Search

Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cover products due to dense-cloud-contaminated and/or technical issues. This study proposes a novel approach (Ensemble Learning Updating Classifier/ELUC), which can be applied with various classification algorithms and data sets to simplistically generate new classifications or renew existing classifications with a remarkable accuracy improvement. Applying miscellaneous features of Landsat-8 images, the ELUC of a random-forest-based algorithm produces sequences of single-time classifications with a mean overall accuracy of 84%. Through the study period, these sequences of individual classifications were then joined to achieve a final classification which reaches an overall accuracy of 94%. Also, the ELUC of the random-forest-based algorithm outperforms that of Kernel-Density-Estimation with a 5% overall accuracy higher. These outcomes confirm the effectiveness of the ELUC for a remarkably consistent land use/cover estimation with a data-rich environment. [ABSTRACT FROM AUTHOR]

A land use/land cover map is an important input for different applications. However, the accuracy of land cover maps remains a great uncertainty and mapping accuracy assessment is not well-documented. The objective of this paper is to examine the relationship between overall accuracy and the number of classification classes by conducting a literature review of land cover/land use studies. The results revealed a weak negative correlation between the map's accuracy and the number of classes. The paper suggests a decrease of 0.77% map's overall accuracy with respect to the increase of 1 land cover class. The average overall accuracy produced by 05 sensor types does not show the big difference. In addition, high spatial resolution sensor such as Airborne might not be always advantageous for producing high overall accuracy map since its accuracy depends on several factors including the number of land cover classes. [ABSTRACT FROM AUTHOR]