Fine-scale Mapping Research Articles

Visualising rural access index and not served rural population in Africa

The rural access index is beneficial to monitor accessibility in rural areas. However, the rural access index cannot indicate how many rural people have not been served (called not served rural population or NSRP), and it has only been mapped at a national and/or regional scale. This study visualises both the rural access index and not served rural population in Africa, and also visualises the not served rural population at a fine scale (i.e. 10 km × 10 km grid). The results show that: First, the spatial pattern of the not served rural population is quite different with that of the rural access index, and thus we suggest to use the not served rural population indicator as a supplement of the rural access index. Second, the not served rural population varies within a country, and the fine-scale mapping can be helpful for policy makers and planners to decide where there is a priority need to improve rural road accessibility.

Modeling the Marked Presence-Only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi

Certain subpopulations like female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have higher prevalence of HIV/AIDS and are difficult to map directly due to stigma, discrimination, and criminalization. Fine-scale mapping of those populations contributes to the progress toward reducing the inequalities and ending the AIDS epidemic. In 2016 and 2017, the PLACE surveys were conducted at 3290 venues in 20 out of the total 28 districts in Malawi to estimate the FSW sizes. These venues represent a presence-only dataset where, instead of knowing both where people live and do not live (presence–absence data), only information about visited locations is available. In this study, we develop a Bayesian model for presence-only data and utilize the PLACE data to estimate the FSW size and uncertainty interval at a -km resolution for all of Malawi. The estimates can also be aggregated to any desirable level (city/district/region) for implementing targeted HIV prevention and treatment programs in FSW communities, which have been successful in lowering the incidence of HIV and other sexually transmitted infections. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

ABSTRACT Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

Reefscape Genomics: Leveraging Advances in 3D Imaging to Assess Fine-Scale Patterns of Genomic Variation on Coral Reefs

Coral reefs across the world are undergoing rapid deterioration, and understanding the ecological and evolutionary processes that govern these ecosystems is critical to our ability to protect them. Molecular ecological studies have been instrumental in advancing such understanding, and while initially focused primarily on broad-scale patterns, they have gradually uncovered the prevalence of local genetic structuring. Genome-wide sequencing approaches have provided new opportunities to understand both neutral and adaptive contributions to this largely unexplained diversity, but fine-scale assessments have been hampered by challenges associated with aquatic environments, in terms of (geo)referencing, seafloor characterization, and in situ phenotyping. Here, we discuss the potential of “reefscape genomics,” leveraging recent advances in underwater imaging to enable spatially explicit genomic studies on coral reefs. More specifically, we consider how (close-range) photogrammetry approaches enable (1) fine-scale spatial mapping of benthic target organisms, (2) repeatable characterization of the abiotic and biotic reefscape, and (3) simultaneous in situ mass-phenotyping. The spatially explicit consideration of genomic data –combined with detailed environmental and phenotypic characterization– opens up the opportunity for fine-scale landscape genomic approaches on coral reefs (and other marine ecosystems). Such approaches enable assessment of the spatio-temporal drivers and adaptive potential of the extensive genetic structuring and cryptic diversity encountered in benthic invertebrates, such as reef-building corals. Considering the threats that coral reefs are facing worldwide, we believe that reefscape genomics represents a promising advancement of our molecular ecological toolkit to help inform how we can most effectively conserve and restore coral reef ecosystems into the future.

Spatial extrapolation of topographic models for mapping soil organic carbon using local samples

Accurate prediction of soil organic carbon (SOC) at fine scales is frequently limited by data availability but fine-scale maps of soil properties are often of great importance for local agricultural land management and watershed processes assessment. Terrain attributes derived from elevation have been commonly used predictor variables in SOC mapping. The aim of this study is to map SOC density (0–30 cm) using hybrid methods that combine regional topographic models and a residual kriging procedure using local samples from an area of interest and evaluate the effects of different sampling designs for residual kriging on final map quality. The regional topographic model was developed based on light detection and ranging (lidar)-derived topographic covariates using random forest to map SOC density on a cropland site intensively sampled for SOC (n = 224). To test ability to generate a local calibration of this regional model, a second intensively sampled cropland site (n = 225) was used to generate a reference SOC density map where residual kriging was implemented involving different numbers of local samples (n = 10–255) and different sample selection schemes: stratified random sampling (SRS) and spatial coverage sampling with close-pairs inclusions (SCS+). To generate a population of local sample sets, 500 repeats were conducted for each scheme from the reference map. By analyzing the distribution of relative mean error (RME) of the overall map for the second site with repeated selection of local samples, we found a smaller variation in RME for SCS+ than that for SRS for a given sample size. However, the E(RME) estimates for SCS+ slightly varied with number of local samples used while the E(RME) for SRS was stabilized at zero over that range in local sampling intensity. There was negligible difference between the distributions of RME for the two sampling schemes at individual points, except a finding of somewhat lower RME for SCS+ based point estimates. These results indicate that SCS+ can generate smaller variation in overall map quality while SRS can provide unbiased mean estimate for an area of interest. This study demonstrates the utility of extrapolating a regional topographic model by use of a limited number of new samples for local calibration and provides insight for practitioners who are interested in mapping soil carbon distribution in sparsely sampled areas of interest.

Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74–0.81, p = 3.1 × 10−31).

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.

ExTraMapper: exon- and transcript-level mappings for orthologous gene pairs.

Access to large-scale genomics and transcriptomics data from various tissues and cell lines allowed the discovery of wide-spread alternative splicing events and alternative promoter usage in mammalians. Between human and mouse, gene-level orthology is currently present for nearly 16k protein-coding genes spanning a diverse repertoire of over 200k total transcript isoforms. Here, we describe a novel method, ExTraMapper, which leverages sequence conservation between exons of a pair of organisms and identifies a fine-scale orthology mapping at the exon and then transcript level. ExTraMapper identifies more than 350k exon mappings, as well as 30k transcript mappings between human and mouse using only sequence and gene annotation information. We demonstrate that ExTraMapper identifies a larger number of exon and transcript mappings compared to previous methods. Further, it identifies exon fusions, splits and losses due to splice site mutations, and finds mappings between microexons that are previously missed. By reanalysis of RNA-seq data from 13 matched human and mouse tissues, we show that ExTraMapper improves the correlation of transcript-specific expression levels suggesting a more accurate mapping of human and mouse transcripts. We also applied the method to detect conserved exon and transcript pairs between human and rhesus macaque genomes to highlight the point that ExTraMapper is applicable to any pair of organisms that have orthologous gene pairs. The source code and the results are available at https://github.com/ay-lab/ExTraMapper and http://ay-lab-tools.lji.org/extramapper. Supplementary data are available at Bioinformatics online.

Small-scale distributions of polycyclic aromatic hydrocarbons in urban areas using geospatial modeling: A case study using the moss Orthotrichum lyellii in Portland, Oregon, U.S.A.

Polycyclic aromatic hydrocarbons (PAHs) are widespread pollutants that are costly to measure with air quality instruments. We used a spatially balanced sample of an epiphytic moss, Orthotrichum lyellii, to develop fine-scale maps of atmospheric PAHs across residential areas of Portland, Oregon. The unusual abundance of this stress-tolerant species enabled us to systematically collect a large sample (n = 350) on a 1 × 1km grid across the city. This provided the unique opportunity to investigate potential effects of more than 20 factors on PAH concentrations in moss, including emissions sources, tree canopy cover, relative elevation, short-term weather, and nuance of sample collection. We measured 20 PAHs in moss samples and assigned them to 4 groups for analysis based on molecular weight (MW): the Naphthalene, Low, Medium, and High MW groups. Spatial regression models explained a modest amount of variation in PAH concentrations in moss, with spatial structure increasing as MW increased. Motor vehicle emissions were the only PAH source we detected and was highly significant in all models. Deciduous tree cover correlated with lower PAH concentrations in moss for all except the high MW group. Short-term weather and nuance of sample collection, like height on the sampled tree, tree taxonomic family, and whether sample was primarily collected from a branch or bole, had surprisingly large effects on all groups. Overall, results aligned with expectations based on each group's dispersal potential due to MW. All PAH groups were highest over downtown Portland, along major highways, and lowest in parks and outer neighborhoods, although the details varied. Our results supported two common themes in studies directly measuring PAHs: the importance of roads as an emissions source in residential areas, and the potential for trees to reduce atmospheric concentrations. Moreover, the large effects of weather and sampling nuance have critical implications for the design of future studies.

