Sampling Technology Based on Classification of SPOT5 Images

Taking You County in Hunan Province as the research area, SPOT5 images in 2009 and 2010 were used to designed schemes on systematic sampling, stratified sampling and simple random sampling under the sampling reliability index of 95%. Classification of images by SVM, combined with continuous inspection data from Hunan Province in 2009 to verify the accuracy of the plan, to obtain a sampling plan suitable for the study area. Results show that among 3 sampling schemes, the optimal scheme for investigating forest resources in You County was 4 km × 6 km (Layer Ⅰ), 4 km × 4 km (Layer Ⅱ) and 4 km × 4 km (Layer Ⅲ). In addition, the overall classification accuracy reached 90.48%. In systematic sampling, the overall accuracy of the schemes with sampling intervals of 4 km × 4 km and 2 km × 2 km was 88.10%, but the number of training samples in the former was small. It showed that in actual survey, the number of training samples and the overall accuracy of sampling were not always positively correlated. In stratified sampling, the optimal sampling scheme for each layer was not necessarily the same, and it also had a difference with the optimal sample for systematic sampling. When the sampling intervals were the same, the overall accuracy of stratified sampling was higher than the overall accuracy of systematic sampling, but the number of training samples was less than the number of training samples sampled by the system. Therefore, in actual surveys, stratified sampling was more accurate than systematic sampling. Thus, it meant stratified sampling can consume less manpower and material resources and was more efficient.