Modeling and prediction of wind damage in forest ecosystems of the Sudety Mountains, SW Poland
Pawlik, Łukasz
Lukasz Pawlik is a PhD candidate at University of Silesia in Katowice at the Institute of Earth Sciences. His research focuses on the effects of windstorms on European forest ecosystems. In his presentation, Lukasz explained how the combination of several data sets on tree features, bioclimatic and geomorphic conditions, and the level of forest damage in the Sudety Mountains over the period 2004-2010, to map forest damage probability based on forest data from 202. Eventually, Lukasz focusses on how,using only 11 variables based on the open source datasets, his research effort was able to obtain predictive models of good accuracy.
 My name is Lucas and I currently work as associate professor at the University of Silesia in Poland. And this is just a small project developed in cooperation with Professor Sandy Harrison from the University of Reading, NewPage and the project is still in progress so we hope to improve it in a new future. This is modeling and prediction of wind damage in the first ecosystem of the Silesia in one region in Poland, in the southwestern part of Poland. So first of all, I'd like to tell you about the motivations. This region is particularly susceptible to forest damage to the strong wind. And one of the reasons for this particular susceptibility is complex forest history. Forests were largely changing this part of Poland due to human impact. First it was the forest species for agricultural purposes and damage in the last two centuries. It was intensive forest management different practices which resulted in changes in forest structure. And this region process were introduced in large amount and and is dominated for us. And this is a distributed species dominated for us so basically we did in this project with my life for us. And this project is linked with a concept of the biome of a dynamic so it is like we tried to. We tried to look at how living organisms and in this case trees how the impact geomorphic processes on his soaps and soil development. And the last thing no past attempts have been made in terms of damage forest damage a model using machine learning methods. And the most important question is where are forest most susceptible to hurricane wind different origin. And, of course, it would be really useful to find potential strong predictors of forest damage cause by wind so we tested several machine learning methods. And we selected the best models and then we tried to build much damage probability. Here's our study region. This southwest part of Poland is so that in mountain range and part of this motor is on Polish territory, and it is a part of Bohemian massive. And we can see the 234 dominate this area especially loses a large part of this area would that for us to and some important information about climate especially wind climate of this area. So here on this plot you can see, they really mean they will speak so you can see some seasonal component but it is even a better visible on this plot where you have number of hours per day with wind speed over 10 meters, the over 10 meters per second. And this is our component is ever better visible. Basically, the highest winds with winter season is new interseason, and it is correlated or related to ethical cycle events. And here are some examples from three jobs. This is the highest part of the city of the mountains, the car connoisseur mountain can see some forest changes, a code based trunk wind and selected plots from 2001 2012 based on auto for the map. And this example from the middle part of the city of the mountains and this damage was caused by the kill Winston 2007. So, workflow, workflow was like combining three data sets. First one for us features before damage obtained from for states, and then based on unique ID number with combined with forest damage from 2004 and 2010 and this information was in cubic meters per basic forest unit. So we tried to fill it with some environmental predictors, and basically bio climate features from Charles database from a word clean, and that was monthly, monthly wind speed and then some turn properties. So we tried to rather photography, mission product. And then we calculate some, some terrain properties in the subject areas. And basically, we tried to test it in four scenarios. And first, first to we divided, or we use all three species versus on this process. And then in another two scenarios, damage to cubic meters versus damage to be the heck. And then these values, this information, we transform to transform to be narrow data. And we tried to use machine learning models to solve classification problem tools mostly it was a card or package. And then we perform maps in quantum gels and for some terrain features inside that area. And for some, for some parallel computing we use infrastructure from beer grade this this is a photo computer