Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Identify the Extreme Rainfall Event for Analysis
2.2. Preprocessing Planet Images
2.3. Classifying Ponding Areas Using Random Forest
2.4. Spatiotemporal Analysis of Ponding Dynamics
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Trenberth, K.E. Conceptual framework for changes of extremes of the hydrological cycle with climate change. Weather Clim. Extremes 1999, 42, 327–339. [Google Scholar]
- Groisman, P.Y.; Knight, R.W.; Karl, T.R. Heavy precipitation and high streamflow in the contiguous United States: Trends in the twentieth century. Bull. Am. Meteorol. Soc. 2001, 82, 219–246. [Google Scholar] [CrossRef] [Green Version]
- Kunkel, K.E. North American trends in extreme precipitation. Nat. Hazards 2003, 29, 291–305. [Google Scholar] [CrossRef]
- Wobus, C.; Lawson, M.; Jones, R.; Smith, J.; Martinich, J. Estimating monetary damages from flooding in the United States under a changing climate: Climate change and damaging floods. J. Flood Risk Manag. 2014, 7, 217–229. [Google Scholar] [CrossRef]
- Catalán, N.; Schiller, D.V.; Marcé, R.; Koschorreck, M.; Gomez-gener, L.; Obrador, B. Carbon Dioxide Efflux During the Flooding Phase of Temporary Ponds. Limnetica 2014, 33, 349–360. [Google Scholar]
- Borken, W.; Davidson, E.A.; Savage, K.; Gaudinski, J.; Trumbore, S.E. Drying and Wetting Effects on Carbon Dioxide Release from Organic Horizons. Soil Sci. Soc. Am. J. 2003, 67, 1888–1896. [Google Scholar] [CrossRef] [Green Version]
- Borken, W.; Matzner, E. Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Glob. Change Biol. 2009, 15, 808–824. [Google Scholar] [CrossRef]
- Angel, R.; Claus, P.; Conrad, R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 2012, 6, 847–862. [Google Scholar] [CrossRef] [Green Version]
- Janssen, E.; Wuebbles, D.J.; Kunkel, K.E.; Olsen, S.C.; Goodman, A. Observational- and model-based trends and projections of extreme precipitation over the contiguous United States. Earth’s Future 2014, 2, 99–113. [Google Scholar] [CrossRef]
- Kelly, S.A.; Takbiri, Z.; Belmont, P.; Foufoula-Georgiou, E. Human amplified changes in precipitation–runoff patterns in large river basins of the Midwestern United States. Hydrol. Earth Syst. Sci. 2017, 21, 5065–5088. [Google Scholar] [CrossRef] [Green Version]
- Peacock, S. Projected Twenty-First-Century Changes in Temperature, Precipitation, and Snow Cover over North America in CCSM4. J. Clim. 2012, 25, 4405–4429. [Google Scholar] [CrossRef]
- Córdoba, M.A.; Bruno, C.I.; Costa, J.L.; Peralta, N.R.; Balzarini, M.G. Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosyst. Eng. 2016, 143, 95–107. [Google Scholar] [CrossRef]
- Davis, S.C.; Parton, W.J.; Grosso, S.J.D.; Keough, C.; Marx, E.; Adler, P.R.; DeLucia, E.H. Impact of second-generation biofuel agriculture on greenhouse-gas emissions in the corn-growing regions of the US. Front. Ecol. Environ. 2012, 10, 69–74. [Google Scholar] [CrossRef] [Green Version]
- Kovacic, D.A.; David, M.B.; Gentry, L.E.; Starks, K.M.; Cooke, R.A. Effectiveness of Constructed Wetlands in Reducing Nitrogen and Phosphorus Export from Agricultural Tile Drainage. J. Environ. Qual. 2000, 29, 1262–1274. [Google Scholar] [CrossRef] [Green Version]
- Blann, K.L.; Anderson, J.L.; Sands, G.R.; Vondracek, B. Effects of Agricultural Drainage on Aquatic Ecosystems: A Review. Crit. Rev. Environ. Sci. Technol. 2009, 39, 909–1001. [Google Scholar] [CrossRef]
- Fausey, N.R.; Brown, L.C.; Belcher, H.W.; Kanwar, R.S. Drainage and Water Quality in Great Lakes and Cornbelt States. J. Irrig. Drain. Eng. 1995, 121, 283–288. [Google Scholar] [CrossRef] [Green Version]
- McCorvie, M.R.; Lant, C.L. Drainage District Formation and the Loss of Midwestern Wetlands, 1850–1930. Agric. Hist. 1993, 67, 13–39. [Google Scholar]
- Jia, K.; Liang, S.; Zhang, N.; Wei, X.; Gu, X.; Zhao, X.; Yao, Y.; Xie, X. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS J. Photogramm. Remote Sens. 2014, 93, 49–55. [Google Scholar] [CrossRef]
