A Study on the Difference of LULC Classification Results Based on Landsat 8 and Landsat 9 Data

Abstract

Landsat 9 enhances the radiation resolution of the operational land imager from the 12 bits of Landsat 8 to 14 bits. The higher radiation resolution improves the sensitivity of the sensor to detect many subtler differences, especially in the case of dense forests or water. However, it remains unclear whether the difference in radiation resolution between Landsat 8 and Landsat 9 actually affects the classification results of water and tree species. Accordingly, the spectral reflectance and vegetation indices were extracted in this study, based on Landsat 8 and Landsat 9 images. Then, the classification models of land use and land cover (LULC) and tree species were developed by using a gradient tree boosting algorithm. Subsequently, the results were analyzed to further investigate how the differences in radiation resolution affect the classification results of LULC and tree species. It is shown that the LULC classification results of Landsat 8 and Landsat 9 are relatively favorable in most cases. However, the LULC classification results are relatively poor in test areas with a lower classification accuracy of water. Further analysis, in the case of test areas with poor classification results, indicates that there are significant differences in the water classification results between the two datasets. In other words, Landsat 9 produces better water classification results than Landsat 8 in most test areas. However, a temperature close to zero may lead to inverse water classification results. In addition, it indicates that the difference in forest classification results between the two datasets is small, but the results of forest tree species classification based on Landsat 9 are superior to those based on Landsat 8, with an improvement in overall accuracy of 6.01%. The results demonstrate that the difference in radiation resolution between Landsat 8 and Landsat 9 has little impact on the results of LULC classification in most cases. Nevertheless, in the case of some test areas, Landsat 9 is better suited for enhancing the classification accuracy of water and tree species.

Lineament Mapping in Batie Area (West-Cameroon) Using Landsat-9 Operational Land Imager/Thermal Infrared Sensor and Shuttle Radar Topography Mission Data: Hydrogeological Implication

“…The cartographic database used in this study includes geological maps at 1:500,000 scale [23]. Landsat 9 was successfully launched on 27 September 2021, equipped with OLI-2 and TIRS-2 [25,26]. The OLI and OLI-2 sensors are almost similar and will provide consistent images of the earth's surface with similar spatial, spectral, geometric, and radiometric qualities.…”

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

“…Launched on 27 September 2021, and operational since 2023, Landsat 9 utilizes the OLI-2 and TIRS-2 sensors to capture data in the visible, infrared, and TIR spectra, providing high-resolution and multispectral capabilities for Earth observation [67]. Research by You [70] shows that Landsat 9 outperforms Landsat 8 in water body and tree species classification, attributed to its increased radiometric resolution from 12 bits (Landsat 8) to 14 bits, improved sensitivity to brightness and color, and enabling the detection of subtle differences, particularly in darker areas like water bodies. However, as a new satellite, Landsat 9 may present uncertainties and challenges related to data quality, sensor performance, and data processing algorithms [70].…”

“…Research by You [70] shows that Landsat 9 outperforms Landsat 8 in water body and tree species classification, attributed to its increased radiometric resolution from 12 bits (Landsat 8) to 14 bits, improved sensitivity to brightness and color, and enabling the detection of subtle differences, particularly in darker areas like water bodies. However, as a new satellite, Landsat 9 may present uncertainties and challenges related to data quality, sensor performance, and data processing algorithms [70].…”

“…Band (B), wavelength (L), and resolution (S) information of Landsat (L) 4-5/7/8/9, modified from[27,67,70].…”

Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest

“…Está equipado con un generador operativo de imágenes terrestres (oli-2) y un sensor térmico infrarrojo (tirs-2), que producen once bandas espectrales y resolución máxima de 15 m, con frecuencia periódica de 16 días [7]. Estas imágenes han ganado una considerable atención en la investigación gracias a su acceso libre y cobertura global [8].…”