Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
[EN] Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscap...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universidad de León |
| Repositório: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/20717 |
| Acesso em linha: | https://www.mdpi.com/1999-4907/13/11/1824 https://hdl.handle.net/10612/20717 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Ingeniería forestal Endmember IES Fraction image Spectral library MESMA |
| Resumo: | [EN] Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscapes in which types of habitats vary at fine spatial scales. The objective of this work is to analyze the influence of spectral libraries of various characteristics on the performance of MESMA. To this end, eight spectral libraries from Landsat satellite images were elaborated with different characteristics in terms of size, composition, and temporality. The spectral libraries were optimized using the iterative selection of endmembers (IES) method with the MESMA technique to obtain the fraction images considering five habitat classes (forest, shrubland, grassland, water, and rock and bare soil). The application of MESMA resulted in the classification of more than 95% of pixels in all cases with a root mean square error (RMSE) less than or equal to 0.025. Validation of the fraction images through linear regressions resulted in an RMSE ≥ 0.35 for the shrubland and grassland classes, with a lower RMSE for the remaining classes. A significant influence of library size was observed, as well as a signi |
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