clusters, and we use one of them with 24 cores, just to speed up. So this is a list of machine learning models we tried to test and some on this side with some information about hyper parameter tuning. So we tried to try to apply great search, in order to choose the best hyper parameters and then obtain the best best model for our approach. Before model training, we removed the highly correlated variables so when to buy this will have a collated so the absolute correlation curve was over 0.75 one of this value was removed. And then we split our data set on the relation, shipping with with other variables. And then we split our data set through training and test set, but just for years 2007 2013 for the time and other years were used as additional test sets for model validation. And depending on scenario, we could use from over 57,000 observations to 20,000 observations. And for model validation we use 10 cross, so 10 fold cross validation you could have five, five times. So here on this PC plus you can see the final number and relationship between by using for the first two scenarios so we had like 11 by years. And you can see that even after removing some highly value, high cold and correlated by it was still some of them were closely related. So it's time for some results. This is for the first scenario we use for as them as a QB meters for all three species, and generally speaking, and so for for evaluation of this models we use accuracy and the curve receiver operator, my character is the curve and general speaking, the results are quite similar so for most of the models and accuracy. The curve is slightly slightly below open seven slightly over open seven and the best models. The best predictive power was offered by your under force model and Javier machine. And in this plot, down here you can see that those are quite, quite similar, and the best accuracy the best predictive power was offered by one of course and try to machine for the first scenario. In terms of future importance. And so that was especially for, I mean, generally, generally for all models but especially for the trend for it and for getting a machine and some other variables like bio climate variables over quite important. So, precipitation over the coldest water or mention when state, and as you can see topographic variables were less important. And another, another slide, and using accumulated local effects plus we can show how individual value influence damage probability. So, as mentioned before trivolume in CH is the most important, the most important variables and damage probability increase according to this is I think he's with trivolume, three age but only for some age class between maybe 70 years old up to 110 years old. And so the other damage probability increases with wind exposition index topographic witness index but just to certain threshold level and then valley. Okay, so the final product was a map of damage probability. And I'm not going to explain this in detail, smash. I'm not going to be much, much complicated but what you can see that damage probability for years 1998 2016 much higher than for 2000 and 20 those two maps. And based on the random for its model and this difference between years is even more better visible for grab industry machine. So now important question is why, why you have these differences. We still try to find the best explanation, but maybe two reasons are important to highlight here. First of all, a proportion of process for 2020 was less for the recent data was led and for data between 1998 and 2006, but we know that process. There was a skeptical trip for them into high winds. And so it might be a good explanation that with less, less proportion, lower proportion of as process we can expect lower damage probability and another. And so the first one argument would be changes in age classes can see that was quite on this density clock is quite well visible changes between three age classes between previous previous period and recent data from 2000 and 20 and local, and local effect was show that damage probability was, was increasing was increasing for three age class between 70 and 110 years. Okay, now it's time for some conclusions. We tested five machine models for four scenarios. The most important predictors were two volume and three age and geomorphic and climate predictors were less important and through models offered the best predictive power for random forest algorithm and machine and four stand features might significantly influence probability of course. Thank you for that. Thank you. Yeah, because we have this data is in it was in vector vector like that. Yes, yes, yes, yes, yes, yes, yes. This is the Yeah, we didn't test it. In terms of, in terms of specifying this yes. So, previous analysis or Yeah, of course, I mean, yeah, I wanted to say that the next steps would, would include some things we learned in this session so it would be great to do that.
