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

doi:10.12912/27197050/199323

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

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

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12912/27197050/199323

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

sustainability tourism GIS zoning land suitability anthropogenic environment subak tourism planning

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2025

Tom

Vol. 26, iss. 3

Strony

1--16

Opis fizyczny

Bibliogr. 68 poz., rys., tab

Twórcy

autor

Ustriyana I Nyoman Gede

Sustainable Development and Finance Post Graduate Program, Udayana University, Denpasar, Indonesia

https://orcid.org/0000-0002-6477-7439

autor

Saifulloh Moh

m.saifulloh@unud.ac.id

Agroecotechnology Study Program, Faculty of Agriculture, Udayana University, Denpasar, Indonesia

https://orcid.org/0000-0001-5762-5755

autor

Diara I Wayan

Agroecotechnology Study Program, Faculty of Agriculture, Udayana University, Denpasar, Indonesia

https://orcid.org/0000-0003-2611-3647

autor

Suyarto R

Spatial Data Infrastructure Development Center (PPIDS), Udayana University, Denpasar, Indonesia

https://orcid.org/0009-0000-9364-8663

Bibliografia

1. Abel, C., Horion, S., Tagesson, T., De Keersmaecker, W., Seddon, A. W. R., Abdi, A. M., & Fensholt, R. (2021). The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nature Sustainability, 4(1). https://doi.org/10.1038/s41893-020-00597-z
2. Adhika, I. M., & Putra, I. D. G. A. D. (2021). Reinvigorating cultural landscapes for planning cultural tourism in Bali. In Geojournal of Tourism and Geosites 33(4). https://doi.org/10.30892/gtg.334spl03-594
3. Adnyana, I. W. S., As-syakur, A. R., Suyarto, R., Sunarta, I. N., Nuarsa, I. W., Diara, I. W., Saifulloh, M., & Wiyanti. (2024). Geospatial Technology for Climate Change: Influence of EN-SO and IOD on Soil Erosion. In Technological Approaches for Climate Smart Agriculture (pp. 249–275). Springer.
4. Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sensing, 14(11). https://doi.org/10.3390/rs14112654
5. Amir, F. L. (2023). Analysis of the potential of subak as a sustainable tourism attraction based on agro-tourism in the Village of Jatiluwih. Pusaka: Journal of Tourism, Hospitality, Travel and Business Event, 5(1). https://doi.org/10.33649/pusaka.v5i1.198
6. Bao Pham, Q., Ajim Ali, S., Parvin, F., Van On, V., Mohd Sidek, L., Đurin, B., Cetl, V., Šamanović, S., & Nguyet Minh, N. (2024). Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network. Advances in Space Research, 74(1). https://doi.org/10.1016/j.asr.2024.03.027
7. Bhayunagiri, I. B. P., & Saifulloh, M. (2022). Mapping of subak area boundaries and soil fertility for agricultural land conservation. Geographia Technica, 17(2). https://doi.org/10.21163/GT_2022.172.17
8. Bhayunagiri, I. B. P., & Saifulloh, M. (2023). Urban footprint extraction derived from worldview-2 satellite imagery by random forest and k-nearest neighbours algorithm. IOP Conference Series: Earth and Environmental Science, 1200(1), 12043.
9. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2). https://doi.org/10.1007/s11749-016-0481-7
10. Breiman, L. (2001). Random forests. Machine Learning, 45(1). https://doi.org/10.1023/A:1010933404324
11. Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G., & Ryan, P. (2017). Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment, 151. https://doi.org/10.1016/j.atmosenv.2016.11.066
12. Budiasa, I. W., & Ambarawati, I. G. A. (2014). Community based agrotourism as an innovative integrated farming system development model towards sustainable agriculture and tourism in Bali. Journal of the International Society for Southeast Asian Agricultural Sciences, 20(1).
13. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11). https://doi.org/10.1890/07-0539.1
14. Chi, X., Lee, S. K., Ahn, Y. joo, & Kiatkawsin, K. (2020). Tourist-perceived quality and loyalty intentions towards rural tourism in China. Sustainability (Switzerland), 12(9). https://doi.org/10.3390/su12093614
15. Diara, I. W., Susila, K. D., Wiyanti, W., Sunarta, I. N., Kusmiyarti, T. B., & Saifulloh, M. (2024). Exploring the Influence of Various Land Use Land Cover on Land Surface Temperature of Coastal Tourism Areas in Bali Using Landsat 9. Environmental Research, Engineering and Man-agement, 80(2), 118–132.
