Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Workflow
3. Results
3.1. Relationships between Variables and Mathematical Models
3.2. Estimated Errors
3.3. Applicability of Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Lavado Contador, J.F.; Schnabel, S.; Gómez Gutiérrez, Á.; Pulido Fernández, M. Mapping sensitivity to land degradation in Extremadura. SW Spain. Land Degrad. Dev. 2009, 20, 129–144. [Google Scholar] [CrossRef]
- Pulido Diaz, F.; Picardo, A. Libro Verde de la Dehesa; Junta de Castilla y León, SECF, SEEP, AEET, SEO, Virtual Book; Consejería de Medio Ambiente, Junta Castilla La Mancha: Toledo, Spain, 2010. [Google Scholar]
- Plieninger, T. Habitat loss, fragmentation, and alteration-Quantifying the impact of land-use changes on a Spanish dehesa landscape by use of aerial photography and GIS. Landsc. Ecol. 2006, 21, 91–105. [Google Scholar] [CrossRef]
- Marín Comitre, U. Evaluación de los Recursos Hídricos en Explotaciones de Ganadería Extensiva del Suroeste Ibérico. Ph.D. Thesis, Universidad de Extremadura, Cáceres, Spain, 2022. [Google Scholar]
- Abdennour, M.A.; Lavado Contador, J.F.; Barrena González, J.; Piccini, C.; Gabourel Landaverde, A.; Pulido Fernández, M. Characterization of the water bodies of Extremadura (SW Spain). Environ. Monit. Assess. 2023, 195, 564. [Google Scholar] [CrossRef]
- Marín-Comitre, U.; Schnabel, S.; Pulido-Fernández, M. Hydrological characterization of watering ponds in rangeland farms in the Southwest Iberian Peninsula. Water 2020, 12, 1038. [Google Scholar] [CrossRef]
- Tramblay, Y.; Koutroulis, A.; Samaniego, L.; Vicente-Serrano, S.M.; Volaire, F.; Boone, A.; Le Page, M.; Llasat, M.C.; Albergel, C.; Burak, S. Challenges for drought assessment in the Mediterranean region under future climate scenarios. Earth-Sci. Rev. 2020, 210, 103348. [Google Scholar] [CrossRef]
- Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
- Schnabel, S.; Gómez-Gutiérrez, A. The role of interannual rainfall variability on runoff generation in a small dry sub-humid watershed with disperse tree cover. Cuad. Investig. Geogr. 2013, 39, 259–285. [Google Scholar] [CrossRef]
- Zhang, X. Impacts of Water Resources Management on Land Water Storage in the Lower Lancang River Basin: Insights from Multi-Mission Earth Observations. Remote Sens. 2023, 15, 1747. [Google Scholar] [CrossRef]
- Marín-Comitre, U.; Gómez-Gutiérrez, Á.; Lavado-Contador, F.; Sánchez-Fernández, M.; Alfonso-Torreño, A. Using geomatic techniques to estimate volume–area relationships of watering ponds. ISPRS Int. J. Geo-Inf. 2021, 10, 502. [Google Scholar] [CrossRef]
- O’Connor, D.J. Seasonal and long-term variations of dissolved solids in lakes and reservoirs. J. Environ. Eng. 1989, 115, 1213–1234. [Google Scholar] [CrossRef]
- Wise, W.R.; Annable, M.D.; Walser, J.A.E.; Switt, R.S.; Shaw, D.T. A wetland–aquifer interaction test. J. Hydrol. 2000, 227, 257–272. [Google Scholar] [CrossRef]
- Trigg, M.A.; Cook, P.G.; Brunner, P. Groundwater fluxes in a shallow seasonal wetland pond: The effect of bathymetric uncertainty on predicted water and solute balances. J. Hydrol. 2014, 517, 901–912. [Google Scholar] [CrossRef]
- Mishra, V.; Cherkauer, K.A.; Bowling, L.C. Parameterization of lakes and wetlands for energy and water balance studies in the great lakes region. J. Hydrometeorol. 2010, 11, 1057–1082. [Google Scholar] [CrossRef]
- Luke, G.J. Consumption of Water by Livestock; Department of Primary Industries and Regional Development: Perth, Australia, 1987.
