Open Source Geospatial Production of United States Forest Disturbance Maps from Landsat Time Series
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Toney, Chris
The North American Forest Dynamics (NAFD) project is completing nationwide processing of historic Landsat data to provide annual, wall-to-wall analysis of US disturbance history over nearly the last three decades. Because understanding the causes of disturbance (e.g., harvest, fire, stress) is important to quantifying carbon dynamics, work was conducted to attribute causal agents to the nationwide change maps. This case study describes the production of disturbance agent maps at 30-m resolution across 434 Landsat path/rows covering the conterminous US. Geoprocessing was based entirely on open source software implemented at the NASA Advanced Supercomputing facility. Several classes of predictor variables were developed and tested for their contribution to classification models. Predictors included the geometric attributes of disturbance patches, spectral indices, topographic metrics, and vegetation types. New techniques based on shape-restricted splines were developed to classify patterns of spectral signature across Landsat time series, comprising another class of predictor variables. Geospatial Data Abstraction Library (GDAL) and the R statistical software were used extensively in all phases of data preparation, model development, prediction, and post-processing. Parallel processing on the Pleiades supercomputer accommodated CPU-intensive tasks on large data volumes. Here we present our methods and resultant 30-m resolution maps of forest disturbance and causes for the conterminous US, 1985 Ð 2011. We also discuss the computing approach and performance, along with some enhancements and additions to open source geospatial packages that have resulted.
 So my name is Chris Tony. I work for the US Forest Service in the Rocky Mountain Research Station And I'll be talking about forest disturbance mapping using Landsat time series I'd like to acknowledge my co-authors Gretchen Moyes and Karen slave ice and Todd Schroeder who are responsible for a lot of the work I'm talking about and I'd also like to point out that this work is part of a larger project With other research teams contributing so I'll have some citations throughout the presentation to acknowledge their work as well this work is part of a Larger project called North American Forest Dynamics. It's funded by NASA And is a collaboration among NASA University of Maryland the research and the research branch of the US Forest Service It's designed to characterize patterns and recovery rates of forests across the continent With a goal of determining the role of forest dynamics in North American carbon balance So one part of North American Forest Dynamics was to map the Recent disturbance history for the determinist United States Using Landsat time series since 1984 That was done with a change detection algorithm called vegetation change tracker developed at University University of Maryland, so I'll start with just a brief overview of the the vegetation change tracker VCT products which indicate the presence of Disturbance in 30 meter pixels for each year during the time series But VCT does not provide information on the causes of disturbance So the focus of the team I've been working with is to determine causal agents of disturbance So things like harvest wildfire insect and disease outbreaks Using the VCT products as a starting point This is done with predictive modeling at the pixel level and I'll describe the production of raster products for conus using open source and I'll describe the software implementation in NASA's high performance computing environment So I'll start with the end products from VCT and then the next slide I'll show the algorithm but The products include a land water mass so every pixel is classified as forest non forest or water And then there are annual raster layers where each pixel indicates presence or absence of disturbance within the forest mask for each year And then there are some disturbance Magnitude metrics associated with the disturbed pixels so this map on the screen is Shows all the years overlaid are composited with each other so the the green color Indicates areas where there was no change detected during the period and then the the other colors indicate a year of disturbance So this has been produced by the team at University of Maryland for conus The algorithm and the products have been described in detail in the literature It's an automated process the input is a Landsat time series stack there's Step for image selection To get images from peak growing season and minimal cloud cover and there also steps in there for image compositing from multiple dates in cases where cloud contamination is excessive And then the selected images for each year undergo some cloud and shadow masking and Then four different spectral indices are calculated from the original Landsat bands so things like NDVI are used and there's a Custom index in there that was developed specifically for the algorithm these spectral indices are used in time series analysis to Detect shifts in the spectral trajectory that indicate a forest disturbance So with the time series analysis DCT is looking across the spectral trajectory and trying to find Some kind of shift that's consistent with a disturbance event so in the example on the right is for a Harvest followed by rapid regeneration so there's this sudden shift in the in the spectral signature when the trees are removed and then there's that's followed by rapid recovery over just a few years Back to something that looks like the original forest cover So the With