and detailed account
     
      
      

was absent in that analysis which was focused
on identifying the cloud forest die-back. Work
  

  
      
cloud forest die-back areas probably due to
the low resolution and limited interpretation of
satellite images as mentioned by Abayasinghe
       
      
  (2014) where they have generated
more detailed and accurate vegetation maps.


Futhermore, the images that were used in these
 
the present study (used 2017 satellite images)
can be considered timely to compare the change
that has occurred after about 10 years gap from
the previous analysis. We observed a slight
increase in the cloud forest cover (~3%) from
       
      
     
changes in the vegetation. Interestingly, our
results suggest a considerable increase (~4%) in
the carpet grass cover and a reduction in tussock
    

, 2002) and we posit tentatively that there
could be a possible increse in the sambar deer
       

     . 2015) we
  
     
     
detailed investigations, there is the possibility
      
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 133
subhabitats based on vegetation maps provided
in the present study.
        
comparisons with past and future vegetation/land
    
of vegetation classes and habitat types would
support the future ecological research work

maps generated through the present study can be
used for management and conservation decision
making. We recommend similar initiations in
     
use of the available technological tools to
implement modern solutions .

We appreciate the generous cooperation of



to conduct this research. We would also like to
       
Jayewardenepura (Department of Zoology and
      
     
to conduct this research under the university
   
   
    
   
      


    
    


management: a practical geoinformatics
approach. 
, 2:113-131.
     
Ellepola, (2015). Evaluation of damage
caused by elephants ( 
) to the woody vegetation in
   ,
3(1):20-30.
       
    
and manual for standardized habitat
mapping.  7:55-74.
    
    
10.1093/obo/9780199830060-0115
       
   
     
Island (Azores).  
, 13(8):1519-1539.
    
(2006).     
    
   
   

   
    
  
  



   
    
 
 
   
   
Environment and Natural Resources,
p.40.
    

    
D.S. Karunarathna, (2015). Avifaunal
diversity in the peripheral areas of the
    
With conservation and management
implications.   
, 8(2):121-132.
      
    
   

    
  
WILDLANKA [Vol. 9 No. 1134
Estimation of above ground biomass
     
    
RADAR remote sensing data. 
, 26(4):608-623.
Ichter, J., D. Evans, and D. Richard, (2014).
    
    

     
  
densities and conservation assessment
of three threatened agamid species in
 
    
4(3):70-79.
      
    
forest management and monitoring in Sri

     
, pp.13-15.
    
   


Remote Sensing,  

, 8(5):42-52.
    
     
   . [online]
    
ianlockwood.blog/2019/09/16/
preliminary-analysis-of-land-cover-

dove-imagery/> [Accessed 14 July
2021].
   
Kuruppuarachchi, (2014). Detecting
   
    
     
    
, 4(1):50-58.
   
   
  
     

  


       
mapping in cropland dominated area
using information on vegetation

images. 
, 1(1): 1-16.
       
environment for statistical computing.


    
    
(    
   
   
   
and Environment Symposium.
   



Anthropogenic or natural phenomenon?.
     
. 13:23-45.
     
 
,
2:103-112.
      
    
     
      
series. , 9(1):1-19.
    
    

    
    
, 2(10):473:482.

    
    
   
March, 2021] MAPPING THE VEGETATION COVER AND HABITAT CATEGORIZATION 135
, 8(4):37-
40.
     
