Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico
| Autor: | Martin Enrique Romero-Sanchez, Steven E. Franklin, Raul Ponce-Hernandez, Carlos Arturo Aguirre-Salado |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | International Journal of Remote Sensing. 36:2786-2799 |
| ISSN: | 1366-5901 0143-1161 |
| DOI: | 10.1080/01431161.2015.1047991 |
| Popis: | A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper ETM+ scan-line corrector SLC are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: 1 local linear histogram matching LLHM; 2 neighbourhood similar pixel interpolator NSPI; 3 geostatistical neighbourhood similar pixel interpolator GNSPI; and 4 weighted linear regression WLR. All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients κ, and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis ISODATA land-cover classification maps. In addition, Pearson correlation coefficients r and root mean squares of the error RMSEs were employed for estimates agreement with fractional land cover. The best results visually overall accuracy > 85%, κ 0.84 and RMSE |
| Databáze: | OpenAIRE |
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