The term land-use refers to the system of how the land is being used and influencedby human activities for different needs. It forms a direct link between land-use and land-cover, and the action of the people in the environment [1
]. The dynamics of land-use are of great concern on the Earth’s surface [2
]. Significant changes in land-use cause a constant strain on the ecology [3
]. Generally, anthropogenic activities in land-use have been accepted as significant factors in global change [6
]. These changes have been reviewed as the critical driving forces of ecological change on all spatial and temporal scales [9
]. Environmental issues have attracted the attention of the people of the world and they are now becoming increasingly conscious of a variety of environmental challenges to well-being by unplanned urban development. Therefore, current environmental problems can be classified into three parts in urban centers: the problems arising from, and associated with, poverty and underdevelopment; the problems arising as the negative effects of every process of development; and the problems related to man-made pollution.
Land-use change is not static because of dynamic and continuous processes [10
]. Monitoring of these changes is needed overall in environmental and ecosystem services [11
]. Geographical information systems (GISs) and remote sensing technology platforms that monitor the temporal changes of land-use have become the most effective methods possible, at a low-cost and with better accuracy for data analysis, update, and retrieval [12
]. Landsat remote sensing data have provided valuable and continuous information about the Earth’s landscape for approximately the past four decades. The archive of this series of data is now made freely available for scientific decision-making and, thus, it serves as a repertoire of information for identifying and monitoring the changes imposed on the physical and human environment [15
]. Remote sensing data is the most important for application and is widely used forupdating land-use change maps [16
]. At present, cities are growing twice as fast because of rapid population growth [17
]. Consequently, this indicates that changes in urban land-use could produce a near tripling in the global urban land in the future, adding hundreds of thousands of additional square kilometers to urban levels of density [17
]. Such urban expansions threaten to destroy ecosystem habitats and contribute to the carbon emissions associated with tropical deforestation and land-use change [18
]. Nowadays, Earth resource satellite data are useful for estimating land-use change and detection analysis [16
]. Land-use change has been applied in numerous studies in the spatially apparent scientific field [6
The land change modeler (LCM) is an estimating and predicting process that determines land-use change [21
]. The LCM is a simplification of reality that offers an important means of predicting the weight of land-use change [22
]. The analysis of land-use change has produced a strong model by integrating multilayer perception in the LMC process in raster data form between land-use classes [5
]. These models reflect the dynamic changes in land-use over time. However, land-use might be static during a short time. Therefore, a Markov chain model can be used to predict the process from past to future [7
]. Many scholars have widely used this model on land-use changes, including on both urban and nonurban areas, for large spatiotemporal scales [23
]. The Markov chain model was applied for sustainable land demand in recent urban studies [2
]. On the basis of this concept, the model might apply to regions that experience rapid land-use change. Land-use has been changing because of the inappropriate processes of the administrative area of theBatticaloa Municipal Council (BMC). However, a few studies have been applied to assess land-use changes through the spatial analysis concept. These concepts are not good enough to utilize the land for forthcoming purposes in effective ways. The empirical land-use and Markov chain model were employed to simulate the land-use change in the present study. The main objective of the study is to measure the land-use changes that have occurred in the BMC during the last thirty years.
We address the following research questions:
What is the extent and magnitude of land-use change in the BMC from 1990 to 2020?
What are the primary drivers of land-use change in the BMC?
How do historic land-use changes differ from simulated land-uses in the BMC?
2. Study Area
The study area is laid between north of 7°39′53″ to 7°44′36″ and 81°39′17″ to 81°41′54″ on the east, in the central part of the Eastern Province of Sri Lanka. The BMC is divided into forty-eight (48) GNDs (Figure 1
), which consist of some parts of the suburb. The study consists of an area of about 4570.6 ha. The mean annual rainfall and temperature are 1500 mm and range from 28 °C to 34 °C, respectively [30
]. In the BMC, remarkable changes in land-use are observed due to urban development and the rapidly increasing population. The rapid development of sectors, such as the residential, commercial, industrial, service, and recreational sectors, has been improved by the utilization of land. This has revealed the changes in land-use in the spatial domain. Therefore, it is important to monitor land-use changes when considering the existing scenario, forecasting future change, and planning for the future development of the urban area. The latest geospatial technology is necessary to simulate contemporary and projected land-use changes for the BMC.
