## 5 Discussion  
We used a cloud-based computing platform (GEE) to acquire data (median composite, derivative indices and terrain) for Kaski District, Nepal and performed LC mapping using the ELMs in the CARET package of R. This has replaced the tedious and expensive process of map-making using traditional  
Figure 9: Changes in the LC area from the year 2010 to the year 2020: (a) Bar diagram; (b) Sankey diagram. methods. This study presents an effective workflow of LC mapping and change detection using openly available tools. In this case study, we obtained good accuracy for all LC maps, resulting in a satisfactory outcome. For the RF classifier, the OA of 0.8792 +/\(-\) 0.0472 at the 95% confidence level was obtained for the 2010 LC map, whereas for 2020, the accuracy was 0.875 +/\(-\) 0.0575 at the 95% confidence level. For XGBoost, we obtained an accuracy of 0.8926 +/\(-\) 0.0448 at the 95% confidence level for the 2010 LC map, whereas for 2020, the accuracy was 0.8603 +/\(-\) 0.053 at the 95% confidence level. From this study, we can conclude that the Landsat imagery from EO data and GEE with ELMs are best for mapping the LC for a larger area. As RF performed better during the 2020 mapping and XGBoost model during the 2010 LC mapping, a clear distinction was not made on ELM's performance. LC mapping has been very limited in Nepal. The only national mapping organization of Nepal, the survey department (SD), still uses the traditional digitization technique for LC mapping for creating the national topographic database [10]. Although it is spatially more accurate, this process is labor-intensive and time-consuming. The process presented in this paper can be used for LC mapping with higher resolution satellite imagery to obtain a better result. The results of the LC mapping procedure were visually like that of the labor-intensive digitization mapping procedure of the SD. Both the model-simulated and observed LC map from SD are represented in Figure 10.  
We also compared our LC map of the year 2010 with the LC map prepared by the International Centre for Integrated Mountain Development (ICIMOD). ICIMOD has produced a national LC map database of 2010 [69]. Although our map is not divided into broad categories as ICIMOD's LC map, we can get a relatively similar result as ICIMOD's LC map. Figure 11 shows the comparison of our LC map with ICIMOD's LC map. By comparing both the maps, we can see the difference in the built-up area. This is because ICIMOD's map is generalized, as it is performed for a larger area. With the exception of built-up, all classes showed similar characteristics when compared. Few works have been  
Figure 10: LC map of Pokhara area of the year 2020: (a) Map obtained from our study procedure; (b) map from a topographical map from the survey department. performed utilizing the ELMs. [PERSON] [42], in his study, used the RF and XGBoost classifiers to perform LC mapping using Sentinel 2 data. The author achieved an overall accuracy of \(0.739\pm 0.018\) for RF classifier and accuracy of \(0.751\pm 0.017\) for XGBoost. On the contrary, we obtained better accuracy by utilizing DEM data from SRTM using the GEE repository for mitigating the effects of hills and shadows and its misclassification. Another study by [PERSON] et al. [40] implemented the RF classifier in the riparian landscape of the Ganges river and obtained an accuracy of 0.91. However, this study was conducted on a flat surface of India that has an elevation ranging from 15 m to 90 m, whereas our study was conducted in an area having elevation ranging from 300 m to 8091 m.  
From the beginning, there were various limitations and challenges to this work. The first challenge was related to the study area. As the Kaski district is heavily covered with clouds in the Landsat scene, it was challenging for us to get the perfect scene for mapping. The only possible solution was to consider the median composite of the annual Landsat imagery after cloud and snow masking.