16. Diara, I. W., Suyarto, R., & Saifulloh, M. (2022). Spatial distribution of landslide susceptibility in new road construction Mengwitani-Singaraja, Bali-Indonesia: based on geospatial data. International Journal of GEOMATE, 23(96). https://doi.org/10.21660/2022.96.3320
17. Diara, I. W., Wahyu Wiradharma, I. K. A., Suyarto, R., Wiyanti, W., & Saifulloh, M. (2023). Spatiotemporal of landslide potential in upstream areas, Bali tourism destinations: remote sensing and geographic information approach. Journal of Degraded and Mining Lands Management, 10(4). https://doi.org/10.15243/jdmlm.2023.104.4769
18. Erythrina, E., Anshori, A., Bora, C. Y., Dewi, D. O., Lestari, M. S., Mustaha, M. A., Ramija, K. E., Rauf, A. W., Mikasari, W., Surdianto, Y., Suriadi, A., Purnamayani, R., Darwis, V., & Syahbuddin, H. (2021). Assessing opportunities to increase yield and profit in rainfed lowland rice systems in Indonesia. Agronomy, 11(4). https://doi.org/10.3390/agronomy11040777
19. Ferreira, D. I. R., & Sánchez-Martín, J. M. (2022). Agricultural Landscapes as a Basis for Promoting Agritourism in Cross-Border Iberian Regions. Agriculture (Switzerland), 12(5). https://doi.org/10.3390/agriculture12050716
20. Fox, E. W., Hill, R. A., Leibowitz, S. G., Olsen, A. R., Thornbrugh, D. J., & Weber, M. H. (2017). Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology. Environmental Monitoring and Assessment, 189(7). https://doi.org/10.1007/ s10661-017-6025-0
21. Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14). https://doi.org/10.1016/j.patrec.2010.03.014
22. Getachew, M., Tolassa, K., De Frenne, P., Verheyen, K., Tack, A. J. M., Hylander, K., Ayalew, B., & Boeckx, P. (2022). The relationship between elevation, soil temperatures, soil chemical characteristics, and green coffee bean quality and biochemistry in southwest Ethiopia. Agronomy for Sustainable Development, 42(4). https://doi.org/10.1007/s13593-022-00801-8
23. Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-Label Confusion Matrix. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3151048
24. Islam, K. I., Elias, E., Carroll, K. C., & Brown, C. (2023). Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed. Remote Sensing, 15(16). https://doi.org/10.3390/rs15163999
25. Jun, M. J. (2021). A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area. International Journal of Geographical Information Science, 35(11). https://doi.org/10.1080/13 658816.2021.1887490
26. Kartini, N. L., Saifulloh, M., Trigunasih, N. M., & Narka, I. W. (2023). Assessment of Soil Degradation Based on Soil Properties and Spatial Analysis in Dryland Farming. Journal of Ecological Engineering, 24(4). https://doi.org/10.12911/22998993/161080
27. Kartini, N. L., Saifulloh, M., Trigunasih, N. M., Sukmawati, N. M. S., & Mega, I. (2024). Impact of Long-Term Continuous Cropping on Soil Nutrient Depletion. Ecological Engineering & Envi-ronmental Technology (EEET), 25(11).
28. Lanya, I., Subadiyasa, N., Sardiana, K., & Ratna Adi, G. P. (2018). Planning of Agro-Tourism Development, Specific Location in Green Open Space Sarbagita Area, Bali Province. IOP Conference Series: Earth and Environmental Science, 123(1). https://doi.org/10.1088/1755-1315/123/1/012038
29. Mudana, I. G., Ernawati, N. M., & Voda, M. (2021). Analysis of the evolving cultural tourism implementation in Bali Indonesia. Multicultural Education, 7(6).
30. Munier, N., & Hontoria, E. (2021). Shortcomings of the AHP Method. https://doi.org/10.1007/978-3-030-60392-2_5
31. Mustafa, O. M., Ahmed, O. M., & Saeed, V. A. (2024). Comparative analysis of decision tree algorithms using gini and entropy criteria on the forest covertypes dataset. The International Conference on Innovations in Computing Research, 185–193.
32. N, P., Triatmadja, R., Legono, D., & Nurrochmad, F. (2023). Subak irrigation system: A heritage of a sustainable hydro-environment. In Water Projects and Technologies in Asia 315–326. CRC Press.
33. Pimenta, F. M., Speroto, A. T., Costa, M. H., & Dionizio, E. A. (2021). Historical changes in land use and suitability for future agriculture expansion in Western Bahia, Brazil. Remote Sensing, 13(6). https://doi.org/10.3390/rs13061088
