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
Open AccessFeature PaperArticle

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
Remote Sens. 2016, 8(8), 687; https://doi.org/10.3390/rs8080687
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)
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
Show Figures

Graphical abstract

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.

AMA 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 Sensing. 2016; 8(8):687.

Chicago/Turabian Style

Hais, Martin; Wild, Jan; Berec, Luděk; Brůna, Josef; Kennedy, Robert; Braaten, Justin; Brož, Zdeněk. 2016. "Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance" Remote Sens. 8, no. 8: 687.

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop