STRUCTURAL CHARACTERIZATION OF CANOPIES OF Eucalyptus spp. USING RADIOMETRIC DATA FROM TM/Landsat 5

Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capao Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to field data collection). Results indicate that stronger correlations were identified between crown dimensions and canopy height with near-infrared spectral band data (rho(s)4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a significant difference between models based on distinct data acquisition dates.

Multitemporal assessment of the geomorphologic evolution of the Restinga of Marambaia, Rio de Janeiro, Brazil

The Restinga of Marambaia is an emerged sand bar located between the Sepetiba Bay and the South Atlantic Ocean, on the south-east coast of Brazil. The objective of this study was to observe the geomorphologic evolution of the coastal zone of the Restinga of Marambaia using multitemporal satellite images acquired by multisensors from 1975 to 2004. The images were digitally segmented by a region growth algorithm and submitted to an unsupervised classification procedure (ISOSEG) followed by a raster edit based on visual interpretation. The image time-series showed a general trend of decrease in the total sand bar area with values varying from 80.61km(2) in 1975 to 78.15km(2) in 2004. The total area calculation based on the 1975 and 1978 Landsat MSS data was shown to be super-estimated in relation to the Landsat TM, Landsat ETM+, and CBERS-2 CCD data. These differences can also be associated to the relatively poorer spatial resolution of the MSS data, nominally 79m, against the 20m of the CCD data and 30m of the TM and ETM+ data. For the estimates of the width in the central portion of the sand bar the variation was from 158m (1975) to 100m (2004). The formation of a spit in the northern region of the study area was visually observed. The area of the spit was estimated, with values varying from 0.82km(2) (1975) to 0.55km(2) (2004).

Recent shift from forest to savanna burning in the Amazon Basin observed by satellite

The numbers of fires detected on forest, savanna and transition lands during the 2002-10 biomass burning seasons in Amazonia are shown using fire count data and co-located land cover classifications from the Moderate Resolution Imaging Spectroradiometer (MODIS). The ratio of forest fires to savanna fires has varied substantially over the study period, with a maximum ratio of 0.65:1 in 2005 and a minimum ratio of 0.27:1 in 2009, with the four lowest years occurring in 2007-10. The burning during the droughts of 2007 and 2010 is attributed to a higher number of savanna fires relative to the drought of 2005. A decrease in the regional mean single scattering albedo of biomass burning aerosols, consistent with the shift from forest to savanna burning, is also shown. During the severe drought of 2010, forest fire detections were lower in many areas compared with 2005, even though the drought was more severe in 2010. This result suggests that improved fire management practices, including stricter burning regulations as well as lower deforestation burning, may have reduced forest fires in 2010 relative to 2005 in some areas of the Amazon Basin.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

A comparison for a multiscale study of structural lineaments in southern Brazil: LANDSAT-7 ETM+ and shaded relief images from SRTM3-DEM

This paper presents a comparison of descriptive statistics obtained for brittle structural lineaments extracted manually from LANDSAT images and shaded relief images from SRTM 3 DEM at 1:100, 000 and 1:500, 000 scales. The selected area is located in the southern of Brazil and comprises Precambrian rocks and stratigraphic units of the Paraná Basin. The application of this methodology shows that the visual interpretation depends on the kind of remote sensing image. The resulting descriptive statistics obtained for lineaments extracted from the images do not follow the same pattern according to the scale adopted. The main direction obtained for Proterozoic rocks using both image types at a 1:500, 000 scale are close to NS±10, whereas at a 1:100, 000 scale N45E was obtained for shaded relief images from SRTM 3 DEM and N10W for LANDSAT images. The Paleozoic sediments yielded the best results for the different images and scales (N50W). On the other hand, the Mesozoic igneous rocks showed greatest differences, the shaded relief images from SRTM 3 DEM images highlighting NE structures and the LANDSAT images highlighting NW structures. The accumulated frequency demonstrated high similarity between products for each image type no matter the scale, indicating that they can be used in multiscale studies. Conversely, major differences were found when comparing data obtained using shaded relief images from SRTM 3 DEM and Landsat images at a 1:100, 000 scale.

Overview of semi-sinusoidal stellar variability with the CoRoT satellite

Context. To date, the CoRoT space mission has produced more than 124 471 light curves. Classifying these curves in terms of unambiguous variab ility behavior is mandatory for obtaining an unbi ased statistical view on th eir controlling root-causes. Aims. The present study provides an overview of semi-sinusoidal light curves observed by the CoRoT exo-field CCDs. Methods. We selected a sample of 4206 light curves presenting well-defined semi-si nusoidal signatures. Th e variability periods were computed based on Lomb-Scargle periodograms, harmonic fits, and visual inspection. Results. Color–period diagrams for the present sample show the trend of an increase of the variability periods as long as the stars evolve. This evolutionary behavior is also noticed when comparing the period distribution in the Galactic center and anti-center directions. These aspect s indicate a compatibility with stellar rotation, although more inform ation is needed to confirm their root- causes. Considering this possi bility, we identified a subset of th ree Sun-like candidates by their photometric peri od. Finally, the variability period versus color diagr am behavior was found to be highly depe ndent on the reddening correction.

Identifying clouds over the Pierre Auger Observatory using infrared satellite data

We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ∼2.4 km by ∼5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.