Abstract
Observations of water surface elevation (WSE) and bathymetry of the lagoons and cenotes of the Yucatán Peninsula (YP) in southeast Mexico are of hydrogeological interest. Observations of WSE (orthometric water height above mean sea level, amsl) are required to inform hydrological models, to estimate hydraulic gradients and groundwater flow directions. Measurements of bathymetry and water depth (elevation of the water surface above the bed of the water body) improve current knowledge on how lagoons and cenotes connect through the complicated submerged cave systems and the diffuse flow in the rock matrix. A novel approach is described that uses unmanned aerial vehicles (UAVs) to monitor WSE and bathymetry of the inland water bodies on the YP. UAV-borne WSE observations were retrieved using a radar and a global navigation satellite system on-board a multi-copter platform. Water depth was measured using a tethered floating sonar controlled by the UAV. This sonar provides depth measurements also in deep and turbid water. Bathymetry (wet-bed elevation amsl) can be computed by subtracting water depth from WSE. Accuracy of the WSE measurements is better than 5–7 cm and accuracy of the water depth measurements is estimated to be ~3.8% of the actual water depth. The technology provided accurate measurements of WSE and bathymetry in both wetlands (lagoons) and cenotes. UAV-borne technology is shown to be a more flexible and lower cost alternative to manned aircrafts. UAVs allow monitoring of remote areas located in the jungle of the YP, which are difficult to access by human operators.
Résumé
Les observations de l’élévation de la surface de l’eau (ESE) et de la bathymétrie des lagunes et cénotes de la péninsule du Yucatán (PY) dans le Sud-Est du Mexique sont d’intérêt hydrogéologique. Les observation d’ESE (hauteur d’eau orthométrique au-dessus du niveau moyen de la mer (m)) sont nécessaires pour nourrir les modèles hydrologiques, pour estimer les gradients hydrauliques et les directions d’écoulement des eaux souterraines. Les mesures de la bathymétrie et de la profondeur de l’eau (élévation de la surface de l’eau au–dessus du lit du plan d’eau) améliorent les connaissances actuelles sur la façon dont les lagunes et les cénotes sont connectées via des systèmes complexes de cavités noyées et l’écoulement diffus dans la matrice rocheuse. Une nouvelle approche qui utilise des drones pour surveiller ESE la bathymétrie des masses d’eau intérieures sur la PY est décrite. Les observations par drone de l’ESE ont été obtenues en utilisant un radar et un système de positionnement par satellite, embarqué sur une plateforme multi-copter. La profondeur de l’eau a été mesurée à l’aide d’un sonar flottant contrôlé par le drone. Ce sonar fournit également des mesures de profondeur dans des eaux profondes et troubles. La bathymétrie (élévation en mètres de la partie mouillée) peut être calculée en soustrayant la profondeur de l’eau à partir de l’ESE. La précision des mesures d’ESE est supérieure à 5–7 cm et des mesures de la profondeur de l’eau est estimée à ~3.8% de la profondeur réelle de l’eau. La technologie a fourni des mesures précises d’ESE et de bathymétrie aussi bien pour les zones humides (lagunes) que pour les cénotes. La technologie embarquée dans le drone s’est. avérée être une alternative plus flexible et moins coûteuses que des moyens aéroportés par avion pilotés. Les drones permettent de surveiller des zones localisées dans la jungle de la PY, qui sont difficiles d’accès par des opérateurs humains.