 My name is Lucas and I currently work as associate professor at the University of Silesia in  Poland.  And this is just a small project developed in cooperation with Professor Sandy Harrison  from the University of Reading, NewPage and the project is still in progress so we hope to improve it in a new future.  This is modeling and prediction of wind damage in the first ecosystem of the Silesia  in one region in Poland, in the southwestern part of Poland.  So first of all, I'd like to tell you about the motivations.  This region is particularly susceptible to forest damage to the strong wind.  And one of the reasons for this particular susceptibility is complex forest history.  Forests were largely changing this part of Poland due to human impact.  First it was the forest species for agricultural purposes and damage in the last two centuries.  It was intensive forest management different practices which resulted in changes in forest structure.  And this region process were introduced in large amount and and is dominated for us.  And this is a distributed species dominated for us so basically we did in this project with my life for us.  And this project is linked with a concept of the biome of a dynamic so it is like we tried to.  We tried to look at how living organisms and in this case trees how the impact geomorphic processes on his soaps and soil development.  And the last thing no past attempts have been made in terms of  damage forest damage a model using machine learning methods.  And the most important question is where are forest most susceptible to hurricane wind different origin.  And, of course, it would be really useful to find potential strong predictors of forest damage cause by wind so we tested several machine learning methods.  And we selected the best models and then we tried to build much damage probability.  Here's our study region.  This southwest part of Poland is so that in mountain range and part of this motor is on Polish territory, and it is a part of Bohemian massive.  And we can see the 234 dominate this area especially loses a large part of this area would that for us to and some important information about climate especially wind climate of this area.  So here on this plot you can see, they really mean they will speak so you can see some seasonal component but it is even a better visible on this plot where you have number of hours per day with wind speed over 10 meters, the over 10 meters per second.  And this is our component is ever better visible. Basically, the highest winds with winter season is new interseason, and it is correlated or related to ethical cycle events.  And here are some examples from three jobs. This is the highest part of the city of the mountains, the car connoisseur mountain can see some forest changes, a code based trunk wind and selected plots from 2001 2012 based on auto for the map.  And this example from the middle part of the city of the mountains and this damage was caused by the kill Winston 2007.  So, workflow, workflow was like combining three data sets. First one for us features before damage obtained from for states, and then based on unique ID number with combined with forest damage from 2004 and 2010 and this information was in cubic meters per basic forest unit.  So we tried to fill it with some environmental predictors, and basically bio climate features from Charles database from a word clean, and that was monthly, monthly wind speed and then some turn properties.  So we tried to rather photography, mission product. And then we calculate some, some terrain properties in the subject areas.  And basically, we tried to test it in four scenarios. And first, first to we divided, or we use all three species versus on this process. And then in another two scenarios, damage to cubic meters versus damage to be the heck.  And then these values, this information, we transform to transform to be narrow data. And we tried to use machine learning models to solve classification problem tools mostly it was a card or package.  And then we perform maps in quantum gels and for some terrain features inside that area.  And for some, for some parallel computing we use infrastructure from beer grade this this is a photo computer clusters, and we use one of them with 24 cores, just to speed up.  So this is a list of machine learning models we tried to test and some on this side with some information about hyper parameter tuning. So we tried to try to apply great search, in order to choose the best hyper parameters and then obtain the best best model for our  approach.  Before model training, we removed the highly correlated variables so when to buy this will have a collated so the absolute correlation curve was over 0.75 one of this value was removed.  And then we split our data set on the relation, shipping with with other variables. And then we split our data set through training and test set, but just for years 2007 2013 for the time and other years were used as additional test sets for model validation.  And depending on scenario, we could use from over 57,000 observations to 20,000 observations. And for model validation we use 10 cross, so 10 fold cross validation you could have five, five times.  So here on this PC plus you can see the final number and relationship between by using for the first two scenarios so we had like 11 by years.  And you can see that even after removing some highly value, high cold and correlated by it was still some of them were closely related.  So it's time for some results.  This is for the first scenario we use for as them as a QB meters for all three species, and generally speaking,  and so for for evaluation of this models we use accuracy and the curve receiver operator, my character is the curve and general speaking, the results are quite similar so for most of the models and accuracy.  The curve is slightly slightly below open seven slightly over open seven and the best models.  The best predictive power was offered by your under force model and Javier machine.  And in this plot, down here you can see that those are quite, quite similar, and the best accuracy the best predictive power was offered by one of course and try to machine for the first scenario.  In terms of future importance.  