• International Journal of Technology (IJTech)
  • Vol 17, No 4 (2026)

Assessing Watershed Health Using Long-Term Moderate Resolution Imaging Spectroradiometer (MODIS)-Derived Vegetation Dynamics in the Upper Citarum Watershed

Assessing Watershed Health Using Long-Term Moderate Resolution Imaging Spectroradiometer (MODIS)-Derived Vegetation Dynamics in the Upper Citarum Watershed

Title: Assessing Watershed Health Using Long-Term Moderate Resolution Imaging Spectroradiometer (MODIS)-Derived Vegetation Dynamics in the Upper Citarum Watershed
Kuswantoro Marko, Dwita Sutjiningsih, Eko Kusratmoko, Widjojo Adi Prakoso

Corresponding email:


Cite this article as:
Marko, K., Sutjiningsih, D., Kusratmoko, E., & Prakoso, W. A. (2026). Assessing watershed health using long-term moderate resolution imaging spectroradiometer (MODIS)-derived vegetation dynamics in the upper citarum watershed. International Journal of Technology, 17 (4), 1432–1450


3
Downloads
Kuswantoro Marko 1.Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia 2. Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Dwita Sutjiningsih Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Eko Kusratmoko Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Widjojo Adi Prakoso Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Email to Corresponding Author

Abstract
Assessing Watershed Health Using Long-Term Moderate Resolution Imaging Spectroradiometer (MODIS)-Derived Vegetation Dynamics in the Upper Citarum Watershed

Rapid land-use/land-cover (LULC) change, vegetation degradation, and increasing anthropogenic pressures have reduced the ecological stability of many tropical watersheds, creating a need for practical approaches to long-term environmental monitoring. Although watershed health is commonly evaluated using hydrological, ecological, and water-quality indicators, the application of long-term satellite-derived vegetation dynamics within the Reliability–Resilience–Vulnerability (RRV) framework remains limited. This study applied the RRV framework to assess vegetation-based watershed health in the Upper Citarum Watershed (UCW), Indonesia, using MODIS MOD13Q1 Normalized Difference Vegetation Index (NDVI) data (250 m) during the dry season (June–September) from 2000 to 2020. Reliability-Resilience-Vulnerability were integrated into a Watershed Health Index (WHI) using a geometric mean. Correlation and threshold sensitivity analyses were conducted to evaluate land-cover relationships and frame-work robustness. The mean dry-season NDVI of the UCW was 0.60, with higher values in mountainous forested areas than in urbanized regions. Vegetation greenness declined during 2002–2003, 2006–2007, and 2015–2016, corresponding to major El Ni˜no and positive Indian Ocean Dipole (IOD) events. Ciwidey exhibited the highest WHI (0.80), whereas Cikapundung had the lowest (0.34). Forest cover was positively associated with Reliability, Resilience, and WHI, while built-up areas showed the opposite relationship. Although NDVI threshold selection affected absolute indicator values, the spatial pattern and sub-watershed ranking remained stable, demonstrating framework robustness. Compared with conventional NDVI analysis, the proposed RRV–NDVI framework captures vegetation persistence, recovery capacity, and degradation severity, providing a practical and transferable approach for long-term vegetation-based watershed health assessment in data-limited tropical watersheds.

MODIS NDVI; Reliability-Resilience-Vulnerability; Upper Citarum watershed; Vegetation-Based watershed health

References

Abbaszadeh Tehrani, N., Mohd Shafri, H., Salehi, S., Chanussot, J., & Janalipour, M. (2022). Remotely-sensed ecosystem health assessment (RSEHA) model for assessing the changes of ecosystem health of lake urmia basin. International Journal of Image and Data Fusion, 13(2), 180–205. https://doi.org/10.1080/19479832.2021.1924880

Ahn, S., & Kim, S. (2017). Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology. Hydrology and Earth System Sciences, 21, 5583–5602. https://doi.org/10.5194/hess-21-5583-2017

Babaousmail, H., Ayugi, B., Hammad, Z., Alupot, D., Posset, K., Mumo, R., & Rajasekar, A. (2024). Quantifying drought impacts based on the Reliability–Resiliency–Vulnerability framework over east africa. Climate, 12(7), 92. https://doi.org/10.3390/cli12070092

Cao, R., Chen, Y., Shen, M., Chen, J., Zhou, J., Wang, C., & Yang, W. (2018). A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky–Golay filter. Remote Sensing of Environment, 217, 244–257. https://doi.org/10.1016/j.rse.2018.08.022

Chamani, R., Vafakhah, M., & Sadeghi, S. (2023). Changes in Reliability–Resilience–Vulnerability-based watershed health under climate change scenarios in the efin watershed, iran. Natural Hazards, 116, 2457–2476. https://doi.org/10.1007/s11069-022-05774-1