different disturbance types and regeneration dynamics the the pattern or the shape of this trajectory could take several different forms So I'll come back to that idea in a bit, but I just want to point out that VCT does not characterize the The pattern the shape of the trajectory It's just looking across the trajectory and identifying shifts that are consistent with forest disturbance Okay, sorry, so this is an overview of the current work to assign causal agents of disturbance to Pixels based on empirical modeling so it starts with a set of training data These are training samples from locations with known disturbance types The training samples are associated with a set of predictor variables. So things like spectral values and other predictors And then predictive modeling is done with random forest, which is a machine learning technique based on classification and regression trees So once these once the random forest models are developed they can be used to Predict 30 meter pixels across the country as long as we have Raster datasets for all of the predictor variables in the model So I'll go into a few details, but the next couple of slides I'm going to give just a little overview of our software and computing environment Open source was used for all the processing We use the GDAL utility programs extensively in particular we Did some work with the polygon enumeration algorithm in GDAL and the GDAL polygonized command line script I'll talk a little bit about that The combination of Python with GDAL and NumPy was kind of a workhorse approach to a variety of a variety of different processing Some things were done in C++ our research organization is very R-centric for Statistical modeling so we relied on R quite a bit And with the RGDAL package We're able to do to do raster processing directly within R which is really convenient for some modeling or computation that depends on other R code or is already implemented in our packages and Then the snow and snowfall packages for R allow us to do parallel computation and we also use QGIS quite a bit for visualization and and data checking Most processing was done by Landsat path row. So there are 434 Path rows that cover conus Each path row has approximately 46 million total pixels Path rows that are in areas with a lot of forest can have 20 to 30 meter forest pixels in a path row Some path rows have much fewer but Across all the path rows in conus 4.3 billion pixels have been classified as forest So that that's not an aerial estimate because the path rows overlap But that's what we had from a data processing perspective All the processing was done in NASA's high performance computing environment using a cluster called Plades Plades has 11176 compute nodes There are three different node types that have either 12 16 or 20 CPU cores per node So it's a total of just under 185,000 CPU cores has lots of disk storage and then The all of the open source software that we needed is well supported there Okay, so I'll step through a few details of the workflow starting with generation of the predictor data So we we're gonna use the spectral data data obviously as predictors, but we also wanted to derive Information about the spectral trajectories through time and make use of that so I'll I'll talk a little bit about that we also looked at the Geometric attributes of disturbance patches and I'll go into that And then there's other ancillary data for predictors that already exists like terrain a forest type classification and a burn severity product for wildland fires so DCT tells us which pixels have been Disturbed but it doesn't give us the cause of disturbance, but we do know that different types of disturbance look different on the landscape So for example harvest tend to be confined to a certain Range of size and they don't get really large and they tend to be fairly simple shapes that are often kind of squarish Where it's fire wildland fire on the other hand, maybe small but can get very large And often has complex geometric geometric patterns so the The geometry of disturbance patches may be a helpful predictor of causality So the VCT products are raster base, so we went through each of the annual disturbance layers and delineated polygons as regions of connected pixels For this we used And then generated vector data for each year and for this we use the GDL polygonized utility and eight Connectedness mode so eight connectedness just meaning that the pixels can be connected along the edges or at the corners and then we had a simple program to go through that vector data and Calculate geometric attributes of each polygon, so we calculated area perimeter the shape index and the fractal dimension index these two indices are similar and they describe the complexity of the shape based on area to perimeter ratios, so they basically Normalize to a square as the simplest possible shape And then we use GDL rasterized to generate raster versions of all the polygon attributes to use in in modeling and Across all the pathores for conus we delineated a little more than 210 million polygons So for another class of predictor variables, we wanted to try to derive Information about the spectral trajectories through time so this This was based on work by Mary Mary Meyer in shape restricted regression, so it's a non-parametric curve fitting technique where the Curve is constrained to fit a certain shape and In conjunction with this work on the Landsat time series and our package called cone project was developed to implement the computations We're currently working with seven predefined shapes That that are believed to indicate some kind of underlying forest dynamics So the algorithm is to fit Each of these shapes