Geospatial Analysis using Planet Labs

Remote Sensing Workflow

A good example of a remote sensing workflow is a land cover classification project executed by the commercial entity, Planet Labs. Planet Labs uses high-resolution satellite imagery to monitor and classify land cover changes globally. The workflow begins with project scoping, where objectives such as tracking deforestation or urban expansion are defined. The next step is data acquisition, which involves the collection of high-resolution imagery from Planet’s fleet of Dove satellites for frequent revisits to capture temporal changes (Planet, 2024).

Data preprocessing follows, involving radiometric and geometric corrections to standardize images and remove distortions. This step includes atmospheric correction to eliminate atmospheric interference. The images are then turned into a mosaic to represent seamless coverage of the study area. The feature extraction phase uses advanced algorithms for spectral enhancement, followed by supervised classification methods where known land cover types are identified using ground truth data (Planet, 2024).

In the analysis and interpretation phase, classified data is analyzed to determine land cover distribution and changes over time (Kotaridis & Lazaridou, 2021). This step is important for deriving insights such as the rate of deforestation or the extent of urban sprawl. An accuracy assessment is conducted using a confusion matrix to evaluate the classification\\\\\\\'s accuracy against ground truth data. The final step is reporting, where findings are documented in detailed reports, including maps and metadata for transparency and reproducibility.