Results 211 to 220 of about 50,129 (308)

Sampling highbush blueberries for Rhagoletis mendax (Diptera: Tephritidae) reveals shifting activity timing and cultivar susceptibility. [PDF]

J Econ Entomol
Van Timmeren S, Isaacs R.
europepmc   +1 more source

Using the CROPGRO Model to Predict Phenology of Cowpea under Rain-fed Conditions

, 2014
David Lomeling
openalex   +1 more source

UAVs unveil the role of small scale vegetation structure on wader nest survival

Remote Sensing in Ecology and Conservation, EarlyView.
In this study, we combine high‐resolution vegetation structural metrics derived from unmanned aerial vehicle (UAV) imagery with on‐field wader nest survival monitoring. We show that the immediate vegetation height and heterogeneity within a 2‐meter buffer surrounding the clutch of the recorded ground‐nesting wader species positively influenced its ...
Miguel Silva‐Monteiro, Miguel Villoslada, Thaisa Bergamo, Triin Kaasiku, Camilo Carneiro, David Kleijn   +5 more
wiley   +1 more source

Synching with seasonality: Predicting roe deer parturition phenology across its distributional range. [PDF]

J Anim Ecol
Kauffert J, Hewison AJM, Gehr B, Hothorn T, Baur S, Gaillard JM, Kjellander P, König A, Müller J, Panzacchi M, Pellerin M, Tamasi B, Peters W, Menzel A.   +13 more
europepmc   +1 more source

New insights into rice phenology: discovering the effect of insolation on heading response [PDF]

Ju‐Hee Kim, So‐Hye Jo, Ji‐Hyeon Moon, Seo‐Yeong Yang, Jae‐Kyeong Baek, Yeong‐Seo Song, Jiyoung Shon, Hyeon‐Seok Lee   +7 more
openalex   +1 more source

Phenological Phenomena† [PDF]

Nature, 1875
openaire   +1 more source

Wall‐to‐wall Amazon forest height mapping with Planet NICFI, Aerial LiDAR, and a U‐Net regression model

Remote Sensing in Ecology and Conservation, EarlyView.
Tree canopy height is a key indicator of forest biomass and structure, yet accurate mapping across the Amazon remains challenging. Here, we generated a canopy height map of the Amazon forest at ~4.8 m resolution using Planet NICFI imagery and a deep learning U‐Net model trained with airborne LiDAR data.
Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi   +21 more
wiley   +1 more source

Nitrogen addition substantially affects plant phenology in terrestrial ecosystems: a meta-analysis. [PDF]

Front Plant Sci
He Y, Zhou T, Mao J, Li M, Sun R, Liu C, Li W, Ma L, Fu S.   +8 more
europepmc   +1 more source

Herbarium specimens can reveal impacts of climate change on plant phenology; a systematic review of methods and applications

, 2017
Casey Jones, Curtis C. Daehler
openalex   +1 more source

Using phenology to improve invasive plant detection in fine‐scale hyperspectral drone‐based images

Remote Sensing in Ecology and Conservation, EarlyView.
Using drone‐based hyperspectral images of mixed temperate successional forests collected over a growing season, detection algorithms were produced for three invasive species of interest, which are not only invasive in Virginia but also much of the U.S.: Ailanthus altissima (tree of heaven), Elaeagnus umbellata (autumn olive), and Rhamnus davurica ...
Kelsey S. Huelsman, Howard E. Epstein, Xi Yang, Roderick Walker   +3 more
wiley   +1 more source