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Context. On 27 April 2015, when comet 67P/Churyumov-Gerasimenko was at 1.76 au from the Sun and moving toward perihelion, the OSIRIS and VIRTIS-M instruments on board the Rosetta spacecraft simultaneously observed the evolving dust and gas coma during a complete rotation of the comet. Aims. We aim to characterize the spatial distribution of dust, H2O, and CO2 gas in the inner coma. To do this, we performed a quantitative analysis of the release of dust and gas and compared the observed H2O production rate with the rate we calculated using a thermophysical model. Methods. For this study we selected OSIRIS WAC images at 612 nm (dust) and VIRTIS-M image cubes at 612 nm, 2700 nm (H2O emission band), and 4200 nm (CO2 emission band). We measured the average signal in a circular annulus to study the spatial variation around the comet, and in a sector of the annulus to study temporal variation in the sunward direction with comet rotation, both at a fixed distance of 3.1 km from the comet center. Results. The spatial correlation between dust and water, both coming from the sunlit side of the comet, shows that water is the main driver of dust activity in this time period. The spatial distribution of CO2 is not correlated with water and dust. There is no strong temporal correlation between the dust brightness and water production rate as the comet rotates. The dust brightness shows a peak at 0 degrees subsolar longitude, which is not pronounced in the water production. At the same epoch, there is also a maximum in CO2 production. An excess of measured water production with respect to the value calculated using a simple thermophysical model is observed when the head lobe and regions of the southern hemisphere with strong seasonal variations are illuminated (subsolar longitude 270 degrees-50 degrees). A drastic decrease in dust production when the water production (both measured and from the model) displays a maximum occurs when typical northern consolidated regions are illuminated and the southern hemisphere regions with strong seasonal variations are instead in shadow (subsolar longitude 50 degrees-90 degrees). Possible explanations of these observations are presented and discussed.
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
(2022)
We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59-73 degrees N, 97-169 degrees E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr).
Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown.
We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha(-1)), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill., Pinus L., Picea A. Dietr., Abies Mill., Salix L., Betula L., Populus L., Alnus Mill., and Ulmus L.
In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here.
The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data.