GEOSPATIAL
Land use classification over a highly-urbanized region using multi-resolution images

TOOLKIT / TOOLS / GEOSPATIAL / Land Use Classification

Mapping land use classes can be challenging especially in highly-urbanized regions due to the diversity of materials and structures. We aimed to generate a land use classification for Metro Manila by combining spatial data derived from Sentinel-2 image, IFSAR DEM and DSM and segmented WorldView2 images and classified land use using Support Vector Machine.

We were able to generate a land use classification with an overall accuracy of 81.6%. Our results show that the addition of informational layers such as height of the structure, dimensions, texture, distance and density improved the classification accuracy by 13.8% higher than with the RGB image classification. In addition, informal settlements can be classified more accurately (PA=85.86% and UA=79.07%). There was a great difficulty in accurately classifying industrial areas (PA=69.06% and UA=69.32%), commercial areas (PA=66.34% and UA=73.46%), and residential areas (PA=65.92%, UA=70.52%). These results can help in estimating the informal settlement population and exposure to various hazards in Metro Manila.

We also mapped the land use for 2011 using LiDAR DEM, DSM and orthophoto for Metro Manila, Pasig City and Barangay Batasan Hills in Quezon City to apply the methodology using another dataset. Using the same methodology, but without ground truthing activity, results appeared to have overestimated the informal settlements for Metro Manila and Quezon City. The best land use classification was for Barangay Batasan Hills. Even without ground truthing and relying on visual assessment, this exercise showed that adding more spatial information layers enable land use classification. In addition, this study showed that the collection of sufficient training and ground truthing sites are important in improving the land use classification accuracy.

Manila Observatory’s Resilience Collaboratory

The Resilience Collaboratory runs activities and programs that aim to strengthen the resilience capacities of communities that are exposed to frequent hazards, to address local vulnerabilities, and to support efforts to adapt to disaster risks and climate impacts. The Resilience Collaboratory facilitates collaboration among the core laboratories of the Manila Observatory with a shared goal of delivering useful and usable climate and disaster risk information to the most vulnerable, and initiates transdisciplinary partnerships with various institutions outside academia especially civil society organizations, and the public and private sectors.

For more information about the Resilience Collaboratory, visit the Manila Observatory website.

Geomatics for Environment and Development Laboratory

GED applies remote sensing and geographic information systems (RS-GIS) technologies to process social and environmental data in map form. This is in order to provide information and knowledge needed to study and analyze socio-environmental themes, dynamics and spatial patterns of disaster risk, resource utilization, and sustainability. Our outputs guide the use of ancillary tools, policies, and plans for climate change adaptation and mitigation as well as disaster risk reduction and management towards sustainable development of local communities in their wider context. Development theory engendered in GED is that sustainable development is best ladderized although cross-cutting, as follows:

1. Human and Resource Security (Water-Energy-Food Nexus, Human Settlements and Health based on Demand, Supply, Access, Utilization)
Inclusivity
2. Equity with (Smart) Growth
3. Climate Change Mitigation
4. Co-Beneficial Climate Change Adaptation and Disaster Risk Reduction and Management (CCA-DRRM) towards more risk-sensitive Comprehensive Land Use Plans (CLUPs)
5. Resilience
6. Overall and Demonstrable SD Cross-Sectoral Integration

For more information on the GED Laboratory, please visit their page.

RESEARCH TEAM

Dr. May Celine Thelma Vicente

Ms. Flordeliz Del Castillo

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