Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia
Article Main
Abstract
An increase in population increases the rate of urbanization. This results in changes in land cover from vegetation to artificial material. As a result, much of the land surface reflects the sun's energy. Consequently, this increases the surface temperature of the land. An increase in land surface temperature (LST) will increase the intensity of rainfall. Therefore, the present study aimed to investigate the relationship between the increase in LST and rainfall intensity. Changes in land cover can be detected by normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) parameters. Landsat satellite imagery was used to detect NDVI, NDBI, and LST. Image processing was done for imageries scanned in 1995, 2015, 2017, and 2021. Two areas in the East Java Province of Indonesia, namely Malang City and Pasuruan Area, were selected. The daily rainfall intensity data were collected from related rainfall stations in the same year. The Mononobe method was applied to analyze hourly and minute rainfall intensity. IDF curves were drawn from the analyzed results. The relationship between both parameters was analyzed by comparing the LST and hourly rainfall intensity from the IDF curve. The studied results showed that the maximum temperature increase from 1995 to 2021 for the Malang City and Pasuruan Area was 2.60 C and 7.60 C, respectively. For rain, the maximum rainfall intensity increased by 58 mm for Malang City and 18 mm for the Pasuruan Area. LST and rainfall intensity change trends of the two areas had a positive coefficient of regression. The findings can be used to predict the rainfall intensity and floods based on the LST data.
Article Details
Article Details
Keywords
Built-up index, Intensity-duration-frequency curve, Land surface temperature (LST), Normalized difference vegetation index (NDVI), Rainfall intentsity
References
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Sun, Y., Wendi, D., Kim, D. E. & Liong, S. Y. (2019). Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data. Geosci. Lett., 6 (17). https://doi.org/10.1186/s40562-019-0147-x.
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Faisal, A. A., Kafy, A. A., Rakib, A. A., Akter, K. S., Jahir, D. M. A., Sikdar, M. S., Ashrafid, T. J., Mallik, S. & Rahman, M. M. (2021). Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environ. Challenges, 4. https://doi.org/10.1016/j.envc.2021.100192.
Faradiba (2021). Analysis of intensity, duration, and frequency rain daily of Java Island using Mononobe method. J. Phys. Conf. Ser., 1783. https://doi.org/10.1088/1742-6596/1783/1/012107.
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Garouani, M. E., Amyay, M., Lahrach, A. & Oulidi, H. J. (2021). Land surface temperature in response to land use/cover change based on remote sensing data and GIS techniques: Application to Saïss Plain Morocco. J. Ecol. Eng., 22(7). https://doi.org/10.12911/22998993/139065.
Gennaro, S., Cerrato, R., Salvatore, M. C., Salzano, R., Salvatori, R., & Baroni, C. (2023). NDVI analysis for monitoring land-cover evolution on selected deglaciated areas in the Gran Paradiso Group (Italian Western Alps). Remote Sens., 15, 3847. https://doi.org/10.3390/rs15153847.
Guha, S., Govil, H. & Diwan, P. (2020). Monitoring LST-NDVI relationship using premonsoon Landsat datasets. Adv. Meteorol. https://doi.org/10.1155/2020/4539684.
Guha, S., Govil, H., Gill, N. & Dey, A. (2020). A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quat. Int., 575-576, 249-258. https://doi.org/10.1016/j.quaint.2020.06.041.
Han, Z., Zuo, Q., Wang, C. & Gan, R. (2023). Impacts of climate change on natural runoff in the Yellow River Basin of China during 1961–2020. Water, 15, 929. https://doi.org/10.3390/w15050929.
Hartoyo, A. P. P., Sunkar, A., Ramadani, R., Faluthi, S. & Hidayat, S. (2021). Normalized difference vegetation index (NDVI) analysis for vegetation cover in Leuser ecosystem area, Sumatra, Indonesia. Biodiversitas, 22, 1160-1171.
Hasan, M., Hassan, L., Abdullah, Al. M., Abualreesh, M. H., Idris, M. H., & Kamal, A. H. M. (2022). Urban green space mediates spatiotemporal variation in land surface temperature: a case study of an urbanized city, Bangladesh. Environmental Science and Pollution Research, 29, 36376–36391.https://doi.org/10.1007/s11356-021-17480-9.
Husain, M.A., Kumar, P. & Gonencgil, B. (2023). Assessment of Spatio-Temporal Land Use/Cover Change and Its Effect on Land Surface Temperature in Lahaul and Spiti, India. Land, 12, 1294. https://doi.org/10.3390/land12071294
Imran, H. M., Hossain, A., Islam, A. K. M. S., Rahman, A., Bhuiyan, M. A. E., Paul, S. & Alam, A. (2021). Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh. Earth Syst. Environ., 5, 667-693. https://doi.org/10.1007/s41748-021-00243-4.
Kaiser, E. A., Rolim, S. B. A., Grondona, A. E. B., Hackmann, C. L., Linn, R. M., Käfer, P. S., Rocha, N. S. & Diaz, L. R. (2022). Spatiotemporal influences of LULC changes on land surface temperature in rapid urbanization area by using Landsat-TM and TIRS images. Atmosphere, 13 (3), 460. https://doi.org/10.3390/atmos13030460.
Kulsum, U. & Moniruzzaman, M. D. (2022). Exploring the relationship of climate change and land-use dynamics with satellite-derived surface indices and temperature in greater Dhaka, Bangladesh. J. Earth Syst. Sci., 131 (117). https://doi.org/10.1007/s12040-022-01841-0.
