Nationwide mapping of coastal resources in the Philippines using remote sensing

Abstract

The Philippines is an archipelagic country with approximately 7,100 islands and 36,000 kilometres of coastline. Many of its people depend on coastal resources for daily necessities such as food. Due to population growth and industrialization, the coastal environment and its resources are being threatened. Integrated management is widely recognized as the basis for sustainable use and to achieve this, an evaluation and mapping of the resources is needed. Inventory of these resources provide important baseline data for resource management which can help in decision making in planning and development. Using the traditional method of field sampling to map these resources will be too difficult, time consuming and expensive. With the latest technologies such as remote sensing, mapping the coastal resources of the Philippines is not too farfetched. The University of the Philippines Training Center for Applied Geodesy and Photogrammetry (UP TCAGP) funded by the Department of Science and Technology (DOST) has recently embarked in a project (CoastMap) which will enable the mapping of coastal resources in the country. The main objective of CoastMap is to map the high valued coastal resources such as benthic habitats, mangroves and aquaculture of the Philippines. To be able to do that, different workflows were developed, tested, and assessed for extraction of such resources using LiDAR, Landsat and WorldView-2 and aerial imageries for pilot sites which will then be applied to other data available for the whole country. Another objective of the project is capacity building. The CoastMap team was tasked to conduct trainings on basic remote sensing and GIS to the fifteen partner SUCs and HEIs, cascade the workflows the team developed and monitor their progress. Each partner was tasked to map the coastal areas near their location. The CoastMap team was able to develop the following methods for its objectives: object-based image analysis for benthic habitat mapping using LiDAR derivatives, object-based image analysis for extracting aquaculture classes using LiDAR datasets, object-based image analysis for extracting mangroves using LiDAR and orthophoto datasets, extraction of coastal aquaculture features (fish ponds, fish pens, fish cages extraction) from high resolution WorldView-2 satellite images using object-oriented approach, object-based image analysis for benthic habitat mapping using high resolution WorldView-2 satellite images and mangrove mapping from Landsat images. In the extraction of coastal resources from remotely sensed data, field surveys to collect training and validation points are done in order to calibrate the classification processes and validate the resulting maps. The ground data collected aids in the assessment of the quality of the extracted information. The project employs various methods of field data collection in order to produce detailed maps of the coastal resources. Included in these methods are ocular inspections, GPS location sampling, and underwater video tows.