Land Use Change Detection and Prediction Around Lenggong UNESCO World Heritage Site Using ANN–CA Modelling

A significant increase in built-up areas, from 3.6% to 10.5%, has been detected within the 1 km heritage buffer of the Lenggong Valley UNESCO World Heritage Site between 2000 and 2020, with projections suggesting this trend will continue, reaching 13.2% by 2040. This study utilized an Artificial Neural Network (ANN) combined with Cellular Automata (CA) modeling to analyze historical land use changes and predict future patterns in the Lenggong catchment. The model demonstrated high accuracy, achieving an overall Kappa value of 0.71 and validation accuracy of 83.1%. While forest and agriculture remained dominant land uses, the study highlighted a concerning decrease in forest cover (from 58.9% to 54.4%) and an increase in built-up areas, posing potential threats to the heritage landscape's authenticity and environmental quality. These findings underscore the need for proactive heritage-sensitive planning and conservation strategies to mitigate development pressures.

Ramli, M. W. A., Wan Ibrahim, W. M. M., Ramanthan, A., Ahmad, A., Abdul Wahab, Y. F., & Mahamud, M. A. (2026). Land Use Change Detection and Prediction Around Lenggong UNESCO World Heritage Site Using ANN–CA Modelling. Earth, 7(5), 144. https://doi.org/10.3390/earth7050144