133 related articles for article (PubMed ID: 37620395)
1. Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers.
Popp MR; Kalwij JM
Sci Rep; 2023 Aug; 13(1):13892. PubMed ID: 37620395
[TBL] [Abstract][Full Text] [Related]
2. Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery.
Lobo Torres D; Queiroz Feitosa R; Nigri Happ P; Elena Cué La Rosa L; Marcato Junior J; Martins J; Olã Bressan P; Gonçalves WN; Liesenberg V
Sensors (Basel); 2020 Jan; 20(2):. PubMed ID: 31968589
[TBL] [Abstract][Full Text] [Related]
3. Automated mapping of
Galuszynski NC; Duker R; Potts AJ; Kattenborn T
PeerJ; 2022; 10():e14219. PubMed ID: 36262418
[TBL] [Abstract][Full Text] [Related]
4. Scots pine stands biomass assessment using 3D data from unmanned aerial vehicle imagery in the Chernobyl Exclusion Zone.
Holiaka D; Kato H; Yoschenko V; Onda Y; Igarashi Y; Nanba K; Diachuk P; Holiaka M; Zadorozhniuk R; Kashparov V; Chyzhevskyi I
J Environ Manage; 2021 Oct; 295():113319. PubMed ID: 34348433
[TBL] [Abstract][Full Text] [Related]
5. Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery.
Kattenborn T; Eichel J; Fassnacht FE
Sci Rep; 2019 Nov; 9(1):17656. PubMed ID: 31776370
[TBL] [Abstract][Full Text] [Related]
6. Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods.
Fundisi E; Tesfamichael SG; Ahmed F
PLoS One; 2022; 17(12):e0278529. PubMed ID: 36455048
[TBL] [Abstract][Full Text] [Related]
7. Comparative assessment of satellite- and drone-based vegetation indices to predict arthropod biomass in shrub-steppes.
Traba J; Gómez-Catasús J; Barrero A; Bustillo-de la Rosa D; Zurdo J; Hervás I; Pérez-Granados C; García de la Morena EL; Santamaría A; Reverter M
Ecol Appl; 2022 Dec; 32(8):e2707. PubMed ID: 35808937
[TBL] [Abstract][Full Text] [Related]
8. Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data.
Gebrehiwot A; Hashemi-Beni L; Thompson G; Kordjamshidi P; Langan TE
Sensors (Basel); 2019 Mar; 19(7):. PubMed ID: 30934695
[TBL] [Abstract][Full Text] [Related]
9. Explainable identification and mapping of trees using UAV RGB image and deep learning.
Onishi M; Ise T
Sci Rep; 2021 Jan; 11(1):903. PubMed ID: 33441689
[TBL] [Abstract][Full Text] [Related]
10. Locating chimpanzee nests and identifying fruiting trees with an unmanned aerial vehicle.
van Andel AC; Wich SA; Boesch C; Koh LP; Robbins MM; Kelly J; Kuehl HS
Am J Primatol; 2015 Oct; 77(10):1122-34. PubMed ID: 26179423
[TBL] [Abstract][Full Text] [Related]
11. Solutions to fire and shade: resprouting, growing tall and the origin of Eurasian temperate broadleaved forest.
Adie H; Lawes MJ
Biol Rev Camb Philos Soc; 2023 Apr; 98(2):643-661. PubMed ID: 36444419
[TBL] [Abstract][Full Text] [Related]
12. U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.
Boston T; Van Dijk A; Thackway R
J Imaging; 2024 Jun; 10(6):. PubMed ID: 38921620
[TBL] [Abstract][Full Text] [Related]
13. Monitoring of Antarctica's Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI.
Raniga D; Amarasingam N; Sandino J; Doshi A; Barthelemy J; Randall K; Robinson SA; Gonzalez F; Bollard B
Sensors (Basel); 2024 Feb; 24(4):. PubMed ID: 38400222
[TBL] [Abstract][Full Text] [Related]
14. Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests.
Miraki M; Sohrabi H
Environ Monit Assess; 2021 Dec; 194(1):45. PubMed ID: 34958415
[TBL] [Abstract][Full Text] [Related]
15. Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats.
Ventura D; Napoleone F; Cannucci S; Alleaume S; Valentini E; Casoli E; Burrascano S
J Environ Manage; 2022 Nov; 321():115723. PubMed ID: 35994965
[TBL] [Abstract][Full Text] [Related]
16. A framework for precisely thinning planning in a managed pure Chinese fir forest based on UAV remote sensing.
Zhou P; Sun Z; Zhang X; Wang Y
Sci Total Environ; 2023 Feb; 860():160482. PubMed ID: 36464045
[TBL] [Abstract][Full Text] [Related]
17. Seasonal leaf dynamics across a tree density gradient in a Brazilian savanna.
Hoffmann WA; da Silva ER; Machado GC; Bucci SJ; Scholz FG; Goldstein G; Meinzer FC
Oecologia; 2005 Sep; 145(2):307-16. PubMed ID: 15965754
[TBL] [Abstract][Full Text] [Related]
18. Unmanned aerial survey of fallen trees in a deciduous broadleaved forest in eastern Japan.
Inoue T; Nagai S; Yamashita S; Fadaei H; Ishii R; Okabe K; Taki H; Honda Y; Kajiwara K; Suzuki R
PLoS One; 2014; 9(10):e109881. PubMed ID: 25279817
[TBL] [Abstract][Full Text] [Related]
19. A multi-scale approach to detecting standing dead trees in UAV RGB images based on improved faster R-CNN.
Jiang X; Wu Z; Han S; Yan H; Zhou B; Li J
PLoS One; 2023; 18(2):e0281084. PubMed ID: 36827399
[TBL] [Abstract][Full Text] [Related]
20. Cost benefit analysis of survey methods for assessing intertidal sediment disturbance: A bait collection case study.
White SM; Schaefer M; Barfield P; Cantrell R; Watson GJ
J Environ Manage; 2022 Mar; 306():114386. PubMed ID: 35030426
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
