A novel building metric for PlanetScope© data optimization

PlanetScope © data are being widely used by the scientific community in addressing important environmental issues, including cropland and tree cover loss, burned area mapping post-wildfire, imagery for river water masking, and snow-covered area mapping. However, building indices’ applications on PlanetScope data for use in separating blocks and to identify the difference between altered/unaltered constructed areas are not available, primarily due to the absence of the Short-Wave Infrared (SWIR) band. In this research, three types of remote sensing data series are employed, namely, PlanetScope images, Sentinel-2A data, and Google Earth Pro data, to separate the blocks and to capture the differences between damaged and undamaged blocks in the Khan Younis town of the Gaza Strip, utilizing a novel building metric purposely developed to be used with PlanetScope data. Additionally, three change detection methodologies are utilized to assess the efficacy of the new metric, namely, image differencing, Principal Component Analysis (PCA), and metrics, including the one proposed in this paper. The proposed metric exhibited high performance with the PlanetScope data compared to Sentinel-2A data. The results of the three methodologies indicate a strong correlation, with an R-value of 0.793,263 and a P-value of 0.0002456. The average sizes of the affected areas, derived from PlanetScope data from October 2023 to April 2024, are 0, 0.05, 2.4, 2.9, 5.2, 12.8, and 13.1 km 2 . The Sentinel data from October to January shows that the average sizes of the devastated regions are 0, 0.9, 2.1, and 7 km 2 , while the rest of the data series was concealed and unavailable by Google Earth Engine (GEE). The accuracy assessment test conducted with the Google Earth Pro scenes to evaluate the strength of the metric showed a considerably strong percentage of compatibility with PlanetScope data. The new proposed building metric yields results that are very close to the other techniques adopted, with slight variations but clearer visual outputs.