Literature DB >> 26982438

Remote Sensing and Reflectance Profiling in Entomology.

Christian Nansen1, Norman Elliott2.   

Abstract

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Keywords:  biotic stress detection; hyperspectral imaging; image classification; multispectral imaging; phenomics; unmanned aerial vehicles

Mesh:

Year:  2016        PMID: 26982438     DOI: 10.1146/annurev-ento-010715-023834

Source DB:  PubMed          Journal:  Annu Rev Entomol        ISSN: 0066-4170            Impact factor:   19.686


  14 in total

1.  Active optical sensor assessment of spider mite damage on greenhouse beans and cotton.

Authors:  Daniel E Martin; Mohamed A Latheef
Journal:  Exp Appl Acarol       Date:  2018-02-08       Impact factor: 2.132

2.  Reflectance-based determination of age and species of blowfly puparia.

Authors:  Sasha C Voss; Paola Magni; Ian Dadour; Christian Nansen
Journal:  Int J Legal Med       Date:  2016-10-21       Impact factor: 2.686

3.  Using proximal remote sensing in non-invasive phenotyping of invertebrates.

Authors:  Xiaowei Li; Hongxing Xu; Ling Feng; Xiao Fu; Yalin Zhang; Christian Nansen
Journal:  PLoS One       Date:  2017-05-04       Impact factor: 3.240

4.  Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Authors:  Maor Matzrafi; Ittai Herrmann; Christian Nansen; Tom Kliper; Yotam Zait; Timea Ignat; Dana Siso; Baruch Rubin; Arnon Karnieli; Hanan Eizenberg
Journal:  Front Plant Sci       Date:  2017-04-03       Impact factor: 5.753

5.  Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory.

Authors:  Leandro do Prado Ribeiro; Adriana Lídia Santana Klock; João Américo Wordell Filho; Marco Aurélio Tramontin; Marília Almeida Trapp; Axel Mithöfer; Christian Nansen
Journal:  Plant Methods       Date:  2018-07-06       Impact factor: 4.993

6.  Rapid Data Analytics to Relate Sugarcane Aphid [(Melanaphis sacchari (Zehntner)] Population and Damage on Sorghum (Sorghum bicolor (L.) Moench).

Authors:  Minori Uchimiya; Joseph E Knoll
Journal:  Sci Rep       Date:  2019-01-23       Impact factor: 4.379

7.  Remote Sensing Data to Detect Hessian Fly Infestation in Commercial Wheat Fields.

Authors:  Ganesh P Bhattarai; Ryan B Schmid; Brian P McCornack
Journal:  Sci Rep       Date:  2019-04-16       Impact factor: 4.379

8.  Hyperspectral remote sensing to detect leafminer-induced stress in bok choy and spinach according to fertilizer regime and timing.

Authors:  Hoang Dd Nguyen; Christian Nansen
Journal:  Pest Manag Sci       Date:  2020-02-07       Impact factor: 4.845

9.  A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

Authors:  Saleh Yousefi; Hamid Reza Pourghasemi; Sayed Naeim Emami; Soheila Pouyan; Saeedeh Eskandari; John P Tiefenbacher
Journal:  Sci Rep       Date:  2020-07-22       Impact factor: 4.379

10.  Penetration and scattering-Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications.

Authors:  Christian Nansen
Journal:  PLoS One       Date:  2018-10-09       Impact factor: 3.240

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