Estimating Accuracy, Area and Confidence Intervals

To create the reference classification for labelling each sample unit, a combination of Landsat 1049

data from the USGS open archive together with GoogleEarthTM provides a source of cost free 1050

reference data. Our hypothetical map was produced using Landsat, and the good practice 1051

recommendations stipulate that if using the same data for creation of both the map and reference 1052

classifications, the process of creating the latter should be of higher quality than the map-making 1053

process. The process of labelling the sample units thus has to be more accurate than supervised 1054

classification. A manual inspection by three analysts of each of the sample units using a set of 1055

Landsat images together with GoogleEarthTM imagery acquired around the same time as the 1056

images used to make the map is assumed to be a more accurate process than supervised 1057

classification. Suppose tThe error matrix resulting from this response design and sample is 1058

presented in terms of the sample counts displayed in Table 8, and the computations for the

1059

accuracy and area estimates are detailed in the following two subsections. 1060 1061 << TABLE 8 HERE >> 1062 1063 5.2.1. Estimating Accuracy 1064

Because the sampling design is stratified random using the map classes as strata, the cell entries 1065

of the error matrix are estimated using Eq. (4). 1066

1067

<< TABLE 9 HERE >> 1068

1069

We can now estimate user’s accuracy 𝑈̂_𝑖 =𝑝̂𝑖𝑖

𝑝̂𝑖∙; producer’s accuracy 𝑃̂𝑗 = 𝑝̂𝑗𝑗

𝑝̂∙𝑗; and overall 1070

accuracy 𝑂̂ = ∑𝑞_𝑗=1𝑝̂_𝑗𝑗 using the estimated area proportions. Variances for these accuracy 1071

measures are estimated using Eq. (5)-(7). 95% confidence intervals are estimated as 1072

± 1.96√𝑉̂(𝑈̂𝑖) (replace 𝑈̂𝑖 with 𝑃̂𝑗 and 𝑂̂ for the producer’s and overall accuracies). In this case, 1073

the estimated user’s accuracy (± 95% confidence interval) is 0.88 ± 0.07 for deforestation, 1074

0.73 ± 0.10 for forest gain, 0.93 ± 0.04 for stable forest, and 0.96 ± 0.02 for stable non-forest. 1075

The estimated producer’s accuracy is 0.75 ± 0.21 for deforestation, 0.85 ± 0.23 for forest gain, 1076

0.93 ± 0.03 for stable forest, and 0.96 ± 0.01 for stable non-forest. The estimated overall 1077

accuracy is 0.95 ± 0.02. 1078

5.2.2. Estimating Area and Uncertainty 1079

The next step is to use the estimated area proportions in Table 9 to estimate the area of each 1080

class. The row totals of the error matrix in Table 9 give the mapped area proportions (which are 1081

also given by Wi) while the column totals give the estimated area proportions according to the 1082

reference data. Multiplying the latter by the total map area gives the stratified area estimate of 1083

each class according to the reference data. For example, the estimated area of deforestation 1084

according to the reference data is 𝐴̂1 = 𝑝̂∙1× 𝐴𝑡𝑜𝑡 = 0.024 × 10,000,000 pixels = 235,086 1085

pixels = 21,158 ha. The mapped area of deforestation (𝐴_𝑚,1) of 200,000 pixels was thus 1086

underestimated by 35,086 pixels or 3,158 ha. 1087

The second step is to estimate a confidence interval for the area of each class. From Eq. (10), 1088

𝑆(𝑝̂_∙1) = 0.0035 and the standard error for the estimated area of forest loss is 𝑆(𝐴̂₁) = 𝑆(𝑝̂_∙1) ×

1089

𝐴𝑡𝑜𝑡 = 0.0035 × 10,000,000 = 34,097 pixels. The margin of error of the confidence interval 1090

is 1.96 × 34,097 = 68,418 pixels = 6,158 ha. We have thus estimated the area of deforestation 1091

with a 95% confidence interval: 21, 158 ± 6,158 ha. The area estimate with a 95% confidence 1092

interval of the forest gain class is 11,686 ± 3,756 ha; stable forest is 285,770 ± 15,510 ha and 1093

stable non-forest 581,386 ± 16,282 ha. 1094

This example has illustrated the workflow of assessing accuracy, and estimating area and 1095

confidence intervals of area of the classes of a change map. While this is fairly straightforward 1096

once the error matrix has been constructed, the example highlights the need to consider different 1097

objectives when designing the sample. 1098

A tool for estimating unbiased accuracy measures and areas with 95% confidence intervals 1099

can be downloaded from www.people.bu.edu/olofsson/ (click ‘Research’ > 1100

‘Accuracy/Uncertainty’). The tool is implemented in Matlab™. 1101

6. Summary

1102

Conducting an accuracy assessment of a land change map serves multiple purposes. In addition 1103

to the obvious purpose of quantifying the accuracy of the map, the reference sample serves as the 1104

basis of estimates of area of each class where area is defined by the reference classification., and

1105

t The accuracy assessment sample data also contribute to estimates of uncertainty of the area 1106

estimates. Without an accuracy assessment, there is no way to communicate map quality in a 1107

quantitative and meaningful fashion. We acknowledge that there is no singular “best” approach 1108

and the recommendations provided do not preclude the existence of other acceptable practices. 1109

However, by following the “good practice” recommendations presented by this paper, scientific 1110

credibility of the accuracy and area estimates is ensured. The “good practice” recommendations 1111

are summarized as follows, organized by the three major components of the accuracy assessment 1112

methodology, the sampling design, response design, and analysis: 1113

6.1 General 1114

 Visually inspect the map and correct obvious errors before conducting the accuracy 1115

assessment 1116

 Accuracy and area estimates will be determined from a classification (i.e., the reference 1117

classification) that is of higher quality than the land change map being evaluated 1118

 A sampling approach is needed because the cost of obtaining the reference classification 1119

for the entire region of interest will be prohibitive 1120

 The sample used for accuracy assessment and area estimation is separate from 1121

(independent of) the data used to train or develop the classification 1122

6.2 Sampling design