## 4 Discussion and Conclusions
### Discussion
#### 4.1.2 Error Analysis and Insights  
In terms of the relative error of the extracted area, the error in the application of nighttime lighting was significantly higher in areas with broken terrain than in areas with flat and contiguous terrain. In addition, the errors in the extraction of impervious surface area statistics were considerably higher in the Karst gorge, Karst plateau, and Karst fracture basin landform types than in other areas with a flat terrain. The Luojia1-01 data provided a greater advantage in extracting impervious surface from economically developed and light concentrated areas, while the extraction of impervious surfaces from the NPP-VIIRS and Flint data was influenced more by topography than the Luojia1-01 data.  
The results showed that the application of nighttime light images to impervious surfaces was affected by topography, and the impervious surface extraction had poor accuracy. The single use of nighttime light data to extract impervious surfaces was not effective for obtaining impervious surface information in large scale topographic fragmentation areas. At the same time, the extraction of impermeable surfaces in cities with a fragmented topography and a small economic scale showed that the extraction accuracies of different nighttime light images were significantly different. The reasons for this phenomenon may include the following:  
1. Climatic conditions in the Karst mountains themselves. The cloudy and rainy climatic characteristics of the Karst mountains can easily bias the collected light data due to factors such as cloud cover, pollutants, and other light sources [44].
2. Zone modelling approach. There are complex reasons that affect the construction of impermeable surfaces in reality, including topography, social, economic and ecological aspects, etc. More complex factors should be considered in actual modelling, and using only geomorphology as the standard zoning approach may not be suitable for different types of nighttime light data.  
Therefore, the integration of nighttime light image data with a variety of remote sensing data should be studied in detail in the future. This study can develop a highly accurate nighttime light image dataset, as well as investigate the interaction between various natural, social, and economic elements [45]. At the same time, it is necessary in the next stage of research to combine other remote sensing data sources, such as stone desertification data, NDVI, etc., to explore the best way for extracting impermeable surfaces in topographic fragmentation areas. Alternatively, other zoning methods can be used to analyze the nighttime light data of the topographic fragmentation areas in combination with different remote sensing data sources to accurately extract impermeable surfaces. The aforementioned steps are crucial to the monitoring of urban morphological changes.  
Generally speaking, nighttime lights vary considerably over the course of a year and require data from at least a year or multiple years for comparison. However, the number of available data periods is small due to the short launch time of Luojia1-01. At the same time, due to the cloudy and rainy conditions in the Karst mountains, the only image with a minimum number of clouds among the available data periods is the one used in this paper. Therefore, additional multi-period data sources are needed in the future study to confirm the reliability of the conclusions presented in this paper.