Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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, hover, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study  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 Sumava Mountains, in the border region beten the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest re 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 re defined by the linear regression slope of Tasseled Cap components (tness, 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,  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 (tness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. tness slope had the greatest predictive por, even relative to environmental predictors, and was relatively stable in its por over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors re not correlated with other predictors, and therefore bring additional explanatory por to the model. Generally, the predictive por 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.
['Martin Hais', 'Jan Wild', 'Luděk Berec', 'Josef Brůna', 'Robert E. Kennedy', 'Justin Braaten', 'Zdeněk Brož']
I am writing a research paper on a idea Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance
[{"from": "human", "value": "I am writing a research paper on a idea Landsat Imagery Spectral Trajectories\u2014Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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, hover, rarely consider the influence of long-term stressors on forest susceptibility to bark beetle infestation. In this study  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 Sumava Mountains, in the border region beten the Czech Republic and Germany, Central Europe. The areas of bark beetle-infested forest re 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 re defined by the linear regression slope of Tasseled Cap components (tness, 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,  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 (tness slope and Brightness slope) significantly improved predictive ability of bark beetle infestation models. tness slope had the greatest predictive por, even relative to environmental predictors, and was relatively stable in its por over the years. Brightness slope improved the model only in the middle of the disturbance period (1996). Importantly, these pre-disturbance predictors re not correlated with other predictors, and therefore bring additional explanatory por to the model. Generally, the predictive por 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."}]