Using Landsat Images to Determine Water Storing Capacity in Mediterranean Environments
Source: By:Gema Marco Dos Santos, Jose Navarro-Pedreño, Ignacio Meléndez-Pastor, Ignacio Gómez Lucas
DOI: https://doi.org/10.30564/jgr.v4i4.3780
Abstract:Reservoirs play an important role in water management and are key elements for water supply. Monitoring is needed in order to guarantee the quantity and quality of stored water. However, this task is sometimes not easy. The objective of this study was to develop a procedure for predicting volume of stored water with remote sensing in water bodies under Mediterranean climate conditions. To achieve this objective,multispectral Landsat 7 and 8 images (NASA) were analyzed for the following five reservoirs: La Serena,La Pedrera, Beniarrés, Cubillas and Negratín (Spain). Reservoirs water surface was computed with the spectral angle mapper (SAM) algorithm.After that, cross-validation regression models were computed in order to assess the capability of water surface estimations to predict stored water in each of the reservoirs. The statistical models were trained with Landsat 7 images and were validated by using Landsat 8 images. Our results suggest a good capability of water volume prediction from free satellite imagery derived from surface water estimations. Combining free remote sensing images and open source GIS algorithms can be a very useful tool for water management and an integrated and efficient way to control water storage,especially in low accessible sites.
References:[1] Sawunyama, T.; Senzanje, A.; Mhizha, A. (2006).Estimation of small reservoir storage capacities in Limpopo River Basin using geographical information systems (GIS) and remotely sensed surface areas: Case of Mzingwane catchment [C]. Physics and Chemistry of the Earth, 31, 15-16: 935-943.https://doi.org/10.1016/j.pce.2006.08.008. [2] Loumagne, C.; Normand, M.; Riffard, M.; Weisse,A.;Quesney, A.; Lehégarat-Mascle, S.; Alem, F. (2001).Integration of remote sensing data into hydrological models for reservoir management [C].Hydrological Sciences Journal, 46, 1: 89-102.http://doi.org/10.1080/02626660109492802. [3] FAO. (2015). Towards a water and food secure future. Critical Perspectives for Policy-makers. Food and Agriculture Organization of the United Nations (Rome) and World Water Council (Marseille) [R].Available online: http://www.fao.org/3/a-i4560e.pdf (accessed on 18/04/2019). [4] Vargas-Amelin, E.; Pindado, P. (2014). The challenge of climate change in Spain: Water resources,agriculture and land [C]. Journal of Hydrology, 518B: 243-249.https://doi.org/10.1016/j.jhydrol.2013.11.035. [5] Jiménez Álvarez, C.; Mediero Orduña, A.; García Montañés, L. Review and selection of stadistical models to fit maximum annual peak flows distribution function in Spain [R]. CEDEX, Spain. Available online: http://www.cedex.es/NR/rdonlyres/96DF4741-0D23-4D67-9956-4BAF6DA5CD97/129082/CEH1174_v2.pdf (accessed on 15/04/2021). [6] Planas, L. La modernización de regadíos es clave para la sostenibilidad y el futuro de nuestra agricultura [N]. XIX Jornada Técnica de Fenacore.Ministry of Agriculture, Fisheries and Food, Spain.Available online:https://www.mapa.gob.es/es/prensa/ultimas-noticias/luis-planas-la-modernizaci%C3%B3n-de-regad%C3%ADos-es-clave-para-la-sostenibilidad-y-el-futuro-de-nuestra-agricultura/tcm:30-506649 (accessed on 23/04/2021). [7] Rovira, A.; Polo, M.J. (2015). Current and future challenges in water resources management in Spain [S]. Global Water Forum, IRTA-Aquatic Ecosystems; University of Cordoba. Available online:http://www.globalwaterforum.org/2015/04/20/current-and-future-challenges-in-water-resources-management-in-spain/ (accessed on 19/04/2021). [8] MITECO. Desarrollo, situación actual y perspectivas de futuro de las presas en España. Available online:https://www.miteco.gob.es/es/agua/temas/seguridad-de-presas-y-embalses/desarrollo/ (accessed on 28/04/2021). [9] P. Alarcos, A. (2018). Seopan: "La falta de inversión en infraestructuras pone en peligro la competividad de la economía" [N]. Available online: https://www.idealista.com/news/finanzas/inversion/2018/06/19/766177-seopan-alerta-la-falta-de-inversion-en-infraestructuras-pone-en-peligro-la-competividad (accessed on 19/04/2021). [10] Ministry for the Ecological Transition. (2021). Estado de los embalses [N]. Available online:https://www.miteco.gob.es/es/prensa/ultimas-noticias/lareserva-h%C3%ADdrica-espa%C3%B1ola-se-encuentra-al-616-por-ciento-de-su-capacidad-/tcm:30-525179 (accessed on 20/04/2021). [11] González de la Aleja, S.M. (2006). Medida de nivel en embalses. Evolución, posibilidades y tratamiento actual [S]. CONAMA Foundation: SMAGUA Zaragoza, Spain. Available online:http://www.hidrosanco.com/pdf/Medida%20de%20niveles%20en%20 embalses.pdf. [12] MAPAMA. Anuario de aforos 2014-15. Available online:http://ceh-flumen64.cedex.es/anuarioaforos/AnuarioMemoria.pdf (accessed on 16/04/2021). [13] Peng, D.; Guo, S.; Liu, P.; Liu, T. (2006). Reservoir Storage Curve Estimation Based on Remote Sensing Data [C]. Journal of Hydrologic Engineering, 11, 2:165-172.DOI: https://doi.org/10.1061/(ASCE)1084-0699(2006)11:2(165). [14] Marco-Dos Santos, G.; Melendez-Pastor, I.; Navarro-Pedreño, J.; Koch, M. (2019). Assessing Water Availability in Mediterranean Regions Affected by Water Conflicts through MODIS Data Time Series Analysis [C]. Remote Sensing, 11, 11: 1355. https://doi.org/10.3390/rs11111355. [15] Nkwonta, O.I.; Dzwairo, B.; Otieno, F.A.O.; Adeyemo, J.A. (2017). A review on water resources yield model [C]. South African Journal of Chemical Engineering, 23: 107-115. https://doi.org/10.1016/j.sajce.2017.04.002. [16] Avisse, N.; Tilmant, A.; Müller, M.F.; Zhang, H.(2017). Monitoring small reservoirs’ storage with satellite remote sensing in inaccessible areas [C].Hydrology and Earth System Sciences, 21, 12: 6445-6459.DOI: https://doi.org/10.5194/hess-21-6445-2017. [17] E.A. Silva; M.M. Pedrosa; S.C. Azevedo; G.P.Cardim; F.P.S. Carvalho. (2016). Assessment of surface water at the Sobradinho reservoir under the effects of drought using multi-temporal Landsat images [C]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,XLI-B8, XXIII ISPRS Congress.DOI: https://doi.org/10.5194/isprsarchives-XLI-B8-387-2016. [18] Pipitone, C.; Maltese, A.; Dardanelli, G.; Lo Brutto,M.; La Loggia, G. (2018). Monitoring water surface and level of a reservoir using different remote sensing approaches and comparison with dam displacements evaluated via GNSS [C]. Remote Sensing, 10,1: 71. https://doi.org/10.3390/rs10010071. [19] Melendez-Pastor, I.; Navarro-Pedreño, J.; Koch, M.;Gómez, I. (2010). Multi-resolution and temporal characterization of land-use classes in a mediterranean wetland with land-cover fractions [C]. International Journal of Remote Sensing, 31, 20: 5365-5389.DOI: https://doi.org/10.1080/01431160903349065. [20] Fang, H.-b.; Hu, T.-s.; Zeng, X.; Wu, F.-y. (2014).Simulation-optimization model of reservoir operation based on target storage curves [C]. Water Science and Engineering, 7,4: 433-445.DOI: https://doi.org/10.3882/j.issn.1674-2370.2014.04.008. [21] Soti, V.; Tran, A.; Bailly, J.S.; Puech, C. (2009).Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas [C]. International Journal of Applied Earth Observation and Geoinformation, 11, 5: 344-351.https://doi.org/10.1016/j.jag.2009.05.005. [22] Cai, X.; Feng, L.; Hou, X.; Chen. (2016). Remote Sensing of the Water Storage Dynamics of Large Lakes and Reservoirs in the Yangtze River Basin from 2000 to 2014 [C]. Scientific Reports, 6. https://doi.org/10.1038/srep36405. [23] Lacaux, J. P.; Tourre, Y. M.; Vignolles, C.; Ndione, J.A.; Lafaye, M. (2007). Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal [C]. Remote Sensing of the Environment, 106, 1: 66-74. https://doi.org/10.1016/j.rse.2006.07.012. [24] Y. Ma; N. Xu; J. Sun; X.H. Wang; F. Yang; S. Li.(2019). Estimating water levels and volumes of lakes dated back to the 1980s using Landsat imagery and photon-counting lidar datasets [C]. Remote Sensing of the Environment, 232: 111287. https://doi.org/10.1016/j.rse.2019.111287. [25] R. Schaeffer, B.A.; Schaeffer, K.G.; Keith, D.; Lunetta, R.S.; Conmy, R.; W. Goild, (2013).Barriers to adopting satellite remote sensing for water quality management [C]. International Journal of Remote Sensing, 34, 21: 7534-7544. https://doi.org/10.1080/01431161.2013.823524. [26] Venkatesan, V.; Balamurugan, R.; Krishnaveni, M.(2012). Establishing water surface area-storage capacity relationship of small tanks using SRTM and GPS [C]. Energy Procedia, 16, B: 1167-1173. https://doi.org/10.1016/j.egypro.2012.01.186. [27] Abileah, R.; Vignudelli, S.; Scozzari, A. (2011). A completely Remote Sensing approach to monitoring reservoirs water volume [C]. Fifteenth International Water Technology Conference, IWTC 15. Available online:https://www.researchgate.net/publication/228517141_A_Completely_Remote_Sensing_Approach_To_Monitoring_Reservoirs_Water_Volume (accessed on 15/04/2021). [28] Martin, P.H.; LeBoeuf, E.J.; Dobbins, J.P.; Daniel,E.B.; Abkowitz, M.D. (2005). Interfacing GIS with water resource models: a state-of-the-art review [C].Journal of the American Water Resources Association (Jawra), 41, 6: 1471-1487. https://doi.org/10.1111/j.1752-1688.2005.tb03813.x. [29] Valjarević, A.; Filipović, D.; Valjarević, D.; Milanovic, M.; Milosevic, S.; Zivic, N., Lukic, T. (2020) GIS and remote sensing techniques for the estimation of dew volume in the Republic of Serbia [C]. Meteorological Applications, 27, 3: e1930. https://doi.org/10.1002/met.1930. [30] Valjarević, A.; Milanović, M.; Valjarević, D.;Basarin, B.; Gribb, W.; Lukic, T. (2021). Geographical information systems and remote sensing methods in the estimation of potential dew volume and its utilization in the United Arab Emirates [C].Arabian Journal Geosciences 14, 1430. https://doi.org/10.1007/s12517-021-07771-3. [31] Martin, P.H.; LeBoeuf, E.J.; Daniel, E. B.; Dobbins,J.P.; Abkowitz, M.D. (2004). Development of a GISbased Spill Management Information System [C].Journal of Hazardous Materials, 112, 3:239-252,https://doi.org/10.1016/j.jhazmat.2004.05.014. [32] Ogilvie, A.; Belaud, G.; Massuel, S.; Mulligan, M.;Le Goulven, P.; Malaterre P.-O.; Calvez, R.(2018).Combining Landsat observations with hydrological modelling for improved surface water monitoring of small lakes [C]. Journal of Hydrology, 566: 109-121.https://doi.org/10.1016/j.jhydrol.2018.08.076. [33] AEMET-IMP. (2011). Iberian Climate Atlas. Air temperature and precipitation (1971-2000) [M]. Spanish Meteorological Agency (AEMET) and Portuguese Institute of Meteorology (IMP): Madrid, Spain and Lisbon, Portugal. ISBN 978-84-7837-079-5. [34] Guadiana Hydrographic Confederation. Ministry for the Ecological Transition. Available online: https://www.chguadiana.es/ (accessed on 01/03/2021). [35] Segura Hydrographic Confederation. Ministry for the Ecological Transition. Available online: https://www.chsegura.es/chs/index.html (accessed on 01/03/2021). [36] Ministry of Agriculture, Rural Development, Climate Emergency and Ecological Transition. Natural Protected Areas. Available online: https://agroambient.gva.es/es/web/espacios-naturales-protegidos/serra-escalona-presentacio (accessed on 22/10/2021). [37] Júcar Hydrographic Confederation. Ministry for the Ecological Transition. Available online: https://www.chj.es/es-es/Organismo/Paginas/Organismo.aspx (accessed on 01/03/2021). [38] Ministry of Agriculture, Rural Development, Climate Emergency and Ecological Transition.Natura 2000 Areas Network. Available online: https://agroambient.gva.es/es/web/red-natura-2000/listado-lic (accessed on 22/10/2021). [39] Guadalquivir Hydrographic Confederation. Ministry for the Ecological Transition. Available online:https://www.chguadalquivir.es/inicio (accessed on 01/03/2021). [40] MAPAMA. Spanish Yearbook of the Water Gauging Information System. Available online:https://sig.mapama.gob.es/redes-seguimiento/ (accessed on 01/03/2021). [41] Ministry for the Ecological Transition. State Monitoring Networks and Hydrological Information.Available online: https://sig.mapama.gob.es/redes-seguimiento/ (accessed on 02/03/2021). [42] U.S. Department of the Interior. U.S. Geological Survey-Earth Explorer. Available online:https://earthexplorer.usgs.gov/ (accessed on 01/03/2021). [43] Magome, J.; Ishidaira, H.; Takeuchi, K. (2003).Method for satellite monitoring of water storage in reservoirs for efficient regional water management [C]. Hydrological Risk, Management and Development (Proceedings of symposium HS02b held during IUOG2003 at Sapporo, July 2003). IAHS Publ. no.281 Water Resources Systems, 2: 303-310. Available online:http://hydrologie.org/redbooks/a281/iahs_281_303.pdf (accessed on 12/04/2021). [44] Rashmi, S.; Addamani, S.; Venkat; Ravikiran, S.(2014). Spectal Angle Mapper Algorithm for remote Sensing Image Classification [C]. International Journal of Innovative Science, Engineering & Technology, 1, 4: 201-205. ISSN 2348 - 7968. [45] Lobo, F. D. L.; Márcia, E. M.; Faria, C.C.; Soares, L.(2012). Reference spectra to classify Amazon water types [C]. International Journal of Remote Sensing,33, 11: 37-41.http://dx.doi.org/10.1080/01431161.2011.627391. [46] QGIS Development Team. QGIS Geographic Information System. 2018. Available online:https://qgis.org/es/site/. [47] Congedo, L. (2016). Semi-Automatic Classifification Plugin Documentation [S]. Release 6.0.1.1.DOI: https://doi.org/10.13140/RG.2.2.29474.02242/1. [48] Gao, H.; Birkett, C.; Lettenmaier, D.P. (2012). Global monitoring of large reservoir storage from satellite remote sensing [C]. Water Resources Research, 48, 9 https://doi.org/10.1029/2012WR012063. [49] Budak, M.; Gunal, H. (2016). Visible and Near Infrared Spectroscopy Techniques for etermination of Some Physical and Chemical Properties in Kazova Watershed [C]. Advances in Environmental Biology,10, 5: 61-72. ISSN-1995-0756. [50] Cheng-Wen, C.; Laird, D.; Mausbach, M.J.; Hurburgh Jr., C.R. (2001). Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties [C]. Soil Science Society of America Journal, 65, 2: 480-490. https://doi.org/10.2136/sssaj2001.652480x. [51] R Core Team. (2018). R: A Language and Environment for Statistical Computing [S].RFoundation for Statitistical Computing: Vienna, Austria. [52] S. Lu; N. Ouyang; B. Wu; Y. Wei; Z. Tesemma.(2013). Lake water volume calculation with time series remote-sensing images [C]. International Journal of Remote Sensing, 34, 22: 7962-7973.https://doi.org/10.1080/01431161.2013.827814. [53] Marco Dos Santos, G.; Meléndez Pastor, I.; Navarro Pedreño, J.; Gómez Lucas, I. (2018). Water Management in Irrigation Systems by Using Satellite Information [M]. In Satellite Information Classification and Interpretation; Rustam, B.R.; InTech, United Kingdom.DOI: https://doi.org/10.5772/intechopen.82368. [54] W. Zhu; J. Yan; S. Jia. (2017). Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis [C]. Remote Sensing, 9,10:1032;https://doi.org/10.3390/rs9101032. [55] R. Cobo. (2008). Los sedimentos de los embalses españoles [J]. Ingeniería del Agua, 15,4:231-241.https://doi.org/10.4995/ia.2008.2937. [56] Duane Nellis, M.; Harrington, J.A.; Jaiping Wu, Jr.(1998). Remote sensing of temporal and spatial variations in pool size, suspended sediment, turbidity,and Secchi depth in Tuttle Creek Reservoir,Kansas:1993 [C]. Geomorphology, 21, 3-4: 281-293.https://doi.org/10.1016/S0169-555X(97)00067-6.