High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China.

Accurate assessment of coal mine methane (CMM) emissions is a prerequisite for defining baselines and assessing the effectiveness of mitigation measures. Such an endeavor is jeopardized, however, by large uncertainties in current CMM estimates. Here, we assimilated atmospheric methane column concentrations observed by the TROPOMI space borne instrument in a high-resolution regional inversion to estimate CMM emissions in Shanxi, a province representing 15% of the global coal production. The emissions are estimated to be 8.5 ± 0.6 and 8.6 ± 0.6 Tg CH4 yr-1 in 2019 and 2020, respectively, close to upper bound of current bottom-up estimates. Data from more than a thousand of individual mines indicate that our estimated emission factors increase significantly with coal mining depth at prefecture level, suggesting that ongoing deeper mining will increase CMM emission intensity. Our results show robustness of estimating CMM emissions utilizing TROPOMI images and highlight potential of monitoring methane leakages and emissions from satellites.

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery.

Methane (CH4) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH4 emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/h to 600 tCH4/h. We conclude here that the prevalence of ultraemitters (>25tCH4/h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2tCH4/h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.

A spectral algorithm for fast de novo layout of uncorrected long nanopore reads.

MOTIVATION: New long read sequencers promise to transform sequencing and genome assembly by producing reads tens of kilobases long. However, their high error rate significantly complicates assembly and requires expensive correction steps to layout the reads using standard assembly engines. RESULTS: We present an original and efficient spectral algorithm to layout the uncorrected nanopore reads, and its seamless integration into a straightforward overlap/layout/consensus (OLC) assembly scheme. The method is shown to assemble Oxford Nanopore reads from several bacterial genomes into good quality (∼99% identity to the reference) genome-sized contigs, while yielding more fragmented assemblies from the eukaryotic microbe Sacharomyces cerevisiae. AVAILABILITY AND IMPLEMENTATION: https://github.com/antrec/spectrassembler. CONTACT: antoine.recanati@inria.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.