Abstract
The Phil-LiDAR 2 program aims to extract the natural resources of the Philippines from the available two points per square meter LiDAR data. Mangroves, being coastal resources, were one of the foci of this program under the Aquatic Resources Extraction from LiDAR Surveys (CoastMap). The object-based image analysis (OBIA) approach, and support vector machine (SVM) algorithm were utilized to classify three major classes from the LiDAR data, namely: mangrove, other vegetation, and non-vegetation. Object feature values used in the classification include the mean, standard deviation, mode, and texture values from the generated LiDAR derivatives. These derivatives include the Digital Surface Model (DSM), Digital Terrain Model (DTM), Canopy Height Model (CHM), Intensity, Number of Returns, Normalized DSM (NDSM), Slope, and Slope of Slope. Moreover, field data collection and validation provided key references in the supervised SVM classification and contextual editing of the extracted mangrove areas. From the implemented classification, an overall accuracy of above 90% was achieved. Focusing with the final classified mangrove coverage, management of the mangrove resources can be made proper and efficient. Furthermore, high resolution or detailed spatial information can support programs like Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) and biodiversity studies.
Highlights
Mangrove ecosystems are formed along the tropical and sub-tropical coasts
Mangrove ecosystem serves as a home to monitor lizards, fishing cats, monkeys, manatees, sea turtles, and mud-skipper fish
Having a two points per square meter resolution, additional utilization of the LiDAR data was given to the Phil-LiDAR 2 Program for detailed resource extraction
Summary
Mangrove ecosystems are formed along the tropical and sub-tropical coasts. It extends in the brackish water along the streams and rivers [1]. Traditional mapping of resources are costly, only cover small area, and difficult to conduct [10] New technology such as remote sensing and Geographic Information System (GIS) must be used to efficiently map the resources. Light Detection and Ranging or LiDAR, aerial LiDAR, is a technology that can provide a higher resolution image of an area as compared to MODIS, Landsat, and Sentinel satellite images It uses pulse of light energy in measuring the distance between sensor and target object [16]. Previous studies conducted using Support Vector Machine (SVM), in combination with OBIA, were able to provide satisfactory results for mapping land cover, mangrove resources. The parameters used were object texture, basic metrics, and distance to water from the image
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