to the temporal trajectories at each pixel That involved that's uses iterative curve fitting for the non-parametric shapes And then we choose the best fit shape based on an information criterion That includes a penalty for model complexity, so it's handling overfitting by the the more complex shapes And then the the outputs from this are written to raster format, so we We get a the shape classification itself the selected shape and Then the curve for that allows us to derive some some associated parameters such as the the year of the shift Magnitude and duration of shifts and then change rates Before and after disturbance. We can also write out the fitted values of from the curve Which we were doing for future use but the shape itself the classification and these parameters derived from it are then used as predictive variables in the disturbance agent modeling So it's very CPU intensive there's been some work to optimize the shape fitting algorithms in our which Are implemented in C++ So there was a good bit of improvement there, but the it's inherently CPU intensive because It involves iterative curve fitting for the non-parametric shapes, and then we have to fit each of the shapes To to the trajectories at each pixel So To implement this for conus we have to fit 4.3 billion trajectories So our approach has been to do parallel computation So in these graphs, I'm showing the runtime in Hours on the y-axis versus the number of CPUs on the x-axis In this example is for path 12 row 28, which has 30 million forest pixels And it takes a week to run this on one path row Running it sequentially on one CPU So again the cluster has three different node types with 12 to 20 CPUs each So we can get that down to about just under 10 hours on a 20 CPU node or about 16 hours on a 12 CPU node So it scales up really well to multiple CPUs and out in this range of 10 to 20 CPUs we're still getting good good efficiency in the processing So we implemented this with R and Gdoll we So we're running four different versions of this for each path row for the four different spectral indices So we assign a path row for a given index to one compute node So for all 434 path rows we need 1,736 total nodes And then within those path rows on a node we split the data and do the computations in parallel And we use snow and snowfall for that and then the curve fitting relies on comb project if we use the 12 CPU nodes on Pallades in Processing all 4.3 billion forest pixels Would use a total of just under 21,000 CPUs and That would have about a 16 hour runtime if we were able to set it up in a single job We've done this twice now for all the path rows and there is some wait time in the in the job queue because the system is busy But we've been able to turn this around and under a week maybe Four or five days if it's not too busy So again, this is it's a it's an intermediate product for us But it's kind of a new product and we're really optimistic about the potential for it and It works so we can we can do it, but I'm not sure you know how we could do this without this kind of computing resource So that's all say about the predictors I won't say too much about the model development other than it uses training data collected in the project using a photo interpretation technique Training data is limited. We'd like to have a lot more We've supplemented that with some data from land fire, which is another national remote sensing program and from FIA Which is a national forest inventory program The modeling rely uses the random forest package in our and also makes use of the model map package Developed by Liz Freeman also in the Rocky Mountain Research Station and model map has some nice features for working with Random forest in a geospatial context and it has extensive model diagnostic associated with it And then the prediction code is very similar to the shape fitting code. I just described uses based on R and G doll it mass to the VCT Predictions are done with in our with the random forest package Certain predictor variables are generated at runtime There's an option for parallel processing within path rows, but it's not really needed because the processing time has Been a little under an hour For a path row running on one CPU And we can run all 434 path rows in parallel So the map product Is Has predicted values of disturbance agents in 30 meter pixels Again, it's mass to VCT non forest and water So far the Assessment of the product has been based strictly on the random forest model diagnostics and visual assessment. There's just not enough Training data to be able to set aside any any data for an independent validation set So there's some work on going to generate additional ground truth data and As far as data distribution, it's being done through the distributed archive center at Oak Ridge National Laboratory the vegetation change tracker disturbance years and magnitude products are Being prepared for data distribution now They will be available in the near future on this site as far as the predicted causal agents and and associated products there They're still being refined and undergoing quality assessment So some those products will be available in some form But exactly what we distribute and the timing of that is still being determined So that's all I have I'd be happy to take any questions if there's time As interesting talk I have a lot of questions, but I'll try to limit myself to two One is did you try to do any disaggregation by age class? And then the second one is did you in any way attempt to identify? man-made disturbances that are irreversible such as conversion of forest into a developed class The answer to the second question is yes conversion is a