Kumar, P., Husain, A., Singh, R. B. & Kumar, M. (2018). Impact of land cover change on land surface temperature: a case study of Spiti Valley. Journal of Mountain Science. J. Mt. Sci., 15 (8), 1658-1670. https://doi.org/10.1007/s11629-018-4902-9.
Lee, G., Kim, G., Min, G., Kim, M., Jung, S., Hwang, J. & Cho, S. (2022). Vegetation classification in urban areas by combining UAV-based NDVI and thermal infrared image. Appl. Sci., 13, 515. https://doi.org/10.3390/app13010515.
Liu, X., Ouyang, C. & Zhou, Y. (2023). A Low‑Return‑Period Rainfall Intensity Formula for Estimating the Design Return Period of the Combined Interceptor Sewers. Water Resources Management, 37, 289–304. https://doi.org/10.1007/s11269-022-03369-w.
Mansourmoghaddam, M., Rousta, I., Cabral, P., Ali, A. A., Olafsson, H., Zhang, H. & Krzyszczak, J. (2023). Investigation and prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) changes for Mashhad City in Iran during 1990–2030. Atmosphere, 14, (741). https://doi.org/10.3390/atmos14040741.
Martel, J. L., Brissette, F. P., Picher, P. L., Troin, M. & Arsenault, R. (2021). Climate change and rainfall intensity–duration–frequency curves: Overview of science and guidelines for adaptation. J. Hydrol. Eng., 26 (10). https://doi.org/10.1061/(ASCE)HE.1943-5584.0002122.
Meng, Q., Liu, W., Zhang, L., Allam, M., Bi, Y., Hu, X., Gao, J., Hu D. & Jancsó, T. (2022). Relationships between land surface temperatures and neighboring environment in highly urbanized areas: Seasonal and scale effects analyses of Beijing, China. Remote Sens., 14 (17), 4340. https://doi.org/10.3390/rs14174340.
Milanović, M. M., Micić, T., Lukić, T., Nenadović, S. S., Basarin, B., Filipović, D. J., Tomić, M., Samardžić, I., Srdić, Z., Nikolić, G., Ninković, M. M., Sakulski, D. & Ristanović, B. (2019). Application of Landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia. Carpathian J. Earth Environ. Sci., 14 (1), 119-129. https://doi.org/10.26471/cjees/2019/014/064.
Murthy, K. V. N. & Kumar, G. K. (2022). Distribution and Prediction of Monsoon Rainfall in Homogeneous Regions of India: A Stochastic Approach. Pure Appl. Geophys, 179, 2577–2590. https://doi.org/10.1007/s00024-022-03042-8.
Orieschnig, C. A., Belaud, G., Venot, J. P., Massuel, S. & Ogilvie, A. (2021). Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta. Eur. J. Remote Sens., 54 (1). https://doi.org/10.1080/22797254.2021.1948356.
Pei, F., Zhou, Y. & Xia, Y. (2021). Application of Normalized Difference Vegetation Index (NDVI) for the Detection of Extreme Precipitation Change. Forest, 12 (5), 594. https://doi.org/10.3390/f12050594.
Prasetya, T. A. E., Munawar, Taufik, M. R., Chesok, S., Lim, A. & McNeil, D. (2020). Land surface temperature assessment in Central Sumatra, Indonesia. Indones. J. Geogr., 52 (2). https://doi.org/10.22146/ijg.51327.
Roelofs, G. J. & Kamphuis, V. (2019). Cloud processing, cloud evaporation and Angström exponent. Atmos. Chem. Phys., 9, 71-80.
Satterthwaite, D. (2019). The implications of population growth and urbanization for climate change. Environ. Urbanization, 21 (2). https://doi.org/10.1177/09562 47809 344361.
Sekertekin, A. & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens., 12 (2), 294. https://doi.org/10.3390/rs12020294.
Shahfahad, Kumari, B., Tayyab, M., Ahmed, I. A., Baig, M. R. I., Khan, M. F. & Rahman, A. (2020). Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab J Geosci., 13, 1040. https://doi.org/10.1007/s12517-020-06068-1.
Song, Y. & Park, M. (2021). A Study on the appropriateness of the drought index estimation method using damage data from Gyeongsangnamdo, South Korea. Atmosphere, 12 (8), 998. https://doi.org/10.3390/atmos120 80998.
Sun, Y., Wendi, D., Kim, D. E. & Liong, S. Y. (2019). Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data. Geosci. Lett., 6 (17). https://doi.org/10.1186/s40562-019-0147-x.
Suwarno, I., Ma’arif, A., Raharja, N. M., Nurjanah, A., Ikhsan, J. & Mutiarin, D. (2021). IoT-based lava flood early warning system with rainfall intensity monitoring and disaster communication technology. Emerging Sci. J., 4. https://doi.org/10.28991/esj-2021-SP1-011.
USGS, Landsat 7 (L7) data users’ handbook, version 2, 122. (2019). United State Geological Survey. https://www.usgs.gov/media/files/landsat-7-data-users-handbook.
Xu, Y., Liu, C., Wang, L. & Zou, L. (2023). Exploring the spatial autocorrelation in soil moisture networks: Analysis of the bias from upscaling the Texas Soil Observation Network (TxSON). Water, 15, 87. https://doi.org/10.3390/w15010087.
Yeneneh, N., Elias, E. & Feyisa, G. L. (2022). Detection of land use/land cover and land surface temperature change in the Suha Watershed, North-Western highlands of Ethiopia. Environ. Challenges, 7. https://doi.org/10.1016/j.envc.2022.100523.
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How to Cite
Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia. (2024). Journal of Applied and Natural Science, 16(1), 251-262. https://doi.org/10.31018/jans.v16i1.5316
