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

Haotian You, Xu Tang, Weixi Deng, Haoxin Song, Yu Wang () and Jianjun Chen
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Haotian You: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China
Xu Tang: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China
Weixi Deng: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China
Haoxin Song: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China
Yu Wang: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China
Jianjun Chen: College of Geomatics and Geoinformation, Guilin University of Technology, No.12 Jiangan Street, Guilin 541006, China

Sustainability, 2022, vol. 14, issue 21, 1-24

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.

Keywords: Landsat 8; Landsat 9; LULC classification; water; tree species (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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