BibTex Citation Data :
@article{geoplanning72006, author = {Ardiyanto Gai and Ernan Rustiadi and Baba Barus and Akhmad Fauzi}, title = {Sustainable Livelihood Approach (SLA) using Spatial Model: A Case from Labuan Bajo, Indonesia}, journal = {Geoplanning: Journal of Geomatics and Planning}, volume = {13}, number = {1}, year = {2026}, keywords = {Sustainable Livelihood Approach, Poverty, GIS, Labuan Bako}, abstract = { Despite its economic potential, Indonesia’s rapidly developing tourism hub faces socio-economic disparities. While tourism contributes significantly to local incomes, many communities still struggle with poverty, unequal access to resources, and environmental degradation. Labuan Bajo, located in West Manggarai Regency, depends heavily on tourism, fishing, and small-scale agriculture, yet faces challenges such as seasonal water scarcity, limited infrastructure, and land-use conflicts that threaten sustainable development. While some areas benefit from tourism-driven economic growth, others remain marginalized due to inadequate access to capital assets. The Sustainable Livelihood Approach (SLA) provides a framework for understanding how local assets—human, social, natural, financial, and physical—shape livelihood outcomes. However, livelihoods are spatially heterogeneous and require localized analysis to inform targeted interventions. By integrating Geographically Weighted Regression (GWR), this study aims to identify spatial disparities in livelihood sustainability and the key drivers of economic resilience in different areas of Labuan Bajo. The results provide spatially explicit evidence on livelihood assets and poverty clusters, supporting local governments and communities in designing targeted interventions for employment access, infrastructure provision, financial inclusion, and essential services. }, issn = {2355-6544}, pages = {1--20} doi = {10.14710/geoplanning.13.1.1-20}, url = {https://ejournal.undip.ac.id/index.php/geoplanning/article/view/72006} }
Refworks Citation Data :
Despite its economic potential, Indonesia’s rapidly developing tourism hub faces socio-economic disparities. While tourism contributes significantly to local incomes, many communities still struggle with poverty, unequal access to resources, and environmental degradation. Labuan Bajo, located in West Manggarai Regency, depends heavily on tourism, fishing, and small-scale agriculture, yet faces challenges such as seasonal water scarcity, limited infrastructure, and land-use conflicts that threaten sustainable development. While some areas benefit from tourism-driven economic growth, others remain marginalized due to inadequate access to capital assets. The Sustainable Livelihood Approach (SLA) provides a framework for understanding how local assets—human, social, natural, financial, and physical—shape livelihood outcomes. However, livelihoods are spatially heterogeneous and require localized analysis to inform targeted interventions. By integrating Geographically Weighted Regression (GWR), this study aims to identify spatial disparities in livelihood sustainability and the key drivers of economic resilience in different areas of Labuan Bajo. The results provide spatially explicit evidence on livelihood assets and poverty clusters, supporting local governments and communities in designing targeted interventions for employment access, infrastructure provision, financial inclusion, and essential services.
Note: This article has supplementary file(s).
Article Metrics:
Alikhani, S., Nummi, P., & Ojala, A. (2021). Urban Wetlands: A Review on Ecological and Cultural Values. Water, 13(22), 3301. https://doi.org/10.3390/w13223301">[Crossref]
Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258. https://doi.org/10.1016/j.isprsjprs.2003.10.002">[Crossref]
Cook, E. M., Kim, Y., Grimm, N. B., McPhearson, T., Anderson, P., Bulkeley, H., Collier, M. J., Diep, L., Morató, J., & Zhou, W. (2025). Nature-Based Solutions for Urban Sustainability. Proceedings of the National Academy of Sciences, 122(29). https://doi.org/10.1073/pnas.2315909122">[Crossref]
Courouble, M., Davidson, N., Dinesen, L., Fennessy, S., Galewski, T., Guelmami, A., Kumar, R., McInnes, R., Perennou, C., Rebelo, L.-M., Robertson, H., Segura-Champagnon, L., Simpson, M., & Stroud, D. (2021). Global Wetland Outlook: Special edition 2021 (N. Dudley (ed.)). https://doi.org/10.69556/GWO-2021-eng">[Crossref]
Cuellar, Y., & Perez, L. (2023). Multitemporal Modeling and Simulation of the Complex Dynamics in Urban Wetlands: The Case of Bogota, Colombia. Scientific Reports, 13(1), 9374. https://doi.org/10.1038/s41598-023-36600-8">[Crossref]
Cvijanović, D., Novković, M., Milošević, D., Stojković Piperac, M., Galambos, L., Čerba, D., Stamenković, O., Damnjanović, B., Mesaroš, M., Pavić, D., Simić, V., Trbojević, I., Anđelković, A., Drešković, N., Stammel, B., Cyffka, B., & Radulović, S. (2025). Conservation and Ecological Screening of Small Water Bodies in Temperate Riverine Wetlands Using UAV Photogrammetry (Middle Danube). Nature Conservation, 58, 61–82. https://doi.org/10.3897/natureconservation.58.116663">[Crossref]
Davidson, N. C., & Finlayson, C. M. (2018). Extent, Regional Distribution and Changes in Area of Different Classes of Wetland. Marine and Freshwater Research, 69(10), 1525–1533. https://doi.org/10.1071/MF17377">[Crossref]
Delle Grazie, F. M., & Gill, L. W. (2022). Review of the Ecosystem Services of Temperate Wetlands and Their Valuation Tools. Water, 14(9), 1345.https://doi.org/10.3390/w14091345">[Crossref]
Dronova, I., Kislik, C., Dinh, Z., & Kelly, M. (2021). A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data. Drones, 5(2), 45. https://doi.org/10.3390/drones5020045">[Crossref]
Elkhrachy, I. (2015). Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA). The Egyptian Journal of Remote Sensing and Space Science, 18(2), 261–278. https://doi.org/10.1016/j.ejrs.2015.06.007">[Crossref]
Gbagir, A.-M. G., Ek, K., & Colpaert, A. (2023). OpenDroneMap: Multi-Platform Performance Analysis. Geographies, 3(3), 446–458. https://doi.org/10.3390/geographies3030023">[Crossref]
Graser, A., Sutton, T., & Bernasocchi, M. (2025). The QGIS Project: Spatial without Compromise. Patterns, 6(7), 101265. https://doi.org/10.1016/j.patter.2025.101265">[Crossref]
Hamed, K. H., & Rao, A. R. (1998). A Modified Mann-Kendall Trend Test for Autocorrelated Data. Journal of Hydrology, 204(1–4), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X">[Crossref]
Johnson, P. (2011). Topographies of Urbanization: Survey in and around Pompeiopolis. Pompeipolis I, Içinde, 195–245.