The study area has faced challenges to land-use by the civil war, a rapidly increasing population, and anthropogenic activities. Moreover, the lack of proper land-use guidelines has led to temporal land-use changes and conflictsaround land-use. According to Mathanraj et al. [31
], the growth of the city without proper planning leads to the creation of many complex problems. The population growth exploited agricultural land and other land areas. The overcrowding of the population directly affected the other land areas. The correlation relationship is significant between population growth and land-use change in the BMC [31
This study provides information on the spatiotemporal land-use changes that occurred during 1990–2020 in the municipal council limits of Batticaloa and combines the empirical land-use model to simulate the land-use change in 2030. We used theland change modeler as a decision support land planning tool [6
] to evaluate historical land-use changes (from 1990 to 2020) in the BMC, and the Markov chain stochastic simulation model (for example, [7
]) to predict land-uses in 2030. Change detection is important for determining which land-use is changing, and how it is changing, for sustainable land-use planning [40
]. The future forecast map of 2030 is useful for developing suitability modeling for homesteads, which were growing exponentially from 2010 to 2020.The land-uses in the BMC have changed because of human-induced activities, such as illegal mining, plantations, sprawling development, encroachment, and natural events, such as storm surges and floods [31
]. An unpredictable sociopolitical system has caused a rapid change in land-use and land-cover in Batticaloa over the past twenty years [43
Most of our land-use class accuracy (both producer’s and user’s accuracy) is greater than 80%, which is considered a moderate accuracy for classification [7
]. In remote sensing data, the minimum level of accuracy assessment should be determined as at least 85% in land-use types [45
]. The accuracy of sandy land-use is comparatively higher (≥94% ≤100%) than other land-uses that are confined to a narrow strip of shoreline/sandbelt (Table 2
, Figure 4
). The sandy beaches in this area are unique and are characterized by bays and headlands, straight sandy shoreline/beaches, deltas/saline flats, and sparse vegetation [41
]. Bare land has limited resources for supporting life and can be easily identified while training the data and, therefore, the accuracy of bare land is noticeably high (≥96% ≤100%).
There was an increase in homesteads between 2010 and 2020 (Figure 5
) as a consequence of the civil war ending in 2009 and the migration of people to an area with a peaceful environment and infrastructure facilities. Agriculture is declining over time (Figure 4
), and this could be an impact of the tsunami effect in 2004 where the saltwater intrusion onto agricultural lands resulted in infertile or bare lands. Home garden acreage was just over the agriculture lands in the year 2000 and onward and seems to be increasing with the increase in homesteads. Similarly, Partheepan et al. [43
] found that the cultivated areasin Batticaloa have increased by 41.9% from 2000 to 2005 because of the peace process prevailing in the country at that time.
The Markov chain model has coupled with geospatial technology in the descriptive capability of prediction. This spatiotemporal model provides, not only a quantitative description of the changes in earlier times, but also the path and level of change in the future. In this study, based on experimental results and empirical analysis, limitations are present. However, four images were used to analyze the land-use change and the images were acquired on different dates (Figure 3
). In the Markov chain model, the transition probability is expected to be uniform with the images. Therefore, it is challenging to accommodate variables by random influence, such as the climatic condition and human influence [46
]. However, it can be resolved by combining temporal, high-accuracy, and same-dated images for decision-making in long-term forecasts.
The aim of this study is based on the geospatial technology analysis of land-use change and a modeling of future land-use in the BMC. The Markov chain model simulates changes in land-use using multi-dated Landsat images from 1990–2020. The results show that the areas of homestead in the BMC increased by 12.7% to 34.1% from 1990 to 2020. During this period, land-use classes of agriculture, wetland, home garden, sandy, and scrub decreased to 5.0%, 4.3%, 2.5%, 2.3%, and 1.4%, respectively. Our results clearly show that the built-up area has gradually increased from 1990 (37.3%) to 2020 (56.8%), while the non-built-up area has declined from 62.7% to 43.1% for the period. The land-use trends were simulated for the forthcoming decade through the use of the Markov model. The results indicate that homestead and built-up areas will have increased by 37.3% and 61.8% in the year 2030, respectively. This result shows that the natural equilibrium of the BMC is threatened by the population pressure of the last 30 years. The historical and forecasting land-use model is important to make better land-use planning decisionsin the BMC for mitigating climatic (e.g.,a tsunami) and/or anthropogenic impacts.