Comparative evaluation of operational land imager sensor on board landsat 8 and landsat 9 for land use land cover mapping over a heterogeneous landscape

Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat Operational Land Imager (OLI) and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy than OLI. The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.

Automated summary

This study compares the accuracy of Landsat satellites' OLI and OLI-2 sensors in land use land cover (LULC) mapping. Image fusion techniques were applied to improve the spatial resolution of OLI and OLI-2 multispectral images, followed by LULC mapping using a support vector machine (SVM) classifier. The results demonstrate that OLI-2 provides more accurate LULC classification than OLI. Validation of the classified LULC maps reveals better performance of OLI-2 in distinguishing dense and sparse vegetation, as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also demonstrates the superiority of OLI-2 in LULC mapping compared to OLI.