INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
Analysis of Agricultural and Plantation Land Use Changes Using  
Spatial and Temporal Data in Deraniyagala and Dehiowita DS  
Divisions in Sri Lanka  
L. A. W. A. C. Liyanage, A. H. D Athukorala, L. S. H Jayasooriya, S. L. Ranamukhaarachchi  
Department of Agricultural Technology, Sri Lanka Technology Campus, Sri Lanka  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100152  
Received: 10 November 2025; Accepted: 20 November 2025; Published: 03 December 2025  
ABSTRACT  
Comprehending the dynamics of land use, particularly in agricultural and plantation sectors, has become  
increasingly vital for ecological sustainability. This research provides an overview of changes in farming and  
plantation land use in the Deraniyagala and Dehiowita DS divisions of Sri Lanka over the past decade,  
analyzing remote sensing data from different time periods. This study evaluates urban growth and vegetation,  
water, and hydric resources within the area using the Normalized Difference Vegetation Index (NDVI),  
Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The results  
reveal contrasting trends: Deraniyagala showed greater urban and environmental strain, coupled with declining  
vegetation and surface moisture levels, whereas Dehiowita demonstrated comparatively more stable ecological  
conditions, indicating more sustainable land use. Shifts in crop cultivation patterns demonstrated a pivot of  
traditional plantation crops, including tea and rubber, towards alternative crops such as coffee and pepper,  
reflecting demographic pressures and evolving land management practices. This study highlights the potential  
of remote sensing to capture fine-scale environmental dynamics and facilitate spatial analysis, underscoring the  
urgent need for integrated strategies in land and water resource management, along with evidence-based spatial  
planning approaches, to foster sustainable ecological and rural development in environmentally sensitive areas.  
Keywords— Land use changes; Satellite imagery; Commercial crop cultivation; NDVI; NDWI; NDBI  
INTRODUCTION  
Land use changes provide a specific component for determining the environmental dynamic influence on  
ecological sustainability, economic development, and social well-being [1]. Due to population density,  
economic activities, and intensive natural and landscape changes, transformational environmental impacts are  
occurring. Globally, the land use changes in agricultural expansion and infrastructure development affect  
almost 62% of the global land area [2]. In the long-term assessment, the impact is greater due to changes in  
land use. Geographical divergence, land-use changes, afforestation, and global north cropland abandonment  
are evident. However, the southern regions are increasing their agricultural land use. Urban sprawl has also  
accelerated, with the United Nations projecting that more than 68% of the world's population will be in urban  
areas by 2050, leading to agricultural pressure for loss of biodiversity and climate change due to increased  
greenhouse gas emissions, altered hydrological cycles, and ecosystem services [3].  
The historical Sri Lankan economy depended heavily on agriculture, especially on plantation crops like tea and  
rubber [4]. Coconut and spice cultivation are mainly found in the central and southwestern regions. The  
plantation sector significantly changed land cover, leading to the conversion of forests into cultivated farmland  
over many years [5]. Kegalle district, Dehiowita, and Deraniyagala DS divisions are chosen based on  
population density and crop type distribution in the area. The Deraniyagala and Dehiowita regions are  
characterized by lush forests and hilly terrain, and they have experienced fluctuations in forest cover due to  
land conversion to agriculture and settlement growth. Likewise, Dehiowita has faced pressures from suburban  
development and the depletion of its water resources.  
Satellite-based remote sensing technologies have transformed the ability to monitor land use changes over  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025  
large areas and over time [6]. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI)  
and the Normalized Difference Water Index (NDWI) have become essential tools for detecting shifts in  
vegetation cover and water bodies, respectively [1]. These indices allow researchers to quantify and visualize  
vegetation pattern changes over time and space, offering important insights into how land use changes under  
various human influences [7]. The current study aimed to identify land use changes by examining spatial and  
temporal variations in satellite-derived data, specifically in planting areas [4]. Data from the Sri Lanka Census  
Department was used to identify specific plantations and land use types across the Dehiowita and Deraniyagala  
Divisional Secretariats in the Kegalle District. This research addresses gaps in understanding the spatial and  
temporal dynamics of land use in the Dehiowita and Deraniyagala Divisional Secretariats in Sri Lanka. It aims  
to support local planning and management efforts by developing a methodological framework that uses remote  
sensing indices for detailed land cover analysis, tailored for GIS technologies [8]. Finally, by comparing local  
findings with global trends, this research highlights both universal patterns and local factors that shape land-  
use change.  