   
obo in Ecology. doi: 10.1093/
obo/9780199830060-0176
Received Date:
Accepted Date:
... There are several remote sensing approaches initiated for habitat mapping in Sri Lanka (Dahdouh-Guebas et al., 2000;Nandasena et al., 2023), and this technology has been utilized for forest management and monitoring since (Jewell and Legg, 1993). However, only a handful of studies are available where in-depth analyses have been conducted regarding the vegetation classification and habitat mapping using remote sensing methods restricted to several protected areas, such as Wilpattu (Sandamali and Welikanna, 2018), Maduru Oya (Jayasekara et al., 2021), Horton Plains (Jayasekara et al., 2021) and Udawalawe (Perera et al., 2021) governed by the Department of Wildlife and Conservation (DWC) Sri Lanka. Furthermore, we have observed a lack of remote sensing tools utilization for identifying the types of vegetation in national parks located in the eastern and southern regions due to the unavailability of ground-truth observed data for verification. ...
... There are several remote sensing approaches initiated for habitat mapping in Sri Lanka (Dahdouh-Guebas et al., 2000;Nandasena et al., 2023), and this technology has been utilized for forest management and monitoring since (Jewell and Legg, 1993). However, only a handful of studies are available where in-depth analyses have been conducted regarding the vegetation classification and habitat mapping using remote sensing methods restricted to several protected areas, such as Wilpattu (Sandamali and Welikanna, 2018), Maduru Oya (Jayasekara et al., 2021), Horton Plains (Jayasekara et al., 2021) and Udawalawe (Perera et al., 2021) governed by the Department of Wildlife and Conservation (DWC) Sri Lanka. Furthermore, we have observed a lack of remote sensing tools utilization for identifying the types of vegetation in national parks located in the eastern and southern regions due to the unavailability of ground-truth observed data for verification. ...
... Furthermore, we have observed a lack of remote sensing tools utilization for identifying the types of vegetation in national parks located in the eastern and southern regions due to the unavailability of ground-truth observed data for verification. While Jayasekara et al. (2021) have comprehensively illustrated a vegetation map for Maduru Oya, a dry zone national park located near the border of eastern and Uva provinces we observed that detailed vegetation/habitat maps are not available for the national parks located in eastern and southern regions of the island. This motivated us to map the vegetation in Kumana National Park (KNP) located in the south-eastern dry zone of Sri Lanka utilizing remote sensing techniques. ...
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Remote sensing constitutes a broad and influential discipline that has assumed a significant role in vegetation mapping on a global scale in recent years. The availability of an accurate vegetation map assists future ecological studies and the management of protected areas. This study was conducted to identify and map the available habitats in Kumana National Park (KNP), Sri Lanka. We utilized multiple environmental covariates obtained via field surveys and remote sensing techniques for the initial categorization of habitats based on principal component analysis. Vegetation maps for KNP were generated by applying multiple classification algorithms to Sentinel 2 multispectral satellite imagery. The maximum likelihood classification (MLC) model generated the most accurate and detailed vegetation map for KNP, which was verified with ground truth data (overall accuracy of 93%; Kappa, 87%). The study’s findings furnish precise insights into the vegetation cover of KNP, thereby augmenting knowledge on the spatial distribution of habitats to support the future work of researchers and park managers. This map offers significantly improved resolution and spatial detail compared to previous maps. It also increased the number of identified habitat types from four to six. These findings can be used to identify critical areas for both terrestrial and aquatic fauna within KNP and support habitat conservation and management strategies in the park.
... It is a habitat specialist and associated with water/wetlands most of the time (Nowell & Jackson, 1998;Miththapala, 2018). Fishing cat is assessed as endangered (EN) locally and Vulnerable (VU) globally in the IUCN Red Lists (MOE, 2012;IUCN, 2021). This species is distributed in South and Southeast Asia. ...
... In other parts of the world, jungle cats are known to utilize a variety of habitats (Nowell & Jackson, 1998) while in Sri Lanka it is more of a dry zone species (Nekaris, 2003;Wijeyeratne, 2008;Miththapala, 2017). This is the species with the broadest distribution among the focal felids but occurs in patches (IUCN, 2021). The IUCN Red List status is Near Threatened (NT) locally (MOE, 2012) and Least Concern (LC) globally (IUCN, 2021). ...