- Halmai, Á.; Gradwohl-Valkay, A.; Czigány, S.; Ficsor, J.; Liptay, Z.Á.; Kiss, K.; Lóczy, D.; Pirkhoffer, E. Applicability of a recreational-grade interferometric sonar for the bathymetric survey and monitoring of the Drava River. ISPRS Int. J. Geo-Inf. 2020, 9, 149. [Google Scholar] [CrossRef]
- Oliveira Ferreira, I.; Coelho de Andrade, L.; Gibrim Teixeira, V.; Catão Mesquita Santos, F. State of art of bathymetric surveys. Bol. Cienc. Geod. 2022, 28, e2022002. [Google Scholar] [CrossRef]
- Bailly, J.S.; Le Coarer, Y.; Languille, P.; Stigermark, C.J.; Allouis, T. Geostatistical estimations of bathymetric LiDAR errors on rivers. Earth Surf. Process. Landf. 2010, 35, 1199–1210. [Google Scholar] [CrossRef]
- Zhang, X.; Jiang, L.; Kittel, C.M.M.; Yao, Z.; Nielsen, K.; Liu, Z.; Wang, R.; Liu, J.; Andersen, O.B.; Bauer-Gottwein, P. On the Performance of Sentinel-3 Altimetry Over New Reservoirs: Approaches to Determine Onboard A Priori Elevation. Adv. Eatrh Space Sci. 2020, 47, e2020GL088770. [Google Scholar] [CrossRef]
- Zhang, X.; Jiang, L.; Liu, Z.; Kittel, C.M.; Yao, Z.; Druce, D.; Wang, R.; Tøttrup, C.; Liu, J.; Jiang, H. Flow regime changes in the Lancang River, revealed by integrated modeling with multiple Earth observation datasets. Sci. Total Environ. 2023, 862, 160656. [Google Scholar] [CrossRef]
- Smith, M.; Vericat, D.; Gibbins, C. Through-water terrestrial laser scanning of gravel beds at the patch scale. Earth Surf. Process. Landf. 2012, 37, 411–421. [Google Scholar] [CrossRef]
- Taddia, Y.; Stecchi, F.; Pellegrinelli, A. Coastal mapping using DJI Phantom 4 RTK in post-processing kinematic mode. Drones 2020, 4, 9. [Google Scholar] [CrossRef]
- Štroner, M.; Urban, R.; Seidl, J.; Reindl, T.; Brouček, J. Photogrammetry using UAV-mounted GNSS RTK: Georeferencing strategies without GCPs. Remote Sens. 2021, 13, 1336. [Google Scholar] [CrossRef]
- Eker, R.; Alkan, E.; Aydın, A. A comparative analysis of UAV-RTK and UAV-PPK methods in mapping different surface types. Eur. J. For. Eng. 2021, 7, 12–25. [Google Scholar] [CrossRef]
- Marín-Comitre, U.; Schnabel, S.; Barrena-González, J.; Pulido-Fernández, M. Pond water quality for livestock in southwestern Iberian rangelands. Rangel. Ecol. Manag. 2022, 83, 31–40. [Google Scholar] [CrossRef]
- Pulido-Fernández, M.; Schnabel, S.; Lavado-Contador, J.F.; Miralles Mellado, I.; Ortega Pérez, R. Soil organic matter of Iberian open woodland rangelands as influenced by vegetation cover and land management. Catena 2013, 109, 13–24. [Google Scholar] [CrossRef]
- REDcatch-GmbH REDtoolbox, 3.1. 2014. Available online: https://www.redcatch.at/redtoolbox/ (accessed on 25 August 2024).
- PIX4D Pix4Dmapper Pro, 4.5. 2022. Available online: https://www.pix4d.com/es/producto/pix4dmapper-fotogrametria-software/ (accessed on 25 August 2024).