34. Purbo-Hadiwidjojo, M. M. (1971). The status of engineering geology in Indonesia: 1970. Bulletin of the International Association of Engineering Geology - Bulletin de l’Association Internationale de Géologie de l’Ingénieur, 4(1). https://doi.org/10.1007/BF02635379
35. Putri, P. Y. A., & Saputra, K. A. K. (2022). Regulatory impact analysis on local government regulation standards for organizing cultural tourism in Bali. American Research Journal of Humanities & Social Science (ARJHSS), 5(4).
36. Rosardi, R. G., Prajanti, S. D. W., Atmaja, H. T., Juhadi, & Yanti, D. (2022). Sustainable tourism development strategy with AHP (Analytical Hierarchy Process) method in pagilaran tea plantation agrotourism, Indonesia. International Journal of Sustainable Development and Planning, 17(4). https://doi.org/10.18280/ijsdp.170429
37. Sahani, N., & Ghosh, T. (2021). GIS-based spatial prediction of recreational trail susceptibility in protected area of Sikkim Himalaya using logistic regression, decision tree and random forest model. Ecological Informatics, 64. https://doi.org/10.1016/j.ecoinf.2021.101352
38. Sardiana, I. K. (2018). The study of development of urban farming agrotourism Subak-Irrigation- Based in Sanur tourism area, Denpasar City, Bali. Journal of Indonesian Tourism and Development Studies, 6(1), 33.
39. Saroinsong, F. B. (2020). Supporting plant diversity and conservation through landscape planning: A case study in an agrotourism landscape in Tampusu, North Sulawesi, Indonesia. Biodiversitas, 21(4). https://doi.org/10.13057/biodiv/d210432
40. Satriawan, I. K., Pujaastawa, I. B. G., & Sarjana, I. M. (2015). Development of small-scale agro-tourism in the Province of Bali, Indonesia. Advances in Environmental Biology, 9(21).
41. Serrano‐Grijalva, L., Ochoa‐Hueso, R., Veen, G. F., Repeto‐Deudero, I., Van Rijssel, S. Q., Koorneef, G. J., & Van der Putten, W. H. (2024). Normalized difference vegetation index analy-sis reveals increase of biomass production and stability during the conversion from conventional to organic farming. Global Change Biology, 30(8), e17461.
42. Singh, R., Behera, M. D., Das, P., Rizvi, J., Dhyani, S. K., & Biradar, C. M. (2022). Agroforestry suitability for planning site-specific interventions using machine learning approaches. Sustainability (Switzerland), 14(9). https://doi.org/10.3390/ su14095189
43. Soniari, N. N., Trigunasih, N. M., Sumarniasih, M. S., & Saifulloh, M. (2024). Exploring soil erodibility: integrating field surveys, laboratory analysis, and geospatial techniques in sloping agricultural terrains. Journal of Degraded & Mining Lands Management, 12(1).
44. Suamba, I. K., Sumiyati, Krisnandika, A. A. K., Tika, I. W., Sulastri, N. N., & Arisena, G. M. K. (2023). The subak-based agrotourism management model in the world cultural heritage area of Catur Angga Batukaru Tabanan Regency, Bali Province, Indonesia. African Journal of Food, Agriculture, Nutrition and Development, 23(2). https://doi.org/10.18697/ajfand.117.21970
45. Suamba, K., Windia, W., & Mekse Kori Arisena, G. (2020). Subak system social services in Bali. Palarch’s Journal Of Archaeology Of Egypt/ Egyptology, 17(4).
46. Sudarma, I., Saifulloh, M., Diara, I. W., & As- Syakur, A. (2024). Carbon Stocks Dynamics of Urban Green Space Ecosystems Using Time- Series Vegetation Indices. Ecological Engineering & Environmental Technology (EEET), 25(9).
47. Sunarta, I. N., & Saifulloh, M. (2022a). Coastal tourism: impact for built-up area growth and correlation to vegetation and water indices derived from Sentinel-2 remote sensing imagery. Geojournal of Tourism and Geosites , 41(2). https://doi.org/10.30892/gtg.41223-857
48. Sunarta, I. N., & Saifulloh, M. (2022b). Spatial variation of NO2 levels during the COVID-19 pandemic in the Bali tourism area. Geographia Technica, 17(1). https://doi.org/10.21163/ GT_2022.171.11
49. Susila, I., Dean, D., Harismah, K., Priyono, K. D., Setyawan, A. A., & Maulana, H. (2024). Does interconnectivity matter? An integration model of agro-tourism development. Asia Pacific Management Review, 29(1). https://doi.org/10.1016/j. apmrv.2023.08.003
50. Susila, K. D., Ginting, D. C. B., Adnyana, I., Saifulloh, M., & Arthagama, I. (2024). Enhancing soil quality for sustainable agricultural practices in Subak rice fields. Journal of Degraded & Mining Lands Management, 12(1).
