Land cover mapping in cropland dominated area using information on vegetation phenology and multi-seasonal Landsat 8 images

Abstract

Information about land cover is required for economic, agricultural and environmental policy making. Therefore, reliable up-to-date information is always called upon. In this study, we developed a new approach for land cover mapping based on the information of vegetation phenology. The main objective of this approach was to generate a land cover map of large cropland dominated area with high classification accuracy. Our approach consisted of two steps: first, we divided the study area into three land use groups depending on the phenology trend of cereals. Second, we applied a supervised classification for each group using the Maximum Likelihood Classifier and multi-date satellite images. Recent multi-temporal Landsat 8 images and field survey data were used for the classification process. To assess the robustness of this approach, a conventional supervised classification was performed using single date and multi-date images. Results indicated that the proposed approach is able to discriminate between different land cover types which have a similar spectral reflectance such as cereals, vegetables and pasture with high accuracy. The accuracy assessment showed very promising results with an overall accuracy of 86 % and a Kappa of 0.85 (good agreement) as compared to the single date (54–55 %) and the multi-date approach (78 %). Indeed, the application of this method provides accurate information for ecologists, hydrologists and the land development decision-makers. It can also improve the accuracy of environmental models that require high resolution land cover maps as input data.