Resumen
Las observaciones de la elevación del agua de superficie (WSE) y la batimetría de las lagunas y los cenotes de la Península de Yucatán (YP) en el sureste de México son de interés hidrogeológico. Se requieren observaciones de la WSE (altura ortométrica del agua por encima del nivel medio del mar (amsl)) para informar los modelos hidrológicos, estimar los gradientes hidráulicos y las direcciones del flujo del agua subterránea. Las mediciones de la batimetría y profundidad del agua (elevación del agua de superficie sobre el lecho del cuerpo de agua) mejoran el conocimiento actual sobre cómo las lagunas y los cenotes se conectan a través de los complejos sistemas de cavernas sumergidas y el flujo difuso en la matriz rocosa. Se describe un enfoque novedoso que utiliza vehículos aéreos no tripulados (UAV) para monitorear la WSE y la batimetría de las masas de agua continentales en la YP. Las observaciones de la WSE transmitidas por UAV se obtuvieron utilizando un radar y un sistema global de navegación por satélite a bordo de una plataforma de múltiples helicópteros. La profundidad del agua se midió usando un sonar flotante controlado por el UAV. Este sonar proporciona mediciones de profundidad también en aguas profundas y turbias. La batimetría (amsl de elevación del lecho) se puede calcular restando la profundidad del agua de la WSE. La precisión de las mediciones WSE es mayor a 5–7 cm y se estima que la precisión de las mediciones de la profundidad del agua es ~3.8% de la profundidad real del agua. La tecnología proporcionó mediciones precisas de la WSE y la batimetría en ambos humedales (lagunas) y cenotes. La tecnología UAV es una alternativa más flexible y de menor costo que las aeronaves tripuladas. Los UAV permiten el monitoreo de áreas remotas ubicadas en la jungla de la YP, a las cuales los operadores humanos tienen un difícil acceso.
摘要
墨西哥南部尤卡坦半岛泻湖和天然井的地下水文过程是水文地质学界的重要课题。通过对泻湖和天然井的水面高度和深度的测量, 输入水文模型, 可对地下水流方向和水力梯度进行估算, 进而增加对复杂水下洞穴的地质连接以及岩层基质中的水流扩散的认识。本文采用了新型的水文观测方法, 运用旋翼无人机对尤卡坦半岛内陆水体的水面高程及深度进行测量。水面高度通过无人机平台搭载的雷达和高精度差分全球卫星定位系统获取。水体深度通过无人机拖拽栓绳的漂浮声呐系统获取, 相较于传统的依靠光学水深测量方法, 该方法可用于更深和更浑浊的水体。研究结果显示, 无人机系统对水面高度的测量精度优于5–7厘米,水深的测量精度是实际水位的大约3.8%。这充分论证了无人机技术可为湿地(泻湖)和天然井的水面高度和水体深度提供精确测量。相较于载人飞行器, 无人机更加灵活、成本更低, 对于监测例如尤卡坦半岛等人类很难涉足的丛林地带偏僻地区的水文地质过程具有重大潜力。
Resumo
Observações da elevação da superfície da água (ESA) e a batimetria de lagoas e cenotes da Península de Yucatã (PY), no sudeste do Mexico são de interesse hidrogeológico. Observações do ESA (altura ortométrica da água acima do nível médio do mar (anmm)) são requeridas para informar modelos hidrológicos, para estimar gradientes hidráulicos e direções de fluxo da água subterrânea. Medidas de batimetria e profundidade da água (elevação da superfície da água acima do leito do corpo d’agua) aperfeiçoam os conhecimentos atuais em como lagoas e cenotes se conectam através dos complicados sistemas de cavernas submersas e do fluxo difuso na matriz da rocha. Uma nova abordagem é descrita utilizando veículos aéreos não tripulados (VANTs) para monitorar a ESA e batimetria dos corpos d’agua interiores da PY. Observações via VANT da ESA foram coletadas usando um radar e um sistema global de navegação por satélite abordo de uma plataforma multirotor. Esse sonar fornece medições de profundidade também em águas turvas e profundas. Batimetria (elevação do leito molhado anmm) pode ser computado subtraindo a profundidade da água da ESA. A acurácia das medições da ESA é melhor que 5–7 cm e acurácia da medições de profundidade da água é estimada em ~3.8% da profundidade real da água. A tecnologia forneceu medições acuradas da ESA e batimetria nas áreas úmidas (lagoas) e cenotes. Tecnologia via VANTs é uma alternativa mais flexível e de menor custo para as aeronaves tripuladas. Os VANTs permitem o monitoramento de áreas remotas localizadas na floresta da PY, que é de difícil acesso para operadores humanos.