51. Susila, K. D., Trigunasih, N. M., & Saifulloh, M. (2024). Monitoring Agricultural Drought in Savanna Ecosystems Using the Vegetation Health Index--Implications of Climate Change. Ecological Engineering & Environmental Technology (EEET), 25(9).
52. Suyarto, R., Diara, I. W., Susila, K. D., Saifulloh, M., Wiyanti, W., Kusmiyarti, T. B., & Sunarta, I. N. (2023). Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm. IOP Conference Series: Earth and Environmental Science, 1190(1). https://doi.org/10.1088/1755-1315/1190/1/012012
53. Suyarto, R., Wiyanti, Saifulloh, M., Fatahillah, A. W., Diara, I. W., Susila, K. D., & Kusmiyarti, T. B. (2023). Hydrological Approach for Flood Overflow Estimation in Buleleng Watershed, Bali. International Journal of Safety and Security Engineering, 13(5). https://doi.org/10.18280/ ijsse.130512
54. Tavana, M., Soltanifar, M., & Santos Arteaga, F. J. (2023). Analytical hierarchy process: revolu-tion and evolution. Annals of Operations Research, 326(2). https://doi.org/10.1007/ s10479-021-04432-2
55. Tikuye, B. G., Rusnak, M., Manjunatha, B. R., & Jose, J. (2023). Land use and land cover change detection using the random forest approach: The case of the upper blue Nile River Basin, Ethiopia. Global Challenges, 7(10). https://doi.org/10.1002/gch2.202300155
56. Tomej, K., & Liburd, J. J. (2020). Sustainable accessibility in rural destinations: a public transport network approach. Journal of Sustainable Tourism, 28(2). https://doi.org/10.1080/09669582.20 19.1607359
57. Trigunasih, N. M., & Saifulloh, M. (2022). The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Re mote Sensing and Geographic Information Systems. Journal of Environmental Management & Tourism, 13(5), 1467–1480.
58. Trigunasih, N. M., & Saifulloh, M. (2023). Investigation of soil erosion in agro-tourism area: guideline for environmental conservation planning. Geographia Technica, 18(1). https://doi.org/10.21163/GT_2023.181.02
59. Trigunasih, N. M., Narka, I. W., & Saifulloh, M. (2023a). Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques. Journal of Degraded and Mining Lands Management, 11(1). https://doi.org/10.15243/jdmlm.2023.111.5073
60. Trigunasih, N. M., Narka, I. W., & Saifulloh, M. (2023b). Measurement of soil chemical properties for mapping soil fertility status. International Journal of Design and Nature And Ecody-Namics, 18(6). https://doi.org/10.18280/ijdne.180611
61. Valero-Carreras, D., Alcaraz, J., & Landete, M. (2023). Comparing two SVM models through different metrics based on the confusion matrix. Computers and Operations Research, 152. https://doi.org/10.1016/j.cor.2022.106131
62. Wang, J., Sun, X., Cheng, Q., & Cui, Q. (2021). An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting. Science of the Total Environment, 762. https://doi.org/10.1016/j.scitotenv.2020.143099
63. Wicaksono, K. P., Tyasmoro, S. Y., Saitama, A., & Permatasari, P. N. (2024). Analyzing of Agrotourism Potential in Malang City. International Journal of Environment, Agriculture and Biotechnology, 9(2).
64. Wiranatha, A. S., Suryawardani, I. G. A. O., Petr, C., & Kencana, I. P. E. N. (2024). Priority of Criteria for Agritourism Development in Bali. Jurnal Kajian Bali (Journal of Bali Studies), 14(1), 234–258.
65. Xiang, K., Yuan, W., Wang, L., & Deng, Y. (2020). An lswi-based method for mapping irrigated areas in china using moderate-resolution satellite data. In Remote Sensing 12(24). https://doi.org/10.3390/rs12244181
66. Yudhari, I. D. A. S., Darwanto, D. H., Waluyati, L. R., & Mulyo, J. H. (2020). The development of agro-tourism based on arabica coffee plantation in bali. Journal of Environmental Management and Tourism, 11(1). https://doi.org/10.14505/jemt.11.1(41).12
67. Zhang, X., Huang, W., Lin, X., Jiang, L., Wu, Y., & Wu, C. (2020). Complex image recognition algorithm based on immune random forest model. In Soft Computing 24(16). https://doi.org/10.1007/s00500-020-04706-0
68. Zhang, Z., Plathong, S., Sun, Y., Guo, Z., Munnoy, T., Ma, L., Jantharakhantee, C., & Tanboot, L. (2020). Analysis of the island tourism environment based on tourists’ perception—A case study of Koh Lan, Thailand. Ocean and Coastal Management, 197. https://doi.org/10.1016/j.ocecoaman.2020.105326

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-0bdf0cdc-53fc-4558-9424-bb7f0dfb5318