... This is the species with the broadest distribution among the focal felids but occurs in patches (IUCN, 2021). The IUCN Red List status is Near Threatened (NT) locally (MOE, 2012) and Least Concern (LC) globally (IUCN, 2021). The rusty-spotted cat is known as the smallest cat in the world (mean body weight: ...
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Habitat suitability modeling and identification of spatiotemporal niches helps in understanding the ecological requirements of faunal guilds. Small and medium sized felids of wild Sri Lanka include three cat species; fishing cat (Prionailurus viverrinus), jungle cat (Felis chaus) and, rusty-spotted cat (Prionailurus rubiginosus). These felids are hyper-carnivorous elusive predators that play important ecological roles in a variety of habitats. We conducted this study to identify the habitat associations of sympatric small and medium sized felids and model the habitat suitability of Maduru Oya National Park (MONP), Sri Lanka. Spatiotemporal niche overlapping and partitioning was also investigated. Species occurrence data were obtained based on the camera trap capture events, direct observations and roadkill records. Modeling was conducted based on the maximum entropy algorithm (MaxEnt) using the software package Maxent (version 3.4.3). The predictive accuracies (ROC) of the selected models were evaluated to be greater than 0.80 (AUC). Distance to water resources (44.9%), Bio1-mean annual temperature (33.6%), and habitat type (Dense dry-mixed forest; 79.8%) were identified as the most important variables contributing to habitat suitability for fishing cat, jungle cat and rusty-spotted cat respectively. We further identified that spatial variation in habitat use facilitates these three species to coexist in MONP despite the significant temporal (activity) overlapping. The outcome of this research will contribute towards future conservation and management. The findings will be useful in comparative studies in Sri Lanka as well as elsewhere in the world. Keywords: Felidae, MaxEnt modeling, ecological niche, resource partitioning, small carnivores Journal of Wildlife and Biodiversity
... Mist can persist in the day during the wet season . The main habitat types of the park can be identified as cloud forests, wet patana grasslands and cloud forest die-back/low canopy forests (Jayasekara et al. 2021). HPNP is considered as one of the Important Bird Areas (IBAs) in Sri Lanka (BirdLife International, 2009). ...
... Thirty-six camera stations over 2160 camera days were surveyed to collect data in SNHWA. Available habitat types were identified as dense wet evergreen forest, low dense wet evergreen forest, sub-montane forest, and riverine forest (Jayasekara et al., 2021). ...
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The use of remotely triggered cameras for studies of bird ecology is uncommon. We used camera trap data from a survey conducted from January 2018 to April 2021, to analyze the habitat use and activity patterns of Sri Lanka Spurfowl Galloperdix bicalcarata which is known as a shy and secretive forest bird endemic to Sri Lanka. Study sites included protected areas situated in dry, wet, and montane zones of the island. Camera traps were placed representatively in the main habitat types of each study site. A total of 104 independent captures of G. bicalcarata were recorded during the study. The highest occupancy was recorded at Sinharaja National Heritage Wilderness Area followed by cloud forests of Horton Plains national park and dry-mixed evergreen forests of Maduru Oya National Park. The activity of G. bicalcarata was highly diurnal and activity levels ranged from 0.250-0.398 at the study sites. Activity peaks of G. bicalcarata occurred in the morning between 0700-1100h. We identified canopy cover, litter cover, litter depth, NDVI as the covariates that positively influenced the habitat occupancy of spurfowl while thick undergrowth and rocky outcrops reduced the occupancy. The findings of this study will be useful for the conservation and management decisions on Sri Lanka spurfowl and habitats that are vital for its survival.
... However, it is now believed that the native tussock grass has started taking its place in the grasslands by invading back into areas invaded by the exotic carpet grass (Jayasekara et al., 2021), while the Rhododendron trees from the cloud forests on hillocks have started encroaching the grasslands in valleys (De Alwis et al., 2007;Piyathara et al., 2017). Such dynamics in the vegetation could have an effect on bringing the sambar population of HPNP into a new equilibrium in the plains. ...
Article
