New international long-term ecological research on air pollution effects on the Carpathian Mountain forests, Central Europe

Bytnerowicz, Andrzej; Badea, Ovidiu; Barbu, Ion; Fleischer, Peter; Frączek, Witold; Gancz, Vladimir; Godzik, Barbara; Grodzińska, K.; Grodzki, W.; Karnosky, David F.; Koreň, Milan; Krywult, Marek; Krzan, Zbigniew; Longauer, Roman; Maňkovská, B.; Manning, William J.; McManus, Michael L.; Musselman, Robert C.; Novotny, J.; Popescu, Flaviu; Postelnicu, Daniela; Prus-Głowacki, Wiesław; Skawiński, Paweł; Skiba, Stefan; Szaro, Robert C.; Tamás, S.; Vasile, Cristian

doi:10.1016/s0160-4120(02)00172-1

Cited by 35 publications

(23 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though some studies mention no significant relation between forest health and bark beetle attack (e.g., [78]), Bytnerowicz et al [79] suggested that long-term elevated levels of atmospheric N and S depositions and elevated O 3 predispose trees to insect attacks and other stresses.…”

Section: Pre-disturbance Forest Conditionsmentioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Pre-disturbance Forest Conditionsmentioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…monocultures or mixed stands, dominated by European beech (Fagus sylvatica), spruce, and European silver fir (Abies alba). Forests in this area are characterized by highly variable tree vigor and mortality rates due to severe airborne pollution during communist times, as well as unsustainable forest management [37][38][39][40]. The conversion of uneven-aged, mixed species stands to spruce plantations in the 19th and 20th centuries [41] homogenized species composition and stand structure at the stand and landscape scales.…”

Section: Study Areamentioning

confidence: 99%

Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians

et al. 2011

View full text Add to dashboard Cite

Understanding the potential of forest ecosystems as global carbon sinks requires a thorough knowledge of forest carbon dynamics, including both sequestration and fluxes among multiple pools. The accurate quantification of biomass is important to better understand forest productivity and carbon cycling dynamics. Stand-based inventories (SBIs) are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments. Satellite remote sensing may provide a supplement or substitute. We tested the accuracy of aboveground biomass estimates modeled from a combination of Landsat Thematic Mapper (TM) imagery and topographic data, as well as SBI-derived variables in a Picea abies forest in the Western Carpathian Mountains. We employed Random Forests for non-parametric, regression tree-based modeling. Results indicated a difference in the importance of SBI-based and remote sensing-based predictors when estimating aboveground biomass. The most accurate models for biomass prediction ranged from a correlation coefficient of 0.52 for the TM-and topography-based model, to 0.98 for the inventory-based model. While Landsat-based biomass estimates were measurably less accurate than those derived from SBI, adding tree height or stand-volume as a field-based predictor to TM and topography-based models increased performance to 0.36 and 0.86, OPEN ACCESSRemote Sens. 2011, 3 1428 respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity.

show abstract

“…The high-elevation forests are unique and economically important to the region (Bytnerowicz et al 2003). Given evidence of climate change (IPCC 2001) and its possible effect on European forests (Saxe et al 2001) as well as the evidence of physiological and genetic differentiation between high-elevation and low-elevation populations in this region (Oleksyn et al 1998), it is of interest to compare the long-term growth/climate response of Picea abies trees growing in different elevations.…”

Section: Introductionmentioning

confidence: 99%

Interannual growth response of Norway spruce to climate along an altitudinal gradient in the Tatra Mountains, Poland

Savva¹,

Oleksyn

Reich

et al. 2006

Trees

129

View full text Add to dashboard Cite

show abstract

New international long-term ecological research on air pollution effects on the Carpathian Mountain forests, Central Europe

Cited by 35 publications

References 7 publications

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

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

Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians

Interannual growth response of Norway spruce to climate along an altitudinal gradient in the Tatra Mountains, Poland

Contact Info

Product

Resources

About