Conducting a complete inventory using paper or other non-i-Tree applications
STRATUM specific field data template forms have been developed and are available for use (see Manual). Field data collected on paper can be manually entered into electronic template form. Inventory databases developed with other non-i-Tree applications can be electronically manipulated into STRATUM-compatible format (see Manual). Regardless of inventory technique, all complete inventories created without the i-Tree PDA Utility must be converted to a STRATUM-formatted MS Access table for import. In other words, you can collect data using any method as long as you end up with a MS Access table that meets the formatting requirements. Contact i-Tree support for more information.
Critical aspects of field data collection
Field data collected for complete inventories provides the necessary information to calculate and understand the resource's structure, function, value, and management needs. Critical aspects of field data collection include:
Developing and following the protocol
Because the accuracy of the results of a STRATUM project depend upon quality data inputs, accurate and consistent field data collection is essential. Default data fields were developed with volunteer data collectors in mind. There are up to 17 basic data fields that can be used as guidelines for data collection. Detailed protocols for default criteria can be found in the STRATUM Manual. The data collection crew will require careful training and oversight to ensure accurate data collection -- guidelines for managing volunteers are discussed in the i-Tree Manual.
Quality control
To obtain the most reliable and accurate results from a STRATUM project, it is recommended that a quality control system be used. Such a system normally examines two different areas:
Field measurements - Checks on field data collection are made by re-inventorying a sample of trees after the data have been collected. Quality control should be conducted throughout any inventory, but is particularly important at the start of a project to ensure that all data collection personnel or volunteers are trained and using consistent protocol.
Data integrity and coherency - Quality control is also useful after field data have been uploaded or entered into a personal computer. Data checks can find errors such as numbers that are out of range (e.g. trunk diameters greater than normally encountered for a species), missing variables, or inappropriate measures.
