Potential impacts of map error on land cover change detection Nick Cuba Clark University 2/25/12 1

Premise: Post-classification image comparison is a practical and popular land cover (LC) change detection technique, yet error affects classification accuracy and through it, change detection. Research Question: “Given two land cover maps of the same area from different times, which observed categorical differences are explainable by random map error?” Applications: -Contributes to work with LC maps derived from aerial photos, which may have minimal error but no formal accuracy assessment. -Gives researchers some measure of confidence in (or doubt of) the realness of observed changes. -Is a first step toward putting “error bars” on land change maps/predictions Introduction Background Methods Results Summary 2

Google Earth reference image: GeoEye 2012 Introduction Background Methods Results Summary Important Categorical Transitions: Forest  Built : “Deforestation” Forest  Rangeland : “Forest Degradation” Rangeland  Forest : “Forest Regrowth ” 3 Rio Piedras River Watershed, PR

Introduction Background Methods Results Summary 4 Height of each segment/flow proportional to spatial extent of class or transition.

Introduction Background Methods Results Summary 5 Height of each segment/flow proportional to spatial extent of class or transition. Forest  Built : “Deforestation” Forest  Grass/Shrub: “Forest Degradation” Grass/Shrub  Forest : “Forest Regrowth”

Assumptions: - No actual change has occurred in the time period All observed map differences are attributable to spatially random error, in one of the maps. Objectives: -Identify the minimum amount of spatially random error (of the 4 types considered) needed to explain each observed map difference. Introduction Background Methods Results Summary Commission Error: Over-prediction (of wrong category) Omission Error: Under-prediction (of true category) These values can vary HUGELY between categories, e.g. 0% to 47% omission error for 2003 map catgories. 6

Q: “What is the minimum amount of spatially random error needed to account for all of an observed transition?” To Answer, we need to know: (for the example of false deforestation, where Built was underestimated in 1999) Introduction Background Methods Results Summary 1) The magnitude of the observed difference e.g., 2.6% of map was Forest in 1999, but Built in ) The total size of Forest in 1999, that was potentially misclassified e.g., Forest = 16.5% of 1999 map 3) The likelihood that a misclassified Forest pixel is truly Built (and not another category) e.g., There is a 74% chance that a misclassified Forest pixel is actually Built (based on 2003 map) 7

Introduction Background Methods Results Summary 8 Q: “What is the minimum amount of spatially random error needed to account for all of an observed transition?” To Answer, we need to know: (for the example of false deforestation, where Built was underestimated in 1999) 1) The magnitude of the observed difference e.g., 2.6% of map was Forest in 1999, but Built in )The total size of Forest in 1999, that was potentially misclassified e.g., Forest = 16.5% of 1999 map 3)The likelihood that a misclassified Forest pixel is truly Built (and not another category) e.g., There is a 74% chance that a misclassified Forest pixel is actually Built (based on 2003 map)

Introduction Background Methods Results Summary 9 Q: “What is the minimum amount of spatially random error needed to account for all of an observed transition?” To Answer, we need to know: (for the example of false deforestation, where Built was underestimated in 1999) 1)The magnitude of the observed difference e.g., 2.6% of map was Forest in 1999, but Built in ) The total size of Forest in 1999, that was potentially misclassified e.g., Forest = 16.5% of 1999 map 3)The likelihood that a misclassified Forest pixel is truly Built (and not another category) e.g., There is a 74% chance that a misclassified Forest pixel is actually Built (based on 2003 map)

Introduction Background Methods Results Summary Q: “What is the minimum amount of spatially random error needed to account for all of an observed transition?” To Answer, we need to know: (for the example of false deforestation, where Built was underestimated in 1999) 10 Built / (TOTAL – Forest) 1)The magnitude of the observed difference e.g., 2.6% of map was Forest in 1999, but Built in )The total size of Forest in 1999, that was potentially misclassified e.g., Forest = 16.5% of 1999 map 3) The likelihood that a misclassified Forest pixel is truly Built (and not another category) e.g., There is a 74% chance that a misclassified Forest pixel is actually Built (based on 2003 map)

Introduction Background Methods Results Summary 1)The magnitude of the observed difference 2)The total size of Forest in 1999 (that was potentially misclassified) 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 11 Total Study Area (1999)

Introduction Background Methods Results Summary 1)The magnitude of the observed difference 2)The total size of Forest in 1999 (that was potentially misclassified) 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 12 Forest Area (1999) Total Study Area (1999)

Introduction Background Methods Results Summary 1)The magnitude of the observed difference 2)The total size of Forest in 1999 (that was potentially misclassified) 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 13 Forest Area (1999) Area of Error (1999) Total Study Area (1999)

Introduction Background Methods Results Summary 1)The magnitude of the observed difference 2)The total size of Forest in 1999 (that was potentially misclassified) 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 14 Forest Area (1999) Area of Error (1999) Total Study Area (1999) Area that is truly BUILT, GRASS/SHRUB, etc. (from 2003 map)

Introduction Background Methods Results Summary 1)The magnitude of the observed difference 2)The total size of Forest in 1999 (that was potentially misclassified) 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 15 Forest Area (1999) Area of Error (1999) Total Study Area (1999) Is the observed Forest  Built transition smaller than the area that is truly BUILT?

Introduction Background Methods Results Summary These equations apply to EVERY observed map difference 1)The magnitude of the observed difference 2)The total size of Forest in 1999, that was potentially misclassified 3)The error threshold itself 4)The likelihood that a misclassified Forest pixel is truly Built (and not another category) 16

Introduction Background Methods Results Summary Interpretation (LEVEL OF CONFIDENCE) : Deforestation : even low amounts of error are able to explain this map difference : LOW Forest Degradation: very high/impossible amounts of error needed to explain : HIGH Forest Regrowth: impossible amounts of random error needed to explain: HIGHEST 17

Introduction Background Methods Results Summary A method was presented to approximate the impact of spatially random error on analysis of land change based on post- classification land cover maps. Future work needed: accounting for non-random error affecting change detection interpreting marginal output thresholds (e.g. under 100%) improving underlying assumptions of error-free landscape 18

Introduction Background Methods Results Summary Thank You 19