Detecting forest and linear woody feature change between 1954 and 2019 in Southeastern Canadian agroecosystems for regional biodiversity assessment

Effectively mapping and detecting changes in forests and linear woody features (LWFs) is crucial for assessing their impact on biodiversity and ecosystem services. This study investigated this capability using heterogeneous, high-resolution aerial imagery, in terms of spectral and spatial properties. Mitigating the influence of these factors, arising from differences in sensor specifications and acquisition conditions, is essential for robust detection and analysis of temporal change across historical image datasets. The deep learning model developed here successfully mapped forests and LWFs between 1954 and 2019 using just a single image band, enabling reliable change estimation. Assessment at the pixel scale showed forest mapping achieved an accuracy of 90%, while LWF accuracy was lower at 69%, primarily due to their narrow widths and boundary errors in both the reference and predicted results. For LWFs an object-based assessment was undertaken to reduce the effect of precise delineation achieving a higher accuracy of 77%. As a final assessment, comparison of area within 200 by 200 m extents showed good agreement, with a mean absolute error of 1.3% for LWFs. For forests this was 2.7%. In terms of change detection, the accuracy was greater than 81% for both forests and LWFs. Change analysis indicated an 8.5% net increase in forests since 1954, along with a small net loss of less than 1% in LWFs. LWF loss was mainly attributed to forest gains. In areas without significant forest gain, LWFs slightly increased. These changes are generally seen as beneficial for biodiversity and ecosystem services in the region. However, other factors such as urban development and larger agricultural field sizes need to be considered in future studies.