LiDAR Data Processing, Modeling and Validation by HEIs for the Detailed Resources Assessment in Luzon: Region 3 and Pangasinan

Central Luzon State University

A. Significance

Signals of changing climate are already evident in the Philippines as shown in the increasing temperature trends, sea level rise and extreme climate event occurrences. The Philippines runs the risk of being affected by more frequent precipitation events due to increased south-west monsoon activities and severe storm occurrences have been taking place lately, causing massive landslides and flashfloods (Amadore, 2005). Central Luzon is considered the food basket for the Philippines. Other regions are dependent on Central Luzon for food and other resources. Since Central Luzon is one of the key players in the agricultural sector in the Philippines, it plays a vital part in the food security of the nation most especially the province of Nueva Ecija, which is considered as the “Rice Granary of the Philippines” where 50% of the country’s total produce are coming from. The fact the province and the region is vital in the country’s food security, it is vulnerable to disasters just like other regions of the country. The contributing factors to disaster risks are environmental degradation, settlement patterns, livelihood choices and human behavior. Results of which are even more harmful on human development and environmental assets, thus, the chances of disaster risk is very high considering that the environment, development and disasters are interconnected. Climate change is likely to have a significant impact on global environment. In general, the faster the climate changes, the greater will be the risk of damage. Some agricultural regions will be threatened by climate change, while others may benefit. The impact on crop yields and productivity will vary considerably. Hence, determination of the vulnerability of a certain agroecosystem towards climate change impacts is very vital. In recent years, earth surface height data has become a vital component of many geospatial planning strategies and is widely used by government agencies and the commercial sector for a variety of applications from flood risk modeling to urban development. The emergence of new spatial data acquisition systems such as Light Intensity Detection and Ranging (liDAR) and Airborne Radar Interferometry (INSAR) presents complementary or alternative solutions to the acquisition of spatial information unanswered by existing technologies such as aerial photography or satellite imagery. The coverage and accuracy of topographic data extracted by these systems, complemented by the features detected by an onboard digital aerial camera, provide rich information that would greatly benefit agencies using spatial data. Data verification and validation project acts as a quality control for the output of the program. LiDAR is a rapid geospatial data acquisition system that can produce robust datasets and collect data in a wide range conditions. But just like any other data acquisition system, it is subject to random and systematic errors. This process will carry out management activities prior to data collection to ensure that the raw and derived data pass the quality requirements of succeeding projects. This includes the calibration, ground validation surveys, and review of data collection activities. It also involves consistent checking to ensure the integrity, correctness and completeness of the data. This project can provide a baseline data in determining the vulnerability of the province towards climate change using LiDAR to determine the spatial distribution of high value crops in the agroecosystems, aquatic resources, forest resources and renewable energy resources.

B. Project Objectives

Generally, it aims to produce detailed resource maps using LiDAR for various applications: production of high value crops; irrigation assessment; aquaculture production; forest protection and discovery of renewable energy sources. Specifically, it aims to: a. Select and improve existing algorithms and workflows for extracting agricultural features from LiDAR data and various types of remotely sensed data b. Characterize feature extracted from LiDAR data and various types of remotely sensed data c. Produce detailed and high-accuracy nationwide resource maps to determine spatial distribution of major resource using RS/GIS d. Produce vulnerability assessment maps of high value crops and aquatic resources and other resources to climate change e. Formulate recommendations to help address future local supply and demand in agriculture, coastal, forest and renewable resources

B. Co-Implementing Agencies

1. Training Center for Applied Geodesy and Photogrammetry, UP Diliman
2. Advanced Science and Technology Institute
3. UP Diliman
4. Isabela State University
5. Mariano Marcos State University
6. Ateneo de Naga
7. UP Los Baños
8. Mapua Institute of Technology
9. Visayas State University
10. UP Cebu
11. University of San Carlos
12. Central Mindanao University
13. CARAGA State University
14. Mindanao State University-Iligan Institute of Technology
15. UP Mindanao