Spectral information that an image can supply is an important factor on classification accuracy. Compared to other satellite images, RapidEye satellites include a Red Edge band, which is sensitive to changes in chlorophyll content (Munden et al., 1994). To analyze the function of the Red Edge band in land cover classification an atmospheric correction has been firstly applied to the images. Then a vegetation index considering the Red Edge band is proposed and the spectral pattern analysis for this index has been conducted using some sample data.
3.2.1.1.1 Atmospheric correction for RapidEye data
The main reason for atmospheric correction is making multi-temporal scenes from RapidEye become comparable, because digital numbers are substituted by surface reflectances. For the work at hand, the software ATCOR is used for atmospheric correction. It was originally developed at the German Aerospace Center (DLR), and integrated in ERDAS IMAGINE software. The satellite version of ATCOR supports all major commercially available small-to- medium sensors with a sensor-specific atmospheric database of look-up tables containing the results of pre-calculated radiative transfer calculations (Richter and Schläpfer, 2013).
ATCOR supports atmospheric correction including water vapor, aerosol, and visibility. As the RapidEye sensor does not possess spectral bands in water vapor regions (bands around 940 or 1130 nm), an estimate of the water vapor column based on the season (summer or winter) is necessary, such as middle latitude summer, tropical conditions, dry desert or winter. The aerosol type includes the absorption and scattering properties of the particles.
ATCOR supports four basic aerosol types: rural, urban, maritime, and desert. Usually the user can choose the aerosol type based on the geographic location. For example, for an area close to the sea, the maritime aerosol would be a logical choice if the wind is coming from the sea. Visibility can be automatically calculated from Red/NIR bands, if the scene contains dense dark vegetation (e.g. coniferous forest). Detailed explanation about the atmospheric parameters can be found in the user’s guide of ATCOR (Richter and Schläpfer, 2013). Figure 3.4 shows the window of ATCOR for processing the RapideEye image acquired on September 25th 2009. For RapidEye data the sensor geometry parameters can be found in the metadata file of the image, including solar (zenith angle), solar azimuth, sensor tilt, satellite azimuth.
Figure 3.4: The window of ATCOR2 for the atmospheric correction of the RapideEye image of Rathen, Germany
3.2.1.1.2 The use of Red Edge band in vegetation classification
As mentioned before, RapidEye is the first commercial satellite that offers a Red Edge band (690nm-730nm). Previous studies have proved this Red Edge band is sensitive for the chlorophyll content of vegetation (Gitelson et al., 1996; Munden et al., 1994). Related vegetation indices have been proposed based on the Red Edge, for example the NDVI-RE, which is derived from NDVI (Normalized Difference Vegetation Index, Equation 1) by replacing the NIR band with Red Edge band (Schuster et al., 2012) or replacing the Red band with Red Edge band (Tapsall et al., 2010). Several tests have been conducted to demonstrate the improved variance measurement in vegetation using the Red Edge band, e.g. species separation and land-cover classification (Bindel et al. 2011; Schuster et al. 2012). However, the experimental results show two formations of NDVI-RE have no significant difference from NDVI for vegetation classification (Schuster et al., 2012; Tapsall et al., 2010). In order to exploit the potential of Red Edge band for vegetation classification, different formulations of vegetation index incorporated with the Red Edge band have been tested in this work. As a result, a modified vegetation index is formulated (Equation 2), named as REVI (Red Edge Vegetation Index).
Equation 1: RED NIR RED - NIR NDVI + = Equation 2: = ×100 2 RE NIR REVI ρ ρ
; where ρ= reflectance value
In order to explain the use of REVI and NDVI in vegetation classification, RapidEye images of Rathen in Germany on May 25th, August 1st, and August 31st 2009 (see details in chapter 4.1) are chosen as test data. The main vegetation in this region is forest (coniferous and broad-leaved), grassland, and farmland. Because of farmland cultivation, a multi-temporal approach is useful to classify crops according to seasonal changes in spectral signals. Since different crops would be planted each year in this region, the detailed crop content is not the focus in this work. From the color-infrared images (Figure 3.5), three types of crops can be visually identified according to their color difference. From each crop typical samples are selected for the calculation of average and standard deviation of NDVI and REVI based on a multi-temporal layerstack from three RapidEye images. Standard deviation values are shown as error bar to the average values (see Figure 3.6). The signal pattern comparison shows both NDVI and REVI indicate the seasonal changes in different crops. From May to August the NDVI and REVI values for crop1 show an increasing trend contrary to the decreasing trends for crop2 and crop3, which makes crop1 easier to differentiate. In the case of crops 2 and crop 3, there is more significant difference on REVI than NDVI in May, which makes the varying REVI a significant feature enabling farmland plots to be distinguished.
May 25th August 1st August 31st
Figure 3.5: Color-infrared images composed of RapidEye band 5, 4, 2; three types of crop can be visually identified from the time series images.
Figure 3.6: Comparison between NDVI and REVI values for crop classes based on multi-temporal RapidEye images.
Likewise, samples of forest and grassland are selected for the calculation of the average and standard deviation values of REVI and NDVI for the time series images. Figure 3.7 shows the signal patterns of NDVI and REVI with respect to the acquisition dates. It reveals that there is a significant difference on REVI between forest and grassland, especially on August 1st. On the other hand, the NDVI value of grassland is not as stable as REVI, and partly mixed with forest. However, the REVI values of forest sub-classes (broad-leaved forest, coniferous forest, and mixed forest) are almost completely overlapped on multi-temporal images (see Figure 3.8). It seems that the REVI can minimize the signal difference among the forest sub-classes and allows classifying the forest as a whole. Thus the optimal solution is to use REVI firstly to differentiate forest and grassland, and then to use NDVI to classify forest sub-classes.
From the spectral analysis, it can be seen that the ability of RapidEye satellite to capture multi-temporal images within a brief interval of time combined with its Red Edge band capability can significantly improve the accuracy of vegetation classification. This is a big advantage and especially useful for farmland monitoring (Tapsall et al. 2010).
Figure 3.7: Comparison between NDVI and REVI values for forest and grassland based on multi- temporal RapidEye images.
Figure 3.8: Comparison between NDVI and REVI values for forest sub-classes (broad-leaved, coniferous, and mixed forest) based on multi-temporal RapidEye images.
Besides the vegetation index layers, NDWI (Normalized Difference Water Index, Equation 3) (McFeeters, 1996) can also be derived from RapidEye data. This index is useful for water classification and together with REVI and NDVI used as the main features for the classification of RapidEye images.
Equation 3: RED GREEN RED - GREEN NDWI + =