Deep learning for detecting landscape features in agricultural lands

Agricultural landscape features are small fragments of natural or semi-natural vegetation in agricultural land, which, compared to their relatively small size, are essential in providing various ecosystem services and supporting biodiversity in the agricultural landscape. The Common Agricultural Policy (CAP) includes landscape features in its payment instruments, allowing farmers to receive incentives for preserving landscape features on their land. However, to effectively manage and monitor the status of landscape features requires their mapping, which is often done manually. The potential of deep learning methods has been promising in automatically segmenting particular objects on remote sensing images, but they require large amounts of labelled data to train the model, which is time-consuming to prepare manually. 

The aim of our study was to develop a deep learning methodology to automate the detection of landscape features in agricultural lands. We leveraged the publicly available dataset of landscape features’ polygons that has been created manually by farmers in Estonia to create labelled training data. To ensure that all landscape features in the database still actually exist, we filtered the dataset by applying a threshold of Normalized Difference Vegetation Index (NDVI) value difference between each field island and its surrounding arable land from three Sentinel-2 seasonal composites. Additionally, we checked the digitization quality of field island polygons by comparing them to orthophotos and the digital elevation model. The labelled training data were used to train a U-Net deep learning model to detect landscape features from orthophotos. We also experimented with adding elevation data as input to improve detection accuracy. We used F1-score and Intersection over Union (IoU) to evaluate the model performance. The results showed that the model is reliable for automated landscape feature detection and can be adopted by the relevant stakeholders to automate their workflow in delineating landscape features for incentive schemes to preserve small landscape features.