Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2021, Номер 173, С. 24 - 49

Опубликована: Янв. 18, 2021

Язык: Английский

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Complexity revealed in the greening of the Arctic

Nature Climate Change, Год журнала: 2020, Номер 10(2), С. 106 - 117

Опубликована: Янв. 31, 2020

Язык: Английский

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Essential biodiversity variables for mapping and monitoring species populations

Nature Ecology & Evolution, Год журнала: 2019, Номер 3(4), С. 539 - 551

Опубликована: Март 11, 2019

Species distributions and abundances are undergoing rapid changes worldwide. This highlights the significance of reliable, integrated information for guiding assessing actions policies aimed at managing sustaining many functions benefits species. Here we synthesize types data approaches that required to achieve such an integration conceptualize 'essential biodiversity variables' (EBVs) a unified global capture species populations in space time. The inherent heterogeneity sparseness raw overcome by use models remotely sensed covariates inform predictions contiguous time extent. We define population EBVs as space-time-species-gram (cube) simultaneously addresses distribution or abundance multiple species, with its resolution adjusted represent available evidence acceptable levels uncertainty. essential enables monitoring single aggregate spatial taxonomic units scales relevant research decision-making. When combined ancillary environmental data, this fundamental directly underpins range ecosystem function indicators. concept present links disparate downstream uses informs vision which collection is closely infrastructure support effective assessment.

Язык: Английский

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Remote sensing of terrestrial plant biodiversity

Remote Sensing of Environment, Год журнала: 2019, Номер 231, С. 111218 - 111218

Опубликована: Июнь 13, 2019

Язык: Английский

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Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

Remote Sensing, Год журнала: 2019, Номер 11(11), С. 1309 - 1309

Опубликована: Июнь 1, 2019

Remote sensing can transform the speed, scale, and cost of biodiversity forestry surveys. Data acquisition currently outpaces ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been long-standing challenge remote available algorithms produce mixed results. show that models leverage existing Light Detection Ranging (LIDAR)-based unsupervised generate trees are used training initial model. Despite limitations original approach, this noisy data may contain information from which neural network learn tree features. then refine model small number higher-quality hand-annotated images. validate our proposed open-canopy site National Ecological Observation Network. Our results 434,551 self-generated with addition 2848 yields accurate predictions natural landscapes. Using intersection-over-union threshold 0.5, full had average recall 0.69, precision 0.61 visually-annotated data. The rate 0.82 field collected stems. improved performance over self-supervised This demonstrates overcome lack labeled by generating methods retraining resulting quality

Язык: Английский

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UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data

Remote Sensing of Environment, Год журнала: 2019, Номер 227, С. 61 - 73

Опубликована: Апрель 12, 2019

Язык: Английский

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Uncovering Ecological Patterns with Convolutional Neural Networks

Trends in Ecology & Evolution, Год журнала: 2019, Номер 34(8), С. 734 - 745

Опубликована: Май 9, 2019

Язык: Английский

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Plant pangenomes for crop improvement, biodiversity and evolution

Nature Reviews Genetics, Год журнала: 2024, Номер 25(8), С. 563 - 577

Опубликована: Фев. 20, 2024

Язык: Английский

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Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics

Nature Ecology & Evolution, Год журнала: 2024, Номер 8(3), С. 430 - 441

Опубликована: Янв. 26, 2024

Язык: Английский

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A global test of ecoregions

Nature Ecology & Evolution, Год журнала: 2018, Номер 2(12), С. 1889 - 1896

Опубликована: Окт. 24, 2018

Язык: Английский

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