This report aims to analyze land cover change of Metro Manila from 2009 to 2018. Major drivers of land cover change are urban development, agriculture and mining as their rate exceeded reforestation rate.

This part aims to analyze land cover change of Metro Manila from 2009 to 2018. We collected and pre-processed multi-temporal satellite images from Landsat 5, 7, and 8 and Sentinel 2. The pre-processed images were classified using pixel-based supervised classification techniques including Support Vector Machine (SVM), Neural Network (NN), Random Forest (RF) and Maximum Likelihood Classifier (MLC). The average overall accuracy of NN was roughly 1% higher than SVM and RF and about 8% higher than ML. Less than 33% of the study site’s land cover changed. Built-up area gained 10.3 % in nine years with urban expansion rate doubling in the past four years. Vegetation cover lost 12.4% in the same period. Tree cover dropped from 48.9% in 2009 to 42.64% in 2014 but slightly recovered to 43.4% in 2018 which is slightly higher than the 42.8% built-up cover in 2018. Major drivers of land cover change here are urban development, agriculture and mining as their rate exceeded reforestation rate. Our results call for strengthening of the implementation of the UMRB Protected Landscape law to protect and preserve the vegetal cover in the study site. This also calls for equitable land use planning for the watershed and proper implementation for sustainable resource use as less space becomes available for the competing demands.

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.

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.