4.1. Urban Area Extraction by the MNUACI
Landsat 8 multi-spectral reflectance data from the four capital cities in China were adopted to calculate MNDWI and NDVI. The MNUACI was then derived by integrating MNDWI, NDVI and Luojia 1-01 NTL. Before performing the calculation for MNUACI, the parameters and were determined by Equation (3) based on samples collected from the urban cores. The parameter r was calculated according to the farthest distance between () and (, ) from the sample data. The parameters (, , r) from Beijing, Nanjing, Guangzhou and Haikou are (0.73, 0.54, 0.35), (0.37, 0.55, 0.51), (0.41, 0.46, 0.32) and (0.36, 0.36, 0.49), respectively.
MNUACI is used to distinguish between light-intensity differences in urban core areas, and therefore, to improve pixel resolution in light-saturated areas and allow recognition of urban core structures. The Temple of Heaven Park in Beijing and Hongcheng Lake in Haikou were selected to evaluate the effectiveness of MNUACI. As illustrated in
Figure 5, the urban areas (cyan) extracted from Landsat 8 were regarded as reference data, to which NTL, EANTLI, HSI, NUACI, and MNUACI were compared. It can be seen that NTL and EANTLI have similar results: neither buildings nor roads are recognized in the middle of the park due to the lack of nighttime luminosity. Although NUACI shows more fractions of urban areas, only a small amount of impervious surface in the park can be recognized, due to the lack of a sufficient luminous condition. HSI and MNUACI identified detail structures of impervious surfaces, but MNUACI extracted impervious surfaces more accurately. As illustrated in
Figure 6, NTL and EANTLI mistakenly identify most urban areas as pervious surfaces and increase omission errors. Although HSI identifies more urban areas, it recognizes the lake as urban areas by mistake, resulting in many commission errors. The recognition results of urban areas from NUACI and MNUACI are similar, both showing the detailed urban structure. However, MNUACI exhibits higher accuracy of urban areas extraction resulting from the reduction of the impact of water bodies on urban areas using MNDWI.
Taking Nanjing as an example,
Figure 7 illustrates a latitudinal transect of NTL, NUACI and MNUACI. These three types of curve variation are similar, but DN values of MNUACI and NUACI in urban areas are distinctly higher than those of NTL, which suggests that MNUACI and NUACI can enhance the NTL effect in urban areas. For urban areas, MNUACI has higher peaks and lower valleys than NUACI, which reflects more characteristics of inner-urban variability and spatial differentiation. This suggests an easier process of urban area extraction using MNUACI. For peri-urban areas, NUACI and NTL present similar low DN values, proving it difficult to identify small towns with them. In contrast, DN values are higher when MNUACI extracts urban areas in suburban regions. In addition, NTL cannot eliminate blooming effects due to a small quantity of luminosity values occurring in water and vegetation areas, while MNUACI and NUACI solve these blooming problems by introducing vegetation and water indexes.
4.2. Performance Assessment of the MNUACI
In terms of urban area recognition, a combination of NTL and auxiliary data is better than the use of NTL alone. Different extraction methods for urban areas demonstrate different performances on the same composited NTL index [
46]. To assess the feasibility and effectiveness of MNUACI, several supervised and unsupervised classification approaches were separately applied to identify urban areas on NTL, EANTLI, HSI, NUACI and MNUACI images. Because the optimal thresholding method is time-consuming and laborious, the genetic algorithm (GA) was used instead of automatically determining the image segmentation threshold for the extraction of urban areas [
47]. Deep learning (DL), GA, fuzzy C-means (FCM) and SVM methods were used to extract urban areas from NTL, EANTLI, HSI, NUACI and MNUACI images of Beijing, Nanjing, Guangzhou and Haikou. Moreover, urban area references of the four sample cities were obtained from Landsat 8 images using the maximum likelihood classifier (MLC) method. Corresponding urban areas from each city were derived from NTL, EANTLI, HSI, NUACI and MNUACI images associated with DL, GA, FCM and SVM approaches. The point-to-point comparison method was applied to test images and reference images. Then, precision indicators such as the Kappa coefficient, overall accuracy and the Jaccard similarity index were calculated to analyze the performance of the combination of different indexes and approaches.
The OA and KC of reference images from Beijing, Nanjing, Guangzhou and Haikou are (93.42%, 0.87), (95.88%, 0.92), (98.16%, 0.96) and (96.94%, 0.94), respectively, which suggests that the classification accuracies of Landsat 8 images from the four cities are reliable.