Chanda, K., Maity, R., Sharma, A., & Mehrotra, R. (2014). Spatiotemporal variation of long-term drought propensity through Reliability-Resilience-Vulnerability based drought management index. Water Resources Research, 50(9), 7662–7676. https://doi.org/10.1002/2014WR015703

Chen, J., J¨onsson, P., Tamura, M., Gu, Z., Matsushita, B., & Eklundh, L. (2012). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment, 91(3–4), 332–344. https://doi.org/10.1016/j.rse.2004.03.014 

D’Arrigo, R., & Smerdon, J. (2008). Tropical climate influences on drought variability over java, indonesia. Geophysical Research Letters, 35(5), L05707. https://doi.org/10.1029/2007GL032589

Didan, K. (2015). MOD13Q1 MODIS/terra vegetation indices 16-day l3 global 250 m SIN grid V006 user guide.

Dimyati, M., Rustanto, A., Shidiq, I., & Indratmoko, S. (2024). Spatiotemporal relation of satellite-based meteorological to agricultural drought in the downstream citarum watershed, indonesia. Environmental and Sustainability Indicators, 21, 100381. https://doi.org/10.1016/j.indic.2024.100381

Eklundh, L., & J¨onsson, P. (2015). TIMESAT: A software package for time-series processing and assessment of vegetation dynamics. In C. Kuenzer, S. Dech, & W. Wagner (Eds.), Remote sensing time series: Revealing land surface dynamics (pp. 141–158). Springer. https://doi.org/10.1007/978-3-319-15967-6_7

Ellison, D., Morris, C., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., Gutierrez, V., van Noordwijk, M., Creed, I., Pokorny, J., Gaveau, D., Spracklen, D., Tobella, A., Ilstedt, U., Teuling, A., Gebrehiwot, S., Sands, D., Muys, B., Verbist, B., & Sullivan, C. (2017). Trees, forests and water: Cool insights for a hot world. Global Environmental Change, 43, 51–61. https://doi.org/10.1016/j.gloenvcha.2017.01.002

Eng, L., Ismail, R., Hashim, W., & Baharum, A. (2019). The use of VARI, GLI, and VIgreen formulas in detecting vegetation in aerial images. International Journal of Technology, 10(7), 1385–1394. https://doi.org/10.14716/ijtech.v10i7.3275

Erasmi, S., Propastin, P., Kappas, M., & Panferov, O. (2009). Spatial patterns of NDVI variation over indonesia and their relationship to ENSO warm events during the period 1982–2006. Journal of Climate, 22(24), 6612–6623. https://doi.org/10.1175/2009JCLI2460.1

Fayech, D., & Tarhouni, J. (2021). Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area. Modeling Earth Systems and Environment, 7, 1231–1245. https://doi.org/10.1007/s40808-020-00896-6

Filoso, S., Bezerra, M., Weiss, K., & Palmer, M. (2017). Impacts of forest restoration on water yield: A systematic review. PLoS ONE, 12(8), e0183210. https://doi.org/10.1371/journal.pone.0183210

Flotemersch, J., Leibowitz, S., Hill, R., Stoddard, J., Thoms, M., & Tharme, R. (2016). A watershed integrity definition and assessment approach to support strategic management of watersheds. River Research and Applications, 32(7), 1654–1671. https://doi.org/10.1002/rra.2978

Foley, J., DeFries, R., Asner, G., Barford, C., Bonan, G., Carpenter, S., Chapin, F., Coe, M., Daily, G., Gibbs, H., Helkowski, J., Holloway, T., Howard, E., Kucharik, C., Monfreda, C., Patz, J., Prentice, I., Ramankutty, N., & Snyder, P. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772

Gessesse, A., & Melesse, A. (2019). Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in amhara region, ethiopia. In A. Melesse, W. Abtew, & G. Senay (Eds.), Extreme hydrology and climate variability: Monitoring, modelling, adaptation and mitigation (pp. 291–305). Elsevier. https://doi.org/10.1016/B978-0-12-815998-9.00008-7

Griffith, J., Martinko, E., & Whistler, J. (2002). Interrelationships among landscapes, NDVI, and stream water quality in the US central plains. Ecological Applications, 12(6), 1702–1718. https://doi.org/10.1890/1051-0761(2002)012[1702:IALNAS]2.0.CO;2

Gunawan, G., Sutjiningsih, D., Soeryantono, H., & Sulistioweni, W. (2013). Soil erosion estimation based on GIS and remote sensing for supporting integrated water resources conservation management. International Journal of Technology, 4(2), 147–156. https://doi.org/10.14716/ijtech.v4i2.110

Hanifa, S., Tambunan, M., & Marko, K. (2020). Rainfall distribution in relation to flooding in upper citarum watershed, indonesia. IOP Conference Series: Earth and Environmental Science, 500(1), 012088. https://doi.org/10.1088/1755-1315/500/1/012088

