Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982–2015 Time Series

A Bayesian classifier mapped the Burned Area (BA) in the Northeastern Siberian boreal forest (70°N 120°E–60°N 170°E) from 1982 to 2015. The algorithm selected the 0.05° (~5 km) Long-Term Data Record (LTDR) version 3 and 4 data sets to generate 10-day BA composites. Landsat-TM scenes of the entire study site in 2002, 2010, and 2011 assessed the spatial accuracy of this LTDR-BA product, in comparison to Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD45A1 and MCD64A1 BA products. The LTDR-BA algorithm proves a reliable source to quantify BA in this part of Siberia, where comprehensive BA remote sensing products since the 1980s are lacking. Once grouped by year and decade, this study explored the trends in fire activity. The LTDR-BA estimates contained a high interannual variability with a maximum of 2.42 million ha in 2002, an average of 0.78 million ha/year, and a standard deviation of 0.61 million ha. Going from 6.36 in the 1980s to 10.21 million ha BA in the 2010s, there was a positive linear BA trend of approximately 1.28 million ha/decade during these last four decades in the Northeastern Siberian boreal forest

Determinación de la temperatura superficial del mar mediante la sinergia de sensores AVHRR y TOVS: Aplicación a Canarias

Se ha desarrollado un algoritmo original para la determinación de la temperatura superficial del mar desde los satélites NOAA. Está basado en la técnica "Split-Window", considerando unos coeficientes variables dependiendo del contenido total en vapor de agua atmosférico. Para la estimación de este componente se propone un método que utiliza las temperaturas radioméricas de los canales 12, 11 y 8 del Hirs-2 subsitema del Tovs. En la validación del modelo de corrección atmosférica propuesto se han usado temperaturas tomadas "in situ" mediante un radiómetro de infrarrojos. Finalmente se presenta una de las aplicaciones más importantes que tiene la obtención de la SST en el área de Canarias, la oceanografía pesquera

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined

Locus coeruleus complex of the family Delphinidae

The locus coeruleus (LC) is the largest catecholaminergic nucleus and extensively projects to widespread areas of the brain and spinal cord. The LC is the largest source of noradrenaline in the brain. To date, the only examined Delphinidae species for the LC has been a bottlenose dolphin (Tursiops truncatus). In our experimental series including different Delphinidae species, the LC was composed of five subdivisions: A6d, A6v, A7, A5, and A4. The examined animals had the A4 subdivision, which had not been previously described in the only Delphinidae in which this nucleus was investigated. Moreover, the neurons had a large amount of neuromelanin in the interior of their perikarya, making this nucleus highly similar to that of humans and non-human primates. This report also presents the first description of neuromelanin in the cetaceans' LC complex, as well as in the cetaceans' brain

Evaluación de un algoritmo para detección de áreas quemadas en bosques de Canarias

Se aplicó un clasificador bayesiano, inicialmente desarrollado y validado con éxito para la detección de áreas quemadas en regiones de bosque boreal usando el conjunto de datos LTDR (Long-Term Data Record) de 0,05º (~ 5 km) de resolución espacial, a una serie temporal de imágenes diarias Terra-MODIS de zonas forestales de Monteverde y Pinar de las Islas Canarias para el periodo 2002-2012. A partir de los dos productos MODIS, MOD09GQ (250 m) y MOD11A1 (1 km), que representan las imágenes diarias de reflectancia y temperatura de superficie respectivamente, se construyeron compuestos de 10 días mediante el criterio de máxima temperatura. Las variables estadísticas utilizadas en el clasificador bayesiano fueron los índices de vegetación GEMI y BBFI, junto con la banda espectral NIR, todos ellos relativos al año anterior y al año de ocurrencia del incendio. Se crearon polígonos de referencia de los 14 incendios mayores de 100 hectáreas identificados en el periodo analizado, utilizando conjuntamente imágenes LANDSAT post-fuego e información oficial de la base de datos nacional de incendios forestales del Ministerio de Agricultura, Alimentación y Medio Ambiente (MAGRAMA). El algoritmo de detección se entrenó usando un incendio producido en el sur de Tenerife en julio de 2012 que afectó a más de 6000 ha. Los resultados muestran que 13 de los 14 incendios registrados en ese periodo en el conjunto de las Islas Canarias, fueron detectados. El área total quemada detectada supone un 64,9% de los datos de referencia del MAGRAMA y un 78,6% según los datos obtenidos a partir de las imágenes LANDSAT. La aplicación de la metodología propuesta podría mejorar estos resultados considerando otros criterios de composición, índices de vegetación, variables estadísticas y/o región de entrenamiento, que mejor caractericen la respuesta espectral de la dinámica de la cobertura forestal de las Islas Canarias afectada por el fueg

