Results 151 to 160 of about 559 (234)

Tidal marsh species mapping using commercial satellite imagery for enhanced coastal management in Chesapeake Bay. [PDF]

Remote Sens Appl
Coffer MM, Trinh R, Mitchell M, Angstadt K, Stanhope D, Lv Z, Nunez K, Bartlett ND, Wiltsie D, Sullivan S, Schaeffer BA.   +10 more
europepmc   +1 more source

Advancing High‐Resolution Lake Bathymetry Reconstruction Through Geomorphologically Informed Deep Learning in High Mountain Asia

Water Resources Research, Volume 62, Issue 7, July 2026.
Abstract Accurate three‐dimensional (3D) lake bathymetry reconstruction is critical for water resources assessment and hydrological modeling yet remains constrained by data scarcity and oversimplified geometric assumptions. To address these challenges, we propose the Geomorphologically informed deep learning (GIDL) framework for high‐resolution 3D lake
Minglei Hou, Jiamu Chen, Jiahua Wei, Yang Shi, Wenqian Zhang, Shengling Hou, Zhen Qiao, Jianshi Zhao   +7 more
wiley   +1 more source

National scale estimation of above-ground biomass density in Nepal's forests at 30 m resolution (circa 2015). [PDF]

Sci Rep
Gilani H, Shrestha HL, Chave J, Hanan NP.   +3 more
europepmc   +1 more source

Vertical Habitat Use by Japanese Jack Mackerel Trachurus japonicus Inferred From a Biologging Study in Tokyo Bay

Fisheries Oceanography, Volume 35, Issue 4, Page 518-531, July 2026.
ABSTRACT The movement ecology of Trachurus japonicus in the adult stage remains poorly understood because observing their underwater behavior over long periods is challenging. This study aimed to examine vertical habitat use by T. japonicus using electronic tags. Ninety fish were tagged and released in November 2022 in Tokyo Bay, Japan.
Junji Kinoshita, Yuki Fujinami, Junichi Imoto, Tohya Yasuda   +3 more
wiley   +1 more source

A daily three-dimensional dataset of the Kuroshio axis and boundaries in the East China Sea and Luzon Strait. [PDF]

Sci Data
Wang J, Chen X, Mao K, Zhang C.
europepmc   +1 more source

Documenting biodiversity with digital data: comparing and contrasting the efficacy of specimen‐based and observation‐based approaches

New Phytologist, Volume 251, Issue 2, Page 721-736, July 2026.
Summary Digitized herbarium specimens and iNaturalist observations provide invaluable plant biodiversity data. Combining these two data sources could create a more holistic representation of local biodiversity; however, understanding biases inherent to each is critical to determine how to best combine and utilize these data.
Rebecca C. Wilcox, Anthony E. Baniaga, Avery P. Hill, Alison Young, Rebecca F. Johnson, Sarah J. Jacobs   +5 more
wiley   +1 more source

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method. [PDF]

Front Plant Sci
Xu L, Yu J, Shu Q, Luo S, Zhou W, Duan D.   +5 more
europepmc   +1 more source

Using herbarium collections to study genetic responses to global change

New Phytologist, Volume 251, Issue 2, Page 637-644, July 2026.
Summary Earth's c. 406 million herbarium specimens represent a largely untapped resource of genetic data that could transform our understanding of global plant populations. Advances in DNA sequencing have made the extraction of genetic data from these preserved specimens increasingly feasible, enabling new insights into plant biodiversity and ...
Lucas Eckert, Isaac Eckert, Olivia Rahn, Cameron P. So, Rowan D. H. Barrett   +4 more
wiley   +1 more source

OliveTreeCrownsDb: A high-resolution UAV dataset for detection and segmentation in agricultural computer vision. [PDF]

Data Brief
Hnida Y, Mahraz MA, Achebour A, Yahyaouy A, Riffi J, Tairi H.   +5 more
europepmc   +1 more source

Machine learning‐based prediction of cereal rye cover crop biomass across diverse agroecosystems

Agricultural &Environmental Letters, Volume 11, Issue 1, June 2026.
Abstract Accurate operational predictions of cereal rye (Secale cereale L.) biomass are critical for quantifying the agroecosystem services provided by cover crops and for guiding growers’ management decisions for subsequent cash crops. In this study, we developed machine learning‐based biomass prediction models using two advanced gradient‐boosted tree
Utsab Ghimire, Alakananda Mitra, David Fleisher, John Park, Jinyoung Y. Barnaby, Yonghyun Kim, Eunjin Han   +6 more
wiley   +1 more source