An Assessment of Repeatability for Crown Measurements Taken on Conifer Tree Species James A. Westfall William A. Bechtold KaDonna C. Randolph USDA Forest.

Slides:

Advertisements

Similar presentations

Allometric Crown Width Equations for Northwest Trees Nicholas L. Crookston RMRS – Moscow June 2004.

Advertisements

PNW Research Station’s Plan for Urban Inventories Partially funded with stimulus grant Cooperative Agreements –Oregon Department of Forestry (ODF) –California.

Charley Peterson Resource Monitoring and Assessment Program Pacific Northwest Station, Portland, Oregon Progress on achieving consistency and compatibility.

QA Programs for Local Health Departments

Quality Assurance (Quality Control)

Adeyl Khan, Faculty, BBA, NSU Quality Control Adeyl Khan, Faculty, BBA, NSU Phases of Quality Assurance 10-2 Figure 10.1 Acceptance sampling Process.

THE TECHNICAL SPECIFICATIONS OF THE US FOREST CARBON INVENTORY: RECENT PAST AND NEAR FUTURE Christopher W. Woodall, Research Forester, U.S. Forest Service,

CHAPTER 21 Inferential Statistical Analysis. Understanding probability The idea of probability is central to inferential statistics. It means the chance.

Progress Toward Compatibility Among Regions 2011 FIA National Users’ Group Meeting Sacramento. CA 2011 FIA National Users’ Group Meeting Sacramento. CA.

Hypothesis testing Week 10 Lecture 2.

Chapter 10 Quality Control McGraw-Hill/Irwin

Operations Management

Intermediate methods in observational epidemiology 2008 Quality Assurance and Quality Control.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 10 Quality Control.

1/55 EF 507 QUANTITATIVE METHODS FOR ECONOMICS AND FINANCE FALL 2008 Chapter 10 Hypothesis Testing.

Chapter 9 Audit Sampling: An Application to Substantive Tests of Account Balances McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved.

Chapter 8 Introduction to Hypothesis Testing

T WO W AY ANOVA W ITH R EPLICATION  Also called a Factorial Experiment.  Factorial Experiment is used to evaluate 2 or more factors simultaneously. 

Chapter 10 Quality Control McGraw-Hill/Irwin

10-1Quality Control William J. Stevenson Operations Management 8 th edition.

Chapter 10 Hypothesis Testing

Wisconsin’s Continuous Forest Inventory State Forest Working Group Meeting Plymouth, WI May 10, 2006 Teague PrichardChip Scott WDNR State Lands SpecialistUSDA.

Graphical Analysis. Why Graph Data? Graphical methods Require very little training Easy to use Massive amounts of data can be presented more readily Can.

An Overview of the Forest Inventory and Analysis Program.

Urban FIA Data-- FIA core variables General stand and site characteristics Tree and sapling measurements, health, and condition Seedling data Derived estimates.

Copyright © 2008 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 22 Using Inferential Statistics to Test Hypotheses.

Biomass retrieval algorithm based on P-band BioSAR experiments of boreal forest Lars Ulander 1,2, Gustaf Sandberg 2, Maciej Soja 2 1 Swedish Defence Research.

Plot Level Variables National Manual Version 1.55.

Tree Data National Manual Version 1.61 Section 5.0.

Planning for Inventory & Monitoring Chip Scott National Inventory & Monitoring Applications Center (FIA-NIMAC) Northern Research Station U.S. Forest Service.

FOR 373: Forest Sampling Methods Simple Random Sampling What is it? How to do it? Why do we use it? Determining Sample Size Readings: Elzinga Chapter 7.

2002/4/10IDSL seminar Estimating Business Targets Advisor: Dr. Hsu Graduate: Yung-Chu Lin Data Source: Datta et al., KDD01, pp

Generic Approaches to Model Validation Presented at Growth Model User’s Group August 10, 2005 David K. Walters.

Chapter 10 Quality Control.

Brian Macpherson Ph.D, Professor of Statistics, University of Manitoba Tom Bingham Statistician, The Boeing Company.

Operations Management

Sharon Stanton & FIA National Indicator Leads RECOMMENDATIONS FOR ENHANCED FOREST INDICATORS.

Winter Injury in American chestnut James Sharpe Rebecca Stern April 20, 2015 Stat 231 James Sharpe Rebecca Stern April 20, 2015 Stat 231.

QUALITY ASSURANCE/QUALITY CONTROL

2012 User’s Group Meeting Data Collection Group Topic: Incorporating Flexibilities in FIA Data Collection Effort Bob Ilgenfritz – Data Collection Group.

FIA Progress in Making Full Use of Phase 3 Data 2011 National FIA User Group Meeting Sacramento, CA March 8-10, 2011 Bill Burkman.

Statistical Quality Control/Statistical Process Control

Inferential Statistics. The Logic of Inferential Statistics Makes inferences about a population from a sample Makes inferences about a population from.

Adaptive data correction at remeasurement: What to do when errors are discovered? 2011 National FIA User Group Meeting Sacramento,

Introduction Acid deposition (AD) from air pollution is a chronic stress on forest ecosystems in the northeast and mid-Atlantic regions of the U. S. The.

Crown-Condition Classification Tutorial (Revised January 2008)

Chapter 21 Measurement Analysis. Measurement It is important to define and validate the measurement system before collecting data. –Without measurement.

CONDUCTING TESTS FOR STATISTICALLY-SIGNIFICANT DIFFERENCES USING FOREST INVENTORY DATA James A. Westfall Scott A. Pugh John W. Coulston U.S. Forest Service.

Chapter 20 Classification and Estimation Classification – Feature selection Good feature have four characteristics: –Discrimination. Features.

Maintaining FIA’s Credibility as The Nation’s Forest Census Dave Struble Maine Forest Service.

The Value of Your Urban Forest:

AN IMPROVED VOLUME, BIOMASS, AND CARBON DATABASE FOR U.S. TREE SPECIES James A. Westfall U.S. Forest Service Forest Inventory and Analysis.

2015 Regional Management Team Meeting Albany, New York Group Leader Update Bob Ilgenfritz Data Collection.

Field Monitoring for LULUCF Projects Winrock International Training Seminar for BioCarbon Fund Projects.

Monitoring and Evaluation in the GMS Learning Program 7 – 18 May 2012, Mekong Institute, Khon Kaen, Thailand Randy S. Balaoro, CE, MM, PMP Data Sampling.

Quality Control Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.

TREE DATA National Manual Version Subplot Number Coded separately from tree number.

Confidence Intervals and Hypothesis Testing Mark Dancox Public Health Intelligence Course – Day 3.

Chapter 7: Hypothesis Testing. Learning Objectives Describe the process of hypothesis testing Correctly state hypotheses Distinguish between one-tailed.

Puulajeittainen estimointi ja ei-parametriset menetelmät Multi-scale Geospatial Analysis of Forest Ecosystems Tahko Petteri Packalén Faculty.

Baseline Mean Daily Servings of Coffee and Other Beverages and of Caffeine in Women in the Iowa Women’s Health Study, 1986* *Data are given as.

Chapter 9 Audit Sampling: An Application to Substantive Tests of Account Balances McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved.

Urban FIA Sample Design

10 Quality Control.

10 Quality Control.

Additional Data Collection in 2017

National Forest Inventory for Great Britain

FIA Lichen Indicator Demo

Confidentiality of plot coordinates - Alternatives?

Intermediate methods in observational epidemiology 2008

Presentation transcript:

An Assessment of Repeatability for Crown Measurements Taken on Conifer Tree Species James A. Westfall William A. Bechtold KaDonna C. Randolph USDA Forest Service Forest Inventory and Analysis

FIA QA/QC Data Collection Hot Checks Cold Checks Blind Checks Independent Plot Remeasurement Randomly Chosen Plots Experienced Personnel Target 3%

Conifer Data FIA Tree Crown Indicator (Phase 3) Uncompacted Crown Ratio (nearest 1%) Light Exposure (6 categories) Crown Position (4 categories) Crown Vigor Class (saplings – 3 categories) Crown Density (nearest 5%) Dieback (nearest 5%) Foliage Transparency (nearest 5%)

Analysis Data Matching (i.e., trees) No one-to-one correspondence (independent remeasure) Manually expensive (large # observations) Automate most matches –2-pass approach –Weighted distance = f(dbh, horz. distance, azimuth) –‘Conservatism’ via a decision rule MUST review unmatched trees and add legitimate matches into analysis data set

Analysis MQOs and Tolerances Tolerance A range of acceptable variation Can be specific value or percentage Example: ± 0.1 in. for dbh Measurement Quality Objective (MQO) The desired percentage of measurements that fall within the tolerance range Example: 95% of the time

Analysis Computations Obtain differences between field and QA crews for matched observations Determine percentage of total observations where difference is within the tolerance range Compare with MQO to see if standard is met Optional: compute percentages across range of tolerance values

Results *

Results

Results

Uncompacted CR

Vigor Class

Crown Density

Crown Dieback

Foliage Transparency

Conclusion Crown Light Exposure, Crown Dieback, and Foliage Transparency measurements met the stated repeatability standard. Uncompacted CR, Crown Position, Crown Vigor, and Crown Density measurements did not meet the repeatability standard.

Conclusion With few exceptions, levels of repeatability are similar across geographic regions. The poorest repeatability statistics were generally associated with relatively rarer crown characteristics. For some variables, improved training and/ or re-evaluation of the tolerance/MQO may be needed.

Conclusion Quality assurance data are important for: Evaluating training effectiveness Employee performance feedback Evaluating measurement protocols Identifying significant sources of error for computed attributes, model projections, etc.

Conclusion Further reading: Westfall, J.A., ed FIA national assessment of data quality for forest health indicators. USDA For. Serv. Gen. Tech. Rep. NRS-53. Pollard, J.E., et al FIA national data quality assessment report for USDA For. Serv. Gen. Tech. Rep. RMRS-181.

Questions ??