bitterlich

Bitterlich angle count sampling simulation

The purpose of this is to simulate a forest, more specific a diameter distribution. With a monte-carlo approach, we want to verify if angle-count sampling yields correct results.

purpose

To provide a repeatable, configurable simulation of angle count sampling to verify its correctness and determine its limits, possibly even enabling the comparison of different inventory designs.

parts

• Forest Simulation: can generate different diameter distributions

  • (maybe) Support for "patchwork" landscapes.
  • (maybe) simulation of irregular cross section shapes (ellipsoid or even more arbitrary)
  • See On the Geometry of a Cross Section of a Stem
    • how to generalize this? arbitrary convex polygons?
  • (maybe) height model, adding a 3rd dimension

• Sample Simulation (function of a point)

  • Simulate various sampling methods (Bitterlich, fixed radius, nested plot, etc).
  • (maybe) (temporal) movement and sampling simulation, to optimize for effort
  • (maybe) Simulated measurement errors (overestimating heights or diameters)

tools

• Python based (v3 I guess?)
• (maybe) Matplotlib for vizualization
• NumPy for raster based data types and algorithms

architecture

• Gui framework? Maybe Jupyter notebook for fast scaffolding
• Raster or vector? (vectors seem more sensible, but then are Matplotlib and Numpy still the right tools?)
  • Bitterlich's method is essentially geometric, vector seems the safer bet
  • A usable georeferenced geometry package is OGR
  • If we're going for vectors, we will be building a small GIS. Ideas for corresponding stacks here.
  • Use mapnik for rendering instead of Matplotlib?
  • Or, considering complexity: Maybe we should avoid building a small GIS and script this inside QGIS instead? Rendenering, data structures and interfaces would be already there..
• Metric and USC units as a binary project-wide setting

use cases

• forest simulation
  • generation (wizard)
  • saving and loading (forest file/data type)
  • rendering (trees on a 2d map)
• inventory design
  • definition of inventory (grid / density of plots, angle count factor, etc.)
  • saving and loading (inventory file/data type)
  • rendering (plots as overlay to the forest map)
• inventory execution
  • results output (textual summary and/or graphics)

roadmap

phase 1: setup

• 2d map
• tooling (tests, package manager, etc.)
• rough package structure
• first (non-parametrized) version of forest structure
  • rendered on the map

phase 2: minimally viable product

• all use cases from above are covered (no special cases yet, just the golden path)