References
Alsdorf D, Melack J, Dunne T, Mertes L, Hess L, Smith L (2000) Interferometric radar measurements of water level changes on the Amazon flood plain. Nature 404(6774):174–177. https://doi.org/10.1038/35004560
Alsdorf DE, Smith LC, Melack JM (2001) Amazon floodplain water level changes measured with interferometric SIR-C radar. IEEE Trans Geosci Remote Sens 39(2):423–431. https://doi.org/10.1109/36.905250
Amigos de Sian Ka’an, Colectividad Razonatura A.C. (2012) Censo de Cenotes de Quintana Roo [Census of cenotes of Quintana Roo]. http://www.amigosdesiankaan.org/es/proyectos/agua/54-logros-recientes. Accessed 15 Jun 2017
Asadzadeh Jarihani A, Callow JN, Johansen K, Gouweleeuw B (2013) Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods. J Hydrol 505:78–90. https://doi.org/10.1016/j.jhydrol.2013.09.010
Bandini F, Jakobsen J, Olesen D, Reyna-Gutierrez JA, Bauer-Gottwein P (2017a) Measuring water level in rivers and lakes from lightweight unmanned aerial vehicles. J Hydrol 548:237–250. https://doi.org/10.1016/j.jhydrol.2017.02.038
Bandini F, Olesen D, Jakobsen J, Kittel CMM, Wang S, Garcia M, Bauer-Gottwein P (2017b) Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle. Hydrol Earth Syst Sci Discuss https://doi.org/10.5194/hess-2017-625
Bauer-Gottwein P (2016) SmartUAV: new and innovative data collection platform and sensor technology - DTU Environment. http://www.env.dtu.dk/english/Research_NEW/WRE_NEW/Project-SmartUAV. Accessed 23 April 2017
Bauer-Gottwein P, Gondwe BRN, Charvet G, Marin LE, Rebolledo-Vieyra M, Merediz-Alonso G (2011) Review: the Yucatan Peninsula karst aquifer, Mexico. Hydrogeol J 19:507–524. https://doi.org/10.1007/s10040-010-0699-5
Beddows P, Blanchon P (2007) Los cenotes de la península de Yucatán [The cenotes of the Yucatan Peninsula]. In: Arquelogía Mex. http://www.seduma.yucatan.gob.mx/cenotes-grutas/documentos/cenotes-peninsula.pdf. Accessed 23 April 2017
Bergeron N, Carbonneau PE (2012) Geosalar: innovative remote sensing methods for spatially continuous mapping of fluvial habitat at riverscape scale. In: Fluvial remote sensing for science and management. Wiley, Chichester, UK, pp 193–213
Biancamaria S, Frappart F, Leleu AS, Marieu V, Blumstein D, Desjonqueres JD, Boy F, Sottolichio A, Valle-Levinson A (2017) Satellite radar altimetry water elevations performance over a 200 m wide river: evaluation over the Garonne River. Adv Space Res 59(1):128–146. https://doi.org/10.1016/j.asr.2016.10.008
Calmant S, Seyler F, Cretaux JF (2008) Monitoring continental surface waters by satellite altimetry. Surv Geophys 29(4–5):247–269. https://doi.org/10.1007/s10712-008-9051-1
Carbonneau PE, Lane SN, Bergeron N (2006) Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments. Earth Surf Process Landf 31(11):1413–1423. https://doi.org/10.1002/esp.1341
Cerdeira-Estrada S, Heege T, Kolb M, Ohlendorf S, Uribe A, Muller A, Garza R, Ressl R, Aguirre R, Marino I, Silva R, Martell R (2012) Benthic habitat and bathymetry mapping of shallow waters in Puerto Morelos reefs using remote sensing with a physics based data processing. In: Proc. of 2012 I.E. International Geoscience and Remote Sensing Symposium. IEEE, Piscataway, NJ, pp 4383–4386
Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113(5):893–903. https://doi.org/10.1016/j.rse.2009.01.007
