Sustainable Land Management in a European Context DOI Open Access

Thomas Weith,

Tim Barkmann,

Nadin Gaasch

et al.

Human-environment interactions, Journal Year: 2020, Volume and Issue: unknown

Published: Aug. 28, 2020

This open access book is sustainable land management, focusing on sustainability goals, important drivers, existing challenges, and new ways to address certain aspects challenges through concepts by integrating different disciplines including practitioners.

Language: Английский

Review on Convolutional Neural Networks (CNN) in vegetation remote sensing DOI
Teja Kattenborn,

Jens Leitloff,

Felix Schiefer

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2021, Volume and Issue: 173, P. 24 - 49

Published: Jan. 18, 2021

Language: Английский

Citations

1151
Complexity revealed in the greening of the Arctic DOI
Isla H. Myers‐Smith, Jeffrey T. Kerby, Gareth K. Phoenix

et al.

Nature Climate Change, Journal Year: 2020, Volume and Issue: 10(2), P. 106 - 117

Published: Jan. 31, 2020

Language: Английский

Citations

711
Essential biodiversity variables for mapping and monitoring species populations DOI Creative Commons
Walter Jetz, Mélodie A. McGeoch, Robert Guralnick

et al.

Nature Ecology & Evolution, Journal Year: 2019, Volume and Issue: 3(4), P. 539 - 551

Published: March 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.

Language: Английский

Citations

406
Remote sensing of terrestrial plant biodiversity DOI
Ran Wang, John A. Gamon

Remote Sensing of Environment, Journal Year: 2019, Volume and Issue: 231, P. 111218 - 111218

Published: June 13, 2019

Language: Английский

Citations

311
Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks DOI Creative Commons
Ben Weinstein, Sergio Marconi, Stephanie Bohlman

et al.

Remote Sensing, Journal Year: 2019, Volume and Issue: 11(11), P. 1309 - 1309

Published: June 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

Language: Английский

Citations

222
UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data DOI
Teja Kattenborn, Javier Lopatin, Michael Förster

et al.

Remote Sensing of Environment, Journal Year: 2019, Volume and Issue: 227, P. 61 - 73

Published: April 12, 2019

Language: Английский

Citations

211
Uncovering Ecological Patterns with Convolutional Neural Networks DOI
Philip G. Brodrick, Andrew B. Davies, Gregory P. Asner

et al.

Trends in Ecology & Evolution, Journal Year: 2019, Volume and Issue: 34(8), P. 734 - 745

Published: May 9, 2019

Language: Английский

Citations

148
Plant pangenomes for crop improvement, biodiversity and evolution DOI
Mona Schreiber, Murukarthick Jayakodi, Nils Stein

et al.

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: 25(8), P. 563 - 577

Published: Feb. 20, 2024

Language: Английский

Citations

30
Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics DOI
James S. Sinclair, Ellen A. R. Welti, Florian Altermatt

et al.

Nature Ecology & Evolution, Journal Year: 2024, Volume and Issue: 8(3), P. 430 - 441

Published: Jan. 26, 2024

Language: Английский

Citations

20
A global test of ecoregions DOI
Jeffrey R. Smith, Andrew D. Letten, Po‐Ju Ke

et al.

Nature Ecology & Evolution, Journal Year: 2018, Volume and Issue: 2(12), P. 1889 - 1896

Published: Oct. 24, 2018

Language: Английский

Citations

116