Literature DB >> 28860949

Remote sensing of forest insect disturbances: Current state and future directions.

Cornelius Senf1,2, Rupert Seidl2, Patrick Hostert1,3.   

Abstract

Insect disturbance are important agents of change in forest ecosystems around the globe, yet their spatial and temporal distribution and dynamics are not well understood. Remote sensing has gained much attention in mapping and understanding insect outbreak dynamics. Consequently, we here review the current literature on the remote sensing of insect disturbances. We suggest to group studies into three insect types: bark beetles, broadleaved defoliators, and coniferous defoliators. By so doing, we systematically compare the sensors and methods used for mapping insect disturbances within and across insect types. Results suggest that there are substantial differences between methods used for mapping bark beetles and defoliators, and between methods used for mapping broadleaved and coniferous defoliators. Following from this, we highlight approaches that are particularly suited for each insect type. Finally, we conclude by highlighting future research directions for remote sensing of insect disturbances. In particular, we suggest to: 1) Separate insect disturbances from other agents; 2) Extend the spatial and temporal domain of analysis; 3) Make use of dense time series; 4) Operationalize near-real time monitoring of insect disturbances; 5) Identify insect disturbances in the context of coupled human-natural systems; and 6) Improve reference data for assessing insect disturbances. Since the remote sensing of insect disturbances has gained much interest beyond the remote sensing community recently, the future developments identified here will help integrating remote sensing products into operational forest management. Furthermore, an improved spatiotemporal quantification of insect disturbances will support an inclusion of these processes into regional to global ecosystem models.

Entities:  

Keywords:  Bark beetle; Biotic disturbance; Defoliation; Forest health; Insect disturbance; Insect outbreak

Year:  2017        PMID: 28860949      PMCID: PMC5572637          DOI: 10.1016/j.jag.2017.04.004

Source DB:  PubMed          Journal:  Int J Appl Earth Obs Geoinf        ISSN: 1569-8432


  13 in total

Review 1.  Forest health and global change.

Authors:  S Trumbore; P Brando; H Hartmann
Journal:  Science       Date:  2015-08-21       Impact factor: 47.728

Review 2.  Temperate forest health in an era of emerging megadisturbance.

Authors:  Constance I Millar; Nathan L Stephenson
Journal:  Science       Date:  2015-08-21       Impact factor: 47.728

Review 3.  Complexity of coupled human and natural systems.

Authors:  Jianguo Liu; Thomas Dietz; Stephen R Carpenter; Marina Alberti; Carl Folke; Emilio Moran; Alice N Pell; Peter Deadman; Timothy Kratz; Jane Lubchenco; Elinor Ostrom; Zhiyun Ouyang; William Provencher; Charles L Redman; Stephen H Schneider; William W Taylor
Journal:  Science       Date:  2007-09-14       Impact factor: 47.728

4.  Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery.

Authors:  Hooman Latifi; Bastian Schumann; Markus Kautz; Stefan Dech
Journal:  Environ Monit Assess       Date:  2013-09-15       Impact factor: 2.513

Review 5.  Global satellite monitoring of climate-induced vegetation disturbances.

Authors:  Nate G McDowell; Nicholas C Coops; Pieter S A Beck; Jeffrey Q Chambers; Chandana Gangodagamage; Jeffrey A Hicke; Cho-ying Huang; Robert Kennedy; Dan J Krofcheck; Marcy Litvak; Arjan J H Meddens; Jordan Muss; Robinson Negrón-Juarez; Changhui Peng; Amanda M Schwantes; Jennifer J Swenson; Louis J Vernon; A Park Williams; Chonggang Xu; Maosheng Zhao; Steve W Running; Craig D Allen
Journal:  Trends Plant Sci       Date:  2014-12-11       Impact factor: 18.313

6.  A 10-Year Assessment of Hemlock Decline in the Catskill Mountain Region of New York State Using Hyperspectral Remote Sensing Techniques.

Authors:  Ryan P Hanavan; Jennifer Pontius; Richard Hallett
Journal:  J Econ Entomol       Date:  2015-01-21       Impact factor: 2.381

7.  Discriminating Tsuga canadensis Hemlock Forest Defoliation Using Remotely Sensed Change Detection.

Authors:  D D Royle; R G Lathrop
Journal:  J Nematol       Date:  2002-09       Impact factor: 1.402

8.  Development of a satellite-based hazard rating system for Dendroctonus frontallis (Coleoptera: Scolytidae) in the Ouachita Mountains of Arkansas.

Authors:  Stephen Cook; Shane Cherry; Karen Humes; James Guldin; Christopher Williams
Journal:  J Econ Entomol       Date:  2007-04       Impact factor: 2.381

9.  Mountain pine beetle and forest carbon feedback to climate change.

Authors:  W A Kurz; C C Dymond; G Stinson; G J Rampley; E T Neilson; A L Carroll; T Ebata; L Safranyik
Journal:  Nature       Date:  2008-04-24       Impact factor: 49.962

10.  Increasing forest disturbances in Europe and their impact on carbon storage.

Authors:  Rupert Seidl; Mart-Jan Schelhaas; Werner Rammer; Pieter Johannes Verkerk
Journal:  Nat Clim Chang       Date:  2014-09-01
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  5 in total

1.  Can Leaf Water Content Be Estimated Using Multispectral Terrestrial Laser Scanning? A Case Study With Norway Spruce Seedlings.

Authors:  Samuli Junttila; Junko Sugano; Mikko Vastaranta; Riikka Linnakoski; Harri Kaartinen; Antero Kukko; Markus Holopainen; Hannu Hyyppä; Juha Hyyppä
Journal:  Front Plant Sci       Date:  2018-03-08       Impact factor: 5.753

2.  Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery.

Authors:  P J Zarco-Tejada; A Hornero; R Hernández-Clemente; P S A Beck
Journal:  ISPRS J Photogramm Remote Sens       Date:  2018-03       Impact factor: 8.979

3.  Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis.

Authors:  Omid Abdi
Journal:  Sensors (Basel)       Date:  2019-09-14       Impact factor: 3.576

4.  Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.

Authors:  Werner Rammer; Rupert Seidl
Journal:  Front Plant Sci       Date:  2019-10-28       Impact factor: 5.753

5.  Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations.

Authors:  Matthias Forkel; Markus Drüke; Martin Thurner; Wouter Dorigo; Sibyll Schaphoff; Kirsten Thonicke; Werner von Bloh; Nuno Carvalhais
Journal:  Sci Rep       Date:  2019-12-10       Impact factor: 4.379

  5 in total

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