It is noteworthy that satellite-based earth observation and GIS have been established as the best tools for observation, measurement and monitoring of land change (Kotze & Fairall 2006; Bolstad & Lillesand 1992). Remote sensing is a process of deriving information about the earth’s land and water surfaces using satellite images acquired from overhead, by employing electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the earth’s surface (Campbell 2007). Image analysis is a systematic method and a framework for examining these digital images in order to extract useful information. This method involves assigning pixels to classes, with each pixel evaluated as a discrete unit composed of values in several spectral bands (Campbell & Wynne 2011). The traditional pixel-based approach has recently been supplemented by OBIA, are widely used. Common image classification techniques include unsupervised and supervised classification (decision trees (DT) and expert systems).
Earth observation data provide large area coverage of features on the face of the earth at near real time. The historical archive of such imagery provides multi-temporal monitoring capability and is therefore well suited to generate land cover and quantify evapotranspiration. There are a number of satellites that can provide useful data in this regard, namely: MODIS, Landsat 8, Spot 5 and Spot 6.
The Moderate-resolution imaging spectroradiometer (MODIS) is located on board the Aqua and Terra satellites, launched by National Aeronautics and Space Agency (NASA) in 2002 and 1999, respectively. It is possible to obtain two MODIS images for each day of any area in the world. The difference between Terra and Aqua is the time of overpass. MODIS has a swath width of 2330 km, making it very useful to gather data for large areas. NASA offers readily available data products through the Distributed Active Archive Centres (DAACs).
The MODIS sensors have the capacity to capture data in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm and at varying spatial resolutions (2 bands at 250 m, 5 bands at 500 m and 29 bands at 1 km). It is also structurally designed to provide measurements in large-scale global dynamics including changes in earth cloud cover, radiation budget and processes occurring in the oceans, on land, and in lower atmosphere. The MODIS sensor provides the possibility to measure the normalised difference vegetation index (NDVI) at a resolution of 250 m, but also has five bands in the visible and near infrared region of the electromagnetic spectrum. These bands have a resolution of 500 m. Furthermore, the MODIS sensor has a large number of bands in the thermal infrared spectrum. Two of those, at 11 and 12 µm, can be used to calculate the surface temperature at a resolution of 1000 m.
Olexa & Lawrence (2014) affirmed the reliability of Terra MODIS on synthetic surface reflectance data and NDVI estimates to assess LULC of semi-arid rangeland by applying the spatial and temporal adaptive reflectance fusion model (STARFM) to five different Landsat TM and concurrent Terra MODIS scenes. Possible uses of the MODIS data using data products Gross primary productivity (GPP) and Net primary productivity (NPP) range from regional strategic planning, such as quantifying decadal harvest targets for large tracts of forest and when to move grazing animals among large pasture areas (Hunt et al. 2003).
The Landsat programme, a series of earth observation satellites termed which was originally instituted by NASA and the U.S Department of the Interior (Lillesand, Kiefer & Chipman 2004) and is the longest continuous spaceborne sensors jointly managed by NASA and the United States Geological Survey (USGS) (NASA 2011a). The area covered by a Landsat image is approximately 185x185 km and is skewed eastwards due to the earth’s rotation (Lillesand, Kiefer & Chipman 2004). The data acquired for land and water resources from Landsat-8 has been an important asset to agriculturists and other public and private sectors. Thus far, NASA has celebrated one year of the success of landsat-8 launch without hitches and a rather rapidly increasing image database of USGS Earth Resources Observation and Science (EROS) at a 16-day repeat cycle.
Referred to as the LDCM (Landsat Data Continuity Mission), the Landsat 8 satellite expands on the 40 years of Landsat satellites recording information. The spacecraft carries multispectral and panchromatic sensors, as well as the Thermal Infrared (TIRS). Landsat 8 provides seven multispectral bands useful for vegetation studies (Table 3.1).
Table 3.1 Spatial and spectral resolutions of OLI and TIRS sensors.
Sensor Spectral bands Electromagnetic spectrum Wavelength (µm) Resolution (m)
OLI Band 1 Costal aerosol 0.43 – 0.45 30
Band 2 Blue 0.45 – 0.51 30
Band 3 Green 0.53 – 0.59 30
Band 4 Red 0.64 – 0.67 30
Band 5 Near Infrared (NIR) 0.85 – 0.88 30
Band 6 Short Wave Infrared Red (SWIR) 1 1.57 – 1.65 30
Band 7 Short Wave infrared (SWIR) 2 2.11 – 2.29 30
Band 8 Panchromatic 0.50 – 0.68 15
Band 9 Cirrus 1.36 – 1.38 30
TIRS (resampled to
30 m) Band 10 Band 11 Thermal Infrared (TIR) 1 Thermal Infrared (TIR) 2 10.60 – 11.19 11.50 – 12.51 100 100
Landsat 5 (TM), Landsat 7 (ETM+) and ASTER are by far the most popular sensors for scientific purposes mainly because archival data from these sensors are available gratis over the Internet. Unfortunately, the scan-line corrector of ETM+ has been inoperative since 2003, rendering large areas of any image unusable. Landsat 4 and 5 carried on-board TM sensors consisting of seven spectral bands at resolution 30 m with thermal infrared band 6 resampled to 30 m from 120 m resolution using cubic convolution resampling method (Gibson 2000). Similar to Landsat TM with respect to resolution scale and band series with exception of the thermal band, Landsat 7 sensor was equipped with ETM+, the instrument which provided a 15 m high resolution panchromatic band 8. Moreover, Landsat 8 consists of two sensors; the Operational Land Imager (OLI) that captures image using nine spectral bands at 15 to 30 m resolution; and the Thermal Infrared Sensor (TIRS) for highly precise measurements of earth’s thermal energy to monitor land and water use. TIRS bands collected at 100 m resolution are resampled to 30 m (Table 3.1).