Use of advanced modelling methods to predict dothistroma needle blight on Pinus radiata at a fine resolution within New Zealand

Dothistroma needle blight (DNB) is a damaging disease of many Pinaceae host taxa including the widely grown species Pinus radiata D. Don. However, little research has modelled the distribution of this disease at a fine spatial resolution from geospatial surfaces using advanced non-parametric methods. This study uses an extensive national dataset (n = 6276 observations) that describes the severity of DNB on plantation grown P. radiata with measurements expressed as a percentage of foliage within the stand affected by the disease (range 0–90%). Using these data the objectives of this research were to (i) compare the predictive precision of models of disease severity constructed using parametric and non-parametric approaches, (ii) determine whether the use of regression kriging is able to improve model fit, (iii) identify the key environmental determinants of disease severity using the most precise modelling methods and (iv) develop a fine scale map showing variation in disease severity throughout New Zealand. All predictions were made on a test dataset (n = 1883) that was not used for model fitting.Disease severity was most precisely predicted by the two non-parametric models eXtreme Gradient Boosting (RMSE = 11.37%, R2 = 0.231) and random forest (RMSE = 11.28%, R2 = 0.246) which both markedly outperformed the parametric multiple regression model (RMSE = 12.17%, R2 = 0.120). The use of regression kriging considerably increased the precision for the multiple regression (RMSE = 11.88%, R2 = 0.161) but did not improve model precision for the two non-parametric models.Random forest was the most precise model and included 14 variables, of which the time of the observation, stand age, relative humidity and solar radiation had the strongest influence on disease severity. A fine resolution map (25 m) created using random forest showed disease severity to be highest in northern and western areas of New Zealand, where relative humidity and rainfall are high. Low disease severity was predicted on the drier east coast of New Zealand and values were particularly low in the far south, where mean annual solar radiation is relatively low. This surface will assist forest managers in narrowing the search radius for the disease and prioritising areas for targeted deployment of disease resistance clones, provenances or alternative species.

Mapping Sub-Metre 3D Land-Sea Coral Reefscapes Using Superspectral WorldView-3 Satellite Stereoimagery

Shallow coral reefs ensure a wide portfolio of ecosystem services, from fish provisioning to tourism, that support more than 500 million people worldwide. The protection and sustainable management of these pivotal ecosystems require fine-scale but large-extent mapping of their 3D composition. The sub-metre spaceborne imagery can neatly produce such an expected product using multispectral stereo-imagery. We built the first 3D land-sea coral reefscape mapping using the 0.3 m superspectral WorldView-3 stereo-imagery. An array of 13 land use/land cover and sea use/sea cover habitats were classified using sea-, ground- and air-truth data. The satellite-derived topography and bathymetry reached vertical accuracies of 1.11 and 0.89 m, respectively. The value added of the eight mid-infrared (MIR) channels specific to the WorldView-3 was quantified using the classification overall accuracy (OA). With no topobathymetry, the best combination included the eight-band optical (visible + near-infrared) and the MIR8, which boosted the basic blue-green-red OA by 9.58%. The classes that most benefited from this MIR information were the land use “roof” and land cover “soil” classes. The addition of the satellite-derived topobathymetry to the optical+MIR1 produced the best full combination, increasing the basic OA by 9.73%, and reinforcing the “roof” and “soil” distinction.

