These historical aerial photographs were made within the context of mapping back in 1958. In my COBECORE project we digitized a large number of these analogue records in order to valorize these data within the context of ecological research.
These data were first stitched together into an orthomosaic (~93K ha), adjusting view angle effects, and georeferenced to local ground control points (buildings and other fixed structures). From this homogeneous forest / non-forest areas were selected for further processing.
Homogeneous areas were then combined into synthetic landscapes, mixing forest and non-forest classes. Landscape patterns were generated using a Gaussian random field mask, with random “sharpness” in the transition of classes (in addition to other augmentation techniques).
The synthetic landscapes were used to train a Deep Learning U-Net image segmentation routine, and resulted in a forest / non-forest land cover map for the whole historical orthomosaic. This data we compared to the current state of forest cover using Global Forest Cover data.
Corroborating our results we ran the segmentation routine on a contemporary panchromatic high resolution Geo-Eye image. The accuracy for the original map exceeded 95%, for the image 60 years later we still found an agreement of 87% (compared to Global Frorest Cover data)!
From these data we calculated changes in the state of the forest in terms of Above Ground Carbon (AGC) and landscape metrics (i.e. how the complexity of the landscape changed over time). We can conclude that for our study area a lot of previously cleared forest (hence AGC) has been reclaimed, and is forest again, offsetting some of the recent losses. Formal homogeneous colonial land clearing made way to a more fragmented ad-hoc deforestation.
Our analysis provides insights into the rate at which deforestation and reforestation has taken place over a multi-decadal scale in the central Congo basin. And, as such, it provides a useful historical context while interpreting past and ongoing forest research in the area.
Hufkens et al. (2019): A curated dataset of aerial survey images over the central Congo Basin, 1958. Zenodo: https://doi.org/10.5281/zenodo.3547767. All data not included in the latter repository can be found bundled with the analysis code as listed below. Proprietary datasets (i.e., Geo-Eye data) are not shared, but purchase order numbers allow for acquisition of these datasets to ensure reproducibility. All analysis code is available as R/python projects (https://khufkens.github.io/orthodrc and https://khufkens.github.io/orthodrc_cnn/).