Principal component analysis applied to remote sensing

[EN] The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images. Three Landsat images were selected from two areas which were located in the municipalities of Gandia and Vallat, both in the Valencia province (Spain). In the first study area, just one Landsat image of the 2005 year was used. In the second study area, two Landsat images were used taken in the 1994 and 2000 years to analyse the most significant changes in the land cover. According to the results, the second principal component of the Gandia area image allowed detecting the presence of vegetation. The same component in the Vallat area allowed detecting a forestry area affected by a forest fire. Consequently in this study we confirmed the feasibility of using PCA in remote sensing to extract land use information.[ES] El objetivo principal de este artículo es mostrar una aplicación del análisis de componentes principales (PCA) que se utiliza en dos grados de la ciencia. En particular, se utilizó el análisis de PCA para obtener información de la cobertura del suelo a partir de imágenes de satélite. Tres imágenes Landsat fueron seleccionadas a partir de dos áreas que se encuentran en los municipios de Gandia y Vallat, ambos en la provincia de Valencia (España). En la primera área de estudio, se utilizó una sola imagen Landsat del año 2005. En la segunda área de estudio, se utilizaron dos imágenes Landsat tomadas en los años 1994 y 2000 para analizar los cambios más significativos en la cobertura de la tierra. Según los resultados, el segundo componente principal de la imagen de área Gandia permitió la detección de la presencia de vegetación. El mismo componente en el área de Vallat permitió detectar un área forestal afectada por un incendio forestal. En consecuencia, en este estudio se confirmó la viabilidad del uso de PCA en teledetección para extraer la información territorial.Estornell, J.; Martí-Gavliá, JM.; Sebastiá, MT.; Mengual, J. (2013). Principal component analysis applied to remote sensing. Modelling in Science Education and Learning. 6(2):83-89. doi:10.4995/msel.2013.1905SWORD838962Xiuping Jia, & Richards, J. A. (1999). Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 538-542. doi:10.1109/36.739109J. R. Eastman, M. Filk. Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing. 59(6) 991-996. (1993)

Principal component analysis applied to remote sensing

The main objective of this article was to show an application of principal component analysis (PCA) which is used in two science degrees. Particularly, PCA analysis was used to obtain information of the land cover from satellite images. Three Landsat images were selected from two areas which were located in the municipalities of Gandia and Vallat, both in the Valencia province (Spain). In the first study area, just one Landsat image of the 2005 year was used. In the second study area, two Landsat images were used taken in the 1994 and 2000 years to analyse the most significant changes in the land cover. According to the results, the second principal component of the Gandia area image allowed detecting the presence of vegetation. The same component in the Vallat area allowed detecting a forestry area affected by a forest fire. Consequently in this study we confirmed the feasibility of using PCA in remote sensing to extract land use information.</p

Mathematical modelling applied to LiDAR data

The aim of this article is to explain the application of several mathematic calculations to LiDAR (Light Detection And Ranging) data to estimate vegetation parameters and modelling the relief of a forest area in the town of Chiva (Valencia). To represent the surface that describes the topography of the area, firstly, morphological filters were applied iteratively to select LiDAR ground points. From these data, the Triangulated Irregular Network (TIN) structure was applied to model the relief of the area. From LiDAR data the canopy height model (CHM) was also calculated. This model allowed obtaining bare soil, shrub and tree vegetation mapping in the study area. In addition, biomass was estimated from measurements taken in the field in 39 circular plots of radius 0.5 m and the 95th percentile of the LiDAR height datanincluded in each plot. The results indicated a high relationship between the two variables (measurednbiomass and 95th percentile) with a coeficient of determination (R2) of 0:73. These results reveal the importance of using mathematical modelling to obtain information of the vegetation and land relief from LiDAR data.</p

Mathematical modelling applied to LiDAR data

The aim of this article is to explain the application of several mathematic calculations to LiDAR (Light Detection And Ranging) data to estimate vegetation parameters and modelling the relief of a forest area in the town of Chiva (Valencia). To represent the surface that describes the topography of the area, firstly, morphological filters were applied iteratively to select LiDAR ground points. From these data, the Triangulated Irregular Network (TIN) structure was applied to model the relief of the area. From LiDAR data the canopy height model (CHM) was also calculated. This model allowed obtaining bare soil, shrub and tree vegetation mapping in the study area. In addition, biomass was estimated from measurements taken in the field in 39 circular plots of radius 0.5 m and the 95th percentile of the LiDAR height datanincluded in each plot. The results indicated a high relationship between the two variables (measurednbiomass and 95th percentile) with a coeficient of determination (R2) of 0:73. These results reveal the importance of using mathematical modelling to obtain information of the vegetation and land relief from LiDAR data.</p

Identification of Phytoplankton Blooms under the Index of Inherent Optical Properties (IOP Index)

Phytoplankton blooms are sporadic events in time and isolated in space. This complex phenomenon is produced by a variety of both natural and anthropogenic causes. Early detection of this phenomenon, as well as the classification of a water body under conditions of bloom or non-bloom, remains an unresolved problem. This research proposes the use of Inherent Optical Properties (IOPs) in optically complex waters to detect the bloom or non-bloom state of the phytoplankton community. An IOP index is calculated from the absorption coefficients of the colored dissolved organic matter (CDOM), the phytoplankton (φ) and the detritus (d), using the wavelength (λ) 443 nm. The effectiveness of this index is tested in five bloom events in different places and with different characteristics from Mexican seas: (1) Dzilam (Caribbean Sea, Atlantic Ocean) a diatom bloom (Rhizosolenia hebetata); (2) Holbox (Caribbean Sea, Atlantic Ocean) a mixed bloom of dinoflagellates (Scrippsiella sp.) and diatoms (Chaetoceros sp.); (3) Campeche Bay in the Gulf of Mexico (Atlantic Ocean) a bloom of dinoflagellates (Karenia brevis); (4) Upper Gulf of California (UGC) (Pacific Ocean) a diatoms bloom (Planktoniella sol) and (5) Todos Santos Bay, Ensenada (Pacific Ocean) a dinoflagellates bloom (Lingulodinium polyedrum). The diversity of sites shows that the IOP index is a suitable method to determine the bloom conditions