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Geo-SAFER Mindanao Program

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Jojene Santillan
added 2 research items
Access to near-real time information on the spatial distribution and detailed characteristics of the current and future (forecasted) flood scenarios is crucial for effective flood forecasting and early warning, especially when formulating decisions related to evacuation and response before, during, and after a flood scenario. In this paper, we present the development and application of a web GIS platform called "Near-real Time Flood Event Visualization and Damage Estimations (NRT-Flood EViDEns) that has the capability to show detailed maps of current and forecasted flood characteristics, including the capabilities to analyze and provide maps and statistics of the impacts of flooding to various infrastructures such as buildings, roads and bridges. The flood information reported by the platform are sourced from a two-dimensional flood model based on HEC RAS 5 that utilizes high-resolution LiDAR data, satellite-derived land-cover, and near-real time hydrological and meteorological data as among its vital inputs. The 2D flood model simulates historical (last 24 hours), current, and future (next 24 hours) flood scenarios at 30-minute interval. A combination of web mapping data storage, visualization and analysis tools that include OpenLayers, Geoserver, GeoDjango, Javascript, and PostgreSQL/PostGIS are utilized to enable the user to perform flood characteristics visualization and spatial overlay analysis for impact assessment. The accuracy of the flood depths and extents generated by the platform was determined to range from 52 to 71% overall accuracies and Root Mean Square Errors ranging from 0.30 to 0.58 m based on historical flood events that were simulated. The web platform is expected to be used for operational flood monitoring and forecasting, and is envisioned to be an important tool for geo-spatially informed decision making in Butuan City.
Above ground biomass (AGB) of mangroves is considered as an important ecological and habitat management indicator of various environmental conditions and processes in mangrove ecosystems. In this study, Sentinel-1 images were used to model and estimate AGB of mangroves in Del Carmen, Siargao Islands, Philippines. There were three predictor variables derived from the Sentinel-1 image used for modeling the AGB: the backscatter value from VV polarization, backscatter value from VH polarization, and the combination of the backscatter values from VV and VH polarizations. The modeling was done through linear regression between the field-measured AGB and the predictor variables, and the coefficient of determination (R 2) and root mean square error (RMSE) were determined. Among the three predictor, the combination of the VV and VH polarizations produced a better model compared to the two predictor variables as it obtained the highest R 2 of 0.43 and the lowest RMSE of 12.65 Mg/ha. Based on the developed model, a map of AGB of mangroves in the study area was generated. The generated map showed that AGB of mangroves in the study site ranges from 73.37 Mg/ha to 225.51 Mg/ha with an average of 140.2 Mg/ha. The AGB model derived in this study can be used for multi-temporal monitoring of mangroves in the study area using Sentinel-1 images.