Hasani, M., Pielesiak, I., Mahiny, A., & Mikaeili, A. (2023). Regional ecosystem health assessment based on landscape patterns and ecosystem services approach. Acta Ecologica Sinica, 43(5), 357–369. https://doi.org/10.1016/j.chnaes.2021.11.004

Hashimoto, T., Stedinger, J., & Loucks, D. (1982). Reliability, resilience, and vulnerability criteria for water resource system performance evaluation. Water Resources Research, 18(1), 14–20. https://doi.org/10.1029/WR018i001p00014

Hazbavi, Z., & Sadeghi, S. (2017). Watershed health characterization using Reliability–Resilience–Vulnerability conceptual framework based on hydrological responses. Land Degradation & Development, 28(5), 1528–1537. https://doi.org/10.1002/ldr.2680

Hazbavi, Z., Sadeghi, S., & Gholamalifard, M. (2018). Land cover based watershed health assessment. AGROFOR International Journal, 3(3), 47–56. https://doi.org/10.7251/AGRENG1803047H

Hazbavi, Z., Sadeghi, S., & Gholamalifard, M. (2019). Dynamic analysis of soil erosion-based watershed health. Geography, Environment, Sustainability, 12(3), 124–141. https://doi.org/10.24057/2071-9388-2018-58

Hoque, Y., Hantush, M., & Govindaraju, R. (2014). On the scaling behavior of Reliability–Resilience–Vulnerability indices in agricultural watersheds. Ecological Indicators, 40, 136–146. https://doi.org/10.1016/j.ecolind.2014.01.027

Hoque, Y., Tripathi, S., Hantush, M., & Govindaraju, R. (2012). Watershed reliability, resilience and vulnerability analysis under uncertainty using water quality data. Journal of Environmental Management, 109, 101–112. https://doi.org/10.1016/j.jenvman.2012.05.010

Hoque, Y., Tripathi, S., Hantush, M., & Govindaraju, R. (2016). Aggregate measures of watershed health from reconstructed water quality data with uncertainty. Journal of Environmental Quality, 45(2), 443–454. https://doi.org/10.2134/jeq2015.10.0508

Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., & Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2

Junengsih, J., Putri, E., & Ismail, A. (2017). Analisis stakeholder dalam pengelolaan DAS citarum dan limbah industri. Jurnal Risalah Kebijakan Pertanian dan Lingkungan, 4(2), 112–124.

Juniarti, N. (2020). Upaya peningkatan kondisi lingkungan di daerah aliran sungai citarum. Jurnal Pengabdian Kepada Masyarakat, 3, 256–271.

Lambin, E., Geist, H., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459

Maity, R., Sharma, A., Kumar, D., & Chanda, K. (2013). Characterizing drought using the Reliability-Resilience-Vulnerability concept. Journal of Hydrologic Engineering, 18(7), 859–869. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000639

Mallick, J., AlMesfer, M., Singh, V., Falqi, I., Singh, C., & Khan, R. (2021). Evaluating the NDVI–rainfall relationship in bisha watershed, saudi arabia using non-stationary modeling technique. Atmosphere, 12(5), 593. https://doi.org/10.3390/atmos12050593

Marko, K., Sutjiningsih, D., & Kusratmoko, E. (2021). Watershed health changes based on vegetated land cover in the upper citarum watershed, west java province, indonesia. IOP Conference Series: Earth and Environmental Science, 940(1), 012045. https://doi.org/10.1088/1755-1315/940/1/012045

Marko, K., Sutjiningsih, D., Kusratmoko, E., & Prakoso, W. (2025). Land use/land cover changes using landsat imagery in the upper citarum watershed, west java province, indonesia. International Journal of Remote Sensing and Earth Sciences, 22(1), 1–10. https://doi.org/10.30536/ijreses.v22i1.13539

McFeeters, S. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714

McGrane, S. (2016). Impacts of urbanisation on hydrological and water quality dynamics, and urban water management: A review. Hydrological Sciences Journal, 61(13), 2295–2311. https://doi.org/10.1080/02626667.2015.1128084

Mehmood, K., Anees, S., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., Ansari, M., Alharbi, S., & Khan, W. (2024). Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. https://doi.org/10.1038/s41598-024-62464-7

Mondal, S., Jeganathan, C., Amarnath, G., & Pani, P. (2017). Time-series cloud noise mapping and reduction algorithm for improved vegetation and drought monitoring. GIScience & Remote Sensing, 54(2), 202–229. https://doi.org/10.1080/15481603.2017.1286726

Musy, A., & Higy, C. (2011). Hydrology: A science of nature. CRC Press. https://doi.org/10.1201/b10426

