Estimating land cover map accuracy and area uncertainty using a confusion matrix: A case study in Kalimantan, Indonesia

Abstract

Abstract Remote sensing is widely used to generate land cover maps, but the maps derived from remote sensing often produce accuracy below expectations for map error. Therefore, quantifying map accuracy is essential for reporting the precision of an estimated area. This study describes a simple framework for assessing map accuracy and estimating land cover area uncertainty for a land cover changes map for Kalimantan in 2012-2018. This study compared simple random sampling and stratified random sampling to determine suitable procedures for estimating accuracy and area uncertainty. The validation relies on the visual assessment of high spatial resolution images such as SPOT 6/7 and high-resolution temporal images from Open Foris Collect Earth. Our results showed that the land cover change map assessed using random sampling had an overall accuracy of 74% while using stratified random sampling had an overall accuracy of 75%. Thus, for tropical regions with high cloud cover, we recommend using stratified random sampling. The major source of map error was in differentiating between native forest and plantation areas. Future map improvement requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.