Tropical forest structure observation with TanDEM-X data

TanDEM-X forms together with TerraSAR-X the first single-pass polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Single-, Dual-, and Quad-Pol-InSAR data without the disturbing effect of temporal decorrelation globally. For this reason, the exploration of TanDEM-X data for forestry is constantly increasing especially concerning forest height estimation, biomass classification and structure characterization. This paper reports the results of recent experiments aimed at investigating the potentials of TanDEM-X in characterizing quantitatively the spatial variability of the canopy top and phase center height, which is a proxy to horizontal structure. It is shown that such characterization can allow to differentiate among e.g. different successional and / disturbance stages in tropical forests

Improving estimates of carbon storage and flux in tropical peatlands

Our limited knowledge of the size of the carbon pool and exchange fluxes in forested lowland tropical peatlands represents a major gap in our understanding of the global carbon cycle. Peat deposits in several regions (e.g. the Congo Basin, much of Amazonia) are only just beginning to be mapped and characterised. Here we consider the extent to which methodological improvements and improved coordination between researchers could help to fill this gap. We review the literature on measurement of the key parameters required to calculate carbon pools and fluxes, including peatland area, peat bulk density, carbon concentration, above-ground carbon stocks, litter inputs to the peat, gaseous carbon exchange, and waterborne carbon fluxes. We identify areas where further research and better coordination are particularly needed in order to reduce the uncertainties in estimates of tropical peatland carbon pools and fluxes, thereby facilitating better-informed management of these exceptionally carbon-rich ecosystems

Improving estimates of tropical peatland area, carbon storage, and greenhouse gas fluxes

Our limited knowledge of the size of the carbon pool and exchange fluxes in forested lowland tropical peatlands represents a major gap in our understanding of the global carbon cycle. Peat deposits in several regions (e.g. the Congo Basin, much of Amazonia) are only just beginning to be mapped and characterised. Here we consider the extent to which methodological improvements and improved coordination between researchers could help to fill this gap. We review the literature on measurement of the key parameters required to calculate carbon pools and fluxes, including peatland area, peat bulk density, carbon concentration, above-ground carbon stocks, litter inputs to the peat, gaseous carbon exchange, and waterborne carbon fluxes. We identify areas where further research and better coordination are particularly needed in order to reduce the uncertainties in estimates of tropical peatland carbon pools and fluxes, thereby facilitating better-informed management of these exceptionally carbon-rich ecosystems

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

MOTIVATION: LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. RESULTS: In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. AVAILABILITY AND IMPLEMENTATION: The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online