RT info:eu-repo/semantics/article
T1 Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
A1 Compains Iso, Leyre
A1 Fernández Manso, Alfonso
A1 Fernández García, Víctor
A2 Ingenieria Agroforestal
K1 Ingeniería forestal
K1 Endmember
K1 IES
K1 Fraction image
K1 Spectral library
K1 MESMA
AB [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
PB MDPI
LK https://hdl.handle.net/10612/20717
UL https://hdl.handle.net/10612/20717
NO Compains Iso, Leyre, Alfonso Fernández-Manso, and Víctor Fernández-García (2022). Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA. Forests 13, no. 11: 1824. https://doi.org/10.3390/f13111824
DS BULERIA. Repositorio Institucional de la Universidad de León
RD Jul 11, 2024