scholarly journals Global ecotypes in the ubiquitous marine clade SAR86

2019 ◽  
Author(s):  
Adrienne Hoarfrost ◽  
Stephen Nayfach ◽  
Joshua Ladau ◽  
Shibu Yooseph ◽  
Carol Arnosti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Diversity ◽  
High Resolution ◽  
Functional Groups ◽  
Environmental Variables ◽  
Gene Clusters ◽  
Environmental Data ◽  
Marine Environments ◽  
Surface Ocean ◽  
Globally Distributed

AbstractSAR86 is an abundant and ubiquitous heterotroph in the surface ocean that plays a central role in the function of marine ecosystems. We hypothesized that despite its ubiquity, different SAR86 subgroups may be endemic to specific ocean regions and functionally specialized for unique marine environments. However, the global biogeographical distributions of SAR86 genes, and the manner in which these distributions correlate with marine environments, have not been investigated. We quantified SAR86 gene content across globally-distributed metagenomic samples and modeled these gene distributions as a function of 51 environmental variables. We identified five distinct clusters of genes within the SAR86 pangenome, each with a unique geographic distribution associated with specific environmental characteristics. Gene clusters are characterized by strong taxonomic enrichment of distinct SAR86 genomes and partial assemblies, as well as differential enrichment of certain functional groups, suggesting differing functional and ecological roles of SAR86 ecotypes. We then leveraged our models and high-resolution, remote sensing-derived environmental data to predict the distributions of SAR86 gene clusters across the world’s oceans, creating global maps of SAR86 ecotype distributions. Our results reveal that SAR86 exhibits previously unknown, complex biogeography, and provide a framework for exploring geographic distributions of genetic diversity from other microbial clades.

scholarly journals An Image is Worth a Thousand Species: Scaling high-resolution plant biodiversity prediction to biome-level using citizen science data and remote sensing imagery

2021 ◽  
Vol 5 ◽  
Author(s):  
Lauren Gillespie ◽  
Megan Ruffley ◽  
Moisés Expósito-Alonso
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Citizen Science ◽  
Environmental Variables ◽  
Aerial Imagery ◽  
Coarse Grained ◽  
Plant Biodiversity ◽  
Distribution Models ◽  
Remote Sensing Imagery ◽  
Bioclimatic Variables

Accurately mapping biodiversity at high resolution across ecosystems has been a historically difficult task. One major hurdle to accurate biodiversity modeling is that there is a power law relationship between the abundance of different types of species in an environment, with few species being relatively abundant while many species are more rare. This “commonness of rarity,” confounded with differential detectability of species, can lead to misestimations of where a species lives. To overcome these confounding factors, many biodiversity models employ species distribution models (SDMs) to predict the full extent of where a species lives, using observations of where a species has been found, correlated with environmental variables. Most SDMs use bioclimatic environmental variables as the dependent variable to predict a species’ range, but these approaches often rely on biased pseudo-absence generation methods and model species using coarse-grained bioclimatic variables with a useful resolution floor of 1 km-pixel. Here, we pair iNaturalist citizen science plant observations from the Global Biodiversity Information Facility with RGB-Infrared aerial imagery from the National Aerial Imagery Program to develop a deep convolutional neural network model that can predict the presence of nearly 2,500 plant species across California. We utilize a state-of-the-art multilabel image recognition model from the computer vision community, paired with a cutting-edge multilabel classification loss, which leads to comparable or better accuracy to traditional SDM models, but at a resolution of 250m (Ben-Baruch et al. 2020, Ridnik et al. 2020). Furthermore, this deep convolutional model is able to accurately predict species presence across multiple biomes of California with good accuracy and can be used to build a plant biodiversity map across California with unparalleled accuracy. Given the widespread availability of citizen science observations and remote sensing imagery across the globe, this deep learning-enabled method could be deployed to automatically map biodiversity at large scales.

scholarly journals Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ke Sun ◽  
Junping Zhang ◽  
Yingying Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Environmental Data ◽  
Tensor Voting ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
The Road ◽  
Road Intersections

Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

scholarly journals Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

2016 ◽  
Vol 46 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Fátima L. BENÍTEZ ◽  
Liana O. ANDERSON ◽  
Antônio R. FORMAGGIO
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
High Resolution ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Remote Sensing Data ◽  
Amazon Rainforest ◽  
Weighted Regression ◽  
Sensing Data ◽  
Geostatistical Techniques

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

scholarly journals Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

2014 ◽  
Vol 3 (4) ◽  
pp. 1352-1371 ◽  
Author(s):  
Vanessa Machault ◽  
André Yébakima ◽  
Manuel Etienne ◽  
Cécile Vignolles ◽  
Philippe Palany ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Data ◽  
Risk Levels

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

High Resolution Displacement of Functional Groups onto Fluorocarbon Resin Surface by Using ArF Excimer Laser

1996 ◽  
Vol 451 ◽  
Author(s):  
T. Shimizu ◽  
M. Murahara
Keyword(s):  
Contact Angle ◽  
High Resolution ◽  
Laser Beam ◽  
Thin Layer ◽  
Functional Groups ◽  
Excimer Laser ◽  
Laser Fluence ◽  
Large Area ◽  
Three Solutions ◽  
Arf Excimer Laser

ABSTRACTA Fluorocarbon resin surface was selectively modified by irradiation with a ArF laser beam through a thin layer of NaAlO2, B(OH)3, or H2O solution to give a hydrophilic property. As a result, with low fluence, the surface was most effectively modified with the NaAlO2 solution among the three solutions. However, the contact angle in this case changed by 10 degrees as the fluence changed only 1mJ/cm2. When modifying a large area of the surface, high resolution displacement could not be achieved because the laser beam was not uniform in displacing functional groups. Thus, the laser fluence was successfully made uniform by homogenizing the laser beam; the functional groups were replaced on the fluorocarbon resin surface with high resolution, which was successfully modified to be hydrophilic by distributing the laser fluence uniformly.

Measuring Exposome Associated with Health

2020 ◽  
pp. 46-50
Keyword(s):  
Mass Spectrometry ◽  
Remote Sensing ◽  
High Resolution ◽  
Biological Effect ◽  
High Resolution Mass Spectrometry ◽  
The Body ◽  
Ecological Environment ◽  
Special Issue ◽  
The Life Course ◽  
Resolution Mass

The concept of exposome has received increasing discussion, including the recent Special Issue of Science –"Chemistry for Tomorrow's Earth,” about the feasibility of using high-resolution mass spectrometry to measure exposome in the body, and tracking the chemicals in the environment and assess their biological effect. We discuss the challenges of measuring and interpreting the exposome and suggest the survey on the life course history, built and ecological environment to characterize the sample of study, and in combination with remote sensing. They should be part of exposomics and provide insights into the study of exposome and health.

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