causal agent class in the in the national legend So we are trying to get at that. I didn't have an example to show but There's there is training data for it and it's being included in the modeling And the shape the shapes of the spectral trajectories are probably pretty helpful in getting at that As far as age class, no, we have not been incorporating age class. That's a good idea We would need You know, we'd need national data for it Which may or may not be possible, but definitely something to consider. It's a good point How deep is your image stack and then second question How much does it cost to run this on on the pleates cluster? The image stack is 1984 through 2012 one image Per year so an annual stack earlier versions of VCT were biennial stacks But we're currently working with all the University of Maryland teams latest products were based on annual stacks for those years, I think that's 29 years and The cost I have no idea we don't get charged for it right now It's a collaboration with NASA and there's some interest in this as sort of a demonstration product to do this type of Processing with the Landsat data So so far it's just strictly collaboration and and there's no charge for it at least in this this kind of this Isolated case normally, you know, there's a whole job scheduling and you get billed and there's accounting for it But we luckily are not having to deal with that so far. Thanks So with your predicted map of causal agents Do you have any sense of do you have products that kind of quantify the uncertainty around those predictions? Does that imagine there would be along the edges predict particularly there would be Cases where you weren't certain exactly what the causal agent was do you have products to demonstrate that? You know, we're looking at that a little bit the model map package that I mentioned It does some of that and so with ran if you're familiar with random forest, you know, it's an ensemble of trees as a It takes a majority vote from a large number of trees like 500 Decision trees so, you know model map has some Functions in there where it looks at the variation in those predictions from the individual trees to kind of get at the uncertainty of the prediction so we're doing that a little bit in and We have a set of test scenes that we we've selected to test things on We're not producing the any of the uncertainty measures for the nationally for all the pathos But we are looking at that a little bit on those test scenes and trying to include that in some of the development and refinement Great. Thank you. And second question. Do you are you aggregating this data back to those shapes that you pulled out of the VCL? In other words, are you saying are you attaching an attribute to those polygons? This says this was a fire or this was a harvest So that you have a vector data set that represents disturbances We have not yet, but the idea is to get there. Yeah once we We we have the raster disturbance agent product at a point We feel we're ready to move forward with that's definitely a next step is to make use of those polygons we've enumerated to To do that and you know one of the questions too was we're predicting at the pixel level So how much speckle is there going to be you know, are we going to get so far? It's not so far the raster output looks like polygons So but those polygons that we have enumerated are definitely going to be helpful in Assessing that variability of the predictions within the polygons and then hopefully labeling those polygons That's that's the next step. Good point. Thank you. Thank you very much
 So my name is Chris Tony. I work for the US Forest Service in the Rocky Mountain Research Station  And I'll be talking about forest disturbance mapping using Landsat time series  I'd like to acknowledge my co-authors Gretchen Moyes and Karen slave ice and Todd Schroeder who are responsible for a lot of the work  I'm talking about and I'd also like to point out that this work is part of a larger project  With other research teams contributing so I'll have some citations throughout the presentation to acknowledge their work as well  this work is part of a  Larger project called North American Forest Dynamics. It's funded by NASA  And is a collaboration among NASA University of Maryland the research and the research branch of the US Forest Service  It's designed to characterize patterns and recovery rates of forests across the continent  With a goal of determining the role of forest dynamics in North American carbon balance  So one part of North American Forest Dynamics was to map the  Recent disturbance history for the determinist United States  Using Landsat time series since 1984  That was done with a change detection algorithm called vegetation change tracker  developed at University University of Maryland, so I'll start with just a brief overview of the  the vegetation change tracker VCT products which indicate the presence of  Disturbance in 30 meter pixels for each year during the time series  But VCT does not provide information on the causes of disturbance  So the focus of the team I've been working with is to determine causal agents of disturbance  So things like harvest wildfire insect and disease outbreaks  Using the VCT products as a starting point  This is done with predictive modeling at the pixel level and I'll describe the production of raster products for  conus using open source and I'll describe the software implementation in NASA's high performance computing environment  So I'll start with the end products from VCT and then the next slide I'll show the algorithm but  The products include a land water mass so every pixel is classified as forest non forest or water  