Abstract
Options for satellite monitoring of deforestation rates over large areas include the use of sampling. Sampling may reduce the cost of monitoring but is also a source of error in estimates of areas and rates. A common sampling approach is systematic sampling, in which sample units of a constant size are distributed in some regular manner, such as a grid. The proposed approach for the 2010 Forest Resources Assessment (FRA) of the UN Food and Agriculture Organization (FAO) is a systematic sample of 10 km wide squares at every 1° intersection of latitude and longitude. We assessed the outcome of this and other systematic samples for estimating deforestation at national, sub-national and continental levels. The study is based on digital data on deforestation patterns for the five Amazonian countries outside Brazil plus the Brazilian Amazon. We tested these schemes by varying sample-unit size and frequency. We calculated two estimates of sampling error. First we calculated the standard errors, based on the size, variance and covariance of the samples, and from this calculated the 95% confidence intervals (CI). Second, we calculated the actual errors, based on the difference between the sample-based estimates and the estimates from the full-coverage maps. At the continental level, the 1°, 10 km scheme had a CI of 21% and an actual error of 8%. At the national level, this scheme had CIs of 126% for Ecuador and up to 67% for other countries. At this level, increasing sampling density to every 0.25° produced a CI of 32% for Ecuador and CIs of up to 25% for other countries, with only Brazil having a CI of less than 10%. Actual errors were within the limits of the CIs in all but two of the 56 cases. Actual errors were half or less of the CIs in all but eight of these cases. These results indicate that the FRA 2010 should have CIs of smaller than or close to 10% at the continental level. However, systematic sampling at the national level yields large CIs unless the sample size is very large, especially if any sub-national stratification of estimates is required.
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