Development of Software Tools for the Planning, Execution and Analysis of Inventory and Monitoring Studies

Research Issue

[screenshot] View of a version of a data recorder software package developed for NIMAC projectsNational Inventory and Monitoring Applications Center (NIMAC) clients often contact us with general goals and desired outcomes of a monitoring study. Without specific goals, however, it is difficult to determine what variables to measure, how to design the inventory, what products will be produced, and how results will be used. Above all, planning and budget formation cannot proceed without these critical first steps. Once the project goals are established, the planning phase can begin. However, without a consistent, documented procedure to design the study and estimate costs given a desired level of precision of results, it is difficult to plan efficiently. Finally, once results are collected, tools are needed to store, process, and report estimates and their precision derived from the monitoring study.

Our Research

Often forest inventory planners start by determining what is to be measured without specifying what the information needs are or the results that must be produced.  Instead NIMAC has developed a series of 15 inventory and monitoring steps to help ensure that the monitoring is effective and efficient.  A set of tools has been developed (and is being enhanced) to assist with these steps. NIMAC created various procedures, tools and utilities to design, establish and conduct the survey, including GIS tools, data recorder programs, and data transfer methodology. Finally, NIMAC has worked to create new and adapt existing database, processing and reporting systems to incorporate clients’ data. 

Design Tool

The Design Tool for Inventory and Monitoring (DTIM) implements a software-driven process whereby National Forests and other clients are guided through the monitoring program planning process.  It helps clients start by determining their true monitoring objectives and questions. Once these monitoring questions have been established, the Design Tool presents attributes to measure that will answer those questions.  The tool then helps them estimate the sample size and resulting inventory costs based on user-specified precision and design constraints. It can be adapted to different clients’ specific monitoring needs. NIMAC scientists previously developed a tool that helps create efficient plot designs, including the one currently used by the FIA program.

[diagram] Image of a space filling curve fractal passing through a white area, with numbers labeling nodes where curve changes directionSurvey Establishment Tools

NIMAC scientists have developed an innovative procedure for establishing a monitoring plot network. This method forces sample plots to be distributed evenly across a study area using a fractal called a space filling curve in order to create a spatially-balanced sample. It has been used to establish sample networks in various NIMAC projects.

Processing, Analysis and Reporting Tools

FIA has existing reporting tools, however, several partners prefer to use their own tools that meet their unique needs. Examples include adaptations of FIA’s Evalidator-PC tool. Tools like this, spreadsheet tools, and tools written in statistical programs like R and SAS have been used by NIMAC staff to help partners meet their resource information needs.

Aerial photo with plot boundaries overlayedExpected Outcomes

NIMAC’s tools have been used by many partners to improve their monitoring systems, and various trainings and tutorials have been developed for their use. For example, a rapid photointerpretation tool tutorial has been adapted for use by FIA, and has been used in several partner countries.

Research Results

Lister, Tonya W.; Lister, Andrew J.; Alexander, Eunice. 2014. Land use change monitoring in Maryland using a probabilistic sample and rapid photointerpretation. Applied Gepgraphy 51: 1-7. https://doi.org/10.1016/j.apgeog.2014.03.002.

Lister, Andrew; Scott, Charles T. 2009. Use of space-filling curves to select sample locations in natural resource monitoring studies. Environmental Monitoring and Assessment. 149: 71-80. https://doi.org/10.1007/s10661-008-0184-y.

Scott, Charles T. 2000. Estimating two-way tables based on forest surveys. In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 234-238.

Scott, C.T., Guiang, E.S., Seubert, C., and T. Stewart. 1996. Proposed forest resource survey for communities in the Philippines. P.305-317 in Multiple Resource Inventory and Monitoring of Tropical Forests. Hassan, H.A., C.Y. Mun, and N. Rahman (eds.). ASEAN Institute of Forest Management, Kuala Lumpur, Malaysia.

Research Participants

Principal Investigators

  • James Westfall, USDA Forest Service Northern Research Station Forest Inventory and Analysis and NIMAC, Research Forester
  • Andrew Lister, USDA Forest Service Northern Research Station Forest Inventory and Analysis and NIMAC, Research Forester
  • Jay Solomakos, USDA Forest Service, Northern Research Station Systems Analyst
  • Pat Miles, USDA Forest Service, Northern Research Station, Science Delivery Specialist / Researcher
  • Last modified: July 2, 2019