ABSTRACT:Considering that the Philippines is archipelagic in nature and is exposed to disasters accentuated by climate change, water resource monitoring and management has been an important concern in the country. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, a 3-year project entitled "Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys", has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs, supplemented with other remotely-sensed imageries and ancillary information. Since the project covers national-scale mapping and inventory, the implementation was structured to be a collaborative effort between fifteen (15) State Universities/Colleges (SUCs) and Higher Education Institutes (HEIs), along with multiple National Government Agencies (NGAs) and Local Government Units (LGUs). This paper presents the project's general structure, focusing mainly on its attempts and accomplishments in strengthening individual capacities of all involved SUCs, HEIs, and stakeholders utilizing hydrologic data for different applications.
Abstract. Updating of seasonal agricultural crop map is limited by the local knowledge of the mapper. Mapping of previously unaccounted agricultural plots involve massive field works aided by very high-resolution images. The phenological cycle of seasonal crops like sugarcane, with a range of ten (10) to twelve (12) months from planting to harvesting, exhibit a unique characteristic in terms of radar backscatter and time. In this paper, a pattern matching algorithm was tested to detect sugarcane plantations. Dynamic Time Warping (DTW), which was originally used for voice recognition, was used to detect sugarcane plantations from multitemporal Sentinel-1A images. Using known sugarcane plots, temporal signatures were gathered and used to detect other plantations in the area. The result helped the Sugar Regulatory Administration (SRA) in updating the inventory of sugarcane plantations faster with detection accuracy of more than 92 percent.
Abstract. The Urban Heat Island (UHI) is a phenomenon where an urban area experiences higher temperatures than its surroundings. A commonly observed phenomenon worldwide and is one of the serious environmental problems related to urbanization. This paper assessed the past and current state of UHI in Baguio City, the Summer Capital of the Philippines. Land Surface Temperature (LST) layers were generated from Landsat images (March 25, 2019, and March 09, 2022) using the Project GUHeat Toolbox and then used to calculate the Urban Thermal Field Variance Index (UTFVI). The study found out that the UHI has intensified in the past three years. In contrast LST in March 2022 was generally lower than that in March 2019, most likely due to differences in weather conditions. This implies that while it is important to examine the spatiotemporal variations of LST, it is critical that UHI indices are also examined. Random Forest regression was used to examine the UHI indices such as Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The explanatory variables used in modelling are (1) NDBI (2) NDVI (3) combination of NDBI and NDVI. The performance of the models is evaluated with Mean Squared Error (MSE) and R-squared (R2). Using NDBI or NDVI alone yielded a less satisfactory model. The combination of NDBI and NDVI resulted in a good prediction of UHI with R2=0.89 and MSE=0.006.
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