A recent email asked how well Assisi Forest works with forest re-measurement data and how it can be applied:
We grow mixed species plantation forest & are looking at moving from MS Access & Excel solutions for management of our plot (permanent & temporary) to some solution more integrated. I have some queries about your Assisi solution.
What are your options to capture & manage data on re-measured trees? What features are there to view previous data / calculation of growth & flagging if outside limits?
How does Assisi calculate & apply growth projections from annually-measured permanent plots & apply to temporary plots for species that don’t exist in your database?
Assisi was basically designed for these purposes! Let me explain…
Absolutely. Assisi was built for re-measurement data from its beginning when research surveys were used as the design template. Oregon State University actually. The field data architecture stores data in a hierarchy of Unit (collections of stands), Stands (collections of cruises), Cruise (collections of plots), Plots (collections of trees), etc. Each stand can have any number of cruises, meaning multiple measurements of the same stands. Stands, a polygon concept can also be strata, meaning multiple cruises are repeats of scattered plots in a strata rather than from one single landscape polygon. Both repeat measurements of trees in a stand or strata organization is supported. The sample database has examples of both.
Re-measurement Field Data in Assisi Forest
The above screen shot has a view of how re-measurement field data is stored for a stand in the OSU Research Forests. The stand was measured in 1984 and 1994. The table is filtered on plot 18 in particular. You can see the same trees in the list for year 1984 and 1994 with 1994 having larger values. You’ll notice plot 13 has been removed, probably from harvest, so the stand boundaries have changed as well.
For comparing periodic growth estimates, I suggest storing each measurement in its own Unit using year as the unit name. Process each unit and you will have an inventory estimate by year, simultaneously stored in Assisi. From here, you can match up each tree by its stand, plot and tree IDs using SQL and look at comparisons. For custom analysis, Assisi supports reports such as Excel Spreadsheets. See the end for more.
For calibrating growth models, Assisi has a tool to match up individual trees (by plot and tree number), compare measured DBH or total height growth to modeled growth and regress the difference. It then calculates a slope, tests for significance and stores the result back to the database. When growth models are used to estimate inventory growth, these factors scale the model results. This is a way of calibrating growth model estimates for local conditions.
Calibration is done on a stand and species basis so each stand and species will get unique calibration factors. But in Assisi, stands can be treated as strata as well. In this case, factors are stored by strata and species rather than by stand and species. This “stratified” calibration can be applied uniquely for each stand’s (or plot’s) strata and species. A mouthful for sure, but this may be more typical of research objectives: to calibrate growth models by strata and species for your forests, then use that calibration for regular inventory work. Assisi facilitates this by letting research do local stratified analysis, then easily applying these results to a stand based inventory.
As an aside, model calibration in Assisi occurs as a normal part of compiling of any single measurement as well. Here, “static” models (total height, merch height, crown, broken top, etc.) are compared to field measurements and factors calculated so that “missing” measurements in the same field survey can be calculated. Below is an example of this type of calibration.
Assisi Forest Sub Sample Processing and Missing Value Estimation
Not Yet Cruised Stands (Expansion)
Your second question sounds like you want to apply “calibrated” growth models to plots (sounds like polygons or “stands” in Assisi vernacular) where you haven’t yet “cruised” the plot. And… there may be species in that un-cruised plot you don’t yet have analysis for. If so, then yes.
Assisi calls this “Expansion”. Here, you add an Expansion record to a stand rather than a Cruise record. An Expansion is a reference to another stand that has inventory estimate. During Expansion, Assisi copied the results from this source stand to the destination stand so that the destination gets a complete inventory estimate identical to the source stand.
Assisi Forest Stratified Inventory Expansion to UnCruised Stands
In the above screenshot, you see the “OSU Expanded” Unit with a list of stands that were not cruised. They each have an “Expansion” record that references stands from the “OSU Stratified” unit that was cruised. When processed they will each get an inventory identical to the sourced stands.
Now, what happens if you know the new stand has a species that is not present in the source stand? To start, add that species into your Calibration so that it has a full set of models for each species you might encounter. You can simple copy and paste an existing species in a Calibration and then change its name and any other settings you know are different. You will be able to process that species like any other species when it shows up in your forests. Note that it is usually the case that calibrations already have some species that are not in your field data. When they do show up, Assisi is ready to go. The second step is to set up a source stand using a copy of the closest cruised stand and then adding tree records by hand. Expansion can then proceed as usual.
Because you are doing research, it may be that you are wanting to source from a strata rather than a polygon (stand). That is in fact what is happening in the OSU example shown above. The idea is the same: the source Unit has a list of all the strata you need using calibrated growth models from your re-measurement data. Research would set up both the stratified sourtce unit and the stratified calibration. Then “regular” inventory, which is typically done by stand (polygons), routinely processes their field data on a stand basis but they will use the research strata and research calibrations in the process. This is truly what Assisi is designed to do.
Both Assisi’s field data and Assisi’s models (taper, growth, volume, etc.) can have unit designations. Either can be Metric or English and they can be mixed as needed. This is important because sometimes models are in different units than data. So in Assisi you can designate what units your field data are in and what units each model you use are in. And Assisi takes care of the translations for you.
Below is a screenshot of Assisi Calibration settings. You will see the setting “Units=Metric” for various models. This tells Assisi that the coefficients for the model are Metric units. If the data being processed is Metric, no translation. If in English, the translation occurs. The Maine data in the sample database has field data in English while the Acadia model it uses have models in metric units. The Univ. of Maine used data from Maine (English) and Canada (Metric) for their model development and chose Metric for model parameters. For the state of Maine to use these same models Assisi translates between English and Metric automatically, using these settings.
Assisi Calibration Settings Showing Both Metric and English Models
Reports and Scripting
As I mentioned in the beginning, Assisi can be scripted and custom reports added. Scripting allows a complex series of analysis to be automated. This could include field data compiling, then growth, then export to external systems, etc. Reports can be added to Assisi using any .Net language (Visual Studio typically) and then called from various places in Assisi.
Assisi includes an example of one called “Non Timber Management Plan” (NTMP) that is used in California for smaller landowners. The NTMP report is an excel sheet with lots of formatting and data summarizing. The report is run from Assisi's Management Plan Simulator to summarize results of potential harvest plans before submitting to the sate for approval. I bet you’ll want to create a custom sheet for summarizing re-measurement data, the way you want to see it.
Hope this helps! I can do an online demo at some point when you are ready.