The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Remote Sensing and Geographic Information Systems
Abstract
Flood disasters always hit densely populated urban areas during the rainy season. The causes of flooding that will examine in this scientific article are the condition of water infiltration into the soil. The case study was conducted in the urban area of Denpasar, Bali, Indonesia. Remote sensing data derived from various satellite images i.e., Sentinel-2 (BSI and NDVI extraction), Alos Palsar Imagery (slope extraction), CHIRPS (annual rainfall), and soil texture by laboratory analysis. Acquisition of remote sensing data using a Cloud Computing platform named Google Earth Engine (GEE). Data analysis using weighted overlay with ArcGIS 10.8 and threshold classification using natural breaks (Jenks). Denpasar City has the potential for water infiltration is good to very critical conditions. The correlation of the water infiltration map was carried out by comparing flood events in Denpasar City. The correlation results show (R2 = 0.84), (r = 0.916), (RMSE = 0.138), and p-value <0.05, these values indicate very high relation. Flood events often occur in zones with very critical water infiltration with high building density and low vegetated land cover. The condition of water infiltration critical to very critical category, spatially at the proportion of land cover vegetation < 1% and built-up area > 37%.
References
[1] Adisanjaya, N. N., et al. 2021. Mapping of Flood Prone Areas in Denpasar Bali with the K-MEANS CLUSTERING method. Jurnal Media Sains, 5(2): 37–46.
[2] Albert, J. S., Destouni, G., Duke-Sylvester, S. M., Magurran, A. E., Oberdorff, T., Reis, R. E., Winemiller, K. O., & Ripple, W. J. 2021. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio, 50(1): 85–94. DOI: https://doi.org/10.1007/s13280-020-01318-8
[3] Assouline, S. 2013. Infiltration into soils: Conceptual approaches and solutions. In Water Resources Research, 4: 1755–1772. DOI: https://doi.org/10.1002/wrcr.20155
[4] Bamweyana, I., Musinguzi, M. and Kayondo, L. 2021. Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda. Atmospheric and Climate Sciences, 11: 797-811. DOI: 10.4236/acs.2021.114046
[5] Beqaj, B., and Çobani, E. 2021. Groundwater quality and risk of pollution from natural, human and urban transport activities in the Drini basin. Scientific Journal of Silesian University of Technology. Series Transport, 111: 5–13. DOI: https://doi.org/10.20858/SJSUTST.2021.111.1
[6] Chen, J., et al. 2022. Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects. In Earth’s Future, 2: 1–33. DOI: https://doi.org/10.1029/2021EF002289
[7] Chen, J., et al. 2013. Research on geographical environment unit division based on the method of natural breaks (Jenks). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 3: 47-50.
[8] Chen, Z., et al. 2020. Water Eco-Nexus Cycle System (WaterEcoNet) as a key solution for water shortage and water environment problems in urban areas. Water Cycle, 1: 71–77. DOI:https://doi.org/10.1016/j.watcyc.2020.05.004
[9] Condon, L. E., et al. 2020. Where Is the Bottom of a Watershed? In Water Resources Research, 3: 1–9. DOI:https://doi.org/10.1029/2019WR026010
[10] Domakinis, C., et al. 2020. Flood susceptibility mapping in erythropotamos river basin with the aid of remote sensing and GIS. Acta Universitatis Carolinae, Geographica, 55(2): 149–164. DOI:https://doi.org/10.14712/23361980.2020.11
[11] Domínguez-Pérez, F., and Mercado-Fernández, T. 2020. Potential infiltration and morphometry in the arroyo grande basin, sucre Colombia. Revista Facultad de Ingenieria, 96: 21–31. DOI:https://doi.org/10.17533/udea.redin.20191043
[12] Driptufany, D. M., Guvil, Q., and Ramadhan, S. 2019. Aplikasi sistem informasi geografis untuk estimasi sebaran daerah potensi resapan air kota padang. Jurnal Momentum, 21(1).
[13] Febrianto, H., Fariza, A., and Hasim, J. A. N. 2016, November. Urban flood risk mapping using analytic hierarchy process and natural break classification (Case study: Surabaya, East Java, Indonesia). In 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC) (pp. 148-154). IEEE.
