Conducting a complete inventory using the i-Tree PDA Utility
i-Tree users interested in conducting a STRATUM inventory are encouraged to use the Pocket PC platform Tree Inventory PDA Utility. Users can tailor data collection for the particular needs of their projects or use the default data collection fields for speed and full utilization of STRATUM's analysis and reporting features. Using the Inventory Utility for STRATUM not only leads to faster data collection and input, but facilitates data transfer directly into STRATUM for analysis.
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.