METHODOLOGY  
Study Site  
The experimental site, Deraniyagala and Dehiowita, was selected under the population density distribution in the  
Deraniyagala and Dehiowita Divisional Secretary in Kegalle District [9].  
Table 01: Population density details of Dehiowita and Deraniyagala DS Divisions [9]  
Population Density  
D.S. Division 1/  
Dehiowita  
Rural Human Population  
Estate Human Population  
72850  
37871  
72149  
37507  
68924  
35830  
13445  
10807  
13135  
10730  
12720  
10224  
Deraniyagala  
Dehiowita  
2024  
2019  
2014  
Deraniyagala  
Dehiowita  
Deraniyagala  
Figure 1: Selected site maps:(a) Deraniyagala DS Division; (b) Dehiowita DS Division.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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Spatial Analysis in NDVI, NDWI, and NDBI: point-based analysis.  
Analysis of the spatial variation of the land use pattern changes by achieving the objective of further examining  
the built-up areas, vegetation cover, and water cover using ArcGIS software version 10.8, and is used to analyze  
the data and collected data sets [10].  
Normalized Different Water Indexes (NDWI)  
Water indices were obtained to determine the influence of water bodies. The NDWI development was produced  
using the Landsat NIR Band–6 and green band–3 for analysis [11].  
(Green Band(3)−NIR Band(6)  
NDWI =  
( )  
(Green Band 3 +NIR Band(6)  
(2.1)  
Normalized Difference Vegetation Index (NDVI)  
Vegetation indices were calculated to determine the influence of vegetation changes. NDVI was calculated using  
the Landsat 05 NIR band-3 and the red band–04. An Infrared spectral (IR) 5 Band with Land 8 OIL (TIRS) Red  
band 4 makeup was modified to the NDVI analysis [7].  
(IR Band(5)−R Band(4)  
NDVI(Land Sat 08) =  
( )  
(IR Band 5 +R Band(4)  
(2.2)  
Normalized Different Built-up Indexes (NDBI)  
Built-up indices were derived to characterize the distribution of built-up areas around the study sites, as well as  
NDBI variables that influence the assessment of the UHI effect. NDBI development procedure based on the SWIR  
band-6 and NIR band-5 was used to determine build-up areas [12].  
(SWIR Band(6)−NIR Band(5)  
NDBI =  
( )  
(SWIR Band 6 +NIR Band(5)  
(2.3)  
Plantation temporal data analysis  
Data collected by the Department of Census and Statistics from 2014 to 2024 were used to determine land-use  
changes under crop cultivation in the Deraniyagala and Dehiowita DS divisions. Population distribution data were  
also used to determine land-use and land-cover changes during this study.  
Statistical analysis  
Stratified sampling (fish net sampling) was used to extract spatial values in ArcGIS 10.8 to determine NDVI,  
NDWI, and NDWI change in the Deraniyagala and Dehiowita DS Divisions. Collected data from the ArcGIS  
software was analyzed using IBM SPSS (Version 25) software [1]  
RESULTS AND DISCUSSION  
Spatial Land use Change analysis (NDVI, NDWI, and NDBI)  
Normalized Difference Vegetation Index Analysis  
Deraniyagala vegetation status data show that mean values for 2014, 2019, and 2024 are -0.1988, -0.2130, and -  
0.2148, respectively (Figure 3.2). The slight decline in means suggests a marginal decrease in vegetation vitality or  
cover. The range from 0.574 to 0.555 is significant, with the maximum value slightly increasing over the years. The  
total sums indicate an increase in vegetation measures, possibly reflecting higher density or biomass in some areas.  
The Dehiowita site shows consistent positive trends, with values rising from just above zero to higher levels,  
indicating improvement in vegetation conditions. The range within Dehiowita remains stable, with maximum values  
likely representing localized patches of denser or healthier vegetation, and the total values indicate an increase in  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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vegetative biomass or coverage. The study compared vegetation conditions between the two sites, Deraniyagala and  
Dehiowita. Deraniyagala shows a slight decline in vegetation conditions over the past decade, indicating  
degradation, and Dehiowita exhibits consistent increases, suggesting improvement with stable conditions. The  
negative average values in Deraniyagala indicate environmental stress, whereas the positive trend in Dehiowita  
suggests successful conservation or improved land management. The results are consistent across measurements.  
Figure 3.1: Normalized Difference Vegetation Index Analysis Results in Dehiowita (A) and Deraniyagala (B) DS  
Divisions 2014 – 2024.  