And then there are annual raster layers where each pixel indicates  presence or absence of disturbance  within the forest mask for each year  And then there are some disturbance  Magnitude metrics associated with the disturbed pixels so this map on the screen is  Shows all the years overlaid are composited with each other so the the green color  Indicates areas where there was no change detected during the period and then the the other colors  indicate a year of disturbance  So this has been produced by the team at University of Maryland for conus  The algorithm and the products have been described in detail in the literature  It's an automated process the input is a Landsat time series stack  there's  Step for image selection  To get images from peak growing season and minimal cloud cover and there also steps in there for image  compositing from multiple dates in cases where cloud contamination is excessive  And then the selected images for each year undergo some cloud and shadow masking  and  Then four different spectral indices are calculated from the original Landsat bands  so things like NDVI are used and there's a  Custom index in there that was developed specifically for the algorithm these spectral indices are used in  time series analysis  to  Detect shifts in the spectral trajectory that indicate a forest disturbance  So with the time series analysis DCT is looking across the spectral trajectory and trying to find  Some kind of shift that's consistent with a disturbance event  so in the example on the right is for a  Harvest followed by rapid regeneration  so there's this sudden shift in the in the spectral signature when the trees are removed and then there's that's followed by rapid  recovery over just a few years  Back to something that looks like the original forest cover  So the  With different disturbance types and regeneration dynamics the the pattern or the shape of this trajectory could take several different forms  So I'll come back to that idea in a bit, but I just want to point out that VCT does not characterize the  The pattern the shape of the trajectory  It's just looking across the trajectory and identifying shifts that are consistent with forest disturbance  Okay, sorry, so this is an overview of the  current work to assign  causal agents of disturbance to  Pixels based on empirical modeling so it starts with a set of training data  These are training samples from locations with known disturbance types  The training samples are associated with a set of predictor variables. So things like spectral values and other predictors  And then predictive modeling is done with random forest, which is a machine learning technique based on classification and regression trees  So once these once the random forest models are developed they can be used to  Predict 30 meter pixels across the country  as long as we have  Raster datasets for all of the predictor variables in the model  So I'll go into a few details, but the next couple of slides  I'm going to give just a little overview of our software and computing environment  Open source was used for all the processing  We use the GDAL utility programs extensively in particular we  Did some work with the polygon enumeration algorithm in GDAL and the GDAL polygonized command line script  I'll talk a little bit about that  The combination of  Python with GDAL and NumPy was kind of a workhorse approach to a variety of  a variety of different processing  Some things were done in C++  our research organization is very R-centric for  Statistical modeling so we relied on R quite a bit  And with the RGDAL package  We're able to do to do raster processing directly  within R which is really convenient for  some modeling or computation that  depends on other R code or is already implemented in our packages and  Then the snow and snowfall packages for R allow us to do parallel computation  and we also use QGIS quite a bit for visualization and  and data checking  Most processing was done by Landsat path row. So there are 434  Path rows that cover conus  Each path row has approximately 46 million total pixels  Path rows that are in areas with a lot of forest can have  20 to 30 meter forest pixels in a path row  Some path rows have much fewer but  Across all the path rows in conus  4.3 billion pixels have been classified as forest  So that that's not an aerial estimate because the path rows overlap  But that's what we had from a data processing perspective  All the processing was done in NASA's high performance computing environment using a  cluster called Plades  Plades has 11176 compute nodes  There are three different node types that have either 12  16 or 20 CPU cores per node  So it's a total of just under 185,000  CPU cores  has lots of disk storage and then  The all of the open source software that we needed is well supported there  Okay, so I'll step through a few details of the workflow starting with generation of the predictor data  So we we're gonna use the spectral data data obviously as predictors, but we also wanted to derive  Information about the spectral trajectories through time and make use of that so I'll I'll talk a little bit about that  we also looked at the  Geometric attributes of disturbance patches and I'll go into that  And then there's other ancillary data for predictors that already exists like terrain  a forest type classification and a burn severity product for  wildland fires so  DCT tells us which pixels have been  Disturbed but it doesn't give us the cause of disturbance, but we do know that different types of disturbance look different on the landscape  So for example harvest  tend to be confined to a certain  Range of size and they don't get really