And so that was especially for, I mean, generally, generally for all models but especially for the trend for it and for getting a machine and some other variables like bio climate variables over quite important.  So, precipitation over the coldest water or mention when state, and as you can see topographic variables were less important.  And another, another slide, and using accumulated local effects plus we can show how individual value influence damage probability.  So, as mentioned before trivolume in CH is the most important, the most important variables and damage probability increase according to this is I think he's with trivolume, three age but only for some age class between maybe 70 years old up to 110 years old.  And so the other damage probability increases with wind exposition index topographic witness index but just to certain threshold level and then valley.  Okay, so the final product was a map of damage probability. And I'm not going to explain this in detail, smash.  I'm not going to be much, much complicated but what you can see that damage probability for years 1998 2016 much higher than for 2000 and 20 those two maps.  And based on the random for its model and this difference between years is even more better visible for grab industry machine.  So now important question is why, why you have these differences.  We still try to find the best explanation, but maybe two reasons are important to highlight here. First of all, a proportion of process for 2020 was less for the recent data was led and for data between 1998 and 2006, but we know that process.  There was a skeptical trip for them into high winds.  And so it might be a good explanation that with less, less proportion, lower proportion of as process we can expect lower damage probability and another.  And so the first one argument would be changes in age classes can see that was quite on this density clock is quite well visible changes between three age classes between previous previous period and recent data from 2000 and 20 and  local, and local effect was show that damage probability was, was increasing was increasing for three age class between 70 and 110 years.  Okay, now it's time for some conclusions. We tested five machine models for four scenarios.  The most important predictors were two volume and three age and geomorphic and climate predictors were less important and through models offered the best predictive power for random forest algorithm and machine and four stand features might significantly influence probability of course.  Thank you for that.  Thank you.  Yeah, because we have this data is in it was in vector vector like that.  Yes, yes, yes, yes, yes, yes, yes.  This is the  Yeah, we didn't test it.  In terms of, in terms of specifying this yes.  So, previous analysis or  Yeah, of course, I mean, yeah, I wanted to say that the next steps would, would include some things we learned in this session so it would be great to do that.
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My name is Lucas and I currently work as associate professor at the University of Silesia in Poland. And this is just a small project developed in cooperation with Professor Sandy Harrison from the University of Reading, NewPage and the project is still in progress so we hope to improve it in a new future. This is modeling and prediction of wind damage in the first ecosystem of the Silesia in one region in Poland, in the southwestern part of Poland. So first of all, I'd like to tell you about the motivations. This region is particularly susceptible to forest damage to the strong wind. And one of the reasons for this particular susceptibility is complex forest history. Forests were largely changing this part of Poland due to human impact. First it was the forest species for agricultural purposes and damage in the last two centuries. It was intensive forest management different practices which resulted in changes in forest structure. And this region process were introduced in large amount and and is dominated for us. And this is a distributed species dominated for us so basically we did in this project with my life for us. And this project is linked with a concept of the biome of a dynamic so it is like we tried to. We tried to look at how living organisms and in this case trees how the impact geomorphic processes on his soaps and soil development. And the last thing no past attempts have been made in terms of damage forest damage a model using machine learning methods. And the most important question is where are forest most susceptible to hurricane wind different origin. And, of course, it would be really useful to find potential strong predictors of forest damage cause by wind so we tested several machine learning methods. And we selected the best models and then we tried to build much damage probability. Here's our study region. This southwest part of Poland is so that in mountain range and part of this motor is on Polish territory, and it is a part of Bohemian massive. And we can see the 234 dominate this area especially loses a large part of this area would that for us to and some important information about climate especially wind climate of this area. So here on this plot you can see, they really mean they will speak so you can see some seasonal component but it is even a better visible on this plot where you have number of hours per day with wind speed over 10 meters, the over 10 meters per second. And this is our component is ever better visible. Basically, the highest winds with winter season is new interseason, and it is correlated or related to ethical cycle events. And here are some examples from three jobs. This is the highest part of the city of the mountains, the car connoisseur mountain can see some forest changes, a code based trunk wind and selected plots from 2001 2012 based on auto for the map. And this example from the middle part of the city of the mountains and this damage was caused by the kill Winston 2007. So, workflow, workflow was like combining three data sets. First one for us features before damage obtained from for states, and then based on unique ID number with combined with forest damage from 2004 and 2010 and this information was in cubic meters per basic forest unit. So we tried to fill it with some environmental predictors, and basically bio climate features from Charles database from a word clean, and