Abstract Understanding land use and land cover (LULC) dynamics is crucial for managing ecological sustainability in fragile mountainous regions. This study analyses decadal LULC changes (2013–2023) and projects future transformations up to 2040 for the Almora district, Uttarakhand, using a hybrid Cellular Automata-Artificial Neural Network (CA-ANN) model within a geospatial framework. The region’s complex topography, steep slopes, dendritic drainage networks, and increasing anthropogenic pressures have accelerated land system transformations. Results reveal a notable expansion of built-up areas, a decline in agricultural land and forest cover, and a significant reduction in water bodies, reflecting intensified urbanisation. The CA-ANN model demonstrated robust predictive accuracy, achieving an overall simulation accuracy of 94.27% and a kappa coefficient of 0.94. If current development trends persist, future LULC scenarios predict further urban proliferation, ecological stress, and hydrological instability. While the modelling approach effectively captures complex spatiotemporal dynamics, limitations remain due to the reliance on historical trends, excluding sudden policy shifts and extreme climatic events. The study underscores the urgent need for integrated sustainable land management practices and policy interventions to mitigate environmental risks and support resilient development trajectories in Himalayan landscapes. Research Highlights <UnorderedList Mark="Bullet"> <ItemContent> The results reflect significant landscape transformations shaped by natural terrain complexities and intensifying anthropogenic pressures. The geomorphological configuration of Almora exerts a profound influence on land utilisation patterns. The decadal LULC analysis highlights a clear trend of urban intensification. </ItemContent> <ItemContent> Water bodies exhibited a significant reduction, raising concerns about declining groundwater recharge and surface water availability. </ItemContent> <ItemContent> These spatial trends portray serious environmental consequences, including increased runoff, higher landslide risks, loss of biodiversity, and hydrological destabilisation. The transformation of land systems in Almora thus mirrors broader patterns observed across fragile mountain ecosystems under the dual pressures of urbanisation and climate variability. </ItemContent> <ItemContent> Results of the CA-ANN model affirm the model’s efficacy in capturing complex, non-linear land transformation processes across heterogeneous landscapes. </ItemContent> <ItemContent> This study emphasises the urgent need for integrated and sustainable land management policies in the Almora region. Targeted interventions such as zoning regulations, slope stabilisation measures, conservation of forest corridors, and protection of hydrological assets are imperative to mitigate the adverse impacts of unplanned urban expansion. </ItemContent> </UnorderedList>

Spatiotemporal dynamics and future prediction of land use scenarios in Almora, Uttarakhand: Integrating cellular automata and neural network

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发表日期：

2025年12月5日

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Radha Krishan,
Ankita Sharma,
Santosh Tudu,
Kuljit Kour

摘要

Abstract

Understanding land use and land cover (LULC) dynamics is crucial for managing ecological sustainability in fragile mountainous regions. This study analyses decadal LULC changes (2013–2023) and projects future transformations up to 2040 for the Almora district, Uttarakhand, using a hybrid Cellular Automata-Artificial Neural Network (CA-ANN) model within a geospatial framework. The region’s complex topography, steep slopes, dendritic drainage networks, and increasing anthropogenic pressures have accelerated land system transformations. Results reveal a notable expansion of built-up areas, a decline in agricultural land and forest cover, and a significant reduction in water bodies, reflecting intensified urbanisation. The CA-ANN model demonstrated robust predictive accuracy, achieving an overall simulation accuracy of 94.27% and a kappa coefficient of 0.94. If current development trends persist, future LULC scenarios predict further urban proliferation, ecological stress, and hydrological instability. While the modelling approach effectively captures complex spatiotemporal dynamics, limitations remain due to the reliance on historical trends, excluding sudden policy shifts and extreme climatic events. The study underscores the urgent need for integrated sustainable land management practices and policy interventions to mitigate environmental risks and support resilient development trajectories in Himalayan landscapes.

Research Highlights

The results reflect significant landscape transformations shaped by natural terrain complexities and intensifying anthropogenic pressures. The geomorphological configuration of Almora exerts a profound influence on land utilisation patterns. The decadal LULC analysis highlights a clear trend of urban intensification.

Water bodies exhibited a significant reduction, raising concerns about declining groundwater recharge and surface water availability.

These spatial trends portray serious environmental consequences, including increased runoff, higher landslide risks, loss of biodiversity, and hydrological destabilisation. The transformation of land systems in Almora thus mirrors broader patterns observed across fragile mountain ecosystems under the dual pressures of urbanisation and climate variability.

Results of the CA-ANN model affirm the model’s efficacy in capturing complex, non-linear land transformation processes across heterogeneous landscapes.

This study emphasises the urgent need for integrated and sustainable land management policies in the Almora region. Targeted interventions such as zoning regulations, slope stabilisation measures, conservation of forest corridors, and protection of hydrological assets are imperative to mitigate the adverse impacts of unplanned urban expansion.