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.
Stream network delineation based on LiDAR-derived digital terrain model (DTM) may produce stream segments that are inexistent or incomplete because of limitations imposed by extraction procedure, terrain and data. The applicability of a common threshold value in defining streams such as those implemented through the D8 algorithm also remains in question because the threshold varies depending on the geomorphology of the area. Flat areas and improper hydrologic conditioning produce erratic stream network. To counteract these limitations, this study proposes a workflow that improves the stream network produced by the D8 algorithm. It incorporates user-defined channel initiation points as inputs to a tool developed to automatically trace the flow of water into the next actual stream segment. Spurious streams along digital dams and flat areas are also manually reshaped. The proposed workflow is implemented in Iligan River Basin, Philippines using LiDARderived DTM of 1-meter resolution. The Flow Path Tracing (FPT) method counteracts the limits imposed by extraction procedure, terrain and data. It is applicable to different typologies of watersheds by eliminating the need to use site-specific threshold in determining streams. FPT is implemented as a Phyton script to automate the tracing of the streams using the flow direction raster. The FPT method is compared to the blue line digitization and the D8 method using morphometric parameters, such as stream number, stream order and stream length, to assess its performance. Results show that streams derived from the FPT method has higher stream order, number and length. An accuracy of 93.5% produced from field validation of the FPT method’s streams strengthens the findings that integrating manual channel head initiation and flow path tracing can be used for nationwide extraction of streams using LiDAR-derived-DTM in the Philippines.
With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.
Water resource monitoring and management has been an important concern in the Philippines, considering that the country is archipelagic in nature and is exposed to a lot of disasters imposed by the global effects of climate change. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of digital maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, the 3-year project “Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys” under the Phil-LiDAR 2 Program (National Resource Inventory), has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has already been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs such as Disaster Risk and Exposure Assessment for Mitigation (DREAM) and Phil-LiDAR 1, supplemented with other remotely-sensed imageries and ancillary information from Local Government Units (LGUs) and National Government Agencies (NGAs). The methodologies implemented are mainly combinations of object-based image analysis, pixel-based image analysis, modeling, and field surveys. This paper presents the PHD project, the methodologies developed, and some sample outputs produced.