Cristina Maria Bentz

Application of remote sensing data for oil spill monitoring in the Guanabara Bay, Rio de Janeiro, Brazil

This paper describes the application of remote sensing data for oil spill monitoring in the Guanabara Bay, Rio de Janeiro, Brazil. During the emergency, Landsat-5/TM (Thematic Mapper) and Radarsat-1 data were acquired to monitor the location of the spill and its movement. Image classification procedures have been utilized to highlight oil-covered areas on the water surface. Ambiguities in the oil detection were resolved with the aid of ancillary information in a GIS (geographic information system) environment. The results obtained helped PETROBRAS to optimize the emergency response procedures and subsequent cleaning efforts.

Multi-sensor synergistic analysis of mesoscale oceanic features: Campos Basin, south-eastern Brazil

This study presents a combined use of multi-sensor remote sensing and in situ data for the analysis and interpretation of oceanic features observed at the continental shelf and slope of the Campos Basin, south-eastern Brazil. Ocean colour (SeaWiFS), thermal infrared (AVHRR), scatterometer winds (QuikSCAT) and SAR (Radarsat-1) data were integrated to associate the different SAR backscatter patterns with physical and biological oceanic processes. The interpreted SAR features included processes such as oceanic fronts, current meandering and eddies, upwelling plumes, wind variability and algae blooms. The interpretation of these features was only feasible through the use of the multi-sensor synergistic approach complemented by timely field verification.

Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3MRAD), Ocean Chlorophyll 4 bands (OC4v4RAD), and Ocean Chlorophyll 2 bands (OC2v4RAD). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3MSAT, and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01SAT), and CarderSAT. In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg/m3. In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m3 (OC2v4RAD). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m3) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m3, respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of RRS by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.

Automatic recognition of coastal and oceanic environmental events with orbital radars

An automatic classification procedure was developed able to identify different oceanic events, detectable in orbital radar images. The procedure was customized to be used in the southeastern Brazilian coast, since the classification training and test used examples extracted from 402 RADARSAT-1 images acquired in this region. Different sets of spectral, geometric and contextual (meteo-oceanographic and location) features of selected low backscatter patches were evaluated. Machine learning procedures (neural networks, decision trees and support vector machines) were used to induce classifiers to differentiate between seven classes, belonging to two categories. The classification procedure involves two steps: first the features area classified in one of two categories - oil spill or meteo- oceanographic phenomena. In the second step, the identification of tree classes of oil spills and four classes of meteo- oceanographic phenomena is done. The oil spill related classes are associated to operational exploration and production spills, ship releases and others. The meteo-oceanographic phenomena include biogenic oils and/or upwellings, algae blooms, low wind areas and rain cells. The models induced by support vector machines and neural networks achieved good results, allowing the operational implementation of the proposed procedures.

A MULTI-SENSOR APPROACH FOR OIL SPILL AND SEA SURFACE MONITORING, IN SOUTHEASTERN BRAZIL

ABSTRACT Since May 2001 PETROBRAS is using spaceborne multi-sensor remote sensing for its sea surface monitoring program at the Campos, Santos and Espirito Santo Basins, southeastern Brazilian coas...

Intelligent hybrid system for dark spot detection using SAR data

Synthetic Aperture Radars (SAR) are the main instrument used to support oil detection systems. In the microwave spectrum, oil slicks are identified as dark spots, regions with low backscatter at sea surface. Automatic and semi-automatic systems were developed to minimize processing time, the occurrence of false alarms and the subjectivity of human interpretation. This study presents an intelligent hybrid system, which integrates automatic and semi-automatic procedures to detect dark spots, in six steps: (I) SAR pre-processing; (II) Image segmentation; (III) Feature extraction and selection; (IV) Automatic clustering analysis; (V) Decision rules and, if needed; (VI) Semi-automatic processing. The results proved that the feature selection is essential to improve the detection capability, keeping only five pattern features to automate the clustering procedure. The semi-automatic method gave back more accurate geometries. The automatic approach erred more including regions, increasing the dark spots area, while the semi-automatic method erred more excluding regions. For well-defined and contrasted dark spots, the performance of the automatic and the semi-automatic methods is equivalent. However, the fully automatic method did not provide acceptable geometries in all cases. For these cases, the intelligent hybrid system was validated, integrating the semi-automatic approach, using compact and simple decision rules to request human intervention when needed. This approach allows for the combining of benefits from each approach, ensuring the quality of the classification when fully automatic procedures are not satisfactory.

Oil slicks detection using a polarimetric region classifier

A new region based classifier for polarimetric synthetic aperture radar data (PolSAR) was tested to evaluate its potential to discriminate different types of oil slicks at sea surface. This classifier uses a supervised approach to compare stochastic distances between complex Wishart distributions and hypothesis tests to associate confidence levels to the classification results. The preliminary results using the Battacharyya distance were promising, returning an overall accuracy of 90.61% at a significance level of 5%. Future works may compare the performance of different stochastic distances, together with the insertion of polarimetric features to improve the oil slicks classification.

Lessons Learned On Oil Spill Environment Impact Assessment: 10 Years of Petrobras Experience Review

ABSTRACT This paper presents an overview of the experience on oil spill impact assessment, preparedness and response, including scientific and forensic approaches. The incidents in Guanabara Bay (2...

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R2(calibration) = 0.89, R2(validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha−1, and RMSE(validation) = 14.80 t·ha−1). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

The contribution of ASTER, CBERS, R99/SIPAM e OrbiSAR-1 data to improve the oceanic monitoring

This paper presents some results using a series of satellite and airborne sensors to monitor the environmental conditions of an oceanic area in the SW South Atlantic off Brazil. The following sensors were used: RADARSAT-1, VNIR- TIR/ASTER Terra, WFI-CCD/CBERS, OrbiSAR-1 and R99 SAR/SIPAM. The analysis presented refers to the data collected during the occurrence of a large meso-scale Brazil Current frontal eddy and a concurrent but uncommon sea floor oil seep event. The surface expression of the oil seep was captured by the eddy. The integrated data set analysis showed that the availability of this set of images made it possible to obtain spatial and physical details of these two features, which could not have been achieved using solely one single sensor alone.