CHAPTER 3: ENHANCED URBAN CLASSIFICATION USING MULTI-SPECTRAL
3.4 Discussion
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3.3.6. Analysis VI: Classification results using OLI spectral bands and VIs
The use of OLI spectral bands and the derived vegetation indices yielded high and comparative similar results with those obtained in Analysis I (i.e. use of reflective bands as standalone dataset) and Analysis V (i.e. use of Landsat 8 thermal bands and the derived vegetation indices).
For example, high overall user (85.49%) and producer accuracies greater than 78% were obtained for all the classes considered under this study. Moreover, an overall accuracy and kappa index of 0.84 was attained (Table 3.6 and Figure 3.2). Producer and user accuracies were greater than 75% for all the LULC classes. A comparison of the results obtained from this analysis (i.e. OLI spectral bands and the derived vegetation indices) and those obtained from Analysis I (i.e. use of the traditional reflective bands as standalone datasets) show significant differences with the McNemar’s Z score of 10 at 95% confidence interval.
3.3.7. Analysis VII: Classification results using OLI, TIRS spectral bands and VIs Table 3.6 shows the urban landscape classification results obtained from running the model based on the integration of Landsat 8 derived OLI reflective bands, TIRS spectral bands and computed vegetation indices. The classification results demonstrate great improvement on the overall, user and producer accuracies for all the classes considered under this study. For example, significantly high user and producer accuracies, greater than 85% for low-medium density residential, water, forested and densely built classes were obtained. The results showed high overall accuracy of 89.33% and a kappa index of 0.86 (Table 3.6). Furthermore, when compared to Analyses 1, II, III, IV, V and VI, the urban landscape classification results obtained from the integration of Landsat 8 derived OLI reflective bands, TIRS spectral bands and computed vegetation indices (i.e. Analysis VII) yielded higher accuracies with McNemar’s Z score of 9 at 95% confidence interval. Overall, these results demonstrate that the integration of TIRS spectral bands from the Landsat 8 sensor with the sensor’s derived reflective bands and computed vegetation indices, improves the classification accuracy of urban landscapes compared to the use of these datasets as standalone datasets.
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the sensor’s traditional reflective bands and computed vegetation indices in discriminating complex and heterogeneous urban landscapes.
The results of this study have shown that the recently-launched Landsat 8 with unique radiometric, as well as new thermal bands, present a strong capability of improving the classification of heterogeneous and complex urban landscapes; especially in areas where the availability of high resolution satellite datasets with strategically positioned spectral bands and band settings remains one of the major limiting factors. When Landsat 8 derived TIRS spectral bands were integrated with the traditional OLI reflective bands, as well as the computed vegetation indices, the classification of urban landscapes significantly improved when compared to the use of these variables as standalone datasets. For instance, based on the integrated datasets, significantly higher overall accuracy (89.33%), along with user and producer accuracies of about 85% were attained for the low-medium density residential, water, forested and densely built land cover classes. The McNemar’s Z score was 9 at 95% confidence interval, implying that there was significant increase in classification accuracy when compared with the traditional use of reflective bands alone. Overall, the use of the integrated datasets outperformed the use of thermal bands and vegetation indices as standalone classification variables. The study showed that the results were almost comparable to those attained using traditional reflective and thermal bands. Higher classification accuracies (i.e. overall, kappa, user and producer) in mapping complex urban environments indicate the high performance associated with the improved Landsat 8 push broom scanner (Jia, et al., 2014).
Also, the performance observed from the results obtained based on the integration of the entire set of variables (i.e. derived thermal, traditional reflective bands, as well as the computed vegetation) concur with findings from the literature (Li et al., 2013; Sun & Schulz, 2015;
Ormsby, 2007). The above studies concluded that thermal remote sensing has the capability of providing crucial information that can enhance robust and reliable monitoring of land cover dynamics. For example, Ormsby (2007) pointed out that the inclusion of thermal bands together with other spectral bands in remote sensing applications influences classification accuracies. Also, the increased performance based on the integrated datasets can be linked to the ability of thermal bands, despite the coarser resolution to separate or separate areas associated with low temperature areas (water, forests and low-medium residential) from high temperature areas (high density residential, grasslands, development areas and densely built
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areas). The results of this study therefore clearly indicate the general importance of thermal bands from the new Landsat 8 sensor, through the provision of complementary information (Panah, et al., 2001), which greatly improves or aids the performance of the traditional reflective bands and the associated derived vegetation indices.