Fine scale mapping of fractional tree canopy cover to support river basin management

AbstractManagement of water, regionally, nationally and globally will continue to be a priority and complex undertaking. In riverine systems, biotic components like flora and fauna play critical roles in filtering water so it is available for human use and consumption. Preservation of ecosystems and associated ecosystem functions is therefore vital. In highly regulated large river basins, natural ecosystems are often supported through provision of environmental flows. Flow delivery, however, should be underpinned by rigorous monitoring to identify and prioritise biotic water requirements. Currently, large‐scale monitoring solutions are scaled from remote sensing data via measurement of field evapotranspiration for woody tree vegetation species. However, as there is generally a mismatch between field data collection area and remote sensing pixel size, new methods are required to proportion tree evapotranspiration based on tree fractional canopy area per pixel. We present a novel method to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi‐arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). We used Sentinel‐1 and Sentinel‐2 (radar and multispectral imagery) in a Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. A predictor model combining the results of both regions was able to explain between 71%–85% of FTCC variation when compared to LiDAR FTCC when output in 10% increments. Development of this method underpins the advancement of woody vegetation monitoring to inform environmental flow management in the Murray–Darling Basin. The method and fine scale outputs will also be of value to other catchment management concerns such as altered catchment water yields related to bushfires and as such has application to water management worldwide.

Fine-scale mapping of urban ecosystem service demand in a metropolitan context: A population-income-environmental perspective

Scientifically characterizing demand for urban ecosystem services (ESs), particularly in a metropolitan context, plays a key role in the optimal allocation of urban ecological resources. However, the definition of urban ES demand is still in the fuzzy conceptual stage. Therefore, this study proposed a conceptual framework for fine-scale mapping of urban ES demands from the perspective of population-income-environmental pressure/preference (PIEP). By integrating multi-source data, the framework was implemented in a metropolitan city of China, Hangzhou, focusing on demands for five exemplary urban ESs, i.e. cooling, air purification, noise reduction, carbon sequestration, and outdoor recreation. The principal component analysis (PCA) was applied to dimensionality reduction and a comprehensively assessment of multiple ES demands. ES demand bundles were identified based on the k-means cluster analysis, where a hierarchical elbow method was devised for setting an optimal cluster number supported by practical decisions. The results showed that the demand for regulating services had more significant spatial aggregation characteristics than that for cultural services. Moreover, the high demand areas were concentrated in the city center and two sub-centers, while demand gradually decreased toward the urban periphery. The ES demand bundles presented three major patterns of composite modes: the multi-demand synergy type, regulating-cultural demand trade-off type, and local-based type. Our findings suggest that the PIEP framework is a promising decision-support tool which can tease out urban ES demands for urban planning and sustainable development.

Differing spatial patterns of the urban heat exposure of elderly populations in two megacities identifies alternate adaptation strategies

Mapping the elderly population exposure to heat hazard in urban areas is important to inform adaptation strategies for increasingly-deadly urban heat under climate change. However, fine-scale mapping is lacking, because global climate change projections have not previously been integrated with urban heat island effects especially with urban three-dimensional characteristics for within-city heat risk analyses. This study compared the spatial patterns of deadly heat exposure for elderly populations in two East Asian megacities, Seoul and Tokyo, using current climate (2006–2015) and two future periods (2040s and 2090s). We integrated global warming projections (the Shared Socioeconomic Pathway 5 based on Representative Concentration Pathway 8.5) with local urban characteristics and demographics. We found that, for the historical period, the overall hotspots of elderly population exposure to urban heat was larger in Tokyo because of relatively higher maximum air temperatures and lack of green spaces, whereas in the future periods, Seoul will have larger hotspots because the elderly population density will have increased. About 20% of the area in Seoul and 0.3–1% of Tokyo will be hotpots in the 2040s, and the size of these hotspots increases to 25–26% and 2–3%, respectively, in the 2090s. The spatial patterns of hotspots identify different types of priority areas and suggest that alternative adaptation strategies for two cities are appropriate. The approach introduced here will be useful for identifying sustainable thermal environments in other cities with high density elderly population and severe heat hazard. • Integrating climate change and UHI effect detects intra-city heat hazard variations. • Tokyo had more heat hazard hotspots for elderly exposure than Seoul in the past. • Climate change will increase the number of hotspots in both Seoul and Tokyo. • Demographic change will increase the hotspots in Seoul but decreases them in Tokyo. • Hotspot results suggest priority location and measures for adaptation strategies.

