Remote Sensing
Remote sensing involves capturing information about the Earth's surface from a distance, often using satellites. It provides crucial spatial data for mapping and analysis. The synergy between remote sensing and geoinformatics allows for comprehensive insights into environmental changes, land use, and natural phenomena.
Remote sensing involves capturing information about the Earth's surface from a distance, often using satellites. It provides crucial spatial data for mapping and analysis. The synergy between remote sensing and geoinformatics allows for comprehensive insights into environmental changes, land use, and natural phenomena.
Land Cover Classification in Bolinao, Pampanga using Landsat 9 Satellite Imagery
Land Cover Classification in Bolinao, Pampanga using Landsat 9 Satellite Imagery
Remote sensing provides the capability to capture information that may go unnoticed by the human eye, offering a nuanced perspective on the landscape. By acquiring data in various wavelengths, it unveils details about land cover, objects, and phenomena that might be otherwise invisible. Indices in remote sensing offer a systematic means to interpret images by assigning distinct spectral bands to represent specific land cover or features. This method enhances the quantitative representation of physical properties. In this study, indices were deployed to accentuate critical features within the research area, aiding in the identification of regions of interest crucial for training data in supervised classification. Both supervised and unsupervised classification algorithms were explored, with diverse band combinations tested to optimize classification accuracy. The obtained results were systematically compared against previously published data, though it is acknowledged that further accuracy assessments are recommended for a comprehensive evaluation.
Remote sensing provides the capability to capture information that may go unnoticed by the human eye, offering a nuanced perspective on the landscape. By acquiring data in various wavelengths, it unveils details about land cover, objects, and phenomena that might be otherwise invisible. Indices in remote sensing offer a systematic means to interpret images by assigning distinct spectral bands to represent specific land cover or features. This method enhances the quantitative representation of physical properties. In this study, indices were deployed to accentuate critical features within the research area, aiding in the identification of regions of interest crucial for training data in supervised classification. Both supervised and unsupervised classification algorithms were explored, with diverse band combinations tested to optimize classification accuracy. The obtained results were systematically compared against previously published data, though it is acknowledged that further accuracy assessments are recommended for a comprehensive evaluation.