Introduction

DRAFT

Context

The Food and Agriculture Organization of the United Nations (FAO), in partnership with Gottingen University have developed a series of training module on National Forest Inventory [LINK]. The overall objective is to complement FAO Voluntary Guidelines on National Forest Monitoring, by providing forest practitioners with pragmatic information on NFI design and implementation. This interactive module is meant as a hands-on tool to apply several concepts presented in the training modules, with a focus on sampling design and data analysis.

Data analysis in a larger context encompasses most all of the different steps of NFI design and implementation. There are numerous definitions for data analysis, one of the earliest being: “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data” (John Tukey, The Future of Data Analysis, 1961).

Different fields touching on data analysis have different steps to encompass the workflow for analyzing data, but they can more or less be divided into:

  1. Data requirements,
  2. Data collection,
  3. Data processing,
  4. Data cleaning,
  5. Exploratory data analysis,
  6. Modelling and algorithms,
  7. Data product,
  8. Communication.

In the NFI training modules, we have already seen most of these steps under different names. Data requirements include NFI sampling and plot designs (NFI Module 3), together with the strategy and objectives of the NFI (NFI Modules 1 and 2). Data collection is the field work part of NFI (NFI Module 4). Data processing is adressed in the data management (NFI Module 5). Data cleaning is maybe the most important part of the data analysis workflow. Several data cleaning tips are provided in QAQC (NFI module 6), although this module is also related to field work and data management.

The data cleaning, exploratory data analysis, modeling and data products are at the heart of this interactive training module. The interactive module also starts with a short practice on sampling design, to help you have a good overall picture on how to start exploring with sampling.

An interactive module to practice sampling and carbon stock calculations

This interactive module is designed to complement the NFI training modules [LINK] with practical hands-on exercises on a fictional island that just emerged in the middle of the Atlantic Ocean. Although this land is not an official country, we will use the acronym NFI when referring to its forest inventory as the method and formulas applied here are valid for nationwide forest inventories.

To limit the scope of this training:

  • This NFI main objective is to estimate the overall average aboveground forest carbon stock (tCO2/ha) of the island.
  • You won’t go to the field! The plot and trees measurements are generated randomly from a large database of anonymized forest measurements [Credits or keep it completely anonymous?]

What you will do is:

  1. Estimate the number of plots necessary to have the desired precision on the target estimator,
  2. Create a sampling a plot design [plot design optimization in V2.0],
  3. Read and clean the field data,
  4. Add ancillary data (wood density, climatic factors, allometric equations) from external sources,
  5. Propagate tree biomass to plot, forest type and nationwide average with confidence interval.

Let’s get started!