A1  
B1  
A2  
B2  
A3  
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Figure 3.2: Descriptive statistics results in NDVI values, Dehiowita (B) and Deraniyagala (A) DS Division 2014–  
2024  
B
A
Normalized Difference Build–Up Index Analysis  
The Deraniyagala dataset shows a steady increase in the mean build-up index over the past decade, indicating  
gradual urbanization. The mean index has risen from 0.3724 to 0.3929, suggesting that urbanization is driven by  
infrastructure development, population growth, or land-use changes. The mean and standard deviation are low,  
indicating high precision in the estimates. The median values closely match the means, and the range and extremes  
are slightly higher. The total build-up index increased from 486.3 to 513.5, confirming the trend of urban growth.  
The mean value of -0.15 to -0.25 at the Dehiowita site has decreased by 0.035-0.036 units, suggesting a reduction in  
built-up areas. Data distribution is centered on negative values, with most areas remaining less developed. The range  
is 0.3679 to 0.4243, indicating that some areas have undergone more significant development. The total build-up  
value has increased from 486.3 to 513.5, possibly reflecting land use reclassification rather than strictly growth. The  
study reveals a significant difference between the two urbanization sites, Deraniyagala and Dehiowita, which shows  
a steady increase in the build-up index over the decade, indicating expanding urbanization or infrastructure  
development. This could be driven by economic growth, population influx, or land-use policies favoring  
development. Dehiowita exhibits a decreasing or stagnant build-up index, with more negative mean values over  
time. Both sites show large portions of their datasets with zero values. This probably means significant parts lack  
development or are in the early stages of urbanization [10]. The positive shift in Deraniyagala suggests more  
extensive development activities or urban sprawl, while the relative decline in Dehiowita suggests stabilization or  
reduction in urban built-up zones.  
Figure 3.3: Normalized Difference Build–Up Index Analysis Results in Dehiowita (A) and Deraniyagala (B) DS  
Divisions 2014 – 2024.  
A1  
B1  
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A2  
B2  
A3  
B3  
Figure 3.4: Descriptive statistics results in NDBI values, Deraniyagala (A) and Dehiowita (B) DS Division 2014 –  
2024  
A
B
Normalized Difference Water Indexes Analysis  
The study reveals a decline in water and moisture conditions in Deraniyagala over the past decade. The mean NDWI  
values are negative, indicating sparse or low moisture content in water bodies or moist areas. The NDWI range  
varies, indicating low or negligible water presence. The total water/moisture presence increases from -259.7 to -  
281.3, indicating a general reduction in water coverage. The dataset in Dehiowita shows a slightly less negative or  
neutral NDWI value over the past decade, fluctuating between -0.16 and -0.15. Over the same period, the NDWI has  
increased slightly, indicating a marginal improvement or stabilization. The data is consistent with slight fluctuations,  
suggesting stable moisture conditions. The NDWI range varies, with the minimum indicating low moisture zones  
and the maximum indicating occasional water or moisture-rich patches. The total water presence is negative but  
decreasing over time.  
The study reveals contrasting water resource dynamics in two sites, Deraniyagala and Dehiowita. A gradual decline  
in NDWI values in Deraniyagala suggests a decrease in water bodies or moisture content, possibly due to climate-  
induced drying trends, water extraction, land-use change, or natural evaporation. Dehiowita shows a slightly  
improving or stable moisture condition, suggesting better management or natural resilience. Both sites maintain a  
mode at zero, implying many areas are dry or lack water features. The decline in NDWI in Deraniyagala could  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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reflect environmental stress, while Dehiowita may be better managed or more resilient to water fluctuation.  
Understanding of these trends is crucial for strategic planning, sustainable water resource management, and  
ecological conservation. The findings highlight the complex interactions between climate, land use, and  
environmental policies that influence water and moisture [13]. The data in the tables provide a comprehensive  
overview of crop cultivation and population density across 2014, 2019, and 2024 for the D.S. Division, highlighting  
significant land-use changes, particularly in the context of highland crop statistics and population dynamics.  
Figure 3.5: Normalized Difference Water Indexes Analysis Results in Dehiowita (A) and Deraniyagala (B)DS  
Divisions 2014 – 2024.  
