This paper discusses a novel way of generating sampling points of hydrologic features, specifically streams, irrigation network and inland wetlands, that could provide a promising measure of accuracy using combinations of traditional statistical sampling methods. Traditional statistical sampling techniques such as simple random sampling, systematic sampling, stratified sampling and disproportionate random sampling were all designed to generate points in an area where all the cells are classified and subjected to actual field validation. However, these sampling techniques are not applicable when generating points along linear features. This paper presents the Weighted Disproportionate Stratified Systematic Random Sampling (WDSSRS), a tool that combines the systematic and disproportionate stratified random sampling methods in generating points for accuracy computation. This tool makes use of a map series boundary shapefile covering around 27 by 27 kilometers at a scale of 1:50000, and the LiDAR-extracted hydrologic features shapefiles (e.g. wetland polygons and linear features of stream and irrigation network). Using the map sheet shapefile, a 10 x 10 grid is generated, and grid cells with water and non-water features are tagged accordingly. Cells with water features are checked for the presence of intersecting linear features, and the intersections are given higher weights in the selection of validation points. The grid cells with non-intersecting linear features are then evaluated and the remaining points are generated randomly along these features. For grid cells with nonwater features, the sample points are generated randomly.