Fine mapping identifies NAD-ME1 as a candidate underlying a major locus controlling temporal variation in primary and specialized metabolism in Arabidopsis.

Plant metabolism is modulated by a complex interplay between internal signals and external cues. A major goal of all quantitative metabolomic studies is to clone the underlying genes to understand the mechanistic basis of this variation. Using fine-scale genetic mapping, in this work we report the identification and initial characterization of NAD-DEPENDENT MALIC ENZYME 1 (NAD-ME1) as the candidate gene underlying the pleiotropic network Met.II.15 quantitative trait locus controlling variation in plant metabolism and circadian clock outputs in the Bay×Sha Arabidopsis population. Transcript abundance and promoter analysis in NAD-ME1Bay-0 and NAD-ME1Sha alleles confirmed allele-specific expression that appears to be due a polymorphism disrupting a putative circadian cis-element binding site. Analysis of transfer DNA insertion lines and heterogeneous inbred families showed that transcript variation of the NAD-ME1 gene led to temporal shifts of tricarboxylic acid cycle intermediates, glucosinolate (GSL) accumulation, and altered regulation of several GSL biosynthesis pathway genes. Untargeted metabolomic analyses revealed complex regulatory networks of NAD-ME1 dependent upon the daytime. The mutant led to shifts in plant primary metabolites, cell wall components, isoprenoids, fatty acids, and plant immunity phytochemicals, among others. Our findings suggest that NAD-ME1 may act as a key gene to coordinate plant primary and secondary metabolism in a time-dependent manner.

Super-Resolution Surface Water Mapping on the Canadian Shield Using Planet CubeSat Images and a Generative Adversarial Network

The Canadian Shield, the world’s largest exposure of glaciated crystalline bedrock, is the most lake-rich region on Earth. Recent studies using high-resolution CubeSat satellite imagery have revealed its surface water hydrology to be surprisingly dynamic at fine spatial scales. Here we test whether super-resolution (SR), the resampling of coarse imagery to a finer-than-native resolution, can detect such changes. We degrade high-resolution Planet CubeSat images of the Shield, then resample the coarsened imagery back to its native resolution using both traditional cubic resampling and a generative adversarial network, a type of neural network often used for SR. To test classification accuracy from the generated SR imagery, we apply the same water classification to both resampling methods and find similar performance based on confusion matrices with the control case of high-resolution imagery. Next, we compare fine-scale shoreline mapping in SR imagery, cubic resampling, and in-situ field surveys. SR shorelines outperform those from cubic resampling, with an increase in the modified kappa coefficient from −0.070 to 0.073. Potential applications include improved mapping of Shield lakes and retroactive application of SR to coarser-resolution satellite datasets to infer historical changes in fine-scale surface water dynamics.

Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations.

Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations. To address this problem, we evaluated a mapping population design where dozens to hundreds of inbred lines are outbred for few generations, which we call the Hybrid Swarm. The Hybrid Swarm approach has likely remained underutilized relative to pre-sequenced inbred lines due to the costs of genome-wide genotyping. To reduce sequencing costs and make the Hybrid Swarm approach feasible, we developed a computational pipeline that reconstructs accurate whole genomes from ultra-low-coverage (0.05X) sequence data in Hybrid Swarm populations derived from ancestors with phased haplotypes. We evaluate reconstructions using genetic variation from the Drosophila Genetic Reference Panel as well as variation from neutral simulations. We compared the power and precision of Genome-Wide Association Studies using the Hybrid Swarm, inbred lines, recombinant inbred lines (RILs), and highly outbred populations across a range of allele frequencies, effect sizes, and genetic architectures. Our simulations show that these different mapping panels vary in their power and precision, largely depending on the architecture of the trait. The Hybrid Swam and RILs outperform inbred lines for quantitative traits, but not for monogenic ones. Taken together, our results demonstrate the feasibility of the Hybrid Swarm as a cost-effective method of fine-scale genetic mapping.

Urban-focused satellite CO2 observations from the Orbiting Carbon Observatory-3: A first look at the Los Angeles megacity

NASA's Orbiting Carbon Observatory-3 (OCO-3) was designed to support the quantification and monitoring of anthropogenic CO2 emissions. Its Snapshot Area Map (SAM) and target mode measurements provide an innovative dataset for carbon studies on sub-city scales. Unlike any other current space-based instrument, OCO-3 has the ability to scan large contiguous areas of emission hot spots like cities, power plants, and volcanoes. These measurements result in dense, fine-scale spatial maps of column averaged dry-air mole fractions of carbon dioxide (XCO2). For the first time, we present and analyze XCO2 distributions over the Los Angeles megacity (LA) derived from OCO-3 SAM and target mode observations. Urban XCO2 enhancements range from 0 − 6 ppm (median enhancements ≃ 2 ppm) relative to a clean background and show excellent agreement with nearby ground-based TCCON measurements of XCO2. OCO-3's dense observations reveal intra-urban variations of XCO2 over the city that have never been observed from space before. The spatial variations are mainly driven by the complex fossil fuel emission patterns and meteorological conditions in the LA Basin and are in good agreement with those from co-located TROPOMI measurements of co-emitted NO2. Differences between measured and simulated XCO2 enhancements from two models (WRF-Chem and X-STILT) are typically below 1 ppm with larger differences for some sub regions. Both models capture the observed intra-urban XCO2 gradients. Further, OCO-3's multi-swath measurements capture about three times as much of the city emissions compared to single-swath overpasses. OCO-3's frequent target and SAM mode observations will pave the way to constrain urban emissions at finer, sub-city scales.

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

BackgroundWhole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision.MethodsThe Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits.ResultsThe prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits.ConclusionsAcross-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

A Fine-Scale Mangrove Map of China Derived from 2-Meter Resolution Satellite Observations and Field Data

Mangrove forests are important ecosystems in the coastal intertidal zone, but China’s mangroves have experienced a large reduction in area from the 1950s, and the remaining mangrove forests are exhibiting increased fragmentation. A detailed mangrove dataset of China is crucial for mangrove ecosystem management and protection, but the fragmented mangrove patches are hardly mapped by medium resolution satellite imagery. To overcome these difficulties, we presented a fine-scale mangrove map for 2018 using the 2-meter resolution Gaofen-1 and Ziyuan-3 satellite imagery together with field data. We employed a hybrid method of object-based image analysis (OBIA), interpreter editing, and field surveying for mangrove mapping. The field survey route reached 9500 km, and 2650 patches were verified during the field work. Accuracy assessment by confusion matrix showed that the kappa coefficient reached 0.98, indicating a highly thematic accuracy of the mangrove dataset. Results showed the total area of mangrove forest in China for 2018 was 25,683.88 hectares, and approximately 91% of mangroves were found in the three provinces of Guangdong, Guangxi, and Hainan. About 64% of mangroves were distributed in or near the nature reserves established by national or local governments, which indicated that China’s mangroves were well protected in recent years. The new fine-scale mangrove dataset was freely shared together with this paper, and it can be used by local authorities and research groups for mangrove management and ecological planning.