We estimated the population density and investigated the social organization of sambar ( Rusa unicolor unicolor ) in Horton Plains National Park (HPNP), Sri Lanka. Distance sampling was conducted along six strip transects every month for a period over 3 years (2018–2020) to estimate the density of the sambar population in grasslands of HPNP (9.4 km ² ), while the antler stage of males and the behavior of individuals were recorded to describe the population's reproductive stage and hence the social organization. Population density estimates showed relative stability over the 3 years and varied over the seasons but with consistent peaks from year to year with the highest population densities recorded in November–December (212.93 ± 25.38 animals/km ² in 2018, 187.91 ± 28.51 in 2019, and 179.76 ± 31.85 in 2020). The highest percentage of males in hard antlers was observed from November through January, while the percentage of antlers cast sambar peaked from March to April each year. Hinds were observed with newborn calves throughout the year, but the highest number of newborn calves were recorded from July to August each year, while the number of calves counted each year varied from 210 to 267 individuals. The mean group size was variable throughout each year with the largest groups recorded from September to December (up to 52), the period accompanied by the most observations of mating and sparring behavior. Although on a tropical island, HPNP is situated on a rolling plateau landscape in the highlands, where sambar showed a degree of reproductive seasonality somewhat similar to temperate cervid species.
... The area of study was 304km 2comprising grasslands, shrublands and the climax habitat of dry mixed evergreen forest. Rocky outcrops can be observed in patches scattered throughout the park (Jayasekara et al. 2021). Most of the grasslands and shrublands are a result of slash and burn cultivation practised over the years, until the area was declared a national park in 1983 (IUCN 1990). ...
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Reliable population estimates are crucial for the conservation and management of faunal species. Population data of meso-mammal carnivores in Sri Lanka, as well as elsewhere in the world, is scarce. We estimated population densities of meso-mammal carnivores in Maduru Oya National Park (MONP) using Random Encounter Model (REM) and Camera Trap Distance Sampling (CTDS) methods in this study. A total of 3,402 camera trapping days yielded 3,357 video captures of 69 different animal taxa including 658 video captures of meso-mammal carnivores. In this study, we recorded all 12 meso-mammal carnivore species found on the island. The two density estimate methods generated similar population estimates indicating that both methods are compatible to be applied in tropical forest habitats for meso-carnivore species. We identify MONP as an area with high richness for the focal species. The study also generated movement speed, activity patterns, activity levels, and day ranges for the focal species, which will be useful for future research. We discuss the population density estimates for different meso-carnivore species and the use of REM and CTDS density estimation methods and their applicability to a tropical meso-carnivore community.
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Snake distribution and habitat modeling serve multiple valuable purposes, one of which involves establishing a systematic approach for collecting and assessing data related to the presence and influence of diverse factors on snake distribution patterns. The main objective of this study is to explore the habitat association of the Sri Lankan Green Pit Viper (Peltopelor trigonocephalus) and develop models to assess the suitability of different habitats within the island Sri Lanka. We modeled the suitable habitat for this species using the maximum entropy algorithm, combining presence data collected over two years (from April 2021 to April 2023) with a set of seven environmental variables (annual precipitation, annual mean temperature, precipitation of coldest quarter, elevation, land use, isothermality, Euclidean distance to water). We assessed the importance of different environmental variables through jackknife tests. We evaluated the predictive ability of the models using the area under the receiver operating characteristic curve. We discovered that the primary explanatory variables influencing the distribution of the species were annual mean temperature, annual precipitation, and elevation, contributing significantly at 35%, 31.6%, and 20%, respectively. The Sri Lankan Green pit viper inhabited riparian, forest, and open habitats, with the highest number of individuals recorded in riparian areas. We presented the first habitat suitability models for the Sri Lankan Green Pit Viper, offering valuable insights for conservation biologists and land managers involved in preserving this species.