[14] Fung, T. K., et al. 2022. Litter decomposition and infiltration capacities in soils of different tropical urban land covers. Urban Ecosystems, 25(1): 21–34. DOI: https://doi.org/10.1007/s11252-021-01126-2
[15] Ghorbanian, A., et al. 2022. Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water (Switzerland), 14(2). DOI:https://doi.org/10.3390/w14020244
[16] Gorelick, N., et al. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18–27. DOI: https://doi.org/10.1016/j.rse.2017.06.031
[17] Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. 2019. Forest stand species mapping using the sentinel-2 time series. Remote Sensing, 11(10).DOI: https://doi.org/10.3390/rs11101197
[18] Halder, B., and Bandyopadhyay, J. 2022. Monitoring the tropical cyclone ‘Yass’ and ‘Amphan’ affected flood inundation using Sentinel-1/2 data and Google Earth Engine. Modeling Earth Systems and Environment. DOI: https://doi.org/10.1007/s40808-022-01359-w
[19] Hapsari, P., Nurlina, and Sota, I. 2013. Analisis daerah resapan di daerah rawan banjir kabupaten banjar menggunakan sistem informasi geografis. Jurnal Fisika FLUX, 10(2): 154–165.
[20] Hou, J., et al. 2019. Experimental study for effects of terrain features and rainfall intensity on infiltration rate of modelled permeable pavement. Journal of Environmental Management, 243: 177–186. DOI:https://doi.org/10.1016/j.jenvman.2019.04.096
[21] Jha, M. K., Chowdhury, A., Chowdary, V. M., and Peiffer, S. 2007. Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water resources management, 21(2): 427-467.
[22] Kaplan, G., and Avdan, U. 2018. Sentinel-1 and Sentinel-2 data fusion for wetlands mapping: Balikdami, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3). DOI: https://doi.org/10.5194/isprs-archives-XLII-3-729-2018
[23] Karra, K., et al. 2021. GLOBAL LAND USE/LAND COVER WITH SENTINEL 2 AND DEEP LEARNING. International Geoscience and Remote Sensing Symposium (IGARSS), 2021-July, 4704–4707. DOI:https://doi.org/10.1109/IGARSS47720.2021.9553499
[24] Kusmiyarti, T. B., Wiguna, P. P. K., and Ratna Dewi, N. K. R. 2018. Flood Risk Analysis in Denpasar City, Bali, Indonesia. IOP Conference Series: Earth and Environmental Science, 123(1): 1–12. DOI:https://doi.org/10.1088/1755-1315/123/1/012012
[25] Lasaponara, R., Tucci, B., and Ghermandi, L. 2018. On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity. Sustainability (Switzerland), 10(11): 1–13. DOI:https://doi.org/10.3390/su10113889
[26] Lu, P., Shi, W., Wang, Q., Li, Z., Qin, Y., and Fan, X. 2021. Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow, red-edge, and SWIR bands. Landslides, 18(6): 2017–2037. DOI:https://doi.org/10.1007/s10346-021-01636-2
[27] Luo, H., and Wu, J. 2022. An Assessment of the Suitability of Sentinel-2 Data for Identifying Burn Severity in Areas of Low Vegetation. Journal of the Indian Society of Remote Sensing. DOI:https://doi.org/10.1007/s12524-022-01518-7
[28] Morbidelli, R., et al. 2019. A new conceptual model for slope-infiltration. Water (Switzerland), 11(4): 1–13. DOI: https://doi.org/10.3390/w11040678
[29] Morbidelli, R., Saltalippi, C., Flammini, A., and Govindaraju, R. S. 2018. Role of slope on infiltration: A review. In Journal of Hydrology, 557: 878–886. DOI: https://doi.org/10.1016/j.jhydrol.2018.01.019
[30] Nguyen, T. T., et al. 2019. Implementation of a specific urban water management - Sponge City. In Science of the Total Environment, 652: 147–162). DOI: https://doi.org/10.1016/j.scitotenv.2018.10.168
[31] Rahardjo, A. P. 2020. Effect of Slope on Infiltration Capacity and Erosion of Mount Merapi Slope Materials. Journal of the Civil Engineering Forum, 1000(1000): 1–13. DOI: https://doi.org/10.22146/jcef.58350
[32] Ren, X., et al. 2020. Effect of infiltration rate changes in urban soils on stormwater runoff process. Geoderma, 363(114158): 1–11. DOI: https://doi.org/10.1016/j.geoderma.2019.114158
[33] Rimba, A. B., and Yastika, P. E. 2020. Indonesia: Threats to physical urban water problems. E3S Web of Conferences, 148: 1–6. DOI: https://doi.org/10.1051/e3sconf/202014806001
[34] Ristianingrum, N. 2019. Analisis Potensi Daerah Resapan Air Kota Depok Menggunakan Citra Satelit Penginderaan Jauh Potential Analysis of Depok City Water Absorption Areas Using Remote Sensing Satellite Imagery. Seminar Nasional Penginderaan Jauh Ke-6 Tahun.
