| Journal: | Boletim de ciencias geodesicas |
| Database: | PERIÓDICA |
| System number: | 000457303 |
| ISSN: | 1413-4853 |
| Authors: | Silva, Murilo Schramm da1 Vibrans, Alexander Christian1 Nicoletti, Adilson Luiz1 |
| Institutions: | 1Universidade Regional de Blumenau, Blumenau, Santa Catarina. Brasil |
| Year: | 2021 |
| Volumen: | 27 |
| Number: | 3 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English abstract | A challenge for the use of medium spatial resolution imagery for land use change detection consists of the reduced availability of ground reference data for previous dates. This study aims to obtain invariant training points using the backdating process for supervised classification of images that have no field data available. The study area comprises 1,353 km² in Santa Catarina, southern Brazil. We compared the accuracy performance of invariant area sets (binary change maps) generated by using three methods (IR-MAD - Iteratively Reweighted Multivariate Alteration Detection, CVA - Change Vector Analysis and SGD - Spectral Gradient Difference) for two periods (2017-2011 and 2011-2006). The classification of the Landsat-5 TM image of 2006 was performed using as training data the sets of points indicated as invariant in the binary maps resulted from the three abovementioned methods. The accuracies for seven land-use classes were computed. The overall accuracy was greater (80,5% and 80,2%) when using training areas achieved by CVA and SGD, respectively than IR-MAD (76%). Were obtained accuracies greater than 80% for the forest class. The results stress that the combination of the IR-MAD and SGD is preferable since the CVA is more time consuming due to the subjective application of thresholds |
| Disciplines: | Geociencias |
| Keyword: | Geodesia, Cobertura del suelo, Píxeles invariantes, Retrodatación, Bosques tropicales, Brasil |
| Keyword: | Geodesy, Land cover, Invariant pixels, Backdating, Tropical forests, Brazil |
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