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Tree Inventories and Sampling Overview Inventories Inventories in i-Tree differ Scope Type Sampling Pervasive in i-Tree Concept important www.treesaremyfriends.org/.../ photos1.htm Scope of inventories Individual tree MCTI Day-to-day management Goal: accurate data for every tree Population of trees STRATUM, UFORE Long-term planning Goal: accurate analysis of forest Types of Inventories Complete Inventory Day-to-day field management Costly, time-consuming Partial Inventory Complete inventory of some segment Sample Inventory Randomly-selected trees inventoried for large-scale interpretation Cost-efficient Good for planning Types II Sample inventory benefits Increase public safety Facilitate short- and long-term planning Improve public relations Justify budgets Estimate tree benefits Large gain for small investment i-Tree promotes the value of sampling Sampling I Traditional sampling techniques valid, but tedious for larger areas i-Tree v. 1.0 includes applications to automate the process for two types of plots: Linear (street) plots/segments STRATUM/MCTI, SDAP Spatial (park, any area) plots UFORE Sampling II Linear plot selectors STRATUM/MCTI SDAP Requirements ArcView 3.x (legacy program) OR ArcMap 8.3 or 9.x GIS files Polygon file delimiting study area boundary Road shape file (TIGER/Line data) Manual selection also possible TIGER/Line files Topologically Integrated Geographic Encoding and Referencing, or TIGER/Line Format used by the United States Census Bureau to describe land attributes such as roads, buildings, rivers, and lakes. Shape files free from ESRI for use in a GIS Sampling III Spatial plot selector UFORE Still testing… Requirements ArcMap 8.3 or 9.x Study area boundary Sub-areas or strata--e.g., land uses Digital aerial photos (optional) Manual methods also possible Concepts I Random sample Data collection in which every member of the population has an equal chance of being selected Can sometimes break population into subgroups (stratification) for better numbers Mind tricks easily, so need rigorous method Concepts II Variance (= square of SD) Measure of how much individual samples vary The less the individual measurements vary from the mean (average), the more reliable the mean In an urban forest, different traits to investigate (variables) may have different variances E.g., species distribution (high?) vs. population size (low) Source: Dave Nowak and Jeff Walton, personal communication (DRG data) Concepts III Sample size How big? Sample size depends on The relationships to be detected (weak more) The significance level sought (high more) The size of the smallest subgroup (small more) The variance of the variables (high more) Can be smaller as these factors change, especially as variance goes down Source: Dave Nowak, personal communication Concepts IV Standard error (SEM) The Standard Error (Standard Error of the Mean) calculates how accurately a sample mean estimates the population mean. Formula: SEM = SD/N , where SD = “standard deviation” of the sample, and N = sample size. Note that as SD goes down or N goes up, SEM gets smaller—i.e., estimate becomes better. Commonly represented by “±” after a number. Are you done yet?! Source: blogaloutre http://www.ontabec.com/fatigue.jpg Final sampling thoughts Sampling is our friend Both tool and product in i-Tree The validity of i-Tree depends critically on understanding the process and capability of sampling