SpatialModelingTutorials was developed to demonstrate function modeling, batch processing, and raster_tools functionality, along with various other coding and modelling strategies.
This collection of Notebooks is meant to demonstrate spatial modeling techniques that use parallel processing and the newly developed Raster Tools package. Most notebooks are designed to run within Google's Colab. However, they can also be used locally if Raster Tools is properly installed link.
To open a notebook within Colab, expand the Notebooks directory and click on a given notebook. After the notebook opens in GitHub, click on the “Open in Colab” link (top left). Once in Colab you can step through each notebook cell.
Additional resouces can be found at https://sites.google.com/view/hoglandsspatialsolutions/learning
- ANF_ML – A short course notebook that demonstrates various vector, machine learning, and visualization techniques using the Raster_Tools package.
- DeliveredCost – A notebook that demonstrates the delivered cost routine described in New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas using Raster_Tools.
- EGAM_example – A notebook used to demonstrate the EGAM modeling procedure described in Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA.
- PODs_Integration – A notebook demonstrating 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West.
- Python_Automation_ANFS – A short course notebook that demonstrates python automation strategies and basic vector processing using Raster_Tools.
- Quick_example_in_R – A short notebook demonstrating how to create a R Colab notebook.
- Raster_tools_surface – A short notebook demonstrating the surface module within Raster Tools.
- Rumple_python – A short notebook demonstrating how to create fast for loops using Numba and automated parallelization through Dask.
- ACCEL – A short notebook demonstrating how to perform a cost revenue analysis (CRA) using Raster-Tools in the TCSI landscape. This notebook requires more memory than Colab provides and must be run locally or on web services that have more than 15 gb of Ram.
- TCSI_Demo - A short demonstration of how to use ipywidgets to set values and call the delivered cost model to produce potential cost surfaces.
- Download Lidar Data - A short notebook demonstrating how to download lidar point cloud data and derived outputs from the 3Dep program.
- Process Lidar Data - A short notebook demonstrating how to process lidar point cloud data collected from the 3Dep program.
- SampleDesign - A short notebook demonstrating how to setup a sample design that is well spread using surfaces derived from Lidar.
- Estimating BAA - A short notebook demonstrating how to create a random forest regression model that predicts basal area per acre from lidar based raster surfaces.
- Ft Stewart Sample Design - A short notebook demonstrating how to determine sample size and location using ancillary data.
- ANF Gopher Tortious Habitat - A short notebook demonstrating how to identify and prioritize gopher tortoise restoration sites.
- Fire Resilience - A short notebook demonstrating how to define and quantify costs and revenues associated with treatments design to make landscapes fire resilent.