Connors M, Hildebrand AR, Pilkington M, OrtizAleman C, Chavez RE, UrrutiaFucugauchi J, GranielCastro E, CamaraZi A, Vasquez J, Halpenny JF (1996) Yucatan karst features and the size of Chicxulub crater. Geophys J Int 127(3):F11–F14. https://doi.org/10.1111/j.1365-246X.1996.tb04066.x
Domeneghetti A, Castellarin A, Tarpanelli A, Moramarco T (2015) Investigating the uncertainty of satellite altimetry products for hydrodynamic modelling. Hydrol Process 29(23):4908–4918. https://doi.org/10.1002/hyp.10507
DTU, Sky-Watch (2017) Smart UAV-video. https://www.youtube.com/watch?v=No4zbFxnJFM. Accessed 24 May 2017
Escolero OA, Marin LE, Steinich B, Pacheco J (2000) Delimitation of a hydrogeological reserve for a city within a karstic aquifer: the Merida, Yucatan example. Landsc Urban Plan 51(1):53–62. https://doi.org/10.1016/S0169-2046(00)00096-7
Eugenio F, Marcello J, Martin J (2015) High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. Geosci Remote Sensing, IEEE Trans 53(7):3539–3549. https://doi.org/10.1109/TGRS.2014.2377300
Feurer D, Bailly J-S, Puech C, Le Coarer Y, Viau AA (2008) Very-high-resolution mapping of river-immersed topography by remote sensing. Prog Phys Geogr 32(4):403–419. https://doi.org/10.1177/0309133308096030
Flener C, Vaaja M, Jaakkola A, Krooks A, Kaartinen H, Kukko A, Kasvi E, Hyyppä H, Hyyppä J, Alho P (2013) Seamless mapping of river channels at high resolution using mobile liDAR and UAV-photography. Remote Sens 5(12):6382–6407. https://doi.org/10.3390/rs5126382
Gondwe BRN, Lerer S, Stisen S, Marín L, Rebolledo-Vieyra M, Merediz-Alonso G, Bauer-Gottwein P (2010a) Hydrogeology of the South-Eastern Yucatan Peninsula: new insights from water level measurements, geochemistry, geophysics and remote sensing. J Hydrol 389:1–17. https://doi.org/10.1016/j.jhydrol.2010.04.044
Gondwe BRN, Hong SH, Wdowinski S, Bauer-Gottwein P (2010b) Hydrologic dynamics of the ground-water-dependent Sian Ka’an wetlands, Mexico, derived from InSAR and SAR data. Wetlands 30(1):1–13. https://doi.org/10.1007/s13157-009-0016-z
Google Earth (2017) SIO, NOAA, U.S. Navy, NGA, GEBCO. Image Landsat / Copernicus. 2017 INEGI. https://www.google.com/earth/. Accessed 26 June 2017
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2016) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ. https://doi.org/10.1016/j.rse.2017.06.031
Groves P (2013) Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech, Norwood, MA
Hall FG (1936) Physical and chemical survey of cenotes of Yucatan. Carnegie Inst. Washington, DC
Hildebrand AR, Penfield GT, Kring DA, Pilkington M, Camargo ZA, Jacobsen SB, Boynton WV (1991) Chicxulub crater: a possible cretaceous/tertiary boundary impact crater on the Yucatán Peninsula, Mexico. Geology 19:867
Hildebrand AR, Pilkington M, Connors M, Ortizaleman C, Chavez RE (1995) Size and structure of the Chicxulub crater revealed by horizontal gravity gradients and cenotes. Nature 376(6539):415–417. https://doi.org/10.1038/376415a0
INEGI (2013) National Geodetic Active Network. http://www.inegi.org.mx/geo/contenidos/geodesia/rgna.aspx?p=22. Accessed 14 April 2017
Jagalingam P, Akshaya BJ, Hegde AV (2015) Bathymetry mapping using Landsat 8 satellite imagery. Procedia Eng 116:560–566
Jerlov NG (1976) Marine optics. Elsevier, Amsterdam
Klemas VV (2015) Coastal and environmental remote sensing from unmanned aerial vehicles: an overview. J Coast Res 315(5):1260–1267. https://doi.org/10.2112/JCOASTRES-D-15-00005.1
Kløve B, Allan A, Bertrand G, Druzynska E, Ertürk A, Goldscheider N, Henry S, Karakaya N, Karjalainen TP, Koundouri P, Kupfersberger H, Kvœrner J, Lundberg A, Muotka T, Preda E, Pulido-Velazquez M, Schipper P (2011) Groundwater dependent ecosystems: part II, ecosystem services and management in Europe under risk of climate change and land use intensification. Environ Sci Pol 14(7):782–793. https://doi.org/10.1016/j.envsci.2011.04.005
Knudsen P, Linden-Vørnle M, Jakobsen J (2015) New drone combines helicopter and aircraft capabilities - DTU. http://www.space.dtu.dk/english/news/Nyhed?id=98e9fed8-3b1f-49f1-ba54-7990ae6a625c. Accessed 25 April 2017
Lane SN, Widdison PE, Thomas RE, Ashworth PJ, Best JL, Lunt IA, Sambrook Smith GH, Simpson CJ (2010) Quantification of braided river channel change using archival digital image analysis. Earth Surf Process Landf 35(8):971–985. https://doi.org/10.1002/esp.2015
Legleiter CJ (2012) Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry. Earth Surf Process Landf 37(5):499–518. https://doi.org/10.1002/esp.2262
Legleiter CJ (2014) A geostatistical framework for quantifying the reach-scale spatial structure of river morphology: 2. application to restored and natural channels. Geomorphology 205:85–101. https://doi.org/10.1016/j.geomorph.2012.01.017
Legleiter CJ, Roberts DA (2005) Effects of channel morphology and sensor spatial resolution on image-derived depth estimates. Remote Sens Environ 95(2):231–247. https://doi.org/10.1016/j.rse.2004.12.013
Legleiter CJ, Roberts DA, Marcus WA, Fonstad MA (2004) Passive optical remote sensing of river channel morphology and in-stream habitat: physical basis and feasibility. Remote Sens Environ 93(4):493–510. https://doi.org/10.1016/j.rse.2004.07.019
Legleiter CJ, Roberts DA, Lawrence RL (2009) Spectrally based remote sensing of river bathymetry. Earth Surf Process Landf 34(8):1039–1059. https://doi.org/10.1002/esp.1787
Lejot J, Delacourt C, Piégay H, Fournier T, Trémélo M-L, Allemand P (2007) Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surf Process Landf 32(11):1705–1725. https://doi.org/10.1002/esp.1595
Lu Z, Kwoun OI (2008) Radarsat-1 and ERS InSAR analysis over southeastern coastal Louisiana: implications for mapping water-level changes beneath swamp forests. IEEE Trans Geosci Remote Sens 46(8):2167–2184. https://doi.org/10.1109/TGRS.2008.917271
Lu Z, Crane M, Kwoun O-I, Wells C, Swarzenski C, Rykhus R (2005) C-band radar observes water level change in swamp forests. EOS Trans Am Geophys Union 86(14):141. https://doi.org/10.1029/2005EO140002
Lyzenga DR (1981) Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int J Remote Sens 2(1):71–82. https://doi.org/10.1080/01431168108948342
Mandlburger G, Pfennigbauer M, Wieser M, Riegl U, Pfeifer N (2016) Evaluation of a novel Uav-borne topo-bathymetric laser profiler. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XLI-B1:933–939. https://doi.org/10.5194/isprs-archives-XLI-B1-933-2016
Marcus WA, Fonstad MA, Legleiter CJ (2012) Management applications of optical remote sensing in the Active River Channel. In: Fluvial remote sensing for science and management. Wiley, Chichester, UK, pp 19–41
Marín LE (1990) Field investigations and numerical simulation of groundwater flow in the karstic aquifer of northwestern Yucatan, Mexico, PhD Thesis, Northern Illinois University, DeKalb, IL
Merediz-Alonso G (2007) Sustainable management of groundwater in Mexico. In: Holliday L, Marin L, Vaux H (eds) Proceedings of a workshop (series: strengthening science-based decision making in developing countries). National Academies Press, Washington, DC
Mishra D, Narumalani S, Lawson M, Rundquist D (2004) Bathymetric mapping using IKONOS multispectral data. GIScience Remote Sens 41(4):301–321. https://doi.org/10.2747/1548-1603.41.4.301
Mohamed H, Negm A, Zahran M, Saavedra OC (2016) Bathymetry determination from high resolution satellite imagery using ensemble learning algorithms in Shallow Lakes: case study El-Burullus Lake. Int J Environ Sci Dev 7(4):295–301. https://doi.org/10.7763/IJESD.2016.V7.787
Monroe WH (1970) A glossary of karst terminology. US Geol Surv Water Suppl Pap 1899-K
Navarro-Mendoza M (1988) Inventario íctico y estudios ecológicos preliminares en los cuerpos de agua continentales en la reserva de la biósfera de Sian Ka’an y áreas circunvecinas en Quintana Roo, México [Ichthyic inventory and preliminary ecological studies in the continental water bodies of the Sian Ka’an biosphere reserve and surrounding areas in Quintana Roo, Mexico]. Tech. report, CIQRO/CONACYT/USFWS, Chetumal, Mexico
Noureldin A, Karamat TB, Georgy J (2013) Fundamentals of inertial navigation, satellite-based positioning and their integration. Springer, Heidelberg, Germany
O’Loughlin FE, Neal J, Yamazaki D, Bates PD (2016) ICESat-derived inland water surface spot heights. Water Resour Res 52(4):3276–3284. https://doi.org/10.1002/2015WR018237
Ohlendorf S, Müller A, Heege T, Cerdeira-Estrada S, Kobryn HT (2011) Bathymetry mapping and sea floor classification using multispectral satellite data and standardized physics-based data processing. Remote Sens Ocean Sea Ice Coast Waters Large Water Reg 2011, 817503. https://doi.org/10.1117/12.898652
Olesen D, Jakobsen J, Knudsen P (2017) Ultra-Tightly Coupled GNSS/INS for small UAVs. In: Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Portland, OR, September 2017
Ore JP, Elbaum S, Burgin A, Detweiler C (2015) Autonomous aerial water sampling. J F Robot 32(8):1095–1113. https://doi.org/10.1002/rob.21591
Pacheco A, Horta J, Loureiro C, Ferreira (2015) Retrieval of nearshore bathymetry from Landsat 8 images: a tool for coastal monitoring in shallow waters. Remote Sens Environ 159: 102–116. https://doi.org/10.1016/j.rse.2014.12.004
Perry E, Marin L, McClain J, Velazquez G (1995) Ring of cenotes (sinkholes), Northwest Yucatan, Mexico: its hydrogeologic characteristics and possible association with the Chicxulub impact crater. Geology 23:17–20
Schiller A, Supper R, Schattauer I, Motschka K, Merediz Alonso G, Lopéz Tamayo A (2017) Advanced airborne electromagnetics for capturing hydrogeological parameters over the coastal karst system of Tulum, Mexico. In: Advances in Karst Science. Springer, Cham, Switzerland, pp 35–43
Schmitter-Soto JJ, Comín FA, Escobar-Briones E, Herrera-Silveira J, Alcocer J, Suárez-Morales E, Elías-Gutiérrez M, Díaz-Arce V, Marín LE, Steinich B (2002) Hydrogeochemical and biological characteristics of cenotes in the Yucatan Peninsula (SE Mexico). Hydrobiologia 467:215–228. https://doi.org/10.1023/A:1014923217206
Schumann GJ-P, Domeneghetti A (2016) Exploiting the proliferation of current and future satellite observations of rivers. Hydrol Process 30(16):2891–2896. https://doi.org/10.1002/hyp.10825
