Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

Abstract

Forecasting landscape changes is vital for developing and implementing sustainable urban planning. Presently, apart from lowland coastal cities, mountain cities (i.e., hill stations) are also facing the negative impacts of rapid urbanization due to their economic and social importance. However, few studies are addressing urban landscape changes in hill stations in Asia. This study aims to examine and forecast landscape changes in the rapidly urbanizing hill station of Nuwara Eliya, Sri Lanka. Landsat data and geospatial techniques including support vector machines, urban–rural gradient, and statistical analysis were used to map and examine the land use/land cover (LULC) change in Nuwara Eliya during the 1996–2006 and 2006–2017 periods. The multilayer perceptron neural network-Markov model was applied to simulate future LULC changes for 2027 and 2037. The results show that Nuwara Eliya has been directly affected by rapid urban development. During the past 21 years (1996–2017), built-up areas increased by 1791 ha while agricultural land declined by 1919 ha due to augmented urban development pressure. The pressure of urban development on forest land has been relatively low, mainly due to strict conservation government policies. The results further show that the observed landscape changes will continue in a similar pattern in the future, confirming a significant increase and decrease of built-up and agricultural land, respectively, from 2017 to 2037. The changes in agricultural land exhibit a strong negative relationship with the changes in built-up land along the urban–rural gradient (R2 were 0.86 in 1996–2006, and 0.93 in 2006–2017, respectively). The observed LULC changes could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea. Further, unplanned development could cause several environmental issues. The study is important for understanding future LULC changes and suggesting necessary remedial measures to minimize possible undesirable environmental and socioeconomic impacts.

The Impacts of Landscape Changes on Annual Mean Land Surface Temperature in the Tropical Mountain City of Sri Lanka: A Case Study of Nuwara Eliya (1996–2017)

“…The observed pattern is critical evidence for policymakers to introduce proper mechanisms for minimizing the adverse impacts associated with the rapid changes in the urban landscape of the study area. The negative impacts of urban development on agricultural land could cause several environmental issues [59], such as increased soil erosion [68], and landslides [69][70][71]. The rapid development of built-up land, resulting in increased levels of LST, could also negatively affect the tourism industry.…”

“…Policies should be implemented to maintain the natural beauty of Nuwara Eliya for the sustainable tourism industry. Most tourists wish to enjoy the landscape and comfortable climate of the area [49,59].…”

“…Machine learning methods, such as support vector machines (SVM), K-nearest neighbor, random forest, and neural networks, have been widely used to classify LULC [13,14]. Among these methods, the SVM has provided higher overall accuracy [56][57][58][59]. Thus, in this study, we used the SVM algorithm to conduct LULC mapping.…”

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

“…LULC classification is a significant challenge when it comes to obtaining complex LULC distribution information from Landsat data [18,37]. Several classification methods have been employed in different studies depending on the target area and the spatial resolution of the satellite images [38][39][40]. From a performance perspective, the maximum likelihood supervised classification method provides an accurate outcome [23].…”

Landscape Changes and their Impacts on Land Surface Temperature in Tropical Hill Stations in South Asia: A Case Study of Nuwara Eliya, Sri Lanka

“…This pattern is critical evidence for policymakers to introduce a proper mechanism for minimizing the adverse impacts associated with the rapid changes in the urbanization of the study area. The observed urban development impact on agricultural land could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea.Further, unplanned development could cause several environmental issues[57], such as increasing soil erosion[67], and landslides[68][69][70]. The rapid development of built-up land resulting in increased levels of LST could negatively affect the tourism industry.…”