Historical Land-Use Land-Cover paper published

In a previous post I described how I was using Deep Learning to map forest cover based upon old aerial photographs in the Yangambi region, from which I source most of my wood (core) material.

This work was finished at the end of December and submitted to MDPI Remote Sensing, and went through review rather smoothly. The paper is currently accepted and available for free online (open access). I’ll give a short summary below, for those interested in the details I refer to the link provided.

Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin

Abstract

Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area.

Visual summary

Our paper discusses how historical aerial photography can be turned into a useful land-use and land-cover map in the central Congo Basin using structure-from-motion and Deep Learning based image segmentation.

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.

Data & Code availability

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/).