Nazarova, T., Martin, P., & Giuliani, G. (2020). Monitoring vegetation change in the presence of high cloud cover with Sentinel-2 in a lowland tropical forest region in brazil. Remote Sensing, 12(11), 1829. https://doi.org/10.3390/rs12111829

Pedzisai, E., Mutanga, O., Odindi, J., & Mushore, T. (2022). The use of remote sensing indices to understand flood-recharged soil moisture impacts on trees in semi-arid floodplains: A review. Ecohydrology, 15(8), e2460. https://doi.org/10.1002/eco.2460

Pettorelli, N., Vik, J., Mysterud, A., Gaillard, J., Tucker, C., & Stenseth, N. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011

Pianosi, F., Beven, K., Freer, J., Hall, J., Rougier, J., Stephenson, D., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008

Prudente, V., Martins, V., Vieira, D., Silva, N. d. F. e., Adami, M., & Sanches, I. (2020). Limitations of cloud cover for optical remote sensing of agricultural areas across south america. Remote Sensing Applications: Society and Environment, 20, 100414. https://doi.org/10.1016/j.rsase.2020.100414

Qian, X., Qiu, B., & Zhang, Y. (2019). Widespread decline in vegetation photosynthesis in southeast asia due to the prolonged drought during the 2015/2016 El Ni˜no. Remote Sensing, 11(8), 910. https://doi.org/10.3390/rs11080910

Sadeghi, S., & Hazbavi, Z. (2017). Spatiotemporal variation of watershed health propensity through Reliability–Resilience–Vulnerability based drought index (case study: Shazand watershed in iran). Science of the Total Environment, 587–588, 168–176. https://doi.org/10.1016/j.scitotenv.2017.02.098

Sadeghi, S., Hazbavi, Z., & Gholamalifard, M. (2019). Interactive impacts of climatic, hydrologic and anthropogenic activities on watershed health. Science of the Total Environment, 648, 880–893. https://doi.org/10.1016/j.scitotenv.2018.08.004

Saltelli, A., & Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software, 25(12), 1508–1517. https://doi.org/10.1016/j.envsoft.2010.04.012

Salvadore, E., Bronders, J., & Batelaan, O. (2015). Hydrological modelling of urbanized catchments: A review and future directions. Journal of Hydrology, 529, 62–81. https://doi.org/10.1016/j.jhydrol.2015.06.028

Sapan, E., Riandasenya, S., Yulianingsani, Anisah, Ilmi, M., & Habibie, M. (2022). Health assessment of the upper citarum watershed, west java, indonesia. IOP Conference Series: Earth and Environmental Science, 1109(1), 012082. https://doi.org/10.1088/1755-1315/1109/1/012082

Sung, J., Chung, E.-S., & Shahid, S. (2018). Reliability–Resiliency–Vulnerability approach for drought analysis in south korea using 28 GCMs. Sustainability, 10(9), 3043. https://doi.org/10.3390/su10093043

Syabila, S., Marko, K., & Hernina, R. (2026). Relationship between vegetation greenness (NDVI) and land surface temperature across land cover types in ciwidey sub-watershed (1990–2020). Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments), 9(1), Article 3. https://scholarhub.ui.ac.id/jglitrop/vol9/iss1/3

US Environmental Protection Agency. (2012). Identifying and protecting healthy watersheds: Concepts, assessments, and management approaches (tech. rep. No. EPA 841-B-11-002). Office of Water & Office of Research and Development. https://www.epa.gov/sites/default/files/2015-10/documents/hwi-watersheds-foreword.pdf

Van Hoek, M., Jia, L., Zhou, J., Zheng, C., & Menenti, M. (2016). Early drought detection by spectral analysis of satellite time series of precipitation and normalized difference vegetation index (NDVI). Remote Sensing, 8(5), 422. https://doi.org/10.3390/rs8050422

Vollmer, D., Burkhard, K., Adem Esmail, B., Guerrero, P., & Nagabhatla, N. (2022). Incorporating ecosystem services into water resources management—tools, policies, promising pathways. Environmental Management, 69(4), 627–635. https://doi.org/10.1007/s00267-022-01640-9

Wang, R., Cherkauer, K., & Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sensing, 8(4), 269. https://doi.org/10.3390/rs8040269

Wu, W., Xu, Z., Zhan, C., Yin, X., & Yu, S. (2015). A new framework to evaluate ecosystem health: A case study in the wei river basin, china. Environmental Monitoring and Assessment, 187, 460. https://doi.org/10.1007/s10661-015-4596-1

Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179

Zeng, P., Sun, F., Liu, Y., & Che, Y. (2020). Future river basin health assessment through Reliability-Resilience-Vulnerability: Thresholds of multiple dryness conditions. Science of the Total Environment, 741, 140395. https://doi.org/10.1016/j.scitotenv.2020.140395