Mathews, A. J., Singh, K. K., Cummings, A. R., & Rogers, S. R. (2023). Fundamental Practices for Drone Remote Sensing Research Across Disciplines. Drone Systems and Applications, 11, 1–22. https://doi.org/10.1139/dsa-2023-0021">[Crossref]
Musungu, K., Dube, T., Smit, J., & Shoko, M. (2024). Using UAV Multispectral Photography to Discriminate Plant Species in a Seep Wetland of the Fynbos Biome. Wetlands Ecology and Management, 32(2), 207–227. https://doi.org/10.1007/s11273-023-09971-y">[Crossref]
Observatorio Ambiental de Bogotá. (2023). Plan de Manejo Ambiental: Humedal Santa María del Lago. https://oab.ambientebogota.gov.co/?p=15068&post_type=dlm_download">
Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076">[Crossref]
Patel, S., Chintanadilok, J., Hall-Scharf, B., Zhuang, Y., Strickland, J., & Singh, A. (2024). WebODM: An Open-Source Alternative to Commercial Image Stitching Software for Uncrewed Aerial Systems (AE593) (Issue AE593).
Patino, J. E., & Estupinan-Suarez, L. M. (2016). Hotspots of Wetland Area Loss in Colombia. Wetlands, 36(5), 935–943. https://doi.org/10.1007/s13157-016-0806-z">[Crossref]
QGIS Development Team. (2025). QGIS Geographic Information System. QGIS Association. https://qgis.org">
Ramsar Convention Secretariat. (2022). Global Wetland Outlook: Special Edition 2021 -- The Value of Wetlands and the Threats They Face. Ramsar Convention on Wetlands.
Salas López, R., Terrones Murga, R. E., Silva-López, J. O., Rojas-Briceño, N. B., Gómez Fernández, D., Oliva-Cruz, M., & Taddia, Y. (2022). Accuracy Assessment of Direct Georeferencing for Photogrammetric Applications Based on UAS-GNSS for High Andean Urban Environments. Drones, 6(12), 388. https://doi.org/10.3390/drones6120388">[Crossref]
Secretaría Distrital de Ambiente. (2010b). Resolución 7773 de 2010: Por la cual se ajusta y aprueba el Plan de Manejo Ambiental del Humedal Santa María del Lago y se adoptan otras determinaciones. https://www.alcaldiabogota.gov.co/sisjur/normas/Norma1.jsp?i=41090">
Secretaría Distrital de Ambiente. (2023). Resolución 2026 de 2023: Por la Cual se Adopta la Actualización del Plan de Manejo Ambiental de la RDH Santa María del Lago. https://www.alcaldiabogota.gov.co/sisjur/normas/Norma1.jsp?i=152521">
Toffanin, P. (2019). OpenDroneMap: The Missing Guide. A Practical Guide to Drone Mapping Using Free and Open-Source Software. MasseranoLabs LLC.
Unidad Administrativa Especial de Aeronáutica Civil. (2023). Resolución 1983 de 2023: Por medio de la cual se incorpora la norma RAC 100 -- Operación de sistemas de aeronaves no tripuladas UAS a los Reglamentos Aeronáuticos de Colombia. https://www.suin-juriscol.gov.co/viewDocument.asp?id=30050174">
Van Alphen, R., Rains, K. C., Rodgers, M., Malservisi, R., & Dixon, T. H. (2024). UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification. Drones, 8(3), 113. https://doi.org/10.3390/drones8030113">[Crossref]
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The Influence of Autocorrelation on the Ability to Detect Trend in Hydrological Series. Hydrological Processes, 16(9), 1807–1829. https://doi.org/10.1002/hyp.1095">[Crossref]
Zerrouk, M., Ait El Kadi, K., Sebari, I., & Fellahi, S. (2025). Machine and Deep Learning for Wetland Mapping and Bird-Habitat Monitoring: A Systematic Review of Remote-Sensing Applications (2015–April 2025). Remote Sensing, 17(21), 3605. https://doi.org/10.3390/rs17213605">[Crossref]
Last update:
Last update: 2026-07-11 05:50:03
Start from Volume 11 Number 2 2024, authors who publish in the Geoplanning Journal of Geomatics and Planning retain full copyright ownership (Copyright@Author) of their work. In keeping with the journal’s commitment to open access, all articles are published under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).Before Volume 11 Number 2 2024, copyright was still owned by the publisher, Universitas Diponegoro. To support a better open access program, we have created a new policy so that authors have full copyright ownership.
Readers and third parties may also use the content in accordance with the CC BY-NC-SA license. This includes the ability to reproduce, modify, and build upon the article, as long as proper attribution is given, and any resulting work is distributed under the same license.