Decompression vs. decomposition : distribution, amount, and gas composition of bubbles in stranded marine mammals

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Physiology 3 (2012): 177, doi:10.3389/fphys.2012.00177.Gas embolic lesions linked to military sonar have been described in stranded cetaceans including beaked whales. These descriptions suggest that gas bubbles in marine mammal tissues may be more common than previously thought. In this study we have analyzed gas amount (by gas score) and gas composition within different decomposition codes using a standardized methodology. This broad study has allowed us to explore species-specific variability in bubble prevalence, amount, distribution, and composition, as well as masking of bubble content by putrefaction gases. Bubbles detected within the cardiovascular system and other tissues related to both pre- and port-mortem processes are a common finding on necropsy of stranded cetaceans. To minimize masking by putrefaction gases, necropsy, and gas sampling must be performed as soon as possible. Before 24 h post mortem is recommended but preferably within 12 h post mortem. At necropsy, amount of bubbles (gas score) in decomposition code 2 in stranded cetaceans was found to be more important than merely presence vs. absence of bubbles from a pathological point of view. Deep divers presented higher abundance of gas bubbles, mainly composed of 70% nitrogen and 30% CO2, suggesting a higher predisposition of these species to suffer from decompression-related gas embolism.This work was supported by the Spanish Ministry of Science and Innovation with two research projects: (AGL 2005-07947) and (CGL 2009/12663), as well as the Government of Canary Islands (DG Medio Natural). The Spanish Ministry of Education contributed with personal financial support (the University Professor Formation fellowship). The Woods Hole Oceanographic Institution Marine Mammal Centre and Wick and Sloan Simmons provided funding for the latest stage of this work

Unusual striped dolphin mass mortality episode related to cetacean morbillivirus in the Spanish Mediterranean sea

BACKGROUND In the last 20 years, Cetacean Morbillivirus (CeMV) has been responsible for many die-offs in marine mammals worldwide, as clearly exemplified by the two dolphin morbillivirus (DMV) epizootics of 1990-1992 and 2006-2008, which affected Mediterranean striped dolphins (Stenella coeruleoalba). Between March and April 2011, the number of strandings on the Valencian Community coast (E Spain) increased. CASE PRESENTATION Necropsy and sample collection were performed in all stranded animals, with good state of conservation. Subsequently, histopathology, immunohistochemistry, conventional reverse transcription polymerase chain reaction (RT-PCR) and Universal Probe Library (UPL) RT-PCR assays were performed to identify Morbillivirus. Gross and microscopic findings compatible with CeMV were found in the majority of analyzed animals. Immunopositivity in the brain and UPL RT-PCR positivity in seven of the nine analyzed animals in at least two tissues confirmed CeMV systemic infection. Phylogenetic analysis, based on sequencing part of the phosphoprotein gene, showed that this isolate is a closely related dolphin morbillivirus (DMV) to that responsible for the 2006-2008 epizootics. CONCLUSION The combination of gross and histopathologic findings compatible with DMV with immunopositivity and molecular detection of DMV suggests that this DMV strain could cause this die-off event

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms