Comparison of Landsat-9 and Prisma Satellite Data for Land Use / Land Cover Classification

Abstract

Abstract. Land use and land cover (LU/LC) detection has great significance in management of natural resources and protection of environment. Hence, monitoring LU/LC with the state-of-the-art approaches has gained importance during the recent years and free access satellite images have become valuable data source. The aim of this study is to compare classification abilities of Landsat-9 and PRISMA satellite images while applying Support Vector Machine (SVM) algorithm to distinguish different LU/LC classes. For this purpose, the study area was chosen to be of heterogeneous character that includes industrial area, roads, residential area, airport, sea, forest, vegetation and barren land. When the classification results were visually examined, it was seen that forest, industrial area and airport classes were distinguished more accurately than other classes. On the other hand, qualitative results were validated with quantitative accuracy assessment results. The overall accuracy (OA) and Kappa coefficient values were calculated as 89.33 and 0.88 for Landsat-9 satellite image and as 92.33 and 0.91 for the PRISMA satellite image, respectively. In the accuracy assessment results, although Landsat-9 and PRISMA satellite images showed similar classification performances, a slight improvement was observed by using the PRISMA satellite image. The findings indicated that although both of the Landsat-9 and PRISMA satellite images were proper data to assess the LU/LC of the complex region, a slightly more performance could be achieved by using the PRISMA satellite image.

Estimation of Mangrove Fractional Cover From Multispectral and Hyperspectral Data Using Mixture Tuned Matched Filtering

“…With these hyperspectral satellite images, various applications are assessed vis-à-vis multispectral satellite images, examining whether the use of hyperspectral images provide significant accuracy improvements at various levels of information. As an example, Kokal et al (2022) used PRISMA and LANDSAT-9 in land cover classification using Support Vector Machine classifier and PRISMA was found to produce slightly better overall accuracy. Land cover classes included industrial area, roads, residential area, airport, sea, forest, vegetation, and barren land.…”

“…Land cover classes included industrial area, roads, residential area, airport, sea, forest, vegetation, and barren land. Kokal et al (2022) concluded that this was due to the higher spectral resolution of PRISMA compared to LANDSAT-9's spectral resolution of 20-180 nm. In forest type discrimination, PRISMA yielded better results compared to Sentinel-2 Multi-Spectral Instrument (MSI) using two different nomenclature systems and four separability metrics (Vangi et al, 2021).…”