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.
Rural communities in third world countries are becoming increasingly concerned over land use practices that may lead to major changes in land cover and pose threats to their chances of survival [
During the farming season, local farmers cultivate plots of forestland mainly for rice production. Traditionally, farmers would allow the farmland to fallow for a minimum of 7 years before coming back to that same plot. However, this fallow period has dramatically reduced to about every other year due to increase in other livelihood activities that also depend on the same piece of land. This farming concern became more obvious when the Government of Sierra Leone (GoSL) commissioned the Bumbuna power plant in 2009. The dam filled up and expanded into a reservoir taking over farmlands, communities, and some vegetation.
The stakeholders have been in a heated debate over the impact of these two activities. The dam operators warn that farming along the shoreline would change the fluvial geomorphology of the area and affect electricity generation. The farmers, on the other hand, contend that the water level keeps rising and shifting the riparian buffer inwards towards their farmland. Farming is an integral part of their tradition, in addition to being their major means of livelihood.
In anticipation of this controversy, the GoSL, in 2008, developed a policy to promote best management practices (BMPs) and alleviate environmental and social problems in the watershed [
Meanwhile, no data exists to show the mechanisms of catchment change in the area, the impact on these activities, and to what magnitude they matter. Advances in the availability of remote sensing datasets and the understanding of their benefits and limitations provide the potential to assist in overcoming this challenge [
The underlying principles behind satellite remote sensing for natural resource research include but are not limited to electromagnetic radiation, image acquisition and processing, and field data gathering [
Birth and McVey [
Several studies have followed Birth and McVey [
This work follows from those methodologies in the literature, postulating that Landsat spectral data can enable development of predictive models as early warning systems for impacts of land use changes in the Bumbuna watershed. The objective is to show case the usefulness of Landsat data in providing scientifically proven evidence to pinpoint cause-effect relationships between land-use practices and land cover changes in the study area. To achieve this objective, this work will exemplify a regression algorithm relating water level fluctuations in the reservoir and spectral reflectance values of Landsat 7 Enhanced Thematic Mapper (ETM) and Landsat 8 Optical Land Imager (OLI).
This work will lay the foundation for utilization of Landsat data to solve complex environmental problems in this data scarce region. The results will show case and provide the basis for project management using these approaches. Hence, future actions will not suffer from lack of information in clarifying land cover boundary limits and common interest. In addition, future predictive models from satellite studies will inform management strategies for biodiversity conservation. This is more obvious giving study findings of high species diversity of primates and other large mammals, birds, herptiles, butterflies, bats, and flora [
The Bumbuna watershed drains into Rokel River, one of the major river basins in Sierra Leone. The Rokel basin starts in Koinadugu District, in the northeast, and empties into the Atlantic Ocean in the Western Area.
Several communities, companies, and the GoSL depend on Rokel River for various purposes. Communities depend on it for fishing, transport, farming, and domestic use; ADDAX Bio-energy abstracts water from the river for irrigation; London Mining Ltd. utilizes the river to preprocess iron ore; plenty of mining companies depend on it for sand and gold; the BHP, the nation’s biggest hydroelectric power supply source, depends on the Bumbuna watershed.
The Bumbuna watershed has a 21 Km2 flooded area that drains into a 2,500,000-m3 reservoir, which supports hydroelectric power supply to major cities in the nation [
This study utilized both manual level readings and in situ “Rugged TROLL” absolute pressure data loggers. These loggers respond to changes in water and air pressure and require compensation to remove the effects of air pressure using a separate barometric logger (Rugged BARROW). The rugged troll and rugged barrow, both are easy-to-use software aquatic data logging instruments. The rugged troll measures water temperature and water level while the rugged barrow measures atmospheric pressure. Both instruments have completely sealed bodies that contain non-vented pressure sensor, tem- perature sensor, real-time clock, microprocessor, lithium battery, and internal memory. The Rugged TROLL 100 hangs by a back-shell hanger from a suspension wire.
Deployment of the instruments followed calibration with manual water levels and customization to take readings at 30-minute intervals. The instrument stored the data in a memory chip installed on the logging device. The Win Situ 5 soft ware utilizes a USB connected docking station to download the data, in CSV file formats, on to a computer.
The United States Geological Survey (USGS) provides Landsat data in tagged image file format (tiff). Using the ESRI Image Classification tool, the mean pixel values (30-m resolution) represented surface reflectance for the overpass dates. The resulting reflectance values informed algorithm development for water level fluctuations over time. The USGS has published, on their web page, band characteristics for Landsat 7 and Landsat 8 [
This research also utilized photographs from digital field cameras. The purpose was to demonstrate visual changes in land cover before and after impoundment of the dam.
Data analysis utilized multiple regression with ANOVA in the Minitab 17 statistical software. All the bands went through stepwise elimination to account for multicollinearity. There was also stepwise elimination of statistically insignificant bands. The null hypothesis was that no spectral band could predict water level in the reservoir, on a 95% confidence interval.
Calculations using spectral values from the Landsat images show that water level in the river (now reservoir) has increased by 45.5% from February 2009 to March 2015 while the NDVI, a related indicator [
Band number | Wavelength (nm) | Characteristics | |
---|---|---|---|
Landsat 7 | Landsat 8 | ||
1 | 2 | 450 - 520 | Blue: bathymetry |
2 | 3 | 520 - 600 | Green: peak vegetation |
3 | 4 | 630 - 690 | Red: vegetation slopes |
4 | 5 | 770 - 900 | Near Infrared (NIR): biomass content and shorelines |
This is a cause for concern giving that the increasing trend become prominent in the rainy season, which is also the farming season. These rural areas, depending primarily on rain-fed agriculture, would need to seek livelihood alternatives if this trend continues [
The chart shows a positive linear relationship between water level and band 1, as expected. Depending on the amount of certain substances in a water body, surface reflectance would range in the blue-green-infrared regions of the electromagnetic spectrum. Since water in the reservoir is clear most of the time, the water would reflect high in the blue region [
Equation (1) shows the regression model for water level in the reservoir. The output of the multiple regression analysis indicates that Band 1 (Blue) and Band 4 (NIR) significantly predict water level in the reservoir. In this case, we rejected the null hypothesis, which states that Landsat spectral values would not predict water level in the reservoir. The R2, which indicates the goodness of fit of the model [
Reservoir Level (m) = 241.74 + 0.0487 Band 1 (nm) − 0.03534 Band 4 (nm).
Equation (1) Predictive equation for water level in the reservoir.
Hence, the use of Landsat spectral data can be useful in determining water level in the Bumbuna Watershed. Inputting time factor would enable the development of predictive models for the area. However, since the root mean squared error (4.6) indicates a deviation between the predictive and observed trends, a lower value would make the model more powerful [
The objective of this work was to make a case for the use of Landsat data to predict land cover change in the Bumbuna watershed. Data gathering included both manual and in situ recording of surface water fluctuations in 30-minute intervals, temporally coincident surface reflectance values of Landsat over passing the area, and digital photographs.
The results show a minimum of 33% water level rise since impoundment in 2009. Another related land cover change is the NDVI, a 44% change from 2009. The Landsat data indicated that bathymetry changed by 45.5% since 2009. These results strengthened the possibility of quantifying land cover changes using remotely sensed Landsat images.
Results of the regression analysis showed that Band 1 (Blue) and Band 4 (NIR) are better predictors of water level in the reservoir. However, future trials of the regression equation require consideration of the error margin to account for deviations from the observed data. The recommendation is collection of more ground truth data to develop algorithms for predictive models in the watershed. These would serve as early warning systems, informing BMPs for the area.
Mansaray, A. and Barrie, A. (2016) Utilization of Landsat Data for Quantifying and Predicting Land Cover Change in the Bumbuna Watershed in Sierra Leone. Natural Resources, 7, 495-504. http://dx.doi.org/10.4236/nr.2016.79042