Next Article in Journal
Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm
Next Article in Special Issue
Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images
Previous Article in Journal
Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis
Previous Article in Special Issue
Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series
Article Menu

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2016, 8(8), 687; doi:10.3390/rs8080687

Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

1
Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, Branišovská 31, České Budějovice 37005, Czech Republic
2
Institute of Botany, The Czech Academy of Sciences, Průhonice 25243, Czech Republic
3
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha 6—Suchdol 16521, Czech Republic
4
Department of Ecology, Institute of Entomology, Biology Centre CAS, Branišovská 31, České Budějovice 37005, Czech Republic
5
Institute for Environmental Studies, Faculty of Science, Charles University in Prague, Benátská 2, 12801 Prague 2, Czech Republic
6
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 114 Wilkinson Hall, Corvallis, OR 97331, USA
7
Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Angela Lausch, Marco Heurich, Randolph H. Wynne and Prasad S. Thenkabail
Received: 30 May 2016 / Revised: 12 August 2016 / Accepted: 16 August 2016 / Published: 22 August 2016
(This article belongs to the Special Issue Remote Sensing of Forest Health)
View Full-Text   |   Download PDF [3372 KB, uploaded 24 August 2016]   |  

Abstract

Tree mortality caused by bark beetle infestation has significant effects on the ecology and value of both natural and commercial forests. Therefore, prediction of bark beetle infestations is critical in forest management. Existing predictive models, however, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation together with commonly used environmental predictors. Observations for this study come from forests in the central part of the Šumava Mountains, in the border region between the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest were delineated from aerial photographs taken in 1991 and in every year from 1994 to 2000. The environmental predictors represent forest stand attributes (e.g., tree density and distance to the infested forest from previous year) and common abiotic factors, such as topography, climate, geology, and soil. Pre-disturbance spectral trajectories were defined by the linear regression slope of Tasseled Cap components (Wetness, Brightness and Greenness) calculated from a time series of 16 Landsat TM images across years from 1984 until one year before the bark beetle infestation. Using logistic regression and multimodel inference, we calculated predictive models separately for each single year from 1994 to 2000 to account for a possible shift in importance of individual predictors during disturbance. Inclusion of two pre-disturbance spectral trajectories (Wetness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors were not correlated with other predictors, and therefore bring additional explanatory power to the model. Generally, the predictive power of most fitted model decreases as time progresses and models describing the initial phase of bark beetle outbreaks appear more reliable for conducting near-future predictions. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems. View Full-Text
Keywords: forest disturbance; spectral trajectories; bark beetle; Landsat forest disturbance; spectral trajectories; bark beetle; Landsat
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hais, M.; Wild, J.; Berec, L.; Brůna, J.; Kennedy, R.; Braaten, J.; Brož, Z. Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance. Remote Sens. 2016, 8, 687.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top