Forest disturbance across the conterminous United States from 1985–2012: The emerging dominance of forest decline

Cohen, Warren B.; Yang, Zhiqiang; Stehman, Stephen V.; Schroeder, Todd A.; Bell, David M.; Masek, Jeffrey G.; Huang, Chengquan; Meigs, Garrett W.

doi:10.1016/j.foreco.2015.10.042

Cited by 234 publications

(205 citation statements)

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“…The relief of the study area is created by a peneplain with numerous local hills. The elevation ranges from 700 m to 1453 m with an average elevation of 1093 m. distinguished different defoliator and bark beetle effects [37], duration and severity of insect disturbances [38], and large-scale drivers of primary forest disturbance events [39]. A similar trajectory-based approach could be used to detect less pronounced pre-disturbance forest state changes which predispose the forest to disturbances [40].…”

Section: Study Areamentioning

confidence: 99%

“…The use of spectral trajectories based on Landsat imagery helped to describe forest disturbance history since 1972 [34], encouraging forest recovery [35], and detecting bark beetle-affected forest stands more precisely [36]. The following studies distinguished different defoliator and bark beetle effects [37], duration and severity of insect disturbances [38], and large-scale drivers of primary forest disturbance events [39]. A similar trajectory-based approach could be used to detect less pronounced pre-disturbance forest state changes which predispose the forest to disturbances [40].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Field surveys are the most accurate approach to examine forest In recent years, the two algorithms LandTrendr [24,[41][42][43][44][45] and BFAST [16,33,46,47] often have been used to detect forest disturbance and recovery in North America. There are also other algorithms such as Continuous Change Detection and Classification (CCDC) [48] and Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) [9].…”

Section: Introductionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

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“…, in the study that developed the harmonic modeling approach we adapted to detect microrefugia, demonstrated that dense time series of all available Landsat data enabled detection of subtle forest thinning. Other studies, more commonly using time series of annual images (including composite images; eg, Roy et al, 2010), emphasize the power of LTS to detect gradual processes including forest decline due to diffuse disturbances such as insect outbreaks or drought (Ahmed et al, 2017;Cohen et al, 2016;Deel et al, 2012;Kennedy et al, 2010), forest succession and woodland densification (Vogelmann et al, 2012), and variation in ecosystem recovery following disturbance (Kennedy et al, 2007(Kennedy et al, , 2010Lawrence and Ripple, 1999;Storey et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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(in press). Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics. Remote Sensing of Environment. Accepted 4 August 2017. DOI:https: //doi.org/10.1016/j.rse.2017.08.005 Disclaimer:The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). 2 AbstractMicrorefugia are sites with stable, high quality habitat within landscapes characterized by dynamic environmental conditions driven by climate variability or ecological disturbances. There is considerable interest in the potential of microrefugia to provide climate change resilience to landscapes and to biodiversity conservation. Although attractive conceptually, there is yet little guidance on how to identify climate change microrefugia in order to study and protect them, and the data required to do so are often lacking. This study demonstrates how time series remote sensing, using all available Landsat images of a study area, can be used to directly detect microrefugia maintained by water subsidies in a semi-arid landscape in southwest Western Australia.Microrefugia were identified as pixels with abundant vegetation and consistent vegetation dynamics between wet and dry years. At every pixel, a harmonic model was fit to the intra-annual time series of vegetation index values compiled from the wettest years in the Landsat-5 Thematic Mapper (TM) archive. This model was then used to predict the phenological cycle of the driest years at that pixel. Candidate microrefugia were defined to be those pixels with (1) high vegetation activity in dry years and (2) highly predictable phenologies that are consistent regardless of the weather conditions experienced in a given year. Spatial relationships between candidate microrefugia and landscape features associated with elevated moisture availability (thought to drive climate microrefugia in these semi-arid landscapes) were assessed. The candidate microrefugia show great promise. Evaluations against high-resolution imagery reveal that candidate microrefugia most likely buffer against drought, although refugia from other disturbances, especially fire, were also detected. In contrast, spatial proxies of the physical features expected to maintain microrefugia failed to adequately represent the distribution of microrefugia across the landscape, likely due to data quality and the heterogeneity of microrefugia. Direct detection of microrefugia with Earth observation data is a promising solution in data limited regions. Landsat time series analyses are well suited to this application as they can characterize both the habitat quality and stability aspects of microrefugia.

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Forest disturbance across the conterminous United States from 1985–2012: The emerging dominance of forest decline

Cited by 234 publications

References 64 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

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

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