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Schad, I., Schmitter, P., Saint-Macary, C., Neef, A., Lamers, M., Nguyen, L., Hilger, T. and Hoffmann, V. (2012) Why Do People Not Learn from Flood Disasters? Evidence from Vietnam’s Northwestern Mountains. Natural Hazards, 62, 221-241.
http://dx.doi.org/10.1007/s11069-011-9992-4

has been cited by the following article:

• TITLE: Utilization of Landsat Data for Quantifying and Predicting Land Cover Change in the Bumbuna Watershed in Sierra Leone

  AUTHORS: Abubakarr Mansaray, Abdulai Barrie

  KEYWORDS: Watershed, Hydroelectric Power, Farming, Water Loggers, Landsat, Remote Sensing, Spectral Data

  JOURNAL NAME: Natural Resources, Vol.7 No.9, September 9, 2016

  ABSTRACT: Rural communities in third world countries are concerned over land use changes resulting from resource exploitation. This is the case for the Bumbuna watershed in Sierra Leone following impoundment of the Bumbuna reservoir in 2009. Farmers have increased activities along the riparian zones in protest against inundation of their farmlands. The dam operators warn this practice would threaten sustainable power supply; the farmers contend the reservoir is increasing and taking over their farms. However, it is difficult to resolve this issue without a means of quantifying the change and developing early warning systems for land cover in the watershed. This research presents a case for the use of remotely sensed Landsat data for quantification of land cover change and the development of predictive models to inform preparedness for imminent problems that may arise from land use practices. In situ water loggers, in combination with manual readings, recorded water levels in 30-minute intervals since 2009. These datasets combined with spectral values of Landsat 7 and Landsat 8 for the development of regression algorithms for predictive purposes. Digital photographs and satellite imagery illustrated the changes in land cover over time (a 33% water rise and 44% NDVI change from 2009 to 2015). These visual and spectral pictures confirm the usefulness of remotely sensed data for early warning systems in the watershed. Results of the regression analysis show Band 1 (Blue) and Band 4 (NIR) as statistically significant predictors for water level in the reservoir. The tests accounted for 84% (R2) of the data with p-values less than α at the 0.05 confidence level. However, future trials of the model will consider reducing the 4.6 error margin to minimize deviations from the observed data.