Results 131 to 140 of about 57,543 (231)

Hyperspectral Remote Sensing in North Africa

4th EAGE North African/Mediterranean Petroleum and Geosciences Conference and Exhibition Tunis 2009, 2009
Hyperspectral remote sensing is a technique that uses reflected solar radiation to determine the spectral response within a determined area (pixel) on the surface of the earth. Airborne hyperspectral techniques can aid in the location of macroseeps by direct detection and may be used indirectly to locate microseeps.
openaire   +2 more sources

Spectral Characterization of the Life Stages and Physiological Responses of Diatraea saccharalis Fabricius (Lepidoptera: Crambidae) Larvae Parasitized by Cotesia flavipes Cameron (Hymenoptera: Braconidae)

Entomologia Experimentalis et Applicata, Volume 174, Issue 5, Page 436-452, May 2026.
Hyperspectral proximal sensing was used to characterize the life stages and physiological responses of Diatraea saccharalis and to detect parasitism by Cotesia flavipes. Distinct spectral signatures differentiated eggs, larval instars, pupae, and adults, as well as live, dead, and parasitized larvae.
Souradji I. Bachirou, David L. Rosalen, Sergio A. De Bortoli, Pedro G. Peixoto   +3 more
wiley   +1 more source

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Remote Sensing
Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue.
Chein-I Chang, Yi-Mei Kuo, Kenneth Yeonkong Ma   +2 more
doaj   +1 more source

Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing. [PDF]

Plant Methods, 2023
Qamar F, Dobler G.
europepmc   +1 more source

Assessing functional diversity and composition using multispectral images in Atlantic Forest restoration sites

Restoration Ecology, Volume 34, Issue 4, May 2026.
Abstract Introduction By focusing on the specific functional traits of species, we can better understand how ecosystems function and respond to environmental change. However, field measurements of functional traits can be time‐consuming and costly, whereas remote sensing data can serve as viable alternatives for studying biodiversity at larger scales ...
Júlia Ayres de Oliveira, Ângelo Mantovani Amorim de Freitas Oliveira, André Quintão de Almeida, Catherine Torres de Almeida, Eric Bastos Gorgens, Vinicius Nunes Henrique Silva, Luiz Fernando Duarte de Moraes, Gustavo Bastos Lyra, Jerônimo Boelsums Barreto Sansevero   +8 more
wiley   +1 more source

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean. [PDF]

Plant Phenomics, 2023
Wong CY, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN.   +7 more
europepmc   +1 more source

Long‐Range Transport Pathways of Intense Wildfire Plumes in the Northern Hemisphere Observed by the IASI/METOP Satellite Mission

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 8, 28 April 2026.
Abstract Wildfires emit significant greenhouse gases and pollutants. Extreme fires disperse smoke plumes over long distances, necessitating a deeper understanding of their source regions and variability. This study introduces a plume back‐trajectory algorithm using carbon monoxide (CO) and peroxyacetyl nitrate satellite observations from the Infrared ...
Antoine Ehret, Solène Turquety, Anderson Da Silva, Maya George, Juliette Hadji‐Lazaro, Bruno Franco, Lieven Clarisse, Pierre‐François Coheur, Cathy Clerbaux   +8 more
wiley   +1 more source

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images. [PDF]

Sensors (Basel), 2022
Chen H, Chen Y, Wang Q, Chen T, Zhao H.
europepmc   +1 more source

Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model. [PDF]

Sensors (Basel), 2022
Li J, Wang H, Zhang A, Liu Y.
europepmc   +1 more source

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. [PDF]

Front Plant Sci, 2023
Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C.   +7 more
europepmc   +1 more source