Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.This study has been funded by the Spanish Ministry of Science and Innovation through the research projects HYDRA (No. CGL2006-02247/BOS) and HYDRA2 (CGL2009-09801/BOS), by the National Parks Authority (Organismo Autonomo de Parques Nacionales) of the Spanish Ministry of Environment to project OAPN 042/2007, and by funding from the European Union (EU) Horizon 2020 research and innovation program under grant agreement No. 641762 to ECOPOTENTIAL project. The Espacio Natural de Doñana provided permits for field work in protected areas with restricted access. We are grateful to the Instituto Nacional de Técnica Aeroespacial (INTA), Spain, for performing the airborne campaign and the geometric correction of the images. J.B. has to acknowledge a sabbatical stay at Pye Laboratory of the Commonwealth Scientific and Research Organization (CSIRO) Marine and Atmospheric Sciences, Australia, and at the Climate Change Cluster (C3) of the University of Technology Sydney, Australia, funded by the Spanish Ministry of Education, during data analysis and writing of this paper. This publication is a contribution from CEIMAR and also a contribution from CEICAMBIO. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI

Hyperspectral sensors as a management tool to prevent the invasion of the exotic cordgrass "Spartina densiflora" in the Doñana wetlands

We test the use of hyperspectral sensors for the early detection of the invasive denseflowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.This study has been funded by the Spanish Ministry of Science and Innovation through the research projects HYDRA (No. CGL2006-02247/BOS) and HYDRA2 (CGL2009-09801/BOS), by the National Parks Authority (Organismo Autonomo de Parques Nacionales) of the Spanish Ministry of Environment to project OAPN 042/2007, and by funding from the European Union (EU) Horizon 2020 research and innovation program under grant agreement No. 641762 to ECOPOTENTIAL project. The Espacio Natural de Donana provided permits for field work in protected areas with restricted access. We are grateful to the Instituto Nacional de Tecnica Aeroespacial (INTA), Spain, for performing the airborne campaign and the geometric correction of the images. J.B. has to acknowledge a sabbatical stay at Pye Laboratory of the Commonwealth Scientific and Research Organization (CSIRO) Marine and Atmospheric Sciences, Australia, and at the Climate Change Cluster (C3) of the University of Technology Sydney, Australia, funded by the Spanish Ministry of Education, during data analysis and writing of this paper. This publication is a contribution from CEIMAR and also a contribution from CEICAMBIO

Fusion of airborne LiDAR, multispectral imagery and spatial modelling for understanding saltmarsh response to sea-level rise

Coastal ecosystems are considered to be sensitive to changes in environmental forcing, particularly sea-level rise. Saltmarshes occupy a discrete lateral and vertical position that is fundamentally controlled by the position of sea level, but the nature of other factors such as broader scale shoreline dynamics and anthropogenic ensure that the nature and extent of sea-level rise impacts on saltmarshes are globally variable, and locally complex. Thus, there is a need to understand these controls and to predict the potential response of saltmarsh systems to sea-level change at the local scale. The present research presents a multifaceted methodology for investigating the response of saltmarshes due to sea-level rise at local scales with application to the Odiel saltmarshes (SW-Spain), using elevation data derived from Light detection and ranging (LiDAR), high spatial resolution multispectral imagery and spatial modelling, that in combination with historical estuary evolution and field observation can be applied for effective management and conservation of saltmarshes in the context of sea-level change. SLAMM (Sea Level Affecting Marshes Model) has been used to evaluate coastal wetland habitat response to sea-level rise Accurate model spatial model inputs such as digital elevation models (DEMs) and saltmarsh habitat map are essential to reduce uncertainties in the model outputs, and part of this thesis has been focused on improving accuracy in saltmarsh elevation and habitat maps. Additionally, a sensitivity and uncertainty analysis was undertaken to explore first the relative importance of data quality and resolution (spatial and vertical) in the elevation data and saltmarsh habitat classification layers, and then the global uncertainty of the model outputs using a Monte Carlo approach. Our findings suggested that model is sensitive to DEM and habitat map resolution, and that historical sea-level trend and saltmarsh accretion rates are the predominant factors that influence uncertainty in predictions of change in saltmarsh habitats

MODELIZACIÓN EMPÍRICA DEL ÍNDICE DE ÁREA FOLIAR EN ECOSISTEMAS DE DEHESA: INTEGRACIÓN DE DATOS DE CAMPO, AEROPORTADOS Y DE SATÉLITE

El índice de área foliar es considerado un bioindicador del estado de la salud real de las plantas y de la productividad primaria bruta de la vegetación. Numerosos estudios han demostrado que los modelos basados ee regresión simple lineal son herramientas óptimas que tienen la capacidad de relacionar el LAI medido en campo con información derivada de teledetección óptica, El objetivo del presente Trabajo Fin de Máster es desarrollar un modelo predictivo de LAI a partir de informa-ción multiespectral de media resolución espacial (Landsat) a partir del análisis y modelización pre-via de las relaciones entre información hiperespectral a alta resolución espacial y LAI verdad-te-rreno utilizando la técnica upcaling y, desarrollado para ambientes heterogéneos como son las dehesas. Para ello, se han utilizado datos hiperespectrales derivados del sensor CASI y datos del LAI medida en campo proporcionados por SynerTGE y una gama de índices de Vegetación derivados de los productos Landsat TM y OLI. Un primer análisis se basó en establecer relaciones empíricas entre pseudo-LAI e índices de vegetación. Para seguir evaluando el rendimiento del modelo, se aplicaron análisis de regresión (RLS) para modelizar la relación entre pseudo-LAI e índices de ve-getación. Los resultados establecieron que el método propuesto varía en función de los modelos utilizados. Por otra parte, se desarrolló un modelo para i) aplicar y modelizar las funciones predic-tivas generadas mediante los análisis RLS y, ii) validar los productos mediante estadístico RMSE. Para ello, se utilizaron series multitemporales derivadas de Landast-8 OLI y muestras de LAI total y LAI verde repartidas en 5 jornadas de campo, en cada parcela (11), las muestras fueron tomadas sobre 3 cuadrantes (25x25cm), además, las muestras tomadas se consideran, a priori, represen-tativas a distintos momentos de la dinámica fenológica. Los resultados obtenidos establecen que los modelos predictivos rinden mejor para periodos primaverales-estivales, cuando el pastizal se encuentra en su periodo de máximo crecimiento. Además, el modelo desarrollado sobre pasto y encinares rinde mejor que el modelo A. Si individualizamos los casos, se establece que el modelo predictivo en fecha del 28 de junio de 2015 obtuvo los mejores valores RMSE = 0.196 y RMSE (%) = 6.73 para predecir la variable biofísica LAI verde