Abstract
During the first two weeks of July 2003, heavy precipitation occurred across the northern and central portions of Indiana, resulting in flooding and ponded water that damaged crops. Landsat 5 Thematic Mapper images were used to identify the level of damage in fields. A supervised classification and temporal change detection were performed with the help of ERDAS Imagine. To examine the recovery rate of crops over time, two methods were used: a change detection matrix and Delta Normalized Difference Vegetation Index. Both methods indicated an improvement in the conditions of the crops two weeks after the end of the heavy precipitation. Correlations between precipitation, crop damage, yield and unharvested area were weak. At the end of the season, the damage caused by flooding and excess precipitation did not greatly affect the yield of crops, especially corn. Soybeans suffered slightly from these rainfall events, and their yield was smaller than in previous years.
Similar content being viewed by others
References
Benedetti R, Rossini P (1993) On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens Environ 45:311–326
FEMA (2003) July storms recovery assistance nears $30 million for Indiana Residents, information on federally declared disasters. Released: September 4, 2003, release number: 1476–1445
Hayes JT, O’Rourke PA, Terjung WE, Todhunter PE (1982) YIELD: a numerical crop yield model of irrigated and rainfed agriculture. Publications in Climatology p 35
Indiana Crop and Weather Report (2003) Crop Report for Week Ending July 13, vol 53. No. 28, Released July 14, 2003
Jensen JR (2000) Remote sensing of the environment, an earth resource perspective. Prentice Hall, Upper Saddle River, NJ, p 194
Landis J, Koch G (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174
Lillesand TM, Kiefer RW, Chipman JW (2004) Remote Sensing and Image Interpretation, 5th ed. John Wiley & Sons, New York, NY, pp 763
Mackey, HE (1990) Monitoring seasonal and annual wetland changes in a freshwater marsh with SPOT HRV data. In: American Society for Photogrammetry and Remote Sensing Proceedings 4, 283–292
Midwestern Regional Climate Center (2003) Climate Report for the Midwestern Region – July 2003. Midwest Regional Climate Center, Champaign, IL
Morton TW, Buchleiter GW, Heermann DF (2000) Quantifying the effect of water availability on corn yield under a center pivot irrigation system. In: Second International on Geospatial Information in Agriculture and Forestry Conference Proceedings (Lake Buena Vista, Florida)
Myers MF (1997) Trends in Floods. In: Pielke RA (ed) Workshop on the Social and Economic Impacts of Weather Proceedings, National Center for Atmospheric Research, Boulder, CO, p 77–86
Prasad VK, Chai L, Singh RP, Kafatos M (2005) Crop yield estimation model for Iowa using remote sensing and surface parameters. In Press in: Int J App Earth Observ Geoinform
Quarmby NA, Milnes M, Hindle TL, Silicos N (1993) The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int J Remote Sens 14:199–210
Thenkabail PS (2003) Quantitative biophysical and yield information for precision farming from Near-Real time and historical Landsat TM images. Int J Remote Sens 24(14):2879–2904
Thenkabail SP, Nolte C (1996) Capabilities of Landsat-5 Thematic Mapper (TM) data in regional mapping and characterization of inland valley agroecosystems in West Africa. Int J Remote Sens 17(8):1505–1538
Uchida S (2001) Sub-pixel classification of land use temporal profile of NDVI. J Jpn Soc Photogram Remote Sens 40(1):43–54
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pantaleoni, E., Engel, B.A. & Johannsen, C.J. Identifying agricultural flood damage using Landsat imagery. Precision Agric 8, 27–36 (2007). https://doi.org/10.1007/s11119-006-9026-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-006-9026-5