B1  
A2  
B2  
A3  
B3  
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Figure 3.6: Descriptive statistics results in NDWI values, Deraniyagala (A) and Dehiowita (B)DS Division 2014 –  
2024  
B
A
Crop cultivation distribution analysis for Dehiowita and Deraniyagala DS Divisions  
Figure 3.7: Crop distribution analysis in Dehiowita (B) and Deraniyagala (A) DS Divisions in hectares in  
2014  
A
B
Figure 3.8: Crop distribution analysis in Dehiowita (B) and Deraniyagala (A) DS Divisions in hectares in  
2019  
A
B
Figure 3.9: Crop distribution analysis in Dehiowita (B) and Deraniyagala (A) DS Divisions in hectares in  
2024  
A
B
Initially, examining data from 2014 shows that the total cultivated land area for key crops was relatively modest,  
with tea, rubber, coconut, cinnamon, coffee, pepper, cashew, and cloves occupying specific areas (Figures 3.7, 3.8,  
3.9). The tea cultivation stood at 1,703 hectares, rubber at 10,282 hectares, coconut at 1,689 hectares, cinnamon at 18  
hectares, coffee at 130 hectares, pepper at 103 hectares, cashew at 16 hectares, and cloves at 22 hectares in  
Dehiowita. During this period, the population density was relatively low, with a rural human population of 68,924  
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and an estate human population of 12,720. This indicates a landscape where agricultural activities, especially rubber  
cultivation, appeared to dominate land use, potentially reflecting economic reliance on rubber estates and traditional  
crop farming practices.  
Based on the data from the 2014-2019 period, tea and rubber increased noticeably. The tea cultivation in Dehiowita  
has increased from 1703 to 1831.6 hectares, and rubber expanded more than 3,000 hectares in a short period.  
Coconut cultivation followed a similar trend, and cinnamon, coffee, pepper, cashew, and clove showed stabilized  
conditions, but cinnamon showed a decreasing trend. Coffee increased significantly to 11.9 hectares, and pepper to  
7.1 hectares. The primary agricultural land use may be driven by rubber and tea plantations, which offer short-term  
economic incentives and profitability, or by government policies that support crop cultivation. The rural population  
in 2018 increased to 72,149, and the estate population reached 13,135. In Dehiowita, population density contributed  
to changes in land-use patterns. Densely populated areas require more agricultural land to be occupied by high-tech,  
intensive production systems to meet demand for food and other crops, which are often associated with commercial  
agriculture. Reaching the land area occupied for food crop cultivation further expanded with tea (1631.6 hectares in  
2019 to 1831.6 hectares in 2023), rubber (7040.7 hectare to 2508.2) continues dominance agricultural land use but  
reaching the 2023 Rubber plantation lands in Dehiowita DS Division decreased more than 3000 hectares showing a  
highlight of the change of agricultural land use pattern shifting towards other plantations, like coffee and pepper. The  
parallel increase of crop cultivation and population density indicates an ongoing trend of land use adapted to  
demographic changes and economic pressures. As population density increases in rural regions, cultivated lands are  
increasingly pressured to meet food security and financial demands, driving a shift toward the commercialization and  
monetization of resources in response to market forces. The cultivation transformation to Dehiowita and  
Deraniyagala DS divisions leads to changes in environmental concerns, such as vegetation cover loss and the loss of  
water bodies, which are impacted during land-use shifts under unplanned conditions.  
Crop cultivation and population density align with the vegetation changes, and water bodies disrupt the ecological  
balance and diversity [14]. The shift towards cinnamon and coffee under large-scale monoculture plantations leads to  
soil erosion and reduced water quality due to the loss of dense vegetation cover at both sites [15]. This may lead to  
long-term sustainability challenges. These concerns underscore the importance of an integrated land-use and water-  
body management plan that considers population growth and agricultural productivity, fostering a resilient and  
sustainable rural landscape [16].  
CONCLUSION  
This study thoroughly examined land-use changes in agricultural and plantation areas within the Deraniyagala and  
Dehiowita DS Divisions in Sri Lanka, using spatial and temporal data supported by NDVI, NDWI, and NDBI  
indices. The findings reveal that the two regions have followed different trajectories over the past decade. An  
increase in built-up areas was observed, with Deraniyagala showing signs of urban pressure and environmental  
stress, as indicated by declining vegetation vitality and water availability reflected in negative trends in NDVI and  
NDWI. Conversely, Dehiowita demonstrates better vegetation cover and more stable moisture conditions, although  
slight declines in built-up areas might suggest reclassification or a status quo rather than new development.  
Examining crop cultivation over time, tea and rubber plantations appeared during periods of growth and declined  
before shifting toward coffee and pepper cultivation. These trends align with increasing rural and estate populations  
exerting pressure on land resources and prompting changes in agricultural practices. The results underscore the  
significance of local land use patterns, which are shaped by demographic, economic, and land management factors.  
