Translator Disclaimer
20 October 2015 Development of the Philippine Hydrologic Dataset (PHD) from LiDAR and other remotely-sensed data
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. M. C. Perez, M. C. Gaspa, D. S. Aloc, M. A. P. Mahor, K. A. C. Gonzalez, N. J. B. Borlongan, R. M. De La Cruz, N. T. Olfindo Jr., and A. C. Blanco "Development of the Philippine Hydrologic Dataset (PHD) from LiDAR and other remotely-sensed data", Proc. SPIE 9644, Earth Resources and Environmental Remote Sensing/GIS Applications VI, 964419 (20 October 2015);

Back to Top