[35] Rouibah, K., and Belabbas, M. 2020. Applying multi-index approach from sentinel-2 imagery to extract urban areas in dry season (Semi-arid land in north east algeria). Revista de Teledeteccion, 2020(56): 89–101. DOI:https://doi.org/10.4995/raet.2020.13787
[36] Saha, A. K., and Agrawal, S. 2020. Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnology for Environmental Engineering, 5(2). DOI:https://doi.org/10.1007/s41204-020-00073-1
[37] Segarra, J., Buchaillot, M. L., Araus, J. L., and Kefauver, S. C. 2020. Remote sensing for precision agriculture: Sentinel-2 improved features and applications. In Agronomy (Vol. 10, Issue 5). DOI:https://doi.org/10.3390/agronomy10050641
[38] Sim, L. K., and Balamurugan, G. 1991. Urbanization and urban water problems in Southeast Asia a case of unsustainable development. Journal of Environmental Management, 32(3): 195–209. DOI:https://doi.org/10.1016/S0301-4797(05)80051-9
[39] Somers, L. D., and McKenzie, J. M. 2020. A review of groundwater in high mountain environments. In Wiley Interdisciplinary Reviews: Water, 6: 1–27. DOI: https://doi.org/10.1002/wat2.1475
[40] Tamiminia, H., et al. 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. In ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152–170. DOI:https://doi.org/10.1016/j.isprsjprs.2020.04.001
[41] Tavus, B., Kocaman, S., Nefeslioglu, H. A., and Gokceoglu, C. 2020. A fusion approach for flood mapping using sentinel-1 and sentinel-2 datasets. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B3): 641–648. DOi: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-641-2020
[42] Ticehurst, C., Teng, J., and Sengupta, A. 2022. Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment. Remote Sensing, 14(5). DOI:https://doi.org/10.3390/rs14051158
[43] Wibowo, M. 2006. Model penentuan kawasan resapan air untuk perencanaan tata ruang berwawasan lingkungan. Hidrosfir, 1(1): 1–7.
[44] Wicaksono, W., Prasetyo, Y., and Bashi, N. 2019. Analisis Kondisi Resapan Air Terhadap Perubahan Kawasan Terbangun Menggunakan Metode Index-Based Built-up Index (IBI) dan Urban Index (UI) Kota Pekalongan. Jurnal Geodesi Undip, 8(4).
[45] Zhu, C., Luo, J., Shen, Z., and Huang, C. 2011. Wetland mapping in the Balqash lake basin using multi-source remote sensing data and topographic features synergic retrieval. Procedia Environmental Sciences, 10(PART C). DOI: https://doi.org/10.1016/j.proenv.2011.09.422
[46] Climate Hazards Center. (2020). CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations. University of California Santa Barbara. Available at: https://www.chc.ucsb.edu/data/chirps
[2] Albert, J. S., Destouni, G., Duke-Sylvester, S. M., Magurran, A. E., Oberdorff, T., Reis, R. E., Winemiller, K. O., & Ripple, W. J. 2021. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio, 50(1): 85–94. DOI: https://doi.org/10.1007/s13280-020-01318-8
[3] Assouline, S. 2013. Infiltration into soils: Conceptual approaches and solutions. In Water Resources Research, 4: 1755–1772. DOI: https://doi.org/10.1002/wrcr.20155
[4] Bamweyana, I., Musinguzi, M. and Kayondo, L. 2021. Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda. Atmospheric and Climate Sciences, 11: 797-811. DOI: 10.4236/acs.2021.114046
[5] Beqaj, B., and Çobani, E. 2021. Groundwater quality and risk of pollution from natural, human and urban transport activities in the Drini basin. Scientific Journal of Silesian University of Technology. Series Transport, 111: 5–13. DOI: https://doi.org/10.20858/SJSUTST.2021.111.1
[6] Chen, J., et al. 2022. Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects. In Earth’s Future, 2: 1–33. DOI: https://doi.org/10.1029/2021EF002289
[7] Chen, J., et al. 2013. Research on geographical environment unit division based on the method of natural breaks (Jenks). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 3: 47-50.
