Modeling land use and land cover dynamics of Bale Mountains National Park using Google Earth Engine and cellular automata–artificial neural network (CA-ANN) model

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This research aimed to assess the observed land use and land cover (LULC) changes of Bale Mountains National Park (BMNP) from 1993 to 2023 and its future projections for the years (2033 and 2053). The study utilized multi-date Landsat imagery from 1993, 2003, 2013, and 2023, leveraging Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI-TIRS sensors for LULC classification. Standard image pre-processing techniques were applied, and composite images were created using yearly median values in Google Earth Engine (GEE). In addition to satellite data, both physical and socioeconomic variables were used as input for future LULC modeling. The Random Forest (RF) classification algorithm was used for image classification, while the Cellular Automata Artificial Neural Networks (CA-ANN) model within the Modules for Land Use Change Simulations (MOLUSCE) plugin of QGIS was employed for future LULC projection. The analysis revealed significant LULC changes in BMNP, from 1993 to 2023, primarily due to anthropogenic activities, with further changes anticipated between 2023 and 2053.The results showed a notable increase in woodland and shrubs at the expense of grassland and Erica forest. While woodland and shrubs increased by 87.18% and 36.7%, areas of Erica forest and grassland lost about 25% and 22% of their area, respectively, during this period. The LULC model results also indicated that areas covered by woodland and shrubs are expected to increase by 15.97% and 15.57%, respectively, between 2023 and 2053. Conversely, land areas occupied by cultivated land, Erica forest, grassland, and herbaceous plants are projected to decrease by 28.52%, 3.28%, 19.03%, and 6.55%, respectively. Proximity to roads and urban areas combined with rising temperatures and altered precipitation patterns emerged as critical factors influencing land use conversion patterns in BMNP. These findings underscore the complex interplay between environmental factors and human activities in shaping land cover dynamics. Hence, promoting sustainable land management practices among the park administration and local community as well as enhancing habitat protection efforts are recommended. Additionally, integrating advanced remote sensing technologies with ground truthing efforts will be essential for accurate assessments of LULC dynamics in this critical area of biodiversity.

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