Conducting a sample 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 data collection technique, all sample 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 it is a simple random sample and 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
With sample inventories, field data collection provides the necessary data to make estimates of the resource's structure, function, value, and management needs. Critical aspects of field data collection include:
Field data collection crews typically locate street segments using maps and location information produced by the Street Segment Sample Generator. Street segments vary in length and are randomly distributed amongst the city's public streets. For most segments, beginning and ending addresses make it easy to locate. However, where addresses are non-existent or difficult to find, large scale maps (e.g., 1:600), with streets and segments clearly labeled, make it easy for professional and volunteer data collectors alike to locate and inventory segments.
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.