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Los datos obtenidos con cámaras de trampeo revelan la asociación con los hábitats, las pautas de actividad y la densidad de población del pangolín indio (Manis crassicaudata) en parque nacional Maduru Oya, en Sri Lanka. El pangolín indio (Manis crassicaudata) es un mamífero solitario de talla media nativo de Asia meridional. En el presente estudio utilizamos datos obtenidos mediante cámaras de trampeo durante un estudio sobre mesomamíferos realizado entre enero de 2019 y enero de 2021, con la finalidad de evaluar la ocupación, la asociación con los hábitats, la densidad de población y las pautas de actividad del pangolín indio en el parque nacional Maduru Oya, en Sri Lanka. El hábitat preferido de la especie fue el bosque mixto–seco con una probabilidad de ocupación de 0,42 ± 0,19. Los modelos de ocupación revelaron la asociación de la especie con los hábitats forestales del parque dotados de una cubierta de dosel abundante, un elevado índice normalizado diferencial de la vegetación y gran cantidad de termiteros. La actividad del pangolín indio fue predominantemente nocturna y alcanzó su máximo después de la medianoche. Observamos una superposición espaciotemporal considerable de la actividad del pangolín indio con la actividad humana, lo que puede crear una cierta presión cinegética sobre la especie. La ocupación de la especie y su densidad de población basada en la abundancia (0,73 ± 0,21 indiv./km2) se obtuvieron siguiendo el modelo de encuentro aleatorio por primera vez en la zona de estudio. Los resultados de este estudio serán de utilidad para tomar decisiones relativas a la conservación y la gestión de uno de los mamíferos silvestres con los que más se trafica en el mundo (el pangolín indio) y los hábitats vitales para su supervivencia.
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Land use and land cover change (LULCC) are dynamic over time and space due to human and biophysical factors. Accurate and up-to-date LULCC information is a mandatory part of environmental change analysis and natural resource management. In Sri Lanka, there is a significant temporal gap in the existing LULCC information due to the civil war that took place from 1983 to 2009. In order to fill this gap, this study presents a whole-country LULCC map for Sri Lanka over a 25-year period using Landsat time-series imagery from 1993 to 2018. The LandTrendr change detection algorithm, utilising the normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), was used to develop spectral trajectories over this time period. A land cover change and disturbance map was created with random forest, using 2117 manually interpreted reference pixels, of which 75% were used for training and 25% for validation. The model achieved an overall accuracy of 94.14%. The study found that 890,003.52 hectares (ha) (13.5%) of the land has changed, while 72,266.31 ha (1%) was disturbed (but not permanently changed) over the last 25 years. LULCC was found to concentrate on two distinct periods (2000 to 2004 and 2010 to 2018) when social and economic stability allowed greater land clearing and investment opportunities. In addition, LULCC was found to impact forest reserves and protected areas. This new set of Sri Lanka-wide land cover information describing change and disturbance may provide a reference point for policy makers and other stakeholders to aid in decision making and for planning purposes.
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ABSTRACT Muthurajawela wetland is a coastal wetland system of high biodiversity and ecological significance. At present, this Muthurajawela wetland is being rapidly degraded by inadequately planned development activities and other detrimental activities related to growing human population pressure. As over a time, there will be change in vegetation area. Therefore, an effective method should be used to re-evaluate the change in area. Remote sensing technology is the most effective method and is used in this study. Three Landsat (TM) satellite images (1992, 2001 and 2015) were taken for comparison. The results showed that Multi-temporal Landsat images with the average resolution have the ability to assess the vegetation coverage changes with guaranteed results as, we have established a six-vegetation cover layer classification map with an overall accuracy of 84.66% and a kappa coefficient of 0.81. The total natural land area of Muthurajawela wetland was 6,232 ha in 2015. Of which, 492.95 ha was marsh, 232.94 ha was grass, 281.62 ha was water and 5,225.27 ha was of forest land; The area of mangroves forest in 1992 increased by 317.66 ha compared to in 2001 and decreased by 300.42 ha in 2001 compared with in 2015, increasing only 17.24ha in 1992 compared with in 2015. KEYWORDS:Landsat, Remote sensing,Muthurajawela wetland, Sri Lanka.
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