As illustrated in
Table 2,
Table 3,
Table 4 and
Table 5, based on the OA, KC and JSC of five NTL indexes, the order of accuracy of urban area classification in Beijing, Nanjing, Guangzhou and Haikou respectively are: MNUACI > HSI > NUACI > NTL > EANTLI, MNUACI > NUACI > NTL > HSI > EANTLI, MNUACI > NUACI > HSI > NTL > EANTLI, MNUACI > NUACI> HSI > NTL > EANTLI. MNUACI has a higher classification accuracy than the other four NTL indexes of the four capital cities using four classification approaches. Based on the OA, KC and JSC of the four classification methods in MNUACI, each SVM demonstrates the highest classification accuracy in the four urban area classification methods. Except the classification accuracy of NUACI which ranks third in Beijing, each NUACI accuracy from the other three cities follows the corresponding MNUACI. For MNUACI, the accuracy relationship of the four urban area extraction approaches in Beijing, Nanjing, Guangzhou and Haikou are as follows: SVM > GA > FCM > DL, SVM > DL> GA > FCM, SVM > GA > FCM > DL, SVM > GA > FCM > DL. The SVM method is superior to other methods with the GA method being the second, the FCM method the third, and the DL method the last.
After applying the SVM method, the spatial distribution of the extraction accuracy of urban areas, commission errors and omission errors from Beijing, Nanjing, Guangzhou and Haikou are displayed in
Figure 8. The results of EANTLI and NTL produce a great deal of omission errors on some peri-urban areas lacking in nighttime luminosity. This might be due to the Luojia 1-01 satellite imaging time set at 2:00–3:00 a.m. local time. The primary errors of HSI for the extraction of urban areas are commission errors caused by a large number of water bodies. Although NUACI improves the accuracy of urban area extraction by integrating vegetation and water bodies, NUACI, like EANTLI and NTL, still has difficulty identifying unlit urban areas due to the use of NTL alone. All results of MNUACI in the term of extraction of urban areas illustrate lower commission errors and omission errors contrasting to results of NUACI, HSI, EANTL and NTL. Moreover, the spatial distribution type of MNUACI results is also closer to the MLC results.
The extraction results of urban areas in the Tongzhou District of Beijing based on MNUACI, NUACI, HSI, EANTLI and NTL by the SVM method are shown in
Figure 9, and a Landsat 8 false color composite image (
Figure 9a) is used as a visual reference for urban areas. For two central city areas, MNUACI and HSI show specific spatial distribution patterns and inner-urban differentiation. NUACI and EANTLI extracted non-vegetation and illuminated regions as urban areas, while NTL extracted only illuminated regions as urban areas. For two town areas, NTL, EANTLI and NUACI merely identify road areas within them, missing most town areas, especially for Town II, while MNUACI and HSI recognize more urban areas. For bare land area, NTL, EANTLI and NUACI merely identify minor bare lands within them while MNUACI and HSI recognize most bare lands. For construction sites, NTL almost identifies the whole construction sites as urban areas without any difference, while EANTLI, HSI, NUACI and MNUACI can extract urban areas correctly, among which MNUACI have the best extraction effect. For village areas, the results of urban areas identified by the five indexes are similar, but for Village I, NTL, EANTLI and NUACI, lead to a large number of omission errors, while the results of HSI and MNUACI generate minimum omission errors. Moreover, NTL and EANTLI produce slight commission errors over the river area, and even HSI mistakenly identifies rivers as urban areas. In contrast, MNUACI and NUACI do not generate such errors.
4.3. Correlation between MNUACI and Urban Impervious Surface
Furthermore, one thousand sample points from an ISA image and corresponding MNUACI, NUACI, HSI, EANTLI and NTL images in each city were randomly selected by using the Create Random Points tool as well as the Extract Multi Values to Points tool of the ArcGIS software. The quadratic polynomial regression models were subsequently established with MNUACI, NUACI, HSI, EANTLI and NTL for estimation of ISA. Correlation coefficients and Root-Mean Square Error (RMSE) were employed together to evaluate the performance of the established regression models.
As shown in
Table 6, the average
R2 and RMSE of MNUACI, NUACI, HSI, EANTLI and NTL in Beijing, Nanjing, Guangzhou and Haikou are (0.74, 0.13), (0.49, 0.18), (0.44, 0.19), (0.21, 0.22) and (0.24, 0.22), respectively. According to correlation coefficients and the RMSE of quadratic polynomial regression models, the results of EANTLI and NTL have similar lower fitting accuracy, and the result of HSI is better than that of the previous two indexes. Apart from the result of Beijing, model regression effects of NUACI in the other three cities are better than EANTLI and NTL. In contrast, MNUACI shows the highest correlation coefficients and the lowest RMSE in all four cities. This suggests that the regression model of MNUACI could enormously decrease the blooming effect of Luojia 1-01 NTL and improve identification accuracy for non-luminous ISA better than for other models. As illustrated in
Figure 10, the scatter plots indicate that regression models between MNUACI and ISA in Beijing, Nanjing, and Guangzhou demonstrate the form of a quadratic polynomial regression model, whereas the polynomial regression model at Haikou is closer to a linear regression model. The NTL of urban core areas in developed metropolitan cities, such as Beijing, can contribute to the saturated MNUACI value. The ISA corresponding to PISI might not be the highest, because the differentiation between the blue and the near-infrared band during the daytime is weakened in the urban core area. On the contrary, the NTL of urban core areas in developing cities, such as Haikou, might rarely generate a saturated MNUACI value, which can present a good linear correspondence to ISA derived from a multispectral image.