Abstract
With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.
Highlights
Nepal is a geographically diverse country with flats in the south and increasing hills, to the mighty Himalayas in the north
First, the results of the cross-validated models were assessed, and they were applied to the whole scene for the surface water extraction
neural networks (NNET) and random forest (RF) produced the model with maximum overall accuracy (OA) and kappa coefficient (Kappa)
Summary
Nepal is a geographically diverse country with flats in the south and increasing hills, to the mighty Himalayas in the north. In Nepal, approximately 70% to 90% of the total annual rainfall. Sensors 2019, 19, 2769 occurs during the monsoon period resulting in high runoff and sediment discharge causing surface water area change [1]. It is rich in water resources with approximately 600 rivers [2] and 5358 lakes [3]. Due to such seasonal variation and large surface water area, it is difficult to track changes in surface water [4,5]. The monitoring and estimation of surface water is an essential task
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