Birgit Eben

Accurate and comprehensive tree cover mapping plays a vital role in forest management and ecosystem assessment. However, the lack of spectral information and the low quality of available black and white (B&W) imagery makes it difficult to retroactively predict an historical tree cover map using common methods. Here, a network structure called B&WTreeNet, based on semantic segmentation, is proposed to make full use of the limited labeled training remote sensing data to obtain tree cover information from historical datasets. The proposed B&WTreeNet is capable of cross-temporal semantic segmentation, including various data augmentation methods, to address inconsistent tree cover features caused by variations in image quality. The luminance enhancer and other modules in B&WTreeNet can extract the characteristics of tree cover effectively, successfully compensating for the limited spectral information in B&W image datasets. The countrywide historical tree cover map in Switzerland generated for the 1980s using a limited training dataset from 2018 and 2019 agrees well with manual interpretation results.

Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. We develop a proof of concept for mapping historic habitat distribution for Switzerland from black and white aerial imagery compatible with the present-day habitat map. Recently available orthorectified 1946 aerial imagery (1 m resolution) was segmented based on spectral and shape characteristics for training areas (320–508 km²) representing the main biogeographical regions of Switzerland. Initial training data was derived by manual aerial orthoimage interpretation differentiating 15 habitat classes. A random forest model was trained to classify the segments using variables describing spectral information, image texture, segment shape, topography, climate, and anthropogenic influence. Classification accuracy was improved with additional training samples derived in a stepwise approach, applying three different sampling techniques. Highest class accuracies (producer's and user's accuracies ≥ 0.75) were achieved for the habitats ‘Standing water’, ‘Flowing water’, ‘Glaciers, permanent ice and snow’, and ‘Forests and other wooded land’. Particularly low user's accuracies were found for ‘Wetlands’, ‘Hedges and tree rows’ and ‘Buildings’. The comparison to independent data further revealed minor differences in overall accuracy for the three different sampling strategies. Yet, map predictions sometimes varied substantially, indicating that the sampling strategies address different classification issues. Hence, we conclude that combining different sampling strategies for training data collection has the potential to improve the mapping, particularly in the case of multi-class classifications.

Historical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use of these images is limited by the lack of multispectral information, as well as by their varying quality. It is therefore important to study and develop methods that are capable of automatic and accurate classification of these B&W images while reducing the need for tedious manual work. The goal of this study was to assess changes over 30 years in woody vegetation cover along alpine treeline ecotones using B&W aerial images from two time points. A convolutional neural networks model was firstly set up based on three structure classes calculated from Airborne Laser Scanning data using the B&W aerial images from 2010. Then, the model was improved by active addition of training samples of those that were wrongly predicted from historical B&W aerial images from 1980. A comparison with visual image interpretation revealed generally high agreement for the class "dense forest" and lower agreement for the class "group of trees". The study illustrates that vegetation changes at the treeline ecotone can be detected in order to assess areawide long-term vegetation dynamics at a fine spatial resolution.