large and they tend to be fairly simple shapes that are often kind of squarish  Where it's fire wildland fire on the other hand, maybe small but can get very large  And often has complex geometric geometric patterns  so the  The geometry of disturbance patches may be a helpful predictor of causality  So the VCT products are raster base, so we went through each of the annual disturbance layers and  delineated polygons as regions of  connected pixels  For this we used  And then generated vector data for each year and for this we use the GDL polygonized utility and eight  Connectedness mode so eight connectedness just meaning that the pixels can be connected along the edges or at the corners  and  then we had a simple program to go through that vector data and  Calculate geometric attributes of each polygon, so we calculated area perimeter  the shape index and the fractal dimension index these two indices are  similar and they describe the  complexity of the shape based on  area to perimeter ratios, so they basically  Normalize to a square as the simplest possible shape  And then we use GDL rasterized to generate raster versions of all the polygon attributes to use in in modeling and  Across all the pathores for conus we delineated a little more than 210 million  polygons  So for another class of predictor variables, we wanted to try to derive  Information about the spectral trajectories through time  so this  This was based on work by Mary Mary Meyer in  shape restricted regression, so it's a  non-parametric curve fitting technique where the  Curve is  constrained to fit a certain shape and  In conjunction with this work on the Landsat time series and our package called cone project was developed to implement the  computations  We're currently working with seven predefined shapes  That that are believed to indicate some kind of underlying forest dynamics  So the algorithm is to fit  Each of these shapes to the temporal trajectories at each pixel  That involved that's uses iterative curve fitting for the non-parametric shapes  And then we choose the best fit shape based on an information criterion  That includes a penalty for model complexity, so it's handling overfitting by the the more complex shapes  And then the the outputs from this are written to raster format, so we  We get a the shape classification itself the selected shape and  Then the curve for that allows us to derive some some associated parameters such as the  the year of the shift  Magnitude and duration of shifts and then change rates  Before and after disturbance. We can also write out the fitted values of  from the curve  Which we were doing for future use but the shape itself the classification and these parameters derived from it are then used as  predictive variables in the disturbance agent modeling  So it's very CPU intensive there's been some work to optimize the shape fitting algorithms in our which  Are implemented in C++  So there was a good bit of improvement there, but the it's inherently CPU intensive because  It involves iterative curve fitting for the non-parametric shapes, and then we have to fit each of the shapes  To to the trajectories at each pixel  So  To implement this for conus we have to fit 4.3 billion trajectories  So our approach has been to do parallel computation  So in these graphs, I'm showing the runtime in  Hours on the y-axis versus the number of CPUs on the x-axis  In this example is for path 12 row 28, which has 30 million forest pixels  And it takes a week to run this on one path row  Running it sequentially on one CPU  So again the cluster has three different node types with 12 to 20 CPUs each  So we can get that down to about just under 10 hours on a 20 CPU node or about 16 hours on a 12 CPU node  So it scales up really well to multiple CPUs  and out in this range of  10 to 20 CPUs we're still getting good good efficiency in the processing  So we implemented this with R and Gdoll we  So we're running four different versions of this for each path row for the four different spectral indices  So we assign a path row for a given index to one compute node  So for all 434 path rows we need 1,736 total nodes  And then within those path rows on a node we split the data and do the computations in parallel  And we use snow and snowfall for that and then the curve fitting relies on comb project  if we use the 12 CPU nodes on Pallades in  Processing all 4.3 billion forest pixels  Would use a total of just under 21,000 CPUs  and  That would have about a 16 hour runtime if we were able to set it up in a single job  We've done this twice now for all the path rows and there is some wait time in the in the job queue because the system is busy  But we've been able to turn this around and under a week maybe  Four or five days if it's not too busy  So again, this is it's a it's an intermediate product for us  But it's kind of a new product and we're really optimistic about the potential for it and  It works so we can we can do it, but I'm not sure you know how we could do this without this kind of computing resource  So that's all say about the predictors  I won't say too much about the model development other than it uses training data collected in the project using a photo interpretation technique  Training data is limited. We'd like to have a lot more  We've supplemented that with some data from land fire, which is another national remote sensing program and from FIA  Which is a national forest inventory program  The modeling rely uses the random forest package in our and also makes use of the model map package  Developed by Liz Freeman also in the Rocky Mountain Research Station and model map has some nice features for working with  Random forest