Overall, this research underscores the value of analyzing remote sensing indices of land use change through spatial  
analysis to gain insights into these processes at the local level. These trends highlight the requirement for integrated  
land and water management strategies to support further development of agriculture, urban areas, and ecosystems.  
REFERENCES  
1. Liyanage, L., Dilini, R.M.A., and Amerasinghe, R. (2024). Urban heat island (UHI) impact assessment  
using spatial and temporal data to develop green urban planning strategies in the Colombo district. In  
Sustainable Built Environment 2024, Kandy Associate Professional (Pvt) Ltd., Dec. 2024, 75-88 pp.  
2. Afuye, G.A., Nduku, L., Kalumba, A.M., Santos, C.A.G., Orimoloye, I.R., Ojeh, V.N., Thamaga, K.H.,  
and Sibandze, P. (2024). Global trend assessment of land use and land cover changes: A systematic  
Page 1902  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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approach to future research development and planning. Journal of King Saud University-Science, 36,  
103262pp.  
3. Mishra, H. (2025). Environmental degradation and impacts on agricultural production: A challenge to  
urban sustainability. In Sustainable urban environment and waste management: Theory and practice.  
Singapore: Springer Nature Singapore. 53-92pp.  
4. Gunawardana, P. J., and Somaratne, W. G. (2011). Non-plantation agricultural economy of Sri Lanka:  
Trends, issues and prospects. Sri Lankan Journal of Agricultural Economics, 3.  
5. Ullah, W., Ahmad, K., Ullah, S., Tahir, A. A., Javed, M. F., Nazir, A., and Mohamed, A. (2023).  
Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and  
normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan  
region. Heliyon, 9.  
6. Rogan, J., & Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and  
land-use change. Progress in planning, 61, 301-325pp.  
7. Liyanage, L., Dilini, R. M. A., and Samaranayake, N. A. R. B. (2024). Assessment of Land Use Changes  
on Vegetation Dynamics in Muthurajawela Wetland: A GIS-Based Spatio-Temporal Study, in  
Technology and Innovation for Growth, Sri Lanka Technology Campus, Sri Lanka, Nov. 2024, 68–75pp.  
8. Di Palma, M., Rigillo, M., and Leone, M. F. (2024). Remote sensing technologies for mapping  
ecosystem services: an analytical approach for urban green infrastructure. Sustainability, 16, 6220pp.  
9. Department of Census and Statistics. (2024, March 30). Statistical abstract 2023: Chapter 2.  
http://www.statistics.gov.lk/abstract2023/chapter2.  
10. Ranagalage, M., Estoque, R. C., Zhang, X., and Murayama, Y. (2018). Spatial changes of urban heat  
island formation in the Colombo District, Sri Lanka: Implications for sustainability planning.  
Sustainability, 10, 1367.  
11. Kshetri, T. (2018). NDVI & NDWI calculation using landsat 7, 8. GeoWorld, 2, 32-34.  
12. Alademomi, A. S., Okolie, C. J., Daramola, O. E., Akinnusi, S. A., Adediran, E., Olanrewaju, H. O., and  
Odumosu, J. (2022). The interrelationship between LST, NDVI, NDBI, and land cover change in a  
section of Lagos metropolis, Nigeria. Applied Geomatics, 14, 299-314pp.  
13. Konapala, G., Mishra, A. K., Wada, Y., and Mann, M. E. (2020). Climate change will affect global water  
availability through compounding changes in seasonal precipitation and evaporation. Nature  
communications, 11, 3044.  
14. Mehta, P. (2024). The impact of climate change on the environment, water resources, and agriculture: a  
comprehensive review. Climate, Environment and Agricultural Development: A Sustainable Approach  
Towards Society, 189-201pp.  
15. Peiris, H. M. P., and Gunarathne, N. (2021). The changing landscape of the plantation sector in the  
central highlands of Sri Lanka. In Mountain landscapes in transition: Effects of land use and climate  
change. Cham: Springer International Publishing. 539-554pp.  
16. Ige, O. E., Ojo, F. R., and Onikanni, S. A. (2024). Rural and Urban Development: Pathways to  
Environmental Conservation and Sustainability. In Prospects for Soil Regeneration and Its Impact on  
Environmental Protection. Cham: Springer Nature Switzerland. 307-333pp.  
Page 1903  
www.rsisinternational.org