Furthermore, this work showed that merging the traditional reflective bands, with the four selected vegetation indices (NDVI, NDWI, NDBaI and NDBI) derived from the Landsat 8 OLI sensor for urban land cover mapping slightly increased the overall classification accuracy by 1.84% (i.e. from 82.65% to 85.49%) when compared to the use of the traditional reflective bands as a standalone dataset. For example, the test results showed that the inclusion of the four selected vegetation indices significantly (i.e. McNemar’s Z score had a value of 9.98 at 95% confidence interval) increased the classification accuracy. These results demonstrate the importance of Landsat 8 computed vegetation indices. These findings are in line with findings from previous studies which have demonstrated and reported the unique strength and usefulness of the four indices in separating various land cover types (Chen, et al., 2006; Jia, et al., 2014). Moreover, literature shows that indices, such as the NDWI (Stathakis, et al., 2012;
Jackson, et al., 2004; De Fries et al., 1998) and NDBI, have the capability to efficiently extract built up areas. The major limitation with these vegetation indices is that they do not consider that bare areas also exhibit similar properties with built up areas (Stathakis, et al., 2012). Thus, in this study, the inclusion of the NDBaI was useful in further separating bare areas from built up areas as it provides good contrast between bare and other surfaces (Sharma, et al., 2012).
Contrastingly, the use of four selected vegetation indices as a standalone dataset proved comparatively weak in discriminating the LULC of the complex and heterogeneous urban environments. However, comparatively the use of Landsat 8 derived vegetation indices alone produced slightly lower classification results (i.e. overall, user and producer accuracies), when compared to the use of traditional reflective bands (see Analysis I). For example, for the majority of the land cover classes considered in this study, user and producer accuracies slightly above 70% on average were observed. Similarly, the use of Landsat 8 derived thermal bands as a standalone dataset for classifying complex and heterogeneous LULC in urban environments overall yielded poor results except for water bodies where the model produced high user and producer accuracies above 90%. Effectiveness of thermal bands is thus dependent on land cover type, and climatic and geographic conditions (Panah, et al., 2001). Lo
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et al. (2010) observed that although most land covers had similar thermal radiances at night, water was still separable from the rest as it had lowest thermal radiance values. This study, similarly, observed that based on temperature water was easily distinguishable from other classes as it had lowest temperatures.
Above all, the high overall classification accuracies obtained in this study from the integration of the Landsat 8 TIRs bands, the traditional reflective spectral bands, as well as the derived vegetation indices, although not tested, can hypothetically be largely associated with the sensor’s unique design. For example, the recently-launched Landsat 8 sensor, unlike its predecessors, provides great improvement in numerous aspects. To begin with, Landsat 8 OLI and TIRs applies the push broom technique during data acquisition (Dube & Mutanga, 2015a, 2015b; Roy et al., 2014; Ke et al., 2015; Dube & Mutanga, 2015c). Sensors applying the push broom design in data acquisition are known to receive good and robust signals from the earth’s surface since they use elongated and linear arrays of detectors (Roy, et al., 2014). For example, the study by Dube and Mutanga (2015a) reports that the Landsat 8 makes use of a multiple extended collection of detectors for each spectral waveband, which in turn provides a comprehensive scan of the earth’s surface. Besides, the newly-launched 30m Landsat 8 sensor is associated with a narrower spectral range which is believed to be useful for this dataset to precisely detect and discriminate various land covers or earth surface features (Dube &
Mutanga, 2015a, 2015c). The observed highly accurate land cover classification results (i.e.
overall, kappa, user and producer accuracies) for complex and heterogeneous urban landscapes obtained in this study, projects the recently launched 30m Landsat 8 sensor as the best satellite data that can provide remarkable solutions and breakthroughs for land cover mapping, especially in environments where the availability of high resolution satellite data remains a daunting task due to cost and above all the restricted spatial coverage.
The observed higher accuracy classification results (i.e. overall, kappa, user and producer accuracies), although not tested in this study, can also be attributed to the strength and effectiveness of the SVM algorithm. Amongst most available classification algorithms, literature shows that the support vector machine classification algorithm is currently one of the most powerful and robust non-parametric machine learning algorithms in image classification studies when compared to the most commonly applied image classification techniques, such as Artificial Neural Networks and Mahalanobis classifiers, Maximum Likelihood, Random
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Forest, among others (Adelabu, et al., 2013; Jia, et al., 2014; Forkuor & Cofie, 2011; Yu, et al., 2013).