CHAPTER 3: DEVELOPMENT OF AN AGE MASK FOR COFFEE CONDITION ASSESSMENT
3.5 Discussion
The overall outcome of this study is that it is possible to achieve acceptable accuracies for developing age-specific thematic maps for coffee in a heterogeneous agricultural landscape using Landsat 8 OLI and the RF algorithm. This result provides an opportunity for a cost- effective way of producing reliable age-specific thematic maps that are useful in coffee crop condition assessments and other applications.
3.5.1 Comparison of performance of Landsat 8 OLI and ETM+
The ability to achieve reliable classification accuracy is premised on the significance of the differences in spectral reflectance of the coffee classes of different age categories as well as with other land cover classes. This difference is a factor of the sensor characteristics, particularly its spectral fidelity. The sensor technology in Landsat 8 OLI, particularly the use
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of numerous elongated sets of detectors for each waveband, makes it capable of a detailed scan of the surface along track. Along track scanning is known to increase the sensitivity of the sensor to the important biophysical characteristics that determine vegetation reflectance (Dube
& Mutanga, 2015). In addition, when compared to the Landsat TM/ETM+, Landsat 8 OLI performed better possibly as it has an enhanced image radiometric resolution of 12 bits which permits more accurate detection of variations in vegetation characteristics (Pahlevan & Schott, 2013; Jia et al., 2014). This, when coupled with Landsat 8 OLI prolonged sensor radiation sampling residence-period for each field-of-view, explain the ability of the RF classifier to produce accurate age-specific thematic maps from Landsat 8 OLI data than on Landsat ETM+.
3.5.2 Effect of age on spectral reflectance of coffee
In this study, it was demonstrated that the spectral reflectance of coffee age categories (young, mature and old) classes are significantly different in the spectral range from the red to the SWIR2 bands of Landsat 8 OLI data. These differences are mostly apparent in the NIR and SWIR1 band, which also showed the greatest uniqueness in spectral characteristics for all age groups. This study therefore underscores the importance of analysing the spectral characteristics of classes in order to discriminate age groups of coffee accurately, and to distinguish these from other land cover classes in order to develop age-specific thematic maps at landscape scale. Furthermore, utilising the random forest inherent variable importance assessments, the results showed the contribution of each band in modelling the land cover classes. This is very important in understanding the usefulness of the Landsat 8 OLI bands in general land cover classification and in vegetation discrimination in particular.
The findings that mature coffee had the highest reflectance in the NIR band was somewhat expected. This is because at mature stage, the coffee is most productive in terms of photosynthetic potential and yield, which is then captured in reflectance in these bands where biochemical properties are least absorptive. The difference between mature coffee and old coffee was not discernible from the visible bands but only in the infrared bands. This confirms the importance of the NIR bands in vegetation characterization particularly in intra-species discrimination. The reduced reflectance of old coffee compared to young and mature coffee could be a result of the normal dieback associated with old coffee. Old coffee also has generally reduced nutrient utilisation and increased leaf fall from plant debilitation (Logan & Biscoe, 1987; Wrigley, 1988). The results indicated a higher degree of spectral overlap between mature coffee and old coffee, and this could be explained by the forward and backward transition of coffee into and out of the mature stage in some samples. The influence of general dieback that
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occurs in old coffee was ostensive in the overlaps between the old coffee and young coffee as old coffee droops, thereby having soil background influence similar to that of young coffee.
This influence of crop parameters was also reported for coffee by other studies (Vieira et al., 2012).
The spectral profiles showed that there could be significant variations in reflectance of an age group particularly in the infrared bands. This point to the fact that for large-scale seemingly uniform plantation crops such as coffee, there can be variation in reflectance even from the same age groups. These are because of local specific field factors which may confound the age- reflectance relationships utilised by the classifier (McMorrow, 2001). In this particular case, there is therefore need to consider a more robust classifier that is able to better deal with these subtle differences, which is the premise of the use of the kernel random forest classifier in this study. In other studies, the random forest classifier was reported to be able to discriminate between degraded and healthy grassland, between young and mature sugarcane and between inland and coastal sand (Adam et al., 2014). This ability to deal with marginal spectral class changes makes the RF classifier an ideal candidate for the development of age-specific thematic maps from Landsat 8 OLI data.
3.5.3 Comparison of accuracy performance
When all accuracy evaluation metrics are considered (overall accuracy, Kappa, allocation disagreement and quantity disagreement), it is apparent that splitting coffee into three age classes reduces the classification accuracy. However, the reduction in classification accuracy that occur when coffee is split into three classes is marginal (only 4.1% decrease in overall classification accuracy and only 2% increase in allocation and quantity disagreement). This indicate that it could be worthwhile to trade-off some accuracy to obtain a more functional thematic map with age-specific classes in mapping complex agricultural landscapes.
Although Landsat data has been used for age discrimination in landscape vegetation analysis (McMorrow, 2001; Franklin et al., 2003), most successful approaches have involved mostly high spatial and spectral resolution imagery such as Worldview-2 (Chemura et al., 2015b), IKONOS (Thenkabail et al., 2004b) and RapidEye imagery (Adam et al., 2014). This study therefore points to the potential utilisation of enhanced Landsat 8 OLI data combined with clever machine learning classifiers to achieve what has traditionally been in the domain of high- resolution imagery. This high spatial and spectral resolution data is very expensive, unavailable for most areas, suffers from speckle effect and often have high dimensionality (Dalponte et al.,
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2009; Mutanga et al., 2012). Some earlier studies had dismissed the potential of thematic maps produced from Landsat 8 OLI data for operational applications because of obvious errors that were associated with the common classification methods at the time (Foody, 2002). Even at the same spatial resolution of 30m, with the improvements in spectral characteristics, coupled by intelligent classifiers, the utility of Landsat data can be further extended to areas that previous studies considered not possible.