in a geospatial context and it has extensive model diagnostic associated with it  And then the prediction code is very similar to the shape fitting code. I just described  uses based on R and G doll it  mass to the VCT  Predictions are done with in our with the random forest package  Certain predictor variables are generated at runtime  There's an option for parallel processing within path rows, but it's not really needed because the processing time has  Been a little under an hour  For a path row running on one CPU  And we can run all 434 path rows in parallel  So the map product  Is  Has predicted values of disturbance agents in 30 meter pixels  Again, it's mass to VCT non forest and water  So far the  Assessment of the product has been based strictly on the random forest model diagnostics and visual assessment. There's just not enough  Training data to be able to set aside any any data for an independent validation set  So there's some work on going to generate  additional ground truth data  and  As far as data distribution, it's being done through the  distributed archive center at Oak Ridge National Laboratory the vegetation change tracker  disturbance years and magnitude products are  Being prepared for data distribution now  They will be available in the near future on this site as far as the predicted causal agents and and associated products there  They're still being refined and undergoing  quality assessment  So some those products will be available in some form  But exactly what we distribute and the timing of that is still being determined  So that's all I have I'd be happy to take any questions if there's time  As interesting talk I have a lot of questions, but I'll try to limit myself to two  One is did you try to do any disaggregation by age class?  And then the second one is did you in any way attempt to identify?  man-made disturbances that are irreversible such as conversion of forest  into a developed class  The answer to the second question is yes conversion is a  causal agent class in the in the national legend  So we are trying to get at that. I didn't have an example to show but  There's there is training data for it and it's being included in the modeling  And the shape the shapes of the spectral trajectories are probably pretty helpful in getting at that  As far as age class, no, we have not been incorporating age class. That's a good idea  We would need  You know, we'd need national data for it  Which may or may not be possible, but definitely something to consider. It's a good point  How deep is your image stack and then second question  How much does it cost to run this on on the pleates cluster?  The image stack is 1984 through 2012 one image  Per year so an annual stack earlier versions of VCT were biennial stacks  But we're currently working with all the University of Maryland teams latest products were based on annual stacks  for those years, I think that's 29 years and  The cost I have no idea we don't get charged for it right now  It's a collaboration with NASA and there's some interest in this as sort of a demonstration product to do this type of  Processing with the Landsat data  So so far it's just strictly collaboration and and there's no charge for it at least in this this kind of this  Isolated case normally, you know, there's a whole job scheduling and you get billed and there's accounting for it  But we luckily are not having to deal with that so far. Thanks  So with your predicted map of causal agents  Do you have any sense of do you have products that kind of quantify the uncertainty around those predictions?  Does that imagine there would be along the edges predict particularly there would be  Cases where you weren't certain exactly what the causal agent was do you have products to demonstrate that?  You know, we're looking at that a little bit the model map package that I mentioned  It does some of that and so with ran if you're familiar with random forest, you know, it's an ensemble of trees as a  It takes a majority vote from a large number of trees like 500  Decision trees so, you know model map has some  Functions in there where it looks at the variation in those predictions from the individual trees to kind of get at the  uncertainty of the prediction so we're doing that a little bit in and  We have a set of test scenes that we we've selected to test things on  We're not producing the any of the uncertainty measures for the nationally for all the pathos  But we are looking at that a little bit on those test scenes and trying to include that in some of the development and refinement  Great. Thank you. And second question. Do you are you aggregating this data back to those shapes that you pulled out of the VCL?  In other words, are you saying are you attaching an attribute to those polygons?  This says this was a fire or this was a harvest  So that you have a vector data set that represents disturbances  We have not yet, but the idea is to get there. Yeah once we  We we have the raster disturbance agent product at a point  We feel we're ready to move forward with that's definitely a next step is to make use of those  polygons we've enumerated to  To do that and you know one of the questions too was we're predicting at the pixel level  So how much speckle is there going to be you know, are we going to get so far?  It's not so far the raster output looks like polygons  So but those polygons that we have enumerated are definitely going to be helpful in  Assessing that variability of the predictions within the polygons and then hopefully labeling those polygons  That's that's the next step. Good point. Thank you. Thank you very much
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