Grain Surface Models and Data for Astrochemistry

AbstractThe cross-disciplinary field of astrochemistry exists to understand the formation, destruction, and survival of molecules in astrophysical environments. Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. A broad consensus has been reached in the astrochemistry community on how to suitably treat gas-phase processes in models, and also on how to present the necessary reaction data in databases; however, no such consensus has yet been reached for grain-surface processes. A team of ∼25 experts covering observational, laboratory and theoretical (astro)chemistry met in summer of 2014 at the Lorentz Center in Leiden with the aim to provide solutions for this problem and to review the current state-of-the-art of grain surface models, both in terms of technical implementation into models as well as the most up-to-date information available from experiments and chemical computations. This review builds on the results of this workshop and gives an outlook for future directions

Relações empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5

O objetivo deste trabalho foi ajustar modelos para estimar características dendrométricas da Caatinga brasileira a partir de dados do sensor TM do Landsat 5. Medidas de diâmetro e altura das árvores foram obtidas de 60 parcelas de inventário (400 m2), em dois municípios do Estado de Sergipe. A área basal e o volume de madeira foram estimados com uso de equação alométrica e de fator de forma (f = 0,9). As variáveis explicativas foram obtidas do sensor TM, após correção radiométrica e geométrica, tendo-se considerado, na análise, seis bandas espectrais, com resolução espacial de 30 m, além dos índices de razão simples (SR), de vegetação por diferença normalizada (NDVI) e de vegetação ajustado ao solo (Savi). Na escolha das melhores variáveis explicativas, foram considerados coeficiente de determinação (R2), raiz do erro quadrático médio (RMSE) e critério bayesiano de informação (CBI). A área basal por hectare não apresentou correlação significativa com nenhuma das variáveis explicativas utilizadas. Os melhores modelos foram ajustados à altura média das árvores por parcela (R2 = 0,4; RMSE = 13%) e ao volume de madeira por hectare (R2 = 0,6; RMSE = 42%). As métricas derivadas do sensor TM do Landsat 5 têm grande potencial para explicar variações de altura média das árvores e do volume de madeira por hectare, em remanescentes de Caatinga situados no Nordeste brasileiro

Biodiversity of the world: a study from space

The Earth is undergoing an accelerated rate of native ecosystem conversion and degradation (Nepstad et al. 1999; Myers et al. 2000; Achard et al. 2002) and there is increased interest in measuring, modeling, and monitoring biodiversity using remote sensing from spaceborne sensors (Nagendra 2001; Kerr and Ostrovsky 2003; Turner et al. 2003; Secades et al. 2014). Biodiversity can be defined as the variation of life forms (genetic, species) within a given ecosystem, region or the entire earth. Terrestrial biodiversity, rare, and threatened species tends to be highest near the equator and generally decreases towards the poles because of decreases in temperature and precipitation (Figure 20.1). However, the distribution of biodiversity is complex and based on a number of environmental and anthropogenic factors over different spatial scales (Whittaker et al. 2001; Field et al. 2009; Jenkins et al. 2013)

The spectral species concept at wide geographical scales: estimating ecosystem alpha- and beta-diversity by remote sensing

There is an increasing need to rapidly assess the biodiversity of ecosystems, due to the widely acknowledged trend towards biodiversity loss. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. In particular, if an investigation area is large, it is challenging to collect data providing reliable information. Observer bias, restrictions in accessibility, funding and workload limitations, missing expert knowledge, dynamics in emergence and phenology are just some examples that limit the collection of representative in-situ information in Earth observation. Some of these restrictions in Earth observation can be avoided through using remote sensing approaches. Remote sensing is efficient and cost-effective. Modern sensors allow the identification of biodiversity patterns in vegetation over large areas and provide sensitive information on the dynamics of their biodiversity. Obviously, shortcomings are also present such as sub-canopy diversity, limited representation of very steep slope surfaces, very different sizes (diameter) of plant species, similar spectral traits between species, or ecosystems that are constantly covered by clouds (e.g. laurel forest). Different works have estimated biodiversity on the basis of the Spectral Variation Hypothesis; according to this hypothesis, spectral heterogeneity over the different pixels reflects a higher niche heterogeneity, allowing more organisms to coexist. From this assumption, the concept of spectral species has been derived, following the consideration that the spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. At a local scale, with the use of high resolution remote sensing data, the different subspaces can be identified as different 'spectral species'. This has been done using an unsupervised method based on the clustering of the different subsets. From the distribution of these spectral species (and the derivation of alpha- and beta-diversity) is then possible to approximate the diversity of the species living in an area. Our approach derives from this concept and extends it at a wide spatial extent. We have been able to apply this method to MODIS imagery data, producing a map of the distribution of the spectral species over all Europe and an estimate of the European alpha- and beta-diversity. In this case, the diversity is not evaluated at species level, but at community level, introducing the concept of spectral community. We propose to apply the method on a stack of images of the months of the year, in order to cluster niches taking also into account their response to different seasons, and therefore to elaborate multi-temporal and multi-dimensional data