Reflectance and imaging spectroscopy applied to detection of petroleum hydrocarbon pollution in bare soils.

Accidental releases of hazardous waste related to the extraction, refining, and transport of oil and gas are inevitable. Petroleum facilities and intrinsic pipelines present environmental pollution risks, threatening both human health and ecosystems. Research has been undertaken to enhance the conventional methods for monitoring hazardous waste problems and to improve time-consuming and cost-effective ways for leak detection and remediation process. In this study, both diffuse and imaging (hyperspectral) reflectance spectroscopy are used for detection and characterization of petroleum hydrocarbon (PHC) contamination in latosols. Laboratory and field measurements of PHC-contaminated and PHC-free soils were collected from an oil facility using an ASD FieldSpec-3 high-resolution portable spectrometer (2150 channels) covering visible, near infrared and shortwave infrared wavelengths (VNIR-SWIR: 350-2500 nm). The hyperspectral image dataset was acquired with the ProSpecTIR-VS airborne sensor using 357 channels in the VNIR-SWIR range at 1 m of spatial resolution. Narrow intervals of reflectance spectra were analyzed to identify the primary mineral and PHC absorption bands in soil samples and to investigate the spectral match with airborne hyperspectral data. The Multiple Endmember Spectral Mixture Analysis (MESMA) method was employed in three hierarchical levels to classify the hyperspectral imagery. The classification product yielded from MESMA model at the fourth level was 98% accurate in discriminating contaminated soils. The results demonstrated the applicability of both diffuse reflectance and imaging (hyperspectral) spectroscopy to identify bare soils contaminated by PHC leaks and spills. These technologies can also provide useful information for remediation initiatives, thereby avoiding further problems with hazardous waste.

Geochemical constraints on the Hadean environment from mineral fingerprints of prokaryotes.

The environmental conditions on the Earth before 4 billion years ago are highly uncertain, largely because of the lack of a substantial rock record from this period. During this time interval, known as the Hadean, the young planet transformed from an uninhabited world to the one capable of supporting, and inhabited by the first living cells. These cells formed in a fluid environment they could not at first control, with homeostatic mechanisms developing only later. It is therefore possible that present-day organisms retain some record of the primordial fluid in which the first cells formed. Here we present new data on the elemental compositions and mineral fingerprints of both Bacteria and Archaea, using these data to constrain the environment in which life formed. The cradle solution that produced this elemental signature was saturated in barite, sphene, chalcedony, apatite, and clay minerals. The presence of these minerals, as well as other chemical features, suggests that the cradle environment of life may have been a weathering fluid interacting with dry-land silicate rocks. The specific mineral assemblage provides evidence for a moderate Hadean climate with dry and wet seasons and a lower atmospheric abundance of CO2 than is present today.

Discriminação de fitofisionomias de floresta de várzea a partir do algoritmo Iterated Conditional Modes aplicado aos dados SAR/R99 (QUAD-POL/Banda L) / Discrimination among flooded forest phytophysiognomies from Iterated Conditional Modes algorithm applied to SAR Data R99 (QUAD POL/L-Band)

Utilizando-se dados do sensor aerotransportado SAR R99, adquiridos na banda L (1,28 GHz) em amplitude e com quatro polarizações (HH, VV, HV e VH), avaliou-se a distinção de fitofisionomias de floresta de várzea existentes nas Reservas de Desenvolvimento Sustentável Amanã e Mamirauá e áreas adjacentes, com a aplicação do algoritmo Iterated Conditional Modes (ICM) de classificação polarimétrica pontual/contextual. Os resultados mostraram que o uso das distribuições multivariadas em amplitude, conjuntamente com uma banda de textura, produziu classificações de qualidade superior àquelas obtidas com dados polarimétricos uni/bivariados. Esta abordagem permitiu a obtenção de um índice Kappa de 0,8963, discriminando as três classes vegetacionais de interesse, comprovando assim o potencial dos dados do SAR R99 e do algoritmo ICM no mapeamento de florestas de várzea da Amazônia.

This study seeks to evaluate the capability of data generated by the synthetic aperture radar SAR R99 sensor to map phytophysiognomies found in the Amanã and Mamirauá Sustainable Development Reserves (RDSA and RDSM). By means of L-band (1.28 GHz), full polarimetric (HH, VV, VH, HV), amplitude data acquired with the SAR R99 sensor, distinctions among flooded forest phytophysiognomies in the RDSA and RDSM and around were achieved. The Iterated Conditional Modes (ICM) algorithm was employed to perform the local/contextual polarimetric classification of the data. Results showed that the use of multivariate distributions in amplitude with a band of texture produced classifications of superior quality in relation to those obtained with the uni/bivariate polarimetric data. This approach allowed to obtain a Kappa index of 0,8963 and the distinction of three vegetation classes of interest, demonstrating the potential of SAR R99 and the ICM algorithm to map flooded vegetation of the Amazon.