**5.2 River basin analysis: Opa River basin in Southwest Nigeria**

Opa river basin is a tributary of River Shasha (one of the main tributaries of River Osun) located within latitudes 7°26′56′′–7°35′5′′ N and longitudes 4°24′53′′–4°

#### **Figure 1.**

*Landcover change over Opa river basin in Southwest Nigeria between 1986 and 2016 (see [45] for details of the methodology used to derive this).*

**57**

**Table 1.**

*Remote Sensing and River Basin Management: An Expository Review with Special Reference…*

39′13′′E. The basin covers four local government areas and an important impound-

Awolowo University, Ile-Ife, Nigeria) community in 1978 supply water for more than 10,000 students and staff in the University community. Satellite data used were freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs) and Landsat imageries (mainly TM of December, 1986; ETM+ of December 1999; ETM+ of March, 2002 and OLI/TIR of January, 2016) to provide management decision support on river basins in the region. Main objectives were to examine landuse/landcover change over the river basin, characterize the basin, morphometrically and compare the morphometric characteristics from different sensors and resolutions. Results showed that built-up areas and farmlands have increased in the study area by 262.71 and 7.15%, respectively, at the expense of vegetation cover that has reduced by 23.78% within the study period of 1986–2016 (**Figure 1**). Analysis of the DEMS classified the river basin as belonging to a fifth order class, with about

showed that the remote sensing data can be used to generate distinguishable subbasins that can ease management and allow for creation of sub-basin plans based on each sub-basin's comparative advantage. To achieve the classification, important drainage parameters were investigated across selected (Opa) basin, and their results are presented in **Table 1**. Subsequently, the river basin was classified into five sub-

Despite the suitability of the remote sensing data, comparison of the results across geometric and radiometric differences indicate that 30 and 15 m resolution DEMs from ASTER sensor produced fewer contrasting results than what was obtained from different sensors (but same resolutions) analysis of 30 m ASTER and 30 m SRTM. It this can be recommended that it is better to adopt same product of a particular sensor rather than of different sensor for analysis. Nikolakopoulos et al. [46] had indicated that differences may occur due to variations in mission specifications of different sensors, and that whereas SRTM elevation data are unedited, and contained occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water covered surface, ASTER imageries can be influenced by weather

*Selected morphometric characteristics of identified sub-basins from remotely sensed data. Details of the* 

Opa Dam that was established in a University (Obafemi

area. When subjected to cluster analysis, results

*DOI: http://dx.doi.org/10.5772/intechopen.88681*

ment in the basin is the 68 km2

480 tributaries over the 236 km2

basins (**Figure 2**) that can aid planning.

conditions during the stereo-imagery acquisition.

*parameters and methods are provided in Eludoyin and Adewole [45].*

#### *Remote Sensing and River Basin Management: An Expository Review with Special Reference… DOI: http://dx.doi.org/10.5772/intechopen.88681*

39′13′′E. The basin covers four local government areas and an important impoundment in the basin is the 68 km2 Opa Dam that was established in a University (Obafemi Awolowo University, Ile-Ife, Nigeria) community in 1978 supply water for more than 10,000 students and staff in the University community. Satellite data used were freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM) Digital Elevation Models (DEMs) and Landsat imageries (mainly TM of December, 1986; ETM+ of December 1999; ETM+ of March, 2002 and OLI/TIR of January, 2016) to provide management decision support on river basins in the region. Main objectives were to examine landuse/landcover change over the river basin, characterize the basin, morphometrically and compare the morphometric characteristics from different sensors and resolutions.

Results showed that built-up areas and farmlands have increased in the study area by 262.71 and 7.15%, respectively, at the expense of vegetation cover that has reduced by 23.78% within the study period of 1986–2016 (**Figure 1**). Analysis of the DEMS classified the river basin as belonging to a fifth order class, with about 480 tributaries over the 236 km2 area. When subjected to cluster analysis, results showed that the remote sensing data can be used to generate distinguishable subbasins that can ease management and allow for creation of sub-basin plans based on each sub-basin's comparative advantage. To achieve the classification, important drainage parameters were investigated across selected (Opa) basin, and their results are presented in **Table 1**. Subsequently, the river basin was classified into five subbasins (**Figure 2**) that can aid planning.

Despite the suitability of the remote sensing data, comparison of the results across geometric and radiometric differences indicate that 30 and 15 m resolution DEMs from ASTER sensor produced fewer contrasting results than what was obtained from different sensors (but same resolutions) analysis of 30 m ASTER and 30 m SRTM. It this can be recommended that it is better to adopt same product of a particular sensor rather than of different sensor for analysis. Nikolakopoulos et al. [46] had indicated that differences may occur due to variations in mission specifications of different sensors, and that whereas SRTM elevation data are unedited, and contained occasional voids, or gaps, where the terrain lay in the radar beam's shadow or in areas of extremely low radar backscatter, such as sea, dams, lakes and virtually any water covered surface, ASTER imageries can be influenced by weather conditions during the stereo-imagery acquisition.


#### **Table 1.**

*Selected morphometric characteristics of identified sub-basins from remotely sensed data. Details of the parameters and methods are provided in Eludoyin and Adewole [45].*

*Current Practice in Fluvial Geomorphology - Dynamics and Diversity*

of 0.125° × 0.125° (i.e., 13 km). In addition, dataset from the Global Precipitation Climatology Project is made available from October 1996 to present. The GPCP provides daily, global horizontal resolution of 1 × 1° (i.e., 111 km) gridded fields of precipitation. The GPCP 1-DD draws upon several data sources such as GOES, Meteosat, GMS geostationary satellites and with NOAA AVHRR polar-orbiting IR satellite, given the different available input sources (GPCP-1DD v.1.2; [42]). The Tropical Rainfall Measurement Mission (TRMM) satellite, launched in November 1997 is a joint space mission between the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), and the Japan Aerospace Exploration Agency (JAXA). It is a polar orbiting satellite, having a relatively high temporal resolution, designed to monitor rainfall over the global Tropics [43]. The satellite estimates rainfall and energy exchange on tropical and subtropical regions of the world based on the characteristics of cloud cover, cloud

The great advantage of satellite-based rainfall records is their global coverage, providing information on rainfall frequency and intensity in regions that are in accessible to other observing systems such as rain gauges and radar. Through the aid of satellite weather observing technologies, the influence of viewing and understanding tropical rainfall systems has been greatly improved. In recent studies, several satellite-based rainfall products have been subjected to cross-validation tests over many regions to ascertain the accuracy of their rainfall estimations. The performance of satellite precipitation estimates over land areas has been reported to be highly dependent on the rainfall regime and the temporal and spatial scale of the

**5.2 River basin analysis: Opa River basin in Southwest Nigeria**

Opa river basin is a tributary of River Shasha (one of the main tributaries of River Osun) located within latitudes 7°26′56′′–7°35′5′′ N and longitudes 4°24′53′′–4°

**56**

**Figure 1.**

*methodology used to derive this).*

tops and temperature.

retrievals [44].

*Landcover change over Opa river basin in Southwest Nigeria between 1986 and 2016 (see [45] for details of the* 

**Figure 2.**

*Delineated sub-basins from hierarchical clustering, and their corresponding locations (the methods are presented in detail in [45]). Specific characteristics of the sub-basins are in Table 1.*