that was monthly, monthly wind speed and then some turn properties. So we tried to rather photography, mission product. And then we calculate some, some terrain properties in the subject areas. And basically, we tried to test it in four scenarios. And first, first to we divided, or we use all three species versus on this process. And then in another two scenarios, damage to cubic meters versus damage to be the heck. And then these values, this information, we transform to transform to be narrow data. And we tried to use machine learning models to solve classification problem tools mostly it was a card or package. And then we perform maps in quantum gels and for some terrain features inside that area. And for some, for some parallel computing we use infrastructure from beer grade this this is a photo computer clusters, and we use one of them with 24 cores, just to speed up. So this is a list of machine learning models we tried to test and some on this side with some information about hyper parameter tuning. So we tried to try to apply great search, in order to choose the best hyper parameters and then obtain the best best model for our approach. Before model training, we removed the highly correlated variables so when to buy this will have a collated so the absolute correlation curve was over 0.75 one of this value was removed. And then we split our data set on the relation, shipping with with other variables. And then we split our data set through training and test set, but just for years 2007 2013 for the time and other years were used as additional test sets for model validation. And depending on scenario, we could use from over 57,000 observations to 20,000 observations. And for model validation we use 10 cross, so 10 fold cross validation you could have five, five times. So here on this PC plus you can see the final number and relationship between by using for the first two scenarios so we had like 11 by years. And you can see that even after removing some highly value, high cold and correlated by it was still some of them were closely related. So it's time for some results. This is for the first scenario we use for as them as a QB meters for all three species, and generally speaking, and so for for evaluation of this models we use accuracy and the curve receiver operator, my character is the curve and general speaking, the results are quite similar so for most of the models and accuracy. The curve is slightly slightly below open seven slightly over open seven and the best models. The best predictive power was offered by your under force model and Javier machine. And in this plot, down here you can see that those are quite, quite similar, and the best accuracy the best predictive power was offered by one of course and try to machine for the first scenario. In terms of future importance. And so that was especially for, I mean, generally, generally for all models but especially for the trend for it and for getting a machine and some other variables like bio climate variables over quite important. So, precipitation over the coldest water or mention when state, and as you can see topographic variables were less important. And another, another slide, and using accumulated local effects plus we can show how individual value influence damage probability. So, as mentioned before trivolume in CH is the most important, the most important variables and damage probability increase according to this is I think he's with trivolume, three age but only for some age class between maybe 70 years old up to 110 years old. And so the other damage probability increases with wind exposition index topographic witness index but just to certain threshold level and then valley. Okay, so the final product was a map of damage probability. And I'm not going to explain this in detail, smash. I'm not going to be much, much complicated but what you can see that damage probability for years 1998 2016 much higher than for 2000 and 20 those two maps. And based on the random for its model and this difference between years is even more better visible for grab industry machine. So now important question is why, why you have these differences. We still try to find the best explanation, but maybe two reasons are important to highlight here. First of all, a proportion of process for 2020 was less for the recent data was led and for data between 1998 and 2006, but we know that process. There was a skeptical trip for them into high winds. And so it might be a good explanation that with less, less proportion, lower proportion of as process we can expect lower damage probability and another. And so the first one argument would be changes in age classes can see that was quite on this density clock is quite well visible changes between three age classes between previous previous period and recent data from 2000 and 20 and local, and local effect was show that damage probability was, was increasing was increasing for three age class between 70 and 110 years. Okay, now it's time for some conclusions. We tested five machine models for four scenarios. The most important predictors were two volume and three age and geomorphic and climate predictors were less important and through models offered the best predictive power for random forest algorithm and machine and four stand features might significantly influence probability of course. Thank you for that. Thank you. Yeah, because we have this data is in it was in vector vector like that. Yes, yes, yes, yes, yes, yes, yes. This is the Yeah, we didn't test it. In terms of, in terms of specifying this yes. So, previous analysis or Yeah, of course, I mean, yeah, I wanted to say that the next steps would, would include some things we learned in this session so it would be great to do that.
associate professor at the University of Silesia and damage in the last two centuries. It was amount and and is dominated for us. And this is a case trees how the impact geomorphic processes on damage forest damage a model using machine three data sets. First one for us features before over open seven and the best models. The best similar, and the best accuracy the best try to machine for the first scenario. In terms most important variables and damage probability based on the random for its model and this recent data from 2000 and 20 and local, and local