- Girardeau-Montaut, D. Cloud Compare, 2.12; France. 2022. Available online: https://www.danielgm.net/cc/ (accessed on 25 August 2024).
- Watson, R.; Baste, I.; Larigauderie, A.; Leadley, P.; Pascual, U.; Baptiste, B.; Demissew, S.; Dziba, L.; Erpul, G.; Fazel, A. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; IPBES Secretariat: Bonn, Germany, 2019; pp. 22–47. [Google Scholar]
- Gonzalo Langa, J. El impacto de la aplicación de la PAC en las producciones ganaderas de la Dehesa (1986–2010). In La Agricultura y la Ganadería Extremeñas: Informe 2010; Coleto Martínez, J.M., de Muslera Pardo, E., González Blanco, R., Pulido García, F., Eds.; Caja de Ahorros de Badajoz: Badajoz, Spain, 2011; pp. 181–196. [Google Scholar]
- Benayas, J.R.; Martins, A.; Nicolau, J.M.; Schulz, J.J. Abandonment of agricultural land: An overview of drivers and consequences. CAB reviews: Perspectives in agriculture, veterinary science, nutrition and natural resources. CABI Rev. 2007, 2, 1–14. [Google Scholar]
- Calheiros, T.; Pereira, M.; Nunes, J.P. Assessing impacts of future climate change on extreme fire weather and pyro-regions in Iberian Peninsula. Sci. Total Environ. 2021, 754, 142233. [Google Scholar] [CrossRef] [PubMed]
- Céréghino, R.; Boix, D.; Cauchie, H.-M.; Martens, K.; Oertli, B. The ecological role of ponds in a changing world. Hydrobiologia 2014, 723, 1–6. [Google Scholar] [CrossRef]
- Duesterhaus, J.; Ham, J.; Owensby, C.; Murphy, J. Water balance of a stock-watering pond in the Flint Hills of Kansas. Rangel. Ecol. Manag. 2008, 61, 329–338. [Google Scholar] [CrossRef]
- Pulido Fernández, M.; Schnabel, S. La disponibilidad de agua en explotaciones de ganadería extensiva. In Aportaciones a la Geografía Física de Extremadura; Schnabel, S., Lavado Contador, J.F., Gómez Gutiérrez, Á., García Marín, R., Eds.; Fundicotex: Cáceres, Spain, 2010; pp. 220–234. [Google Scholar]
- Lu, S.; Ouyang, N.; Wu, B.; Wei, Y.; Tesemma, Z. Lake water volume calculation with time series remote-sensing images. Int. J. Remote Sens. 2013, 34, 7962–7973. [Google Scholar] [CrossRef]
Pond | Spillway Level (m) | Minimum Elevation (m) | Height (m) | Interval (cm) | Intervals (n) |
---|---|---|---|---|---|
PAR6 | 381.15 | 379.25 | 1.9 | 10 | 19 |
PAR4 | 401.56 | 398.46 | 3.1 | 10 | 31 |
BAR6 | 314.10 | 310.70 | 3.45 | 10 | 34 |
BRA8 | 316.65 | 312.35 | 4.35 | 20 | 23 |
Acquisition Date | Spatial Resolution (m) | Source |
---|---|---|
1984 | 0.5 | CNIG |
2005 | 0.5 | CNIG |
2007 | 0.5 | CNIG |
2009 | Unknown | Google Earth |
2010 | Unknown | Google Earth |
2011 | 0.5 | CNIG |
2013 | 0.5 | CNIG |
2015 | Unknown | Google Earth |
2016 | 0.5 | CNIG |
2017 | Unknown | Google Earth |
2018 | Unknown | Google Earth |
2019 | 0.5 | CNIG |
2020 | Unknown | Google Earth |
2021 | Unknown | Google Earth |
Pond | V Max (m3) | A Max (m2) | h Max (m) |
---|---|---|---|
Par6 | 771.4 | 860.7 | 1.9 |
Par4 | 5401.2 | 5033.9 | 3.1 |
Bra8 | 6653.6 | 2511.7 | 3.4 |
Bar6 | 2270.2 | 1745.5 | 4.3 |
P4* | 2157.6 | 1957.6 | 2.5 |
B6* | 3350.7 | 2005.5 | 3.4 |
B8* | 2282.0 | 1711.3 | 2.5 |
P3* | 3635.3 | 3392.7 | 2.8 |
P1* | 1680.1 | 1764.7 | 2.5 |
P5* | 4978.0 | 4916.4 | 3.1 |
B3* | 5439.9 | 2923.4 | 3.3 |
Power Functions | Quadratic Functions | |||||
---|---|---|---|---|---|---|
Pond | Equation | R2 | RMSE (%) | Equation | R2 | RMSE (%) |
V-A relationship | ||||||
Par6 | y = 0.0011x1.9968 | 0.997 | 1.71 | y = 0.0009x2 + 0.1219x − 13.186 | 0.999 | 2.06 |
Par4 | y = 0.0036x1.7038 | 0.984 | 9.61 | y = 0.00009x2 + 0.7028x −165.44 | 0.996 | 1.92 |
Bra8 | y = 0.0002x2.1894 | 0.967 | 5.99 | y = 8 × 10−7x3 − 0.0016x2 + 1.6942x − 209.25 | 0.994 | 3.93 |
Bar6 | y = 0.0449x1.4057 | 0.972 | 10.26 | y = 0.0008x2 − 0.063x + 20.355 | 0.998 | 1.55 |
V-h relationship | ||||||
Par6 | y = 195.95x2.3368 | 0.997 | 4.98 | y = 223.93x2 − 21.151x + 0.3621 | 1.000 | 0.16 |
Par4 | y = 367.8x2.2055 | 0.994 | 5.12 | y = 756.09x2 − 741.96x + 223.18 | 0.998 | 1.57 |
Bra8 | y = 420.93x1.9136 | 0.999 | 1.59 | y = 296.9x2 + 291.73x − 122.59 | 0.999 | 0.76 |
Bar6 | y = 33.264x3.6366 | 0.987 | 8.96 | y = 303.98x2 − 433.71x + 130.63 | 0.999 | 1.12 |
A-h relationship | ||||||
Par6 | y = 421x1.1704 | 0.998 | 2.44 | y = 18.796x2 + 417.95x − 13.286 | 0.999 | 0.91 |
Par4 | y = 847.26x1.3545 | 0.979 | 6.12 | y = 457.7x2 + 98.615x + 128.74 | 0.997 | 1.53 |
Bra8 | y = 797.88x0.8695 | 0.981 | 4.91 | y = −88.807x2 + 983.1x − 50.579 | 0.998 | 1.35 |
Bar6 | y = 111.56x2.5498 | 0.905 | 17.06 | y = −71.174x3 + 467.31x2 − 295.75x + 47.039 | 0.996 | 1.94 |
Power Functions | Quadratic Functions | |||
---|---|---|---|---|
Parameters | Equation | R2 | Equation | R2 |
V-A | y = 0.0093x1.6112 | 0.75 | y = −0.00003x2 + 1.3416x − 356.86 | 0.81 |
V-h | y = 210.87x2.5744 | 0.82 | y = 373.77x2 − 11.259x − 60.104 | 0.84 |
A-h | y = 515.67x1.4813 | 0.66 | y = −47.233x2 1035.6x − 275.29 | 0.69 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Castaño-Martín, F.M.; Gómez-Gutiérrez, Á.; Pulido-Fernández, M. Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems. Remote Sens. 2024, 16, 3257. https://doi.org/10.3390/rs16173257
Castaño-Martín FM, Gómez-Gutiérrez Á, Pulido-Fernández M. Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems. Remote Sensing. 2024; 16(17):3257. https://doi.org/10.3390/rs16173257
Chicago/Turabian StyleCastaño-Martín, Francisco Manuel, Álvaro Gómez-Gutiérrez, and Manuel Pulido-Fernández. 2024. "Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems" Remote Sensing 16, no. 17: 3257. https://doi.org/10.3390/rs16173257