Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/11/1824 |
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| author | Leyre Compains Iso Alfonso Fernández-Manso Víctor Fernández-García |
| author_facet | Leyre Compains Iso Alfonso Fernández-Manso Víctor Fernández-García |
| author_sort | Leyre Compains Iso |
| collection | DOAJ |
| description | 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 significant effect of temporality, with the best results obtained for the largest monotemporal libraries. |
| first_indexed | 2024-03-09T19:04:43Z |
| format | Article |
| id | doaj.art-ad24f5e9f98c4a669638eafe414a634f |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T19:04:43Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-ad24f5e9f98c4a669638eafe414a634f2023-11-24T04:43:48ZengMDPI AGForests1999-49072022-11-011311182410.3390/f13111824Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMALeyre Compains Iso0Alfonso Fernández-Manso1Víctor Fernández-García2Agrarian Science and Engineering Department, School of Agricultural and Forestry Engineering, University of León, 24400 Ponferrada, SpainAgrarian Science and Engineering Department, School of Agricultural and Forestry Engineering, University of León, 24400 Ponferrada, SpainEcology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, Universidad de León, 24071 León, SpainSpectral 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 significant effect of temporality, with the best results obtained for the largest monotemporal libraries.https://www.mdpi.com/1999-4907/13/11/1824endmemberIESfraction imagespectral libraryMESMA |
| spellingShingle | Leyre Compains Iso Alfonso Fernández-Manso Víctor Fernández-García Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA Forests endmember IES fraction image spectral library MESMA |
| title | Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA |
| title_full | Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA |
| title_fullStr | Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA |
| title_full_unstemmed | Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA |
| title_short | Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA |
| title_sort | optimizing spectral libraries from landsat imagery for the analysis of habitat richness using mesma |
| topic | endmember IES fraction image spectral library MESMA |
| url | https://www.mdpi.com/1999-4907/13/11/1824 |
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