- Kennedy, R.E.; Townsend, P.A.; Gross, J.E.; Cohen, W.B.; Bolstad, P.; Wang, Y.Q.; Adams, P. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens. Environ. 2009, 113, 1382–1396. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Roy, D.P.; Wulder, M.A.; Loveland, T.R.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Helder, D.; Irons, J.R.; Johnson, D.M.; Kennedy, R.; et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef] [Green Version]
- Salomonson, V.V.; Barnes, W.; Xiong, J.; Kempler, S.; Masuoka, E. An overview of the Earth Observing System MODIS instrument and associated data systems performance. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada, 24–28 June 2002; Volume 2, pp. 1174–1176. [Google Scholar]
- Hillger, D.; Kopp, T.; Lee, T.; Lindsey, D.; Seaman, C.; Miller, S.; Solbrig, J.; Kidder, S.; Bachmeier, S.; Jasmin, T.; et al. First-Light Imagery from Suomi NPP VIIRS. Bull. Am. Meteorol. Soc. 2013, 94, 1019–1029. [Google Scholar] [CrossRef]
- Marta, S. Planet Imagery Product Specifications; Planet Labs Inc.: San Francisco, CA, USA, 2018; pp. 1–98. [Google Scholar]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Planet Team. Planet Application Program Interface: In Space for Life on Earth; Planet Labs Inc.: San Francisco, CA, USA, 2017. [Google Scholar]
- Midwestern Regional Climate Center. cli-MATE: MRCC Application Tools Environment; Illinois State Water Survey; Prairie Research Institute, University of Illinois: Champaign, IL, USA, 2015. [Google Scholar]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Boryan, C.; Yang, Z.; Mueller, R.; Craig, M. Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto Int. 2011, 26, 341–358. [Google Scholar] [CrossRef]
- Leutner, B.; Horning, N.; Schwalb-Willmann, J. RStoolbox: Tools for Remote Sensing Data Analysis; The Earth Observation Center (EOC) at the German Aerospace Center (DLR): Oberpfaffenhofen, Germany, 2019. [Google Scholar]
- Kuhn, M. Building Predictive Models in R Using the caret Package. J. Stat. Soft. 2008, 28, 1–26. [Google Scholar] [CrossRef] [Green Version]
- QGIS Development Team. QGIS Geographic Information System; Open Source Geospatial Foundation: Beaverton, OR, USA, 2018. [Google Scholar]
- Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 1960, 20, 37–46. [Google Scholar] [CrossRef]
- Jones, E.; Oliphant, T.; Peterson, P. SciPy: Open Source Scientific Tools for Python. Available online: http://www.scipy.org/ (accessed on 19 September 2017).
- Peter, L.; Matjaž, M.; Krištof, O. Detection of Flooded Areas using Machine Learning Techniques: Case Study of the Ljubljana Moor Floods in 2010. Disaster Adv. 2013, 6, 4–11. [Google Scholar]
- Malinowski, R.; Groom, G.; Schwanghart, W.; Heckrath, G. Detection and Delineation of Localized Flooding from WorldView-2 Multispectral Data. Remote Sens. 2015, 7, 14853–14875. [Google Scholar] [CrossRef] [Green Version]
- Heine, I.; Stüve, P.; Kleinschmit, B.; Itzerott, S. Reconstruction of Lake Level Changes of Groundwater-Fed Lakes in Northeastern Germany Using RapidEye Time Series. Water 2015, 7, 4175–4199. [Google Scholar] [CrossRef] [Green Version]
- Naz, B.S.; Ale, S.; Bowling, L.C. Detecting subsurface drainage systems and estimating drain spacing in intensively managed agricultural landscapes. Agric. Water Manag. 2009, 96, 627–637. [Google Scholar] [CrossRef]
- Ju, J.; Roy, D.P. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sens. Environ. 2008, 112, 1196–1211. [Google Scholar] [CrossRef]
- Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; Potin, P.; Rommen, B.; Floury, N.; Brown, M.; et al. GMES Sentinel-1 mission. Remote Sens. Environ. 2012, 120, 9–24. [Google Scholar] [CrossRef]
- Cook, K.H.; Vizy, E.K.; Launer, Z.S.; Patricola, C.M. Springtime Intensification of the Great Plains Low-Level Jet and Midwest Precipitation in GCM Simulations of the Twenty-First Century. J. Clim. 2008, 21, 6321–6340. [Google Scholar] [CrossRef]