Sharpton V, Dalrymple G, Marín L (1992) New links between the Chicxulub impact structure and the cretaceous/tertiary boundary. Nature 2(3):173–179. https://doi.org/10.1038/359819a0
Sharpton VL, Burke K, Camargo-Zanoguera A, Hall SA, Lee DS, Marin LE, Suaarez-Reynoso G, Quezada-Muneton JM, Spudis PD, Urrutia-Fucugauchi J (1993) Chicxulub multiring impact basin: size and other characteristics derived from gravity analysis. Science 261(5128):1564–1567. https://doi.org/10.1126/science.261.5128.1564
Stumpf RP, Holderied K, Sinclair M (2003) Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol Oceanogr 48(1/2):547–556. https://doi.org/10.4319/lo.2003.48.1_part_2.0547
Tauro F, Porfiri M, Grimaldi S (2016) Surface flow measurements from drones. J Hydrol 540:240–245. https://doi.org/10.1016/j.jhydrol.2016.06.012
Villadsen H, Andersen OB, Stenseng L, Nielsen K, Knudsen P (2015) CryoSat-2 altimetry for river level monitoring: evaluation in the Ganges–Brahmaputra River basin. Remote Sens Environ 168:80–89. https://doi.org/10.1016/j.rse.2015.05.025
Westaway RM, Lane SN, Hicks DM (2000) The development of an automated correction procedure for digital photogrammetry for the study of wide, shallow, gravel-bed rivers. Earth Surf Process Landf 25(2):209–226. https://doi.org/10.1002/(SICI)1096-9837(200002)25:2<209::AID-ESP84>3.0.CO;2-Z
Westaway RM, Lane SN, Hicks DM (2001) Remote sensing of clear-water, shallow, gravel-bed rivers using digital photogrammetry. Photogramm Eng Remote Sens 67(11):1271–1281
Winterbottom SJ, Gilvear DJ (1997) Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography. Regul Rivers Res Manag 13(6):489–499. https://doi.org/10.1002/(SICI)1099-1646(199711/12)13:6<489::AID-RRR471>3.0.CO;2-X
Woodget AS, Carbonneau PE, Visser F, Maddock IP (2015) Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf Process Landf 40(1):47–64. https://doi.org/10.1002/esp.3613
Acknowledgements
The Innovation Fund Denmark is acknowledged for providing funding for this study via the project Smart UAV [125-2013-5]. Amigos de Sian Ka’an A.C is acknowledged for the hospitality and for the help during the flight campaigns. Amigos de Sian Ka’an (http://www.amigosdesiankaan.org/es/) is one of the leading NGOs in Mexico dedicated to environmental conservation and sustainable development of the YP. Without associations such as Amigos de Sian Ka’an A.C., scientific research aimed to promote public policies and preserve the biodiversity and the natural resources of the peninsula would not be possible. In particular, we thank its “Mayan” co-workers Cornelio Baas and Sebastian Cach Chuc for the support and the valuable assistance during the surveys. From Amigos de Sian Ka’an we also thank Liliana Garcia Ramírez for the support and the help during the planning phase and organisation process.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Figure 11 depicts UAV-borne water depth observations retrieved in Laguna Balam Nah, Pucté, cenote K’ux Chúuk, and cenote Vigía Chico.
Rights and permissions
About this article
Cite this article
Bandini, F., Lopez-Tamayo, A., Merediz-Alonso, G. et al. Unmanned aerial vehicle observations of water surface elevation and bathymetry in the cenotes and lagoons of the Yucatan Peninsula, Mexico. Hydrogeol J 26, 2213–2228 (2018). https://doi.org/10.1007/s10040-018-1755-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10040-018-1755-9

