Jet Propulsion Laboratory

About: Jet Propulsion Laboratory is a facility organization based out in La Cañada Flintridge, California, United States. It is known for research contribution in the topics: Mars Exploration Program & Telescope. The organization has 8801 authors who have published 14333 publications receiving 548163 citations. The organization is also known as: JPL & NASA JPL.

Surface Ices and the Atmospheric Composition of Pluto

Abstract: Observations of the 1.4- to 2.4-micrometer spectrum of Pluto reveal absorptions of carbon monoxide and nitrogen ices and confirm the presence of solid methane. Frozen nitrogen is more abundant than the other two ices by a factor of about 50; gaseous nitrogen must therefore be the major atmospheric constituent. The absence of carbon dioxide absorptions is one of several differences between the spectra of Pluto and Triton in this region. Both worlds carry information about the composition of the solar nebula and the processes by which icy planetesimals formed.

Dusty starburst galaxies in the early Universe as revealed by gravitational lensing

Abstract: In the past decade, our understanding of galaxy evolution has been revolutionized by the discovery that luminous, dusty starburst galaxies were 1,000 times more abundant in the early Universe than at present. It has, however, been difficult to measure the complete redshift distribution of these objects, especially at the highest redshifts (z > 4). Here we report a redshift survey at a wavelength of three millimetres, targeting carbon monoxide line emission from the star-forming molecular gas in the direction of extraordinarily bright millimetre-wave-selected sources. High-resolution imaging demonstrates that these sources are strongly gravitationally lensed by foreground galaxies. We detect spectral lines in 23 out of 26 sources and multiple lines in 12 of those 23 sources, from which we obtain robust, unambiguous redshifts. At least 10 of the sources are found to lie at z > 4, indicating that the fraction of dusty starburst galaxies at high redshifts is greater than previously thought. Models of lens geometries in the sample indicate that the background objects are ultra-luminous infrared galaxies, powered by extreme bursts of star formation.

Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia

Abstract: Understanding the interplay between climate and land-use dynamics is a fundamental concern for assessing the vulnerability of Amazonia to climate change In this study, we analyse satellite-derived monthly and annual time series of rainfall, fires and deforestation to explicitly quantify the seasonal patterns and relationships between these three variables, with a particular focus on the Amazonian drought of 2005 Our results demonstrate a marked seasonality with one peak per year for all variables analysed, except deforestation For the annual cycle, we found correlations above 90% with a time lag between variables Deforestation and fires reach the highest values three and six months, respectively, after the peak of the rainy season The cumulative number of hot pixels was linearly related to the size of the area deforested annually from 1998 to 2004 (r 2 Z084, pZ0004) During the 2005 drought, the number of hot pixels increased 43% in relation to the expected value for a similar deforested area (approx 19 000 km 2 ) We demonstrated that anthropogenic forcing, such as land-use change, is decisive in determining the seasonality and annual patterns of fire occurrence Moreover, droughts can significantly increase the number of fires in the region even with decreased deforestation rates We may expect that the ongoing deforestation, currently based on slash and burn procedures, and the use of fires for land management in Amazonia will intensify the impact of droughts associated with natural climate variability or human-induced climate change and, therefore, a large area of forest edge will be under increased risk of fires

Widespread decline of Congo rainforest greenness in the past decade

Abstract: Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.

Inferring Ground Truth from Subjective Labelling of Venus Images

Abstract: In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite significant in terms of quantifying both human and algorithm detection performance.