[8] Chen, Z., et al. 2020. Water Eco-Nexus Cycle System (WaterEcoNet) as a key solution for water shortage and water environment problems in urban areas. Water Cycle, 1: 71–77. DOI:https://doi.org/10.1016/j.watcyc.2020.05.004
[9] Condon, L. E., et al. 2020. Where Is the Bottom of a Watershed? In Water Resources Research, 3: 1–9. DOI:https://doi.org/10.1029/2019WR026010
[10] Domakinis, C., et al. 2020. Flood susceptibility mapping in erythropotamos river basin with the aid of remote sensing and GIS. Acta Universitatis Carolinae, Geographica, 55(2): 149–164. DOI:https://doi.org/10.14712/23361980.2020.11
[11] Domínguez-Pérez, F., and Mercado-Fernández, T. 2020. Potential infiltration and morphometry in the arroyo grande basin, sucre Colombia. Revista Facultad de Ingenieria, 96: 21–31. DOI:https://doi.org/10.17533/udea.redin.20191043
[12] Driptufany, D. M., Guvil, Q., and Ramadhan, S. 2019. Aplikasi sistem informasi geografis untuk estimasi sebaran daerah potensi resapan air kota padang. Jurnal Momentum, 21(1).
[13] Febrianto, H., Fariza, A., and Hasim, J. A. N. 2016, November. Urban flood risk mapping using analytic hierarchy process and natural break classification (Case study: Surabaya, East Java, Indonesia). In 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC) (pp. 148-154). IEEE.
[14] Fung, T. K., et al. 2022. Litter decomposition and infiltration capacities in soils of different tropical urban land covers. Urban Ecosystems, 25(1): 21–34. DOI: https://doi.org/10.1007/s11252-021-01126-2
[15] Ghorbanian, A., et al. 2022. Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery. Water (Switzerland), 14(2). DOI:https://doi.org/10.3390/w14020244
[16] Gorelick, N., et al. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18–27. DOI: https://doi.org/10.1016/j.rse.2017.06.031
[17] Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. 2019. Forest stand species mapping using the sentinel-2 time series. Remote Sensing, 11(10).DOI: https://doi.org/10.3390/rs11101197
[18] Halder, B., and Bandyopadhyay, J. 2022. Monitoring the tropical cyclone ‘Yass’ and ‘Amphan’ affected flood inundation using Sentinel-1/2 data and Google Earth Engine. Modeling Earth Systems and Environment. DOI: https://doi.org/10.1007/s40808-022-01359-w
[19] Hapsari, P., Nurlina, and Sota, I. 2013. Analisis daerah resapan di daerah rawan banjir kabupaten banjar menggunakan sistem informasi geografis. Jurnal Fisika FLUX, 10(2): 154–165.
[20] Hou, J., et al. 2019. Experimental study for effects of terrain features and rainfall intensity on infiltration rate of modelled permeable pavement. Journal of Environmental Management, 243: 177–186. DOI:https://doi.org/10.1016/j.jenvman.2019.04.096
[21] Jha, M. K., Chowdhury, A., Chowdary, V. M., and Peiffer, S. 2007. Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water resources management, 21(2): 427-467.
[22] Kaplan, G., and Avdan, U. 2018. Sentinel-1 and Sentinel-2 data fusion for wetlands mapping: Balikdami, Turkey. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(3). DOI: https://doi.org/10.5194/isprs-archives-XLII-3-729-2018
[23] Karra, K., et al. 2021. GLOBAL LAND USE/LAND COVER WITH SENTINEL 2 AND DEEP LEARNING. International Geoscience and Remote Sensing Symposium (IGARSS), 2021-July, 4704–4707. DOI:https://doi.org/10.1109/IGARSS47720.2021.9553499
[24] Kusmiyarti, T. B., Wiguna, P. P. K., and Ratna Dewi, N. K. R. 2018. Flood Risk Analysis in Denpasar City, Bali, Indonesia. IOP Conference Series: Earth and Environmental Science, 123(1): 1–12. DOI:https://doi.org/10.1088/1755-1315/123/1/012012
[25] Lasaponara, R., Tucci, B., and Ghermandi, L. 2018. On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity. Sustainability (Switzerland), 10(11): 1–13. DOI:https://doi.org/10.3390/su10113889
[26] Lu, P., Shi, W., Wang, Q., Li, Z., Qin, Y., and Fan, X. 2021. Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow, red-edge, and SWIR bands. Landslides, 18(6): 2017–2037. DOI:https://doi.org/10.1007/s10346-021-01636-2
[27] Luo, H., and Wu, J. 2022. An Assessment of the Suitability of Sentinel-2 Data for Identifying Burn Severity in Areas of Low Vegetation. Journal of the Indian Society of Remote Sensing. DOI:https://doi.org/10.1007/s12524-022-01518-7
[28] Morbidelli, R., et al. 2019. A new conceptual model for slope-infiltration. Water (Switzerland), 11(4): 1–13. DOI: https://doi.org/10.3390/w11040678
[29] Morbidelli, R., Saltalippi, C., Flammini, A., and Govindaraju, R. S. 2018. Role of slope on infiltration: A review. In Journal of Hydrology, 557: 878–886. DOI: https://doi.org/10.1016/j.jhydrol.2018.01.019
[30] Nguyen, T. T., et al. 2019. Implementation of a specific urban water management - Sponge City. In Science of the Total Environment, 652: 147–162). DOI: https://doi.org/10.1016/j.scitotenv.2018.10.168
[31] Rahardjo, A. P. 2020. Effect of Slope on Infiltration Capacity and Erosion of Mount Merapi Slope Materials. Journal of the Civil Engineering Forum, 1000(1000): 1–13. DOI: https://doi.org/10.22146/jcef.58350
[32] Ren, X., et al. 2020. Effect of infiltration rate changes in urban soils on stormwater runoff process. Geoderma, 363(114158): 1–11. DOI: https://doi.org/10.1016/j.geoderma.2019.114158
[33] Rimba, A. B., and Yastika, P. E. 2020. Indonesia: Threats to physical urban water problems. E3S Web of Conferences, 148: 1–6. DOI: https://doi.org/10.1051/e3sconf/202014806001
[34] Ristianingrum, N. 2019. Analisis Potensi Daerah Resapan Air Kota Depok Menggunakan Citra Satelit Penginderaan Jauh Potential Analysis of Depok City Water Absorption Areas Using Remote Sensing Satellite Imagery. Seminar Nasional Penginderaan Jauh Ke-6 Tahun.
[35] Rouibah, K., and Belabbas, M. 2020. Applying multi-index approach from sentinel-2 imagery to extract urban areas in dry season (Semi-arid land in north east algeria). Revista de Teledeteccion, 2020(56): 89–101. DOI:https://doi.org/10.4995/raet.2020.13787
[36] Saha, A. K., and Agrawal, S. 2020. Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnology for Environmental Engineering, 5(2). DOI:https://doi.org/10.1007/s41204-020-00073-1
[37] Segarra, J., Buchaillot, M. L., Araus, J. L., and Kefauver, S. C. 2020. Remote sensing for precision agriculture: Sentinel-2 improved features and applications. In Agronomy (Vol. 10, Issue 5). DOI:https://doi.org/10.3390/agronomy10050641
[38] Sim, L. K., and Balamurugan, G. 1991. Urbanization and urban water problems in Southeast Asia a case of unsustainable development. Journal of Environmental Management, 32(3): 195–209. DOI:https://doi.org/10.1016/S0301-4797(05)80051-9
[39] Somers, L. D., and McKenzie, J. M. 2020. A review of groundwater in high mountain environments. In Wiley Interdisciplinary Reviews: Water, 6: 1–27. DOI: https://doi.org/10.1002/wat2.1475
[40] Tamiminia, H., et al. 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. In ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152–170. DOI:https://doi.org/10.1016/j.isprsjprs.2020.04.001
[41] Tavus, B., Kocaman, S., Nefeslioglu, H. A., and Gokceoglu, C. 2020. A fusion approach for flood mapping using sentinel-1 and sentinel-2 datasets. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B3): 641–648. DOi: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-641-2020
[42] Ticehurst, C., Teng, J., and Sengupta, A. 2022. Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment. Remote Sensing, 14(5). DOI:https://doi.org/10.3390/rs14051158
[43] Wibowo, M. 2006. Model penentuan kawasan resapan air untuk perencanaan tata ruang berwawasan lingkungan. Hidrosfir, 1(1): 1–7.
[44] Wicaksono, W., Prasetyo, Y., and Bashi, N. 2019. Analisis Kondisi Resapan Air Terhadap Perubahan Kawasan Terbangun Menggunakan Metode Index-Based Built-up Index (IBI) dan Urban Index (UI) Kota Pekalongan. Jurnal Geodesi Undip, 8(4).
[45] Zhu, C., Luo, J., Shen, Z., and Huang, C. 2011. Wetland mapping in the Balqash lake basin using multi-source remote sensing data and topographic features synergic retrieval. Procedia Environmental Sciences, 10(PART C). DOI: https://doi.org/10.1016/j.proenv.2011.09.422
[46] Climate Hazards Center. (2020). CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations. University of California Santa Barbara. Available at: https://www.chc.ucsb.edu/data/chirps
Published
2022-09-02
How to Cite
TRIGUNASIH, Ni Made; SAIFULLOH, Moh.
The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Remote Sensing and Geographic Information Systems.
Journal of Environmental Management and Tourism, [S.l.], v. 13, n. 5, p. 1467 - 1480, sep. 2022.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/7216>. Date accessed: 23 apr. 2024.
doi: https://doi.org/10.14505/jemt.v13.5(61).22.
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Article
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.