loss assessment models. Utilization band ratioing methods for flood crop damage
assessment in cropland could address the shortcoming of classification-based counterparts. By deriving the ratio of multiple spectral bands (mainly the ones sensitive to
crop covers) within a single image, various indices (also known as Vegetation
Indices) could be developed to measure the greenness (health) of the crop. The
changes of these indices before and after the flood events are then utilized to quantify
the damages in crop productivity. Among the various indices available to employ in
crop damage assessments using remotely sensed data, NDVI is considered to be the
primary index to be exercised. As NDVI directly reflects the greenness of the
vegetation, a regression model could be established with historical NDVI value
and crop yield. Once these models are set up, they could be used to estimate the
changes in crop yield depending on how NDVI is altered after flooding events.
The case study was presented in this chapter assessing corn loss in parts of
Nebraska and Missouri due to the 2011 Mississippi/Missouri flood by using an
NDVI-based regression model. The achieved result illustrated a high correlation
between the NDVI and corn yield. The developed ASD-based models with NDVI as
independent variables and corn yield as a dependent variable showed a statistically
significant coefficient of deterministic with R
2 as high as 0.92. This suggests that
92% of the time, the corn yield can be explained by its corresponding NDVI.
Furthermore, the model’s predictability result in loss assessment also showed highly
accurate results. In Missouri State, the average percentage difference between
observed and calculated result was 3.81%, whereas, in Nebraska State, it was only
2.88%. Even though the case study was performed in only one flood event and to a
smaller geographic extent, the results suggest the method could be extended to a
larger scale with similar outcomes.
References
Al-Sabhan, W., Mulligan, M., & Blackburn, G. A. (2003). A real-time hydrological model for flood
prediction using GIS and the WWW. Computers, Environment and Urban Systems, 27, 9–32.
Balaghi, R., Tychon, B., Eerens, H., & Jlibene, M. (2008). Empirical regression models using
NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco.
International Journal of Applied Earth Observation and Geoinformation, 10, 438–452. https://
doi.org/10.1016/j.jag.2006.12.001.
Bazi, Y., Bruzzone, L., & Melgani, F. (2005). An unsupervised approach based on the generalized
Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43, 874–887.
Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data.
Remote Sensing of Environment, 114, 1312–1323. https://doi.org/10.1016/j.rse.2010.01.010.
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 Sensing of
Environment, 45, 311–326. https://doi.org/10.1016/0034-4257(93)90113-C.
Beven, K. J., Kirkby, M. J., Schofield, N., & Tagg, A. F. (1984). Testing a physically-based flood
forecasting model (TOPMODEL) for three UK catchments. Journal of Hydrology, 69, 119–143.
16 Flood Monitoring and Crop Damage Assessment
345
assessment in cropland could address the shortcoming of classification-based counterparts. By deriving the ratio of multiple spectral bands (mainly the ones sensitive to
crop covers) within a single image, various indices (also known as Vegetation
Indices) could be developed to measure the greenness (health) of the crop. The
changes of these indices before and after the flood events are then utilized to quantify
the damages in crop productivity. Among the various indices available to employ in
crop damage assessments using remotely sensed data, NDVI is considered to be the
primary index to be exercised. As NDVI directly reflects the greenness of the
vegetation, a regression model could be established with historical NDVI value
and crop yield. Once these models are set up, they could be used to estimate the
changes in crop yield depending on how NDVI is altered after flooding events.
The case study was presented in this chapter assessing corn loss in parts of
Nebraska and Missouri due to the 2011 Mississippi/Missouri flood by using an
NDVI-based regression model. The achieved result illustrated a high correlation
between the NDVI and corn yield. The developed ASD-based models with NDVI as
independent variables and corn yield as a dependent variable showed a statistically
significant coefficient of deterministic with R
2 as high as 0.92. This suggests that
92% of the time, the corn yield can be explained by its corresponding NDVI.
Furthermore, the model’s predictability result in loss assessment also showed highly
accurate results. In Missouri State, the average percentage difference between
observed and calculated result was 3.81%, whereas, in Nebraska State, it was only
2.88%. Even though the case study was performed in only one flood event and to a
smaller geographic extent, the results suggest the method could be extended to a
larger scale with similar outcomes.
References
Al-Sabhan, W., Mulligan, M., & Blackburn, G. A. (2003). A real-time hydrological model for flood
prediction using GIS and the WWW. Computers, Environment and Urban Systems, 27, 9–32.
Balaghi, R., Tychon, B., Eerens, H., & Jlibene, M. (2008). Empirical regression models using
NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco.
International Journal of Applied Earth Observation and Geoinformation, 10, 438–452. https://
doi.org/10.1016/j.jag.2006.12.001.
Bazi, Y., Bruzzone, L., & Melgani, F. (2005). An unsupervised approach based on the generalized
Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43, 874–887.
Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regressionbased model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data.
Remote Sensing of Environment, 114, 1312–1323. https://doi.org/10.1016/j.rse.2010.01.010.
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 Sensing of
Environment, 45, 311–326. https://doi.org/10.1016/0034-4257(93)90113-C.
Beven, K. J., Kirkby, M. J., Schofield, N., & Tagg, A. F. (1984). Testing a physically-based flood
forecasting model (TOPMODEL) for three UK catchments. Journal of Hydrology, 69, 119–143.
16 Flood Monitoring and Crop Damage Assessment
345