- Feng, Z.; Leung, L.R.; Hagos, S.; Houze, R.A.; Burleyson, C.D.; Balaguru, K. More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun. 2016, 7, 13429. [Google Scholar] [CrossRef] [Green Version]
- Joo, E.; Hussain, M.Z.; Zeri, M.; Masters, M.D.; Miller, J.N.; Gomez-Casanovas, N.; DeLucia, E.H.; Bernacchi, C.J. The influence of drought and heat stress on long-term carbon fluxes of bioenergy crops grown in the Midwestern USA. Plant Cell Environ. 2016, 39, 1928–1940. [Google Scholar] [CrossRef]
- McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76–87. [Google Scholar] [CrossRef] [Green Version]
- Perks, M.T.; Russell, A.J.; Large, A.R.G. Technical Note: Advances in flash flood monitoring using unmanned aerial vehicles (UAVs). Hydrol. Earth Syst. Sci. 2016, 20, 4005–4015. [Google Scholar] [CrossRef] [Green Version]
- Turner, D.; Lucieer, A.; Watson, C. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sens. 2012, 4, 1392–1410. [Google Scholar] [CrossRef] [Green Version]
- McClain, M.E.; Boyer, E.W.; Dent, C.L.; Gergel, S.E.; Grimm, N.B.; Groffman, P.M.; Hart, S.C.; Harvey, J.W.; Johnston, C.A.; Mayorga, E.; et al. Biogeochemical Hot Spots and Hot Moments at the Interface of Terrestrial and Aquatic Ecosystems. Ecosystems 2003, 6, 301–312. [Google Scholar] [CrossRef]
- De-Campos, A.B.; Huang, C.; Johnston, C.T. Biogeochemistry of terrestrial soils as influenced by short-term flooding. Biogeochemistry 2012, 111, 239–252. [Google Scholar] [CrossRef]
- De-Campos, A.B.; Mamedov, A.I.; Huang, C. Short-Term Reducing Conditions Decrease Soil Aggregation. Soil Sci. Soc. Am. J. 2009, 73, 550. [Google Scholar] [CrossRef]
- Arduino, E.; Barberis, E.; Boero, V. Iron oxides and particle aggregation in B horizons of some Italian soils. Geoderma 1989, 45, 319–329. [Google Scholar] [CrossRef]
- Duiker, S.W.; Rhoton, F.E.; Torrent, J.; Smeck, N.E.; Lal, R. Iron (Hydr)Oxide Crystallinity Effects on Soil Aggregation. Soil Sci. Soc. Am. J. 2003, 67, 606–611. [Google Scholar] [CrossRef]
- Thompson, A.; Chadwick, O.A.; Boman, S.; Chorover, J. Colloid Mobilization During Soil Iron Redox Oscillations. Environ. Sci. Technol. 2006, 40, 5743–5749. [Google Scholar] [CrossRef]
- Krichels, A.; DeLucia, E.H.; Sanford, R.; Chee-Sanford, J.; Yang, W.H. Historical soil drainage mediates the response of soil greenhouse gas emissions to intense precipitation events. Biogeochemistry 2019, 142, 425–442. [Google Scholar] [CrossRef]
Date | Overall Accuracy | 95% CI | No Information Rate | Sensitivity | Specificity | Kappa |
---|---|---|---|---|---|---|
2017-05-02 | 0.9982 | (0.9973, 0.9989) | 0.9548 | 0.96063 | 1.00000 | 0.979 |
2017-05-05 | 0.9834 | (0.9824, 0.9843) | 0.9399 | 0.95066 | 0.98548 | 0.8642 |
2017-05-06 | 0.9907 | (0.9897, 0.9916) | 0.9083 | 0.92032 | 0.99779 | 0.9426 |
2017-05-07 | 0.9985 | (0.9981, 0.9988) | 0.9156 | 0.99978 | 0.99840 | 0.9905 |
2017-05-09 | 0.9738 | (0.9724, 0.975) | 0.9195 | 0.83919 | 0.98554 | 0.823 |
2017-05-10 | 0.9958 | (0.995, 0.9965) | 0.9794 | 0.93651 | 0.99702 | 0.8993 |
2017-05-13 | 0.9901 | (0.9884, 0.9917) | 0.9288 | 0.86162 | 1.00000 | 0.9204 |
2017-05-14 | 0.9971 | (0.9967, 0.9974) | 0.951 | 0.97559 | 0.99821 | 0.969 |
2017-05-15 | 1 | (0.9999, 1) | 0.9417 | 0.99928 | 1.00000 | 0.9996 |
© 2020 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 (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Paul, R.F.; Cai, Y.; Peng, B.; Yang, W.H.; Guan, K.; DeLucia, E.H. Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images. Remote Sens. 2020, 12, 1942. https://doi.org/10.3390/rs12121942
Paul RF, Cai Y, Peng B, Yang WH, Guan K, DeLucia EH. Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images. Remote Sensing. 2020; 12(12):1942. https://doi.org/10.3390/rs12121942
Chicago/Turabian StylePaul, Robert F., Yaping Cai, Bin Peng, Wendy H. Yang, Kaiyu Guan, and Evan H. DeLucia. 2020. "Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images" Remote Sensing 12, no. 12: 1942. https://doi.org/10.3390/rs12121942