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Mapping agrotourism potential using geospatial data and random forest: A case study from Bali

Ustriyana I Nyoman Gede, Saifulloh Moh, Diara I Wayan, Suyarto R

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 3

1--16

Bali, Indonesia, renowned for its cultural heritage and natural beauty, has untapped potential in agrotourism, offering a sustainable avenue for economic diversification and cultural preservation. This study aims to identify and map agrotourism potential in Gianyar Regency using advanced geospatial analysis and the Random Forest algorithm, integrating antrophogenic and environmental variables. Ten key factors were analyzed, including proximity to tourist attractions, tourism facilities, and road networks, as well as environmental variables such as NDVI, LSWI, elevation, slope, temperature, and rainfall. A total of 410 data points, including 111 existing agrotourism locations, were utilized to develop and validate the model. The Random Forest algorithm demonstrated strong performance, achieving an accuracy of 86%, a recall of 72%, and an F1 score of 78%. The model’s high specificity (92%) and low false positive rate (8%) underscored its reliability in excluding unsuitable areas while accurately classifying high-potential zones. Variable importance analysis revealed NDVI (13.13%) and LSWI (13.11%) as the most critical factors, highlighting the significance of soil fertility and moisture in agrotourism suitability. The zoning map categorized land into five potential levels, with 10.11% identified as having very high potential, concentrated in subdistricts like Tegallalang and Payangan. Tegallalang, with its iconic Subak rice terraces, exemplifies the integration of agricultural sustainability and cultural heritage, while Payangan offers interactive horticulture and plantation experiences. Priority villages for development, including Tampaksiring, Kedewatan, and Keliki, demonstrated >50% agrotourism potential, underscoring their suitability for targeted investment and strategic planning. This study provides a robust framework for data-driven agrotourism development, combining geospatial technology with sustainable tourism strategies. It highlights the importance of optimizing natural and cultural assets to enhance Bali’s global appeal while ensuring economic and environmental sustainability. Future research should refine zoning models with additional parameters and collaborative approaches to maximize the potential of agrotourism in rural areas.

Carbon Stocks Dynamics of Urban Green Space Ecosystems Using Time-Series Vegetation Indices

Sudarma I Made, Saifulloh Moh, Diara I Wayan, As-Syakur Abd. Rahman

Ecological Engineering & Environmental Technology

2024

Vol. 25, iss. 9

147--162

The quantification of carbon stocks has emerged as a critical global issue due to its vital role in ecosystem services amid increasing urbanization and the impacts of global climate change. This study assesses carbon stocks in urban green space (UGS) ecosystems using time-series remote sensing data from 2014 to 2022. Carbon stock computation was derived from vegetation indices obtained from Landsat 8 satellite sensors, specifically the red and near infrared (NIR) bands with central wavelengths of 0.665 µm and 0.705 µm, respectively. The results, based on nine years of annual data, indicate a 24% increase in carbon stocks within UGS ecosystems. However, year-to-year transitions showed significant fluctuations, with a 19% decrease in carbon stocks from 2017 to 2019, and notable increases of 25% and 40% during the 2015–2016 and 2019–2020 periods, respectively. Spatially, carbon stock fluctuations were most pronounced in agricultural ecosystems, which are vulnerable to climate change, especially during El Niño-Southern Oscillation (ENSO) and positive Indian Ocean Dipole (IOD) events that influenced vegetation dynamics, particularly in low-density areas. The most substantial contributors to carbon stocks, exhibiting relatively stable and adaptive patterns to climate change, were mangrove and urban forest ecosystems. From a state-of-the-art perspective, this research addresses a gap in the literature where previous studies focused on calculating carbon for specific periods using various model approaches. Our implementation of a new time series analysis demonstrates that carbon stocks are dynamic, as evidenced by our findings. The results underscore the importance of preserving urban forest ecosystems, which play a significant role in climate change mitigation and the reduction of urban greenhouse gas emissions.