In a rapidly changing world, new data will always come in handy. Such is an important matter for a country like the Philippines which has overwhelmingly rich natural resources but is often troubled with overpopulation, wrong urban planning, and natural hazards that disrupt the environment and leave the people vulnerable.
Hence, the government initiated a project involving data extraction to map the country’s natural resources with the hope of being useful in monitoring, planning, and formulating policies for disaster preparedness and mitigation.
The government used an existing technology called Light Detection and Ranging (LiDAR) under a project called Phil-LiDAR 2, which was simultaneously conducted under Phil-LiDAR 1 from 2014 to 2017. Both projects used LiDAR technology and were under the Disaster Risk and Exposure Assessment for Mitigation (DREAM) Program.
While Phil-LiDAR 1 generated detailed flood hazard maps for major river basins nationwide, the Phil-LiDAR 2 project aimed to map the country’s agricultural, forest, coastal, hydrologic, and renewable energy resources.
Phil-LiDAR 2 was implemented nationwide and for three years, experts from 14 State Universities and Colleges (SUCs) and Higher Education Institutions (HEIs) combined their efforts to achieve the project goals.
The University of the Philippines Los Baños (UPLB) is one of the 14 SUCs in the program, with the Institute of Biological Sciences-College of Arts and Sciences (IBS-CAS) as the main implementing unit. The UPLB team was headed by Dr. Damasa M. Macandog and was composed of young researchers who closely worked with the UP Diliman Training Center for Applied Geodesy and Photogrammetry, the overall lead of the project.
UPLB was assigned in Laguna and the provinces of Oriental Mindoro, Occidental Mindoro, Marinduque, Romblon, and Palawan (MIMAROPA). The team’s main task was to generate and apply various methods for extracting different information on natural resources from LiDAR data.
Data extraction from LiDAR
The Phil-LiDAR 2 was composed of five component projects. These projects focused on generating protocols for coordination and methodologies for extracting five different natural resources maps from the LiDAR data and other datasets.
Four of these component projects were led by UPLB, namely, Agricultural Resources Extraction (PARMAP); Aquatic Resources Extraction (CoastMap); Forest Resource Extraction (FRExLS); and Philippine Renewable Energy Resources Mapping (REMaps). The projects’ respective names give a clear-cut distinction as to what resources are being mapped. The component projects may be different from each other but as a whole, it sought to identify the location of annual crops and trees, or the roads and buildings, or even fishponds and mangroves.
Phil-LiDAR 2 can also identify which areas have vulnerable agriculture and aquatic resources, the areas with the highest concentration of rivers and lakes, and the suitable places where people can access renewable energy.
Various methods and techniques were done for the extraction. Remote sensing, geographic information system (GIS), and use of computer languages were employed to create models and simulations.
Traditional methods of manual digitization of LiDAR- derived images were also put into good use.
PARMAP and CoastMap in particular, made use of Object-based Image Analysis (OBIA) which can classify clusters of colors on a map into useful information.
From the LiDAR images, PARMAP and CoastMap can easily identify rice paddies, fishponds, fish pens and cages, mangrove areas, coconut plantations, various crops, bare lands, buildings, and roads, among other things. Outputs can be generated in a matter of few days, but in the case of FRExLS and REMaps, it took months before results were obtained. Codes and commands were programmed on a Linux-powered computer to get biomass estimation, carbon stock, and renewable energy resource maps.
In the end, FRExLS was able to identify and classify open and closed forests. REMaps on the other hand was able to generate energy potential maps for solar, wind, and biomass resources.
The whole extraction process took several man-hours to carefully monitor the daily progress of computer processing. It sometimes required dividing the researchers’ work into morning and evening shifts. Most challenging times for the researchers were during the typhoon season when there were very frequent power interruptions. It was like hitting the reset button in a video game that has no save option.
It was not always watching computer screens for 6-7 hours though. The Phil-LiDAR 2-UPLB team also spent valuable time in the field to make sure that LiDAR data matches with what is actually on the field. Stationed at the Ecoinformatics Laboratory behind the Biological Sciences Building, researchers welcome field validation and groundtruthing as pleasant breaks.
Spreading the light
As the Phil-LiDAR 2 team from UPLB collaborated with different SUCs, LGUs, and NGOs in Laguna and MIMAROPA in conducting training and fieldwork demonstrations for collecting datasets, it was able to successfully generate the following maps: agricultural and coastal land cover, benthic habitat, carbon stock, biomass estimation, biomass resource, solar resource, and wind resource.
Vulnerability assessment maps for Sta. Cruz, Laguna (agriculture) and Gloria, Oriental Mindoro (mangrove, corals, and seagrass) were made while spectral signatures of selected agricultural and aquatic resources were also collected.
Maps generated from the project were distributed to various municipalities as well as concerned national government agencies that have jurisdiction over the resources. The rich set of data can now provide valuable bases for these LGUs to develop and operationalize programs for local resource management and disaster preparedness.
In Oriental Mindoro for example, the Mansalay LGU has used the mangrove and fishponds map generated from CoastMap to draft its Climate and Disaster Risk Assessment Report. Calapan City also made use of the LiDAR generated maps for its Environmental Impact Assessment Report.
These are only a few of what data from LiDAR can do. It does not only raise awareness to resource vulnerabilities but also establishes a culture of preparedness to natural hazards.
As the government continues to invest in technological innovations for the people, Phil-LiDAR 2 and UPLB hope to carry on spreading that light. A number of projects have already been proposed to various funding agencies and institutions so that processed LiDAR information would continue benefitting the government and the Filipino people.
Through these proposed projects, more value can be put into the government’s investments and generate other promising outputs. These could include near real- time vegetation and built up monitoring system; a biodiversity information system, and a food security information system – all of which can aid planning, decision-making and policy formulation in the face of global climate change and variability. ■