A Study on Land Cover Map of UAV Imagery using an Object-based Classification Method

Abstract

The study of ecosystem assessment(ES) is based on land cover information, and primarily it is performed at the global scale. However, these results as data for decision making have a limitation at the aspects of range and scale to solve the regional issue. Although the Ministry of Environment provides available land cover data at the regional scale, it is also restricted in use due to the intrinsic limitation of on screen digitizing method and temporal and spatial difference. This study of objective is to generate UAV land cover map. In order to classify the imagery, we have performed resampling at 5m resolution using UAV imagery. The results of object-based image segmentation showed that scale 20 and merge 34 were the optimum weight values for UAV imagery. In the case of RapidEye imagery, we found that the weight values, scale 30 and merge 30 were the most appropriate at the level of land cover classes for sub-category. We generated land cover imagery using example-based classification method and analyzed the accuracy using stratified random sampling. The results show that the overall accuracies of RapidEye and UAV classification imagery are each 90% and 91%.

UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)

“…Nowadays, the ecology community is relying on newer technologies to complete its goals of detailed land cover analysis: (UAVs). Recently, a growing number of studies have focused on using UAVs for land cover classification [8,16,[38][39][40][41][42][43]. However, most image classification methods rely on pixel-based techniques that have limitations when